Datasets:

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[ "To follow up on this: the cased and uncased versions actually contain different content, and the cased one is easier since it contains a Summary of the Invention in the input.\r\n\r\nSee the paper describing the issue here:\r\nhttps://aclanthology.org/2022.gem-1.34/", "Thanks for proposing the addition of the cased version of this dataset and for pinging again recently.\r\n\r\nI have just merged a PR that adds the cased version: https://huggingface.co/datasets/big_patent/discussions/3\r\n\r\nThe cased version (2.1.2) is the default one:\r\n```python\r\nds = load_dataset(\"big_patent\", \"all\")\r\n```\r\n\r\nTo use the 1.0.0 version (lower cased tokenized words), pass both parameters `codes` and `version`:\r\n```python\r\nds = load_dataset(\"big_patent\", codes=\"all\", version=\"1.0.0\")\r\n```\r\n\r\nClosed by: https://huggingface.co/datasets/big_patent/discussions/3" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3860). All of your documentation changes will be reflected on that endpoint.", "There are still some `.. code-block:: python` (e.g. see [this](https://huggingface.co/docs/datasets/master/en/package_reference/main_classes#datasets.Dataset.align_labels_with_mapping)) directives in our codebase, so maybe we can remove those as well as part of this PR." ]

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[ "Hi @slvcsl, thanks for reporting.\r\n\r\nYesterday we just made a patch release of our `datasets` library that fixes this issue: version 1.18.4.\r\nhttps://pypi.org/project/datasets/#history\r\n\r\nPlease, feel free to update `datasets` library to the latest version: \r\n```shell\r\npip install -U datasets\r\n```\r\nAnd then you should force redownload of the data file to update your local cache: \r\n```python\r\nds = load_dataset(\"big_patent\", \"g\", split=\"validation\", download_mode=\"force_redownload\")\r\n```\r\n- Note that before the fix, you just downloaded and cached the Google Drive virus scan warning page, instead of the data file\r\n\r\nThis issue was already reported \r\n- #3784\r\n\r\nand its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe already fixed it. See:\r\n- #3787 \r\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3858). All of your documentation changes will be reflected on that endpoint." ]

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[ "I agree using `glob.glob` alone is bad practice because it's not deterministic. Using `sorted` is a nice solution.\r\n\r\nNote that the `xglob` function you are referring to in the `streaming_download_manager.py` code just extends `glob.glob` for URLs - we don't change its behavior. That's why it has no `sorted()`" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3856). All of your documentation changes will be reflected on that endpoint." ]

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[ "We raise the error “ FileNotFoundError: can’t find the dataset” mainly to follow best practice in security (otherwise users could be able to guess what private repositories users/orgs may have)\r\n\r\nWe can indeed reformulate this and add the \"If this is a private repository,...\" part !", "Resolved via https://github.com/huggingface/datasets/pull/4536" ]

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[ "Hi @amanjaiswal777,\r\n\r\nFirst note that the dataset you are trying to load is deprecated: it was the Common Voice dataset release as of Dec 2020.\r\n\r\nCurrently, Common Voice dataset releases are directly hosted on the Hub, under the Mozilla Foundation organization: https://huggingface.co/mozilla-foundation\r\n\r\nFor example, to get their latest Common Voice relase (8.0):\r\n- Go to the dataset page and request access permission (Mozilla Foundation requires this for people willing to use their datasets): https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0\r\n- Looking at the dataset card, you can check that data instances have, among other fields, the ones you are interested in: \"accent\", \"age\",... \r\n- Then you can load their \"en\" language dataset as usual, besides passing your authentication token (more info on auth token here: https://huggingface.co/docs/hub/security)\r\n ```python\r\n from datasets import load_dataset\r\n ds_en = load_dataset(\"mozilla-foundation/common_voice_8_0\", \"en\", use_auth_token=True)\r\n ```\r\n- Finally, you can filter only the data instances you are interested in (more info on `filter` here: https://huggingface.co/docs/datasets/process#select-and-filter):\r\n ```python\r\n ds_england_en = ds_en.filter(lambda item: item[\"accent\"] == \"England English\")\r\n ```\r\n\r\nFeel free to reopen this issue if you need further assistance.", "Hey @albertvillanova trying the same approach as you with the common_voice_16_1 dataset. What I'm trying to do is to filter the valencian accent in the catalan subset. Gave me this error and I have everything it asks for decoding mp3:\r\n\r\n\r\n\r\n\r\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3853). All of your documentation changes will be reflected on that endpoint.", "The CI fail is unrelated to this dataset, merging :)" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3852). All of your documentation changes will be reflected on that endpoint." ]

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[ "Hi @lemoner20, thanks for reporting.\r\n\r\nI'm sorry but I cannot reproduce your problem:\r\n```python\r\nIn [1]: from datasets import load_dataset, load_metric, Audio\r\n ...: raw_datasets = load_dataset(\"superb\", \"ks\", split=\"train\")\r\n ...: print(raw_datasets[0][\"audio\"])\r\nDownloading builder script: 30.2kB [00:00, 13.0MB/s] \r\nDownloading metadata: 38.0kB [00:00, 16.6MB/s] \r\nDownloading and preparing dataset superb/ks (download: 1.45 GiB, generated: 9.64 MiB, post-processed: Unknown size, total: 1.46 GiB) to .../.cache/huggingface/datasets/superb/ks/1.9.0/fc1f59e1fa54262dfb42de99c326a806ef7de1263ece177b59359a1a3354a9c9...\r\nDownloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.49G/1.49G [00:37<00:00, 39.3MB/s]\r\nDownloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 71.3M/71.3M [00:01<00:00, 36.1MB/s]\r\nDownloading data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:41<00:00, 20.67s/it]\r\nExtracting data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:28<00:00, 14.24s/it]\r\nDataset superb downloaded and prepared to .../.cache/huggingface/datasets/superb/ks/1.9.0/fc1f59e1fa54262dfb42de99c326a806ef7de1263ece177b59359a1a3354a9c9. Subsequent calls will reuse this data.\r\n{'path': '.../.cache/huggingface/datasets/downloads/extracted/8571921d3088b48f58f75b2e514815033e1ffbd06aa63fd4603691ac9f1c119f/_background_noise_/doing_the_dishes.wav', 'array': array([ 0. , 0. , 0. , ..., -0.00592041,\r\n -0.00405884, -0.00253296], dtype=float32), 'sampling_rate': 16000}\r\n``` \r\n\r\nWhich version of `datasets` are you using? Could you please fill in the environment info requested in the bug report template? You can run the command `datasets-cli env` and copy-and-paste its output below\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:", "@albertvillanova Thanks for your reply. The environment info below\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-4.19.91-007.ali4000.alios7.x86_64-x86_64-with-debian-buster-sid\r\n- Python version: 3.6.12\r\n- PyArrow version: 6.0.1", "Thanks @lemoner20,\r\n\r\nI cannot reproduce your issue in datasets version 1.18.3 either.\r\n\r\nMaybe redownloading the data file may work if you had already cached this dataset previously. Could you please try passing \"force_redownload\"?\r\n```python\r\nraw_datasets = load_dataset(\"superb\", \"ks\", split=\"train\", download_mode=\"force_redownload\")", "Thanks, @albertvillanova,\r\n\r\nI install the python package of **librosa=0.9.1** again, it works now!\r\n\r\n\r\n", "Cool!", "@albertvillanova, you can actually reproduce the error if you reach the cell `common_voice_train[0][\"path\"]` of this [notebook](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_%F0%9F%A4%97_Transformers.ipynb#scrollTo=_0kRndSvqaKk). Error gets solved after updating the versions of the libraries used in there.", "@jvel07, thanks for reporting and finding a solution.\r\n\r\nMaybe we could tell @patrickvonplaten about the version pinning issue in his notebook.", "Should I update the version of datasets @albertvillanova ? " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq can you review when you have some time?", "Hi @lhoestq -- thanks so much for your review! I just added the stuff you requested to the README.md, including an example from the dataset, the table of contents, and lots of section headers with \"More Information Needed\" below. Let me know if there's anything else I need to do!", "Feel free to also merge `master` into your branch to get the latest updates for the tests ;)", "thanks @lhoestq - just made all the updates you requested!" ]

