Datasets:

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cylee

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[ "I am very unsure on why you tagged me here. I am not a maintainer of the Datasets library and have no idea how to help you.", "fixed", "Ok got it, tensor full of NaNs, cf.\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write_examples_on_file(self)\r\n315 # This check fails with FloatArrays with nans, which is not what we want, so account for that:", "Actually this is is a live bug, documented yet still live so reopening" ]

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[ "Something like this would fix it I think: https://github.com/huggingface/datasets/compare/master...Dref360:HF-3111/concatenate_types?expand=1" ]

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[ "Zenodo has fixed on their side the 1.13.3 release: https://zenodo.org/record/5589150" ]

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[ "Hi @JTWang2000, thanks for reporting.\r\n\r\nHowever, I cannot reproduce your reported bug:\r\n```python\r\n>>> from datasets import load_dataset\r\n\r\n>>> dataset = load_dataset(\"sst\", \"default\")\r\n>>> dataset\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence', 'label', 'tokens', 'tree'],\r\n num_rows: 8544\r\n })\r\n validation: Dataset({\r\n features: ['sentence', 'label', 'tokens', 'tree'],\r\n num_rows: 1101\r\n })\r\n test: Dataset({\r\n features: ['sentence', 'label', 'tokens', 'tree'],\r\n num_rows: 2210\r\n })\r\n})\r\n```\r\n\r\nMaybe, the cause is that you have a quite old version of `huggingface_hub`. Could you please try to update it and confirm if the problem persists?\r\n```\r\npip install -U huggingface_hub\r\n```", "Im facing the same issue. I did run the upgrade command but that doesnt seem to resolve the issue", "Hi @aneeshjain, could you please specify which `huggingface_hub` version you are using?\r\n\r\nBesides that, please run `datasets-cli env` and copy-and-paste its output below.", "The problem seems to be with the latest version of `datasets`. After running `pip install -U datasets huggingface_hub`, I get the following: \r\n\r\n```bash\r\npython -c \"import huggingface_hub; print(f'hbvers={huggingface_hub.