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[ "Hi @jxmorris12, thanks for reporting.\r\n\r\nThe objective of `verify_checksums` is to check that both checksums are equal. Therefore if one is None and the other is non-None, they are not equal, and the function accordingly raises a NonMatchingChecksumError. That behavior is expected.\r\n\r\nThe question is: how did you generate the expected checksum? Normally, it should not be None. To properly generate it (it is contained in the `dataset_infos.json` file), you should have runned: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md\r\n```shell\r\ndatasets-cli test <your-dataset-folder> --save_infos --all_configs\r\n```\r\n\r\nOn the other hand, you should take into account that the generation of this file is NOT mandatory for personal/community datasets (we only require it for \"canonical\" datasets, i.e., datasets added to our library GitHub repository: https://github.com/huggingface/datasets/tree/master/datasets). Therefore, other option would be just to delete the `dataset_infos.json` file. If that file is not present, the function `verify_checksums` is not executed.\r\n\r\nFinally, you can circumvent the `verify_checksums` function by passing `ignore_verifications=True` to `load_dataset`:\r\n```python\r\nload_dataset(..., ignore_verifications=True)\r\n``` ", "Thanks @albertvillanova!\r\n\r\nThat's fine. I did run that command when I was adding a new dataset. Maybe because the command crashed in the middle, the checksum wasn't stored properly. I don't know where the bug is happening. But either (i) `verify_checksums` should properly handle this edge case, where the passed checksum is None or (ii) the `datasets-cli test` shouldn't generate a corrupted dataset_infos.json file.\r\n\r\nJust a more high-level thing, I was trying to follow the instructions for adding a dataset in the CONTRIBUTING.md, so if running that command isn't even necessary, that should probably be mentioned in the document, right? But that's somewhat of a moot point, since something isn't working quite right internally if I was able to get into this corrupted state in the first place, just by following those instructions.", "Hi @jxmorris12,\r\n\r\nDefinitely, your `dataset_infos.json` was corrupted (and wrongly contains expected None checksum). \r\n\r\nWhile we further investigate how this can happen and fix it, feel free to delete your `dataset_infos.json` file and recreate it with:\r\n```shell\r\ndatasets-cli test <your-dataset-folder> --save_infos --all_configs\r\n```\r\n\r\nAlso note that `verify_checksum` is working as expected: if it receives a None and and a non-None checksums as input pair, it must raise an exception: they are not equal. That is not a bug.", "At a higher level, also note that we are preparing the release of `datasets` version 2.0, and some docs are being updated...\r\n\r\nIn order to add a dataset, I think the most updated instructions are in our official documentation pages: https://huggingface.co/docs/datasets/share", "Thanks for the info. Maybe you can update the contributing.md if it's not up-to-date.", "Hi @jxmorris12, we have discovered the bug why `None` checksums wrongly appeared when generating the `dataset_infos.json` file:\r\n- #3892\r\n\r\nThe fix will be accessible once this PR merged. And we are planning to do our 2.0 release today.\r\n\r\nWe are also working on updating all our docs for our release today.", "Thanks @albertvillanova - congrats on the release!" ]

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[ "<s>I think this is because the tokenizer is stateful and because the order in which the splits are processed is not deterministic. Because of that, the hash of the tokenizer may change for certain splits, which causes issues with caching.\r\n\r\nTo fix this we can try making the order of the splits deterministic for map.</s>", "Actually this is not because of the order of the splits, but most likely because the tokenizer used to process the second split is in a state that has been modified by the first split.\r\n\r\nTherefore after reloading the first split from the cache, then the second split can't be reloaded since the tokenizer hasn't seen the first split (and therefore is considered a different tokenizer).\r\n\r\nThis is a bit trickier to fix, we can explore fixing this next week maybe", "Sorry didn't have the bandwidth to take care of this yet - will re-assign when I'm diving into it again !", "I had this issue with `run_speech_recognition_ctc.py` for wa2vec2.0 fine-tuning. I made a small change and the hash for the function (which includes tokenisation) is now the same before and after pre-porocessing. With the hash being the same, the caching works as intended.\r\n\r\nBefore:\r\n```\r\n def prepare_dataset(batch):\r\n # load audio\r\n sample = batch[audio_column_name]\r\n\r\n inputs = feature_extractor(sample[\"array\"], sampling_rate=sample[\"sampling_rate\"])\r\n batch[\"input_values\"] = inputs.input_values[0]\r\n batch[\"input_length\"] = len(batch[\"input_values\"])\r\n\r\n # encode targets\r\n additional_kwargs = {}\r\n if phoneme_language is not None:\r\n additional_kwargs[\"phonemizer_lang\"] = phoneme_language\r\n\r\n batch[\"labels\"] = tokenizer(batch[\"target_text\"], **additional_kwargs).input_ids\r\n\r\n return batch\r\n\r\n with training_args.main_process_first(desc=\"dataset map preprocessing\"):\r\n vectorized_datasets = raw_datasets.map(\r\n prepare_dataset,\r\n remove_columns=next(iter(raw_datasets.values())).column_names,\r\n num_proc=num_workers,\r\n desc=\"preprocess datasets\",\r\n )\r\n```\r\nAfter:\r\n```\r\n def prepare_dataset(batch, feature_extractor, tokenizer):\r\n # load audio\r\n sample = batch[audio_column_name]\r\n\r\n inputs = feature_extractor(sample[\"array\"], sampling_rate=sample[\"sampling_rate\"])\r\n batch[\"input_values\"] = inputs.input_values[0]\r\n batch[\"input_length\"] = len(batch[\"input_values\"])\r\n\r\n # encode targets\r\n additional_kwargs = {}\r\n if phoneme_language is not None:\r\n additional_kwargs[\"phonemizer_lang\"] = phoneme_language\r\n\r\n batch[\"labels\"] = tokenizer(batch[\"target_text\"], **additional_kwargs).input_ids\r\n\r\n return batch\r\n\r\n pd = lambda batch: prepare_dataset(batch, feature_extractor, tokenizer)\r\n\r\n with training_args.main_process_first(desc=\"dataset map preprocessing\"):\r\n vectorized_datasets = raw_datasets.map(\r\n pd,\r\n remove_columns=next(iter(raw_datasets.values())).column_names,\r\n num_proc=num_workers,\r\n desc=\"preprocess datasets\",\r\n )\r\n```", "Not sure why the second one would work and not the first one - they're basically the same with respect to hashing. In both cases the function is hashed recursively, and therefore the feature_extractor and the tokenizer are hashed the same way.\r\n\r\nWith which tokenizer or feature extractor are you experiencing this behavior ?\r\n\r\nDo you also experience this ?\r\n> Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache.", "Thanks ! Hopefully this can be useful to others, and also to better understand and improve hashing/caching ", "`tokenizer.save_pretrained(training_args.output_dir)` produces a different tokenizer hash when loaded on restart of the script. When I was debugging before I was terminating the script prior to this command, then rerunning. \r\n\r\nI compared the tokenizer items on the first and second runs, there are two different items:\r\n1st:\r\n```\r\n('_additional_special_tokens', [AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True)])\r\n\r\n...\r\n\r\n('tokens_trie', <transformers.tokenization_utils.Trie object at 0x7f4d6d0ddb38>)\r\n```\r\n\r\n2nd:\r\n```\r\n('_additional_special_tokens', [AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True)])\r\n\r\n...\r\n\r\n('tokens_trie', <transformers.tokenization_utils.Trie object at 0x7efc23dcce80>)\r\n```\r\n\r\n On every run of this the special tokens are being added on, and the hash is different on the `tokens_trie`. The increase in the special tokens category could be cleaned, but not sure about the hash for the `tokens_trie`. What might work is that the call for the tokenizer encoding can be translated into a function that strips any unnecessary information out, but that's a guess.\r\n", "Thanks for investigating ! Does that mean that `save_pretrained`() produces non-deterministic tokenizers on disk ? Or is it `from_pretrained()` which is not deterministic given the same files on disk ?\r\n\r\nI think one way to fix this would be to make save/from_pretrained deterministic, or make the pickling of `transformers.tokenization_utils.Trie` objects deterministic (this could be implemented in `transformers`, but maybe let's discuss in an issue in `transformers` before opening a PR)", "Late to the party but everything should be deterministic (afaik at least).\r\n\r\nBut `Trie` is a simple class object, so afaik it's hash function is linked to its `id(self)` so basically where it's stored in memory, so super highly non deterministic. Could that be the issue ?", "> But Trie is a simple class object, so afaik it's hash function is linked to its id(self) so basically where it's stored in memory, so super highly non deterministic. Could that be the issue ?\r\n\r\nWe're computing the hash of the pickle dump of the class so it should be fine, as long as the pickle dump is deterministic", "I've ported wav2vec2.0 fine-tuning into Optimum-Graphcore which is where I found the issue. The majority of the script was copied from the Transformers version to keep it similar, [here is the tokenizer loading section from the source](https://github.com/huggingface/transformers/blob/f0982682bd6fd0b438dda79ec45f3a8fac83a985/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L531).\r\n\r\nIn the last comment I have two loaded tokenizers, one from run 'N' of the script and one from 'N+1'. I think what's happening is that when you add special tokens (e.g. PAD and UNK) another AddedToken object is appended when tokenizer is saved regardless of whether special tokens are there already. \r\n\r\nIf there is a AddedTokens cleanup at load/save this could solve the issue, but then is Trie going to cause hash to be different? I'm not sure. ", "Which Python version are you using ?\r\n\r\nThe trie is basically a big dict of dics, so deterministic nature depends on python version:\r\nhttps://stackoverflow.com/questions/2053021/is-the-order-of-a-python-dictionary-guaranteed-over-iterations\r\n\r\nMaybe the investigation is actually not finding the right culprit though (the memory id is changed, but `datasets` is not using that to compare, so maybe we need to be looking within `datasets` so see where the comparison fails)", "Similar issue found on `BartTokenizer`. You can bypass the bug by loading a fresh new tokenizer everytime.\r\n\r\n```\r\n dataset = dataset.map(lambda x: tokenize_func(x, BartTokenizer.from_pretrained(xxx)),\r\n num_proc=num_proc, desc='Tokenize')\r\n```", "Linking in https://github.com/huggingface/datasets/issues/6179#issuecomment-1701244673 with an explanation.", "I got the same problem while using Wav2Vec2CTCTokenizer in a distributed experiment (many processes), and found that the problem was localized in the serialization (pickle dump) of the field `tokenizer.tokens_trie._tokens` (just a python set). I focussed into the set serialization and found it is not deterministic:\r\n\r\n```\r\nfrom datasets.fingerprint import Hasher\r\nfrom pickle import dumps,loads\r\n\r\n# used just once to get a serialized literal\r\n#print(dumps(set(\"abc\")))\r\nserialized = b'\\x80\\x04\\x95\\x11\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x8f\\x94(\\x8c\\x01a\\x94\\x8c\\x01c\\x94\\x8c\\x01b\\x94\\x90.'\r\n\r\nmyset = loads(serialized)\r\nprint(f'{myset=} {Hasher.hash(myset)}')\r\nprint(serialized == dumps(myset))\r\n```\r\n\r\nEvery time you run the python script (different processes) you get a random result. @lhoestq does it make any sense?", "OK, I assume python's set is just a hash table implementation that uses internally the hash() function. The problem is that python's hash() is not deterministic. I believe that setting the environment variable PYTHONHASHSEED to a fixed value, you can force it to be deterministic. I tried it (file `set_pickle_dump.py`):\r\n\r\n```\r\n#!/usr/bin/python3\r\n\r\nfrom datasets.fingerprint import Hasher\r\nfrom pickle import dumps,loads\r\n\r\n# used just once to get a serialized literal (with environment variable PYTHONHASHSEED set to 42)\r\n#print(dumps(set(\"abc\")))\r\nserialized = b'\\x80\\x04\\x95\\x11\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x8f\\x94(\\x8c\\x01b\\x94\\x8c\\x01c\\x94\\x8c\\x01a\\x94\\x90.'\r\n\r\nmyset = loads(serialized)\r\nprint(f'{myset=} {Hasher.hash(myset)}')\r\nprint(serialized == dumps(myset))\r\n```\r\n\r\nand now every run (`PYTHONHASHSEED=42 ./set_pickle_dump.py`) gets tthe same result. I tried then to test it with the tokenizer (file `test_tokenizer.py`):\r\n\r\n```\r\n#!/usr/bin/python3\r\nfrom transformers import AutoTokenizer\r\nfrom datasets.fingerprint import Hasher\r\n\r\ntokenizer = AutoTokenizer.from_pretrained('model')\r\nprint(f'{type(tokenizer)=}')\r\nprint(f'{Hasher.hash(tokenizer)=}')\r\n```\r\n\r\nexecuted as `PYTHONHASHSEED=42 ./test_tokenizer.py` and now the tokenizer fingerprint is allways the same!\r\n", "Thanks for reporting. I opened a PR here to propose a fix: https://github.com/huggingface/datasets/pull/6318 and doesn't require setting `PYTHONHASHSEED`\r\n\r\nCan you try to install `datasets` from this branch and tell me if it fixes the issue ?", "I patched (*) the file `datasets/utils/py_utils.py` and cache is working propperly now. Thanks!\r\n\r\n(*): I am running my experiments inside a docker container that depends on `huggingface/transformers-pytorch-gpu:latest`, so pattched the file instead of rebuilding the container from scratch", "Fixed by #6318.", "The OP issue hasn't been fixed, re-opening", "I think the Trie()._tokens of PreTrainedTokenizer need to be a sorted set So that the results of `hash_bytes(dumps(tokenizer))` are consistent every time", "I believe the issue may be linked to [tokenization_utils.py#L507](https://github.com/huggingface/transformers/blob/main/src/transformers/tokenization_utils.py#L507)，specifically in the line where self.tokens_trie.add(token.content) is called. The function _update_trie appears to modify an unordered set. Consequently, this line:\r\n`value = hash_bytes(dumps(tokenizer.tokens_trie._tokens))`\r\ncan lead to inconsistencies when rerunning the code.\r\n\r\nThis, in turn, results in inconsistent outputs for both `hash_bytes(dumps(function))` at [arrow_dataset.py#L3053](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L3053) and\r\n`hasher.update(transform_args[key])` at [fingerprint.py#L323](https://github.com/huggingface/datasets/blob/main/src/datasets/fingerprint.py#L323)\r\n\r\n```\r\ndataset_kwargs = {\r\n \"shard\": raw_datasets,\r\n \"function\": tokenize_function,\r\n}\r\ntransform = format_transform_for_fingerprint(Dataset._map_single)\r\nkwargs_for_fingerprint = format_kwargs_for_fingerprint(Dataset._map_single, (), dataset_kwargs)\r\nkwargs_for_fingerprint[\"fingerprint_name\"] = \"new_fingerprint\"\r\nnew_fingerprint = update_fingerprint(raw_datasets._fingerprint, transform, kwargs_for_fingerprint)\r\n```\r\n", "Alternatively, does the \"dumps\" function require separate processing for the set?", "We did a fix that does sorting whenever we hash sets. The fix is available on `main` if you want to try it out. We'll do a new release soon :)", "Is there a documentation chapter that discusses in which cases you should expect your dataset preprocessing to be cached. Including do's and don'ts for the preprocessing functions? I think Datasets team does amazing job at tacking this issue on their side, but it would be great to have some guidelines on the user side as well.\r\n\r\nIn our current project we have two cases (text-to-text classification and summarization) and in one of them the cache is sometimes reused when it's not supposed to be reused while in the other it's never used at all 😅", "You can find some docs here :) \r\nhttps://huggingface.co/docs/datasets/about_cache" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3846). All of your documentation changes will be reflected on that endpoint." ]

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{ "diff_url": "https://github.com/huggingface/datasets/pull/3845.diff", "html_url": "https://github.com/huggingface/datasets/pull/3845", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/3845.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3845" }

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3845). All of your documentation changes will be reflected on that endpoint.", "@mariosasko I've reopened it here. Please suggest any changes if required. Thank you.", "Thanks for suggestions. :) I have added update the KWARGS_DESCRIPTION for the missing params and also changed RMSE to MSE.\r\nWhile testing, I noticed that when the input is a list of lists, we get an error :\r\n`TypeError: float() argument must be a string or a number, not 'list'`\r\nCould you suggest the datasets.Value() attribute to support both list of floats and list of lists containing floats ?\r\n", "Just add a new config to cover that case. You can do this by replacing the current `features` dict with:\r\n```python\r\nfeatures=datasets.Features(\r\n {\r\n \"predictions\": datasets.Sequence(datasets.Value(\"float\")),\r\n \"references\": datasets.Sequence(datasets.Value(\"float\")),\r\n }\r\n if self.config_name == \"multioutput\"\r\n else {\r\n \"predictions\": datasets.Value(\"float\"),\r\n \"references\": datasets.Value(\"float\"),\r\n }\r\n),\r\n```\r\nFeel free to suggest a better name for the config than `multioutput`", "Also, could you please move the changes to a new branch and open a PR from there (for the 3rd time 😄) because the diff shows changes from unrelated PRs (maybe due to rebasing?).", "Thanks for the input, I have added new config to support multi-dimensional lists and updated the examples as well.\r\n\r\nSure. Will do that and open a new PR for these changes." ]

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{ "diff_url": "https://github.com/huggingface/datasets/pull/3844.diff", "html_url": "https://github.com/huggingface/datasets/pull/3844", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/3844.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3844" }

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3844). All of your documentation changes will be reflected on that endpoint.", "@dnaveenr This PR is in pretty good shape, so feel free to reopen it." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3843). All of your documentation changes will be reflected on that endpoint.", "Cool ! Looks like it breaks `test_streaming_gg_drive_gzipped` for some reason..." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3842). All of your documentation changes will be reflected on that endpoint.", "We should also add `generator` as a param to `shuffle` to fully align the APIs, no?", "I added the `generator` argument.\r\n\r\nI had to make a few other adjustments to make it work. In particular when you call `set_epoch()` on a streaming dataset, it updates the underlying random generator by using a new effective seed. The effective seed is generated using the previous generator and the epoch number." ]

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[ "Hi! \r\n\r\nThis issue stems from `datasets` having `py.typed` defined (see https://github.com/microsoft/pyright/discussions/3764#discussioncomment-3282142) - to avoid it, we would either have to remove `py.typed` (added to be compliant with PEP-561) or export the names with `__all__`/`from .submodule import name as name`.\r\n\r\nTransformers is fine as it no longer has `py.typed` (removed in https://github.com/huggingface/transformers/pull/18485)\r\n\r\nWDYT @lhoestq @albertvillanova @polinaeterna \r\n\r\n@sgugger's point makes sense - we should either be \"properly typed\" (have py.typed + mypy tests) or drop `py.typed` as Transformers did (I like this option better).\r\n\r\n(cc @Wauplin since `huggingface_hub` has the same issue.)", "I'm fine with dropping it, but autotrain people won't be happy @SBrandeis ", "> (cc @Wauplin since huggingface_hub has the same issue.)\r\n\r\nHmm maybe we have the same issue but I haven't been able to reproduce something similar to `\"load_dataset\" is not exported from module \"datasets\"` message (using VSCode+Pylance -that is powered by Pyright). `huggingface_hub` contains a `py.typed` file but the package itself is actually typed. We are running `mypy` in our CI tests since ~3 months and so far it seems to be ok. But happy to change if it causes some issues with linters.\r\n\r\nAlso the top-level [`__init__.py`](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/__init__.py) is quite different in `hfh` than `datasets` (at first glance). We have a section at the bottom to import all high level methods/classes in a `if TYPE_CHECKING` block.", "@Wauplin I only get the error if I use Pyright's CLI tool or the Pyright extension (not sure why, but Pylance also doesn't report this issue on my machine)\r\n\r\n> Also the top-level [`__init__.py`](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/__init__.py) is quite different in `hfh` than `datasets` (at first glance). We have a section at the bottom to import all high level methods/classes in a `if TYPE_CHECKING` block.\r\n\r\nI tried to fix the issue with `TYPE_CHECKING`, but it still fails if `py.typed` is present.", "@mariosasko thank for the tip. I have been able to reproduce the issue as well. I would be up for including a (huge) static `__all__` variable in the `__init__.py` (since the file is already generated automatically in `hfh`) but honestly I don't think it's worth the hassle. \r\n\r\nI'll delete the `py.typed` file in `huggingface_hub` to be consistent between HF libraries. I opened a PR here: https://github.com/huggingface/huggingface_hub/pull/1329", "I am getting this error in google colab today:\r\n\r\n\r\n\r\nThe code runs just fine too." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3840). All of your documentation changes will be reflected on that endpoint." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3836). All of your documentation changes will be reflected on that endpoint.", "Weird, the logo doesn't seem to be floating on my side (using Chrome) at https://huggingface.co/docs/datasets/master/en/index", "https://huggingface.co/docs/datasets/index\r\n\r\nThe needed css change from moon-landing just got deployed" ]

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[ "It will be indeed really great to add support to GNN datasets. Big :+1: for this initiative.", "@napoles-uach identifies the [TUDatasets](https://chrsmrrs.github.io/datasets/) (A collection of benchmark datasets for graph classification and regression). \r\n\r\nAdded to the Tasks in the initial issue.", "Thanks Omar, that is a great collection!", "Great initiative! Let's keep this issue for these 3 datasets, but moving forward maybe let's create a new issue per dataset :rocket: great work @napoles-uach and @omarespejel!" ]

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[ "Maybe @Rocketknight1 can help here", "Hi @greenned, this is expected behaviour for `to_tf_dataset`. By default, we drop the smaller 'remainder' batch during training (i.e. when `shuffle=True`). If you really want to keep that batch, you can set `drop_remainder=False` when calling `to_tf_dataset()`.", "@Rocketknight1 Oh, thank you. I didn't get **drop_remainder** Have a nice day!", "No problem!\r\n" ]

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[ "Was able to reproduce the issue on Colab; full logs below. \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNotADirectoryError Traceback (most recent call last)\r\n[<ipython-input-2-39967739ba7f>](https://localhost:8080/#) in <module>()\r\n 1 import datasets\r\n 2 \r\n----> 3 train_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"train\")\r\n\r\n5 frames\r\n[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n 1705 ignore_verifications=ignore_verifications,\r\n 1706 try_from_hf_gcs=try_from_hf_gcs,\r\n-> 1707 use_auth_token=use_auth_token,\r\n 1708 )\r\n 1709 \r\n\r\n[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n 593 if not downloaded_from_gcs:\r\n 594 self._download_and_prepare(\r\n--> 595 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 596 )\r\n 597 # Sync info\r\n\r\n[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 659 split_dict = SplitDict(dataset_name=self.name)\r\n 660 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 661 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n 662 \r\n 663 # Checksums verification\r\n\r\n[/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234/cnn_dailymail.py](https://localhost:8080/#) in _split_generators(self, dl_manager)\r\n 253 def _split_generators(self, dl_manager):\r\n 254 dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n--> 255 train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)\r\n 256 # Generate shared vocabulary\r\n 257 \r\n\r\n[/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234/cnn_dailymail.py](https://localhost:8080/#) in _subset_filenames(dl_paths, split)\r\n 154 else:\r\n 155 logger.fatal(\"Unsupported split: %s\", split)\r\n--> 156 cnn = _find_files(dl_paths, \"cnn\", urls)\r\n 157 dm = _find_files(dl_paths, \"dm\", urls)\r\n 158 return cnn + dm\r\n\r\n[/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234/cnn_dailymail.py](https://localhost:8080/#) in _find_files(dl_paths, publisher, url_dict)\r\n 133 else:\r\n 134 logger.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 135 files = sorted(os.listdir(top_dir))\r\n 136 \r\n 137 ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'\r\n```", "Hi @jon-tow, thanks for reporting. And hi @dynamicwebpaige, thanks for your investigation. \r\n\r\nThis issue was already reported \r\n- #3784\r\n\r\nand its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe have already fixed it. See:\r\n- #3787 \r\n\r\nWe are planning to make a patch release today (indeed, we were planning to do it last Friday).\r\n\r\nIn the meantime, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```\r\n\r\nCC: @lhoestq " ]

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[ "Hi ! Yes this is definitely something we'll explore, since optimizing processing pipelines can be challenging and because performance is key here: we want anyone to be able to play with large-scale datasets more easily.\r\n\r\nI think we'll start by documenting the performance of the dataset transforms we provide, and then we can have some tools to help debugging/optimizing them" ]

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[ "Hi @dlwh, thanks for reporting.\r\n\r\nPlease note, that the source data files for \"all\" config are different from the other configurations.\r\n\r\nThe \"all\" config contains the official Pile data files, from https://mystic.the-eye.eu/public/AI/pile/\r\nAll data examples contain a \"meta\" dict with a single \"pile_set_name\" key:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ds = load_dataset(\"the_pile\", \"all\", split=\"train\", streaming=True)\r\n item = next(iter(ds))\r\nDownloading builder script: 9.09kB [00:00, 4.42MB/s]\r\n\r\nIn [3]: item[\"meta\"]\r\nOut[3]: {'pile_set_name': 'Pile-CC'}\r\n```\r\n\r\nOn the other hand, all the other subset configs data files come from the Pile preliminary components directory: https://mystic.the-eye.eu/public/AI/pile_preliminary_components/\r\nFor theses components, the \"meta\" field may have different keys depending on the subset: \"id\", \"language\", \"pmid\",... Because of that, if we had kept the `dict` data format for the \"meta\" field, we would have an error when trying to concatenate different subsets, whose \"meta\" keys are not identical. In order to avoid that, the \"meta\" field is cast to `str` in all these cases, so that there is no incompatibility in their \"meta\" data type when concatenating.\r\n\r\nYou can check, for example, that for \"pubmed_central\" the \"meta\" field is cast to `str`:\r\n```python\r\nIn [4]: from datasets import load_dataset\r\n ds = load_dataset(\"the_pile\", \"pubmed_central\", split=\"train\", streaming=True)\r\n item = next(iter(ds))\r\n\r\nIn [5]: item[\"meta\"]\r\nOut[5]: \"{'id': 'PMC6071596'}\"\r\n```\r\n\r\nFeel free to reopen this issue if you have further questions. " ]

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{ "diff_url": "https://github.com/huggingface/datasets/pull/3827.diff", "html_url": "https://github.com/huggingface/datasets/pull/3827", "merged_at": "2022-03-07T12:37:51Z", "patch_url": "https://github.com/huggingface/datasets/pull/3827.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3827" }

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3827). All of your documentation changes will be reflected on that endpoint." ]

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{ "diff_url": "https://github.com/huggingface/datasets/pull/3826.diff", "html_url": "https://github.com/huggingface/datasets/pull/3826", "merged_at": "2022-03-09T17:23:11Z", "patch_url": "https://github.com/huggingface/datasets/pull/3826.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3826" }

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3826). All of your documentation changes will be reflected on that endpoint.", "Indeed ! If `batch_size` is `None` or `<=0` then the full dataset should be passed. It's been mentioned in the docs for a while but never actually implemented. We can fix that later" ]

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{ "diff_url": "https://github.com/huggingface/datasets/pull/3825.diff", "html_url": "https://github.com/huggingface/datasets/pull/3825", "merged_at": "2022-03-04T17:24:36Z", "patch_url": "https://github.com/huggingface/datasets/pull/3825.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3825" }

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3825). All of your documentation changes will be reflected on that endpoint." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3824). All of your documentation changes will be reflected on that endpoint." ]

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[ "Hi @jacobbieker, thanks for reporting!\r\n\r\nI have transferred this issue to our Hub team and they are investigating it. I keep you informed. ", "Hi @jacobbieker, we are investigating this issue on our side and we'll see if we can fix it, but please note that your repo is considered problematic for git. Here are the results of running https://github.com/github/git-sizer on it:\r\n\r\n```\r\nProcessing blobs: 147448 \r\nProcessing trees: 27 \r\nProcessing commits: 4 \r\nMatching commits to trees: 4 \r\nProcessing annotated tags: 0 \r\nProcessing references: 3 \r\n| Name | Value | Level of concern |\r\n| ---------------------------- | --------- | ------------------------------ |\r\n| Biggest objects | | |\r\n| * Trees | | |\r\n| * Maximum entries [1] | 167 k | !!!!!!!!!!!!!!!!!!!!!!!!!!!!!! |\r\n| | | |\r\n| Biggest checkouts | | |\r\n| * Number of files [2] | 189 k | *** |\r\n\r\n[1] aa057d2667c34c70c6146efc631f5c9917ff326e (refs/heads/main:2016.zarr/unknown)\r\n[2] 6897b7bf6440fdd16b2c39d08085a669e7eaa59d (refs/heads/main^{tree})\r\n```\r\n\r\nYou can check https://github.com/github/git-sizer for more information on how to avoid such pathological structures.", "Hi, thanks for getting back to me so quick! And yeah, I figured that was probably the problem. I was going to try to delete the repo, but couldn't through the website, so if that's the easiest way to solve it, I can regenerate the dataset in a different format with less tiny files, and you guys can delete the repo as it is. Zarr just saves everything as lots of small files to make chunks easy to load, which is why I was preferring that format, but maybne that just doesn't work well for HF datasets.", "Hi @jacobbieker,\r\n\r\nFor future use cases, our Hub team is still pondering whether to limit the maximum number of files per repo to avoid technical issues...\r\n\r\nOn the meantime, they have made a fix and your dataset is working: https://huggingface.co/datasets/openclimatefix/mrms" ]

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[ "Official dataset location : https://icu.ee.ethz.ch/research/datsets.html\r\nIn the \"Biwi Kinect Head Pose Database\" section, I do not find any information regarding \"Downloading the dataset.\" . Do we mail the authors regarding this ?\r\n\r\nI found the dataset on Kaggle : [Link](https://www.kaggle.com/kmader/biwi-kinect-head-pose-database) , but since 🤗 does not host any of the datasets, this would require the user to provide their Kaggle username and API key to download. \r\n\r\nAny inputs on how we could proceed ? Thank you.\r\n[ Need your inputs here, @lhoestq or @mariosasko ]", "Hi @dnaveenr! Thanks for tackling this issue. This link should work: https://data.vision.ee.ethz.ch/cvl/gfanelli/kinect_head_pose_db.tgz", "#self-assign", "Added in https://github.com/huggingface/datasets/pull/3903, thanks @dnaveenr !" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I'm starting to generate the pre-processed data for some of the languages (for backward compatibility).\r\n\r\nOnce this merged, we will create the pre-processed data on the Hub under the Wikimedia namespace.", "All steps have been properly done.\r\n\r\nI'm merging all these commits into master." ]

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[ "Hi @jon-tow, thanks for reporting.\r\n\r\nThis issue was already reported and its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe have already fixed it. See:\r\n- #3787 \r\n\r\nWe are planning to make a patch release today.\r\n\r\nIn the meantime, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3819). All of your documentation changes will be reflected on that endpoint." ]

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[ "Hi, thanks for reporting! We can add a `sources: datasets.Value(\"string\")` feature to the `Features` dict in the `SARI` script to fix this. Would you be interested in submitting a PR?", "Hi Mario,\r\n\r\nThanks for your message. I did try to add `sources` into the `Features` dict using a script for the metric:\r\n```\r\n features=datasets.Features(\r\n {\r\n \"sources\": datasets.Value(\"string\", id=\"sequence\"),\r\n \"predictions\": datasets.Value(\"string\", id=\"sequence\"),\r\n \"references\": datasets.Sequence(datasets.Value(\"string\", id=\"sequence\"), id=\"references\"),\r\n }\r\n ),\r\n```\r\n\r\nBut that only avoids a failure in `encode_batch` in the `add_batch` method:\r\n```\r\n batch = {\"predictions\": predictions, \"references\": references}\r\n batch = self.info.features.encode_batch(batch)\r\n```\r\n\r\nThe real problem is that `add_batch()`, `add()` and `compute()` does not receive a `sources` param:\r\n```\r\ndef add_batch(self, *, predictions=None, references=None):\r\ndef add(self, *, prediction=None, reference=None):\r\ndef compute(self, *, predictions=None, references=None, **kwargs)\r\n```\r\n\r\nAnd then, it fails:\r\n`TypeError: add_batch() got an unexpected keyword argument sources`\r\n\r\nI need this for adding any metric based on SARI or alike, not only for sari.py :)\r\n\r\nLet me know if I understood correctly the proposed solution.\r\n", "The `Metric` class has been modified recently to support this use-case, but the `add_batch` + `compute` pattern still doesn't work correctly. I'll open a PR." ]

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[ "I think the script looks pretty clean and readable now! cool!\r\n" ]

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[ "Looks like I added my comments while you were editing - sorry about that" ]

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[ "I would like to take this up and give it a shot. Any image specific - dataset guidelines to keep in mind ? Thank you.", "#self-assign", "I've started working on adding this dataset. I require some inputs on the following : \r\n\r\nRef for the initial draft [here](https://github.com/dnaveenr/datasets/blob/add_metashift_dataset/datasets/metashift/metashift.py)\r\n1. The dataset does not have a typical - train/test/val split. What do we do for the _split_generators() function ? How do we go about this ?\r\n2. This dataset builds on the Visual Genome dataset, using a metadata file. The dataset is generated using generate_full_MetaShift.py script. By default, the authors choose to generate the dataset only for a SELECTED_CLASSES. The following script is used : \r\nCode : https://github.com/Weixin-Liang/MetaShift/blob/main/dataset/generate_full_MetaShift.py \r\nInfo : https://metashift.readthedocs.io/en/latest/sub_pages/download_MetaShift.html#generate-the-full-metashift-dataset\r\nCan I just copy over the required functions into the metashift.py to generate the dataset ?\r\n3. How do we complete the _generate_examples for this dataset ?\r\n\r\nThe user has the ability to use default selected classes, get the complete dataset or add more specific additional classes. I think config would be a good option here.\r\n\r\nInputs, suggestions would be helpful. Thank you.", "I think @mariosasko and @lhoestq should be able to help here 😄 ", "Hi ! Thanks for adding this dataset :) Let me answer your questions:\r\n\r\n1. in this case you can put everything in the \"train\" split\r\n2. Yes you can copy the script (provided you also include the MIT license of the code in the file header for example). Though we ideally try to not create new directories nor files when generating dataset, so if possible this script should be adapted to not create the file structure they mentioned, but instead yield the images one by one in `_generate_examples`. Let me know if you think this is feasible\r\n3. see point 2 haha\r\n\r\n> The user has the ability to use default selected classes, get the complete dataset or add more specific additional classes. I think config would be a good option here.\r\n\r\nYup ! We can also define a `selected_classes` parameter such that users can do\r\n```python\r\nload_dataset(\"metashift\", selected_classes=[\"cat\", \"dog\", ...])\r\n```", "Great. This is helpful. Thanks @lhoestq .\r\nRegarding Point 2, I'll try using yield instead of creating the directories and see if its feasible. selected_classes config sounds good.", "Closed via #3900 " ]

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[ "I'm closing the PR since we're not going to merge it" ]

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[ "Hi @muelletm, thanks for reporting.\r\n\r\nThis issue was already reported and its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe have already fixed it. See:\r\n- #3787 \r\n\r\nUntil our next `datasets` library release, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```" ]

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[ "Ok - this is still an issue but I believe the root cause is different than I originally thought. I'm now able to get caching to work consistently with the above example as long as I fix the python hash seed `export PYTHONHASHSEED=1234`", "Hi! \r\n\r\nYes, our hasher should work with decorators. For instance, this dummy example:\r\n```python\r\ndef f(arg):\r\n def f1(ex):\r\n return {\"a\": ex[\"col1\"] + arg}\r\n return f1\r\n```\r\ngives the same hash across different Python sessions (`datasets.fingerprint.Hasher.hash(f(\"string1\")` returns `\"408c9059f89dbd6c\"` on my machine).\r\n\r\nCould you please make the example self-contained? This way, we can reproduce the bug. Additionally, you can try to find the problematic object yourself by testing their hash with `datasets.fingerprint.Hasher.hash(obj)`\r\n\r\nThis could be related to https://github.com/huggingface/datasets/issues/3638.", "#3638 was indeed my issue. Thanks!" ]

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[ "@albertvillanova here's a separate issue for a bug similar to #3792", "Hi @afcruzs-ms, thanks for opening this separate issue for your problem.\r\n\r\nThe root problem in the other issue (#3792) was a change in the service of Google Drive.\r\n\r\nBut in your case, the `xcopa` dataset is not hosted on Google Drive. Therefore, the root cause should be a different one.\r\n\r\nLet me look at it... ", "@afcruzs-ms, I'm not able to reproduce the issue you reported:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ...: dataset = load_dataset(\"xcopa\", \"it\")\r\nDownloading builder script: 5.21kB [00:00, 2.75MB/s] \r\nDownloading metadata: 28.6kB [00:00, 14.5MB/s] \r\nDownloading and preparing dataset xcopa/it (download: 627.09 KiB, generated: 76.43 KiB, post-processed: Unknown size, total: 703.52 KiB) to .../.cache/huggingface/datasets/xcopa/it/1.0.0/e1fab65f984b24c8b66bcf7ac27a26a1182f84adfb2e74035861be65e214b9e6...\r\nDownloading data: 642kB [00:00, 5.42MB/s]\r\nDataset xcopa downloaded and prepared to .../.cache/huggingface/datasets/xcopa/it/1.0.0/e1fab65f984b24c8b66bcf7ac27a26a1182f84adfb2e74035861be65e214b9e6. Subsequent calls will reuse this data. \r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 733.27it/s]\r\n\r\nIn [2]: dataset\r\nOut[2]: \r\nDatasetDict({\r\n test: Dataset({\r\n features: ['premise', 'choice1', 'choice2', 'question', 'label', 'idx', 'changed'],\r\n num_rows: 500\r\n })\r\n validation: Dataset({\r\n features: ['premise', 'choice1', 'choice2', 'question', 'label', 'idx', 'changed'],\r\n num_rows: 100\r\n })\r\n})\r\n```\r\n\r\nMaybe you have some issue with your cached data... Could you please try to force the redownload of the data?\r\n```python\r\ndataset = load_dataset(\"xcopa\", \"it\", download_mode=\"force_redownload\")\r\n```", "It works indeed, thanks! ", "unfortunately, i am having a similar problem with the irc_disentaglement dataset :/\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\n\r\nhowever, it produces the same error as @afcruzs-ms \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\n\r\nI attempted to use the `ignore_verifications' as such:\r\n```\r\nds = datasets.load_dataset('irc_disentangle', download_mode=\"force_redownload\", ignore_verifications=True)\r\n\r\n```\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\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\n\r\nnot sure what else to try at this point?\r\nThanks in advanced🤗", "Thanks @labouz for reporting: yes, better opening a new GitHub issue as you did. I'm addressing it:\r\n- #4376" ]

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[ "Gently pinging @lhoestq", "Hi ! Interresting :)\r\n\r\nCould you give more details on what kind of separators you would like to use instead ?", "In my case, I just want to use `\\n` but not `U+2028`.", "Ok I see, maybe there can be a `sep` parameter to allow users to specify what line/paragraph separator they'd like to use", "Related to:\r\n- #3729 \r\n- #3910", "Thanks for requesting this enhancement. We have recently found a somehow related issue with another dataset:\r\n- #3704\r\n\r\nLet me make a PR proposal." ]

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[ "This is awesome ! The dataset card and the dataset script look amazing :)\r\n\r\nI wanted to ask you if you'd be interested to have this dataset under the namespace of you research group at https://huggingface.co/coastalcph ? If yes, then you can actually create a dataset repository under your research group name and upload the files from this PR there", "Hi @lhoestq,\r\n\r\nYeah, I could do that. I see that people do that a lot of models, but not for datasets. \r\n\r\nIs there any good reason to have it under the organization domain instead of the general domain?\r\n\r\n Thanks!", "It's nice to have it under your namespace:\r\n- it will appear on your research group page, along with your models\r\n- you can edit or create datasets at any time - you don't need to open PRs on GitHub\r\n\r\nAll the datasets that are not under a namespace are this way because we started adding datasets from GitHub. Now we encourage users to upload them directly to make things simpler, and aligned with the workflow for models\r\n\r\n(the documentation will be updated in the following days)\r\n\r\nNote that we will keep accepting PRs here though when there is no clear namespace under which a dataset should be, or for users that want a review from us", "Ok, I'll do that. So, I'll just have to upload all the files under the `/fairlex` directory in my PR, right?", "Yes exactly !", "Ok, I uploaded most of them from the UI environment (https://huggingface.co/datasets/coastalcph/fairlex). Can I possibly upload the dummy data as well from the UI environment. I really want to avoid the CLI right now 😄 ", "Yea sure, feel free to use the UI of the website, even for the dummy data ^^", "Did you upload them yourself? Because I see the data preview, and I'm pretty sure, I didn't do that 😄 ", "The preview is computed from the real data ;)\r\n\r\nThe dummy data are used for testing only", "Haha, ok I was shocked! Cool, I close this PR, then. Thanks, again! ", "Thank you 🤗" ]

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[ "Right ! Will add it in another PR :)" ]

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[ "@lhoestq - if you could take a final review here this would be great (if you have 5min :-) ) ", "Don't think the failures are related but not 100% sure", "Yes the CI fail is unrelated - you can ignore it" ]

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[ "same issue. downgrade it to a lower version.", "Thanks for reporting, I'll take a look tomorrow :)" ]

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[ "It seems like the doc can't be compiled right now because of the following:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/usr/local/bin/doc-builder\", line 33, in <module>\r\n sys.exit(load_entry_point('doc-builder', 'console_scripts', 'doc-builder')())\r\n File \"/__w/datasets/datasets/doc-builder/src/doc_builder/commands/doc_builder_cli.py\", line 39, in main\r\n args.func(args)\r\n File \"/__w/datasets/datasets/doc-builder/src/doc_builder/commands/build.py\", line 95, in build_command\r\n build_doc(\r\n File \"/__w/datasets/datasets/doc-builder/src/doc_builder/build_doc.py\", line 361, in build_doc\r\n anchors_mapping = build_mdx_files(package, doc_folder, output_dir, page_info)\r\n File \"/__w/datasets/datasets/doc-builder/src/doc_builder/build_doc.py\", line 200, in build_mdx_files\r\n raise type(e)(f\"There was an error when converting {file} to the MDX format.\\n\" + e.args[0]) from e\r\nTypeError: There was an error when converting datasets/docs/source/package_reference/table_classes.mdx to the MDX format.\r\nexpected string or bytes-like object\r\n```", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3793). All of your documentation changes will be reflected on that endpoint.", "This is due to the injection of docstrings from PyArrow. I think I can fix that by moving all the docstrings and fix them manually.", "> It seems like the doc can't be compiled right now because of the following:\r\n\r\nit is expected since there is something I need to change on doc-builder side.\r\n\r\n> This is due to the injection of docstrings from PyArrow. I think I can fix that by moving all the docstrings and fix them manually.\r\n\r\n@lhoestq I will let you know if we need to change it manually.\r\n\r\n@LysandreJik thanks a lot for this PR! I only had one question [here](https://github.com/huggingface/datasets/pull/3793#discussion_r816100194)", "> @lhoestq I will let you know if we need to change it manually.\r\n\r\nIt would be simpler to change it manually anyway - I don't want our documentation to break if PyArrow has documentation issues", "For some reason it fails when `Installing node dependencies` when running `npm ci` from the `kit` directory, any idea why @mishig25 ?", "Checking it rn", "It's very likely linked to an OOM error: https://github.com/huggingface/transformers/pull/15710#issuecomment-1051737337" ]

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[ "Same issue with `dataset = load_dataset(\"dbpedia_14\")`\r\n```\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k']", "I think this is a side-effect of #3787. The checksums won't match because the URLs have changed. @rafikg @Y0mingZhang, while this is fixed, maybe you can load the datasets as such:\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\", ignore_verifications=True)`\r\n`dataset = load_dataset(\"dbpedia_14\", ignore_verifications=True)`\r\n\r\nThis will, most probably, skip the verifications and integrity checks listed [here](https://huggingface.co/docs/datasets/loading_datasets.html#integrity-verifications)", "Hi! Installing the `datasets` package from master (`pip install git+https://github.com/huggingface/datasets.git`) and then redownloading the datasets with `download_mode` set to `force_redownload` (e.g. `dataset = load_dataset(\"dbpedia_14\", download_mode=\"force_redownload\")`) should fix the issue.", "Hi @rafikg and @Y0mingZhang, thanks for reporting.\r\n\r\nIndeed it seems that Google Drive changed their way to access their data files. We have recently handled that change:\r\n- #3787\r\n\r\nbut it will be accessible to users only in our next release of the `datasets` version.\r\n- Note that our latest release (version 1.18.3) was made before this fix: https://github.com/huggingface/datasets/releases/tag/1.18.3\r\n\r\nIn the meantime, as @mariosasko explained, you can incorporate this \"fix\" by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\nThen, you should force the redownload of the data (before the fix, you are just downloading/caching the virus scan warning page, instead of the data file):\r\n```shell\r\ndata = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\", download_mode=\"force_redownload\")", "@albertvillanova by running:\r\n```\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\ndata = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\", download_mode=\"force_redownload\", ignore_verifications=True)\r\n```\r\n\r\nI had a pickle error **UnpicklingError: invalid load key, '<'** in this part of code both `locally and on google colab`:\r\n\r\n```\r\n\"\"\"Yields examples.\"\"\"\r\nwith open(filepath, \"rb\") as f:\r\n data = pickle.load(f)\r\nfor id_, row in enumerate(data.items()):\r\n yield id_, {\"url\": row[0], \"article\": self._process_article(row[1])}\r\n```\r\n", "This issue impacts many more datasets than the ones mention in this thread. Can we post # of downloads for each dataset by day (by successes and failures)? If so, it should be obvious which ones are failing.", "I can see this problem too in xcopa, unfortunately installing the latest master (1.18.4.dev0) doesn't work, @albertvillanova .\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"xcopa\", \"it\")\r\n```\r\n\r\nThrows\r\n\r\n```\r\nin verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n 38 if len(bad_urls) > 0:\r\n 39 error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n 41 logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n 42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://github.com/cambridgeltl/xcopa/archive/master.zip']\r\n```", "Hi @rafikg, I think that is another different issue. Let me check it... \r\n\r\nI guess maybe you are using a different Python version that the one the dataset owner used to create the pickle file...", "@kwchurch the datasets impacted for this specific issue are the ones which are hosted at Google Drive.", "@afcruzs-ms I think your issue is a different one, because that dataset is not hosted at Google Drive. Would you mind open another issue for that other problem, please? Thanks! :)", "@albertvillanova just to let you know that I tried it locally and on colab and it is the same error", "There are many many datasets on HugggingFace that are receiving this checksum error. Some of these datasets are very popular. There must be a way to track these errors, or to do regression testing. We don't want to catch each of these errors on each dataset, one at a time.", "@rafikg I am sorry, but I can't reproduce your issue. For me it works OK for all languages. See: https://colab.research.google.com/drive/1yIcLw1it118-TYE3ZlFmV7gJcsF6UCsH?usp=sharing", "@kwchurch the PR #3787 fixes this issue (generated by a change in Google Drive service) for ALL datasets with this issue. Once we make our next library release (in a couple of days), the fix will be accessible to all users that update our library from PyPI.", "By the way, @rafikg, I discovered the URL for Spanish was wrong. I've created a PR to fix it:\r\n- #3806 ", "I have the same problem with \"wider_face\" dataset. It seems that \"load_dataset\" function can not download the dataset from google drive.\r\n", "still getting this issue with datasets==2.2.2 for \r\ndataset_fever_original_dev = load_dataset('fever', \"v1.0\", split=\"labelled_dev\")\r\n(this one seems to be hosted by aws though)\r\n\r\nupdate: also tried to install from source to get the latest 2.2.3.dev0, but still get the error below (and also force-redownloaded)\r\n\r\nupdate2: Seems like this issues is linked to a change in the links in the specific fever datasets: https://fever.ai/\r\n\"28/04/2022\r\nDataset download URLs have changed\r\nDownload URLs for shared task data for FEVER, FEVER2.0 and FEVEROUS have been updated. New URLS begin with https://fever.ai/download/[task name]/[filename]. All resource pages have been updated with the new URLs. Previous dataset URLs may not work and should be updated if you require these in your scripts. \"\r\n\r\n=> I don't know how to update the links for HF datasets - would be great if someone could update them :) \r\n\r\n```\r\n\r\nDownloading and preparing dataset fever/v1.0 (download: 42.78 MiB, generated: 38.39 MiB, post-processed: Unknown size, total: 81.17 MiB) to /root/.cache/huggingface/datasets/fever/v1.0/1.0.0/956b0a9c4b05e126fd956be73e09da5710992b5c85c30f0e5e1c500bc6051d0a...\r\n\r\nDownloading data files: 100%\r\n6/6 [00:07<00:00, 1.21s/it]\r\nDownloading data:\r\n278/? [00:00<00:00, 2.34kB/s]\r\nDownloading data:\r\n278/? [00:00<00:00, 1.53kB/s]\r\nDownloading data:\r\n278/? [00:00<00:00, 7.43kB/s]\r\nDownloading data:\r\n278/? [00:00<00:00, 5.54kB/s]\r\nDownloading data:\r\n278/? [00:00<00:00, 6.19kB/s]\r\nDownloading data:\r\n278/? [00:00<00:00, 7.51kB/s]\r\nExtracting data files: 100%\r\n6/6 [00:00<00:00, 108.05it/s]\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNonMatchingChecksumError Traceback (most recent call last)\r\n\r\n[<ipython-input-20-92ec5c728ecf>](https://localhost:8080/#) in <module>()\r\n 27 # get labels for fever-nli-dev from original fever - only works for dev\r\n 28 # \"(The labels for both dev and test are hidden but you can retrieve the label for dev using the cid and the original FEVER data.)\"\" https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md\r\n---> 29 dataset_fever_original_dev = load_dataset('fever', \"v1.0\", split=\"labelled_dev\")\r\n 30 df_fever_original_dev = pd.DataFrame(data={\"id\": dataset_fever_original_dev[\"id\"], \"label\": dataset_fever_original_dev[\"label\"], \"claim\": dataset_fever_original_dev[\"claim\"], \"evidence_id\": dataset_fever_original_dev[\"evidence_id\"]})\r\n 31 df_fever_dev = pd.merge(df_fever_dev, df_fever_original_dev, how=\"left\", left_on=\"cid\", right_on=\"id\")\r\n\r\n4 frames\r\n\r\n[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n 38 if len(bad_urls) > 0:\r\n 39 error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n 41 logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n 42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://s3-eu-west-1.amazonaws.com/fever.public/train.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_dev.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_dev_public.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_test.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/paper_dev.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/paper_test.jsonl']\r\n```\r\n", "I think this has to be fixed on the google drive side, but you also have to delete the bad stuff from your local cache. This is not a great design, but it is what it is.", "We have fixed the issues with the datasets:\r\n- wider_face: by hosting their data files on the HuggingFace Hub (CC: @HosseynGT)\r\n- fever: by updating to their new data URLs (CC: @MoritzLaurer)", "The yelp_review_full datasets has this problem as well and can't be fixed with the suggestion.", "This is a super-common failure mode. We really need to find a better workaround. My solution was to wait until the owner of the dataset in question did the right thing, and then I had to delete my cached versions of the datasets with the bad checksums. I don't understand why this happens. Would it be possible to maintain a copy of the most recent version that was known to work, and roll back to that automatically if the checksums fail? And if the checksums fail, couldn't the system automatically flush the cached versions with the bad checksums? It feels like we are blaming the provider of the dataset, when in fact, there are things that the system could do to ease the pain. Let's take these error messages seriously. There are too many of them involving too many different datasets.", "the [exams](https://huggingface.co/datasets/exams) dataset also has this issue and the provided fix above doesn't work", "Same for [DART dataset](https://huggingface.co/datasets/dart):\r\n```\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-train.json', 'https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-dev.json', 'https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-test.json']\r\n```", "same for multi_news dataset", "- @thesofakillers the issue with `exams` was fixed on 16 Aug by this PR:\r\n - #4853\r\n- @Aktsvigun the issue with `dart` has been transferred to the Hub: https://huggingface.co/datasets/dart/discussions/1\r\n - and fixed by PR: https://huggingface.co/datasets/dart/discussions/2\r\n- @Carol-gutianle the issue with `multi_news` have been transferred to the Hub as well: https://huggingface.co/datasets/multi_news/discussions/1\r\n - not reproducible: maybe you should try to update `datasets`\r\n\r\nFor information to everybody, we are removing the checksum verifications (that were creating a bad user experience). This will be in place in the following weeks.", "auto_gptq is required for real quantization\r\n['/home/sam/Doctorproject/OmniQuant-main/main.py', '--model', '/home/sam/Doctorproject/OmniQuant-main/PATH/TO/LLaMA/llama-7b/', '--epochs', '20', '--output_dir', '/home/sam/Doctorproject/OmniQuant-main/outdir/llama-7b-w3a16/', '--eval_ppl', '--wbits', '3', '--abits', '16', '--lwc', '--net', 'llama-7b', '--aug_loss']\r\n[2024-03-13 17:58:48 root](main.py 262): INFO Namespace(model='/home/sam/Doctorproject/OmniQuant-main/PATH/TO/LLaMA/llama-7b/', cache_dir='./cache', output_dir='/home/sam/Doctorproject/OmniQuant-main/outdir/llama-7b-w3a16/', save_dir=None, resume=None, real_quant=False, calib_dataset='wikitext2', nsamples=128, batch_size=1, seed=2, tasks='', eval_ppl=True, num_fewshot=0, wbits=3, abits=16, group_size=None, alpha=0.5, let_lr=0.005, lwc_lr=0.01, wd=0, epochs=20, let=False, lwc=True, aug_loss=True, symmetric=False, disable_zero_point=False, a_dynamic_method='per_token', w_dynamic_method='per_channel', limit=-1, multigpu=False, deactive_amp=False, attn_implementation='eager', net='llama-7b', act_scales=None, act_shifts=None)\r\nLoading checkpoint shards: 0%| | 0/33 [00:00<?, ?it/s]/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/torch/_utils.py:776: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\r\n return self.fget.__get__(instance, owner)()\r\nLoading checkpoint shards: 100%|██████████| 33/33 [00:11<00:00, 2.98it/s]\r\nvocab size: 32000\r\n[2024-03-13 17:58:59 root](main.py 331): INFO === start quantization ===\r\nget_wikitext2\r\n[2024-03-13 18:02:20 datasets.load](load.py 1586): WARNING Using the latest cached version of the module from /home/sam/.cache/huggingface/modules/datasets_modules/datasets/wikitext/a241db52902eaf2c6aa732210bead40c090019a499ceb13bcbfa3f8ab646a126 (last modified on Wed Mar 13 16:54:26 2024) since it couldn't be found locally at wikitext, or remotely on the Hugging Face Hub.\r\nUsing the latest cached version of the module from /home/sam/.cache/huggingface/modules/datasets_modules/datasets/wikitext/a241db52902eaf2c6aa732210bead40c090019a499ceb13bcbfa3f8ab646a126 (last modified on Wed Mar 13 16:54:26 2024) since it couldn't be found locally at wikitext, or remotely on the Hugging Face Hub.\r\nDownloading data: 243B [00:00, 877kB/s]\r\nGenerating test split: 0%| | 0/4358 [00:00<?, ? examples/s]\r\nTraceback (most recent call last):\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/builder.py\", line 1742, in _prepare_split_single\r\n for key, record in generator:\r\n File \"/home/sam/.cache/huggingface/modules/datasets_modules/datasets/wikitext/a241db52902eaf2c6aa732210bead40c090019a499ceb13bcbfa3f8ab646a126/wikitext.py\", line 187, in _generate_examples\r\n with open(data_file, encoding=\"utf-8\") as f:\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/streaming.py\", line 75, in wrapper\r\n return function(*args, download_config=download_config, **kwargs)\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 507, in xopen\r\n return open(main_hop, mode, *args, **kwargs)\r\nNotADirectoryError: [Errno 20] Not a directory: '/home/sam/.cache/huggingface/datasets/downloads/94be2a7b3fff32ae7379658c8d3821035b666baddad3a06d29b55ab3a4ab3115/wikitext-2-raw/wiki.test.raw'\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/home/sam/Doctorproject/OmniQuant-main/main.py\", line 382, in <module>\r\n main()\r\n File \"/home/sam/Doctorproject/OmniQuant-main/main.py\", line 339, in main\r\n dataloader, _ = get_loaders(\r\n File \"/home/sam/Doctorproject/OmniQuant-main/datautils.py\", line 178, in get_loaders\r\n return get_wikitext2(nsamples, seed, seqlen, model)\r\n File \"/home/sam/Doctorproject/OmniQuant-main/datautils.py\", line 37, in get_wikitext2\r\n traindata = load_dataset(path='wikitext', name='wikitext-2-raw-v1', split='train', download_mode=\"force_redownload\")\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/load.py\", line 2598, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/builder.py\", line 1021, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/builder.py\", line 1783, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/builder.py\", line 1116, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/builder.py\", line 1621, in _prepare_split\r\n for job_id, done, content in self._prepare_split_single(\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/builder.py\", line 1778, in _prepare_split_single\r\n raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\ndatasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset\r\n\r\n\r\n@albertvillanova @Y0mingZhang @kwchurch @HosseynGT @rafikg I tried the solutions you provided above, but none of them worked. Could you please give me some guidance\r\n" ]

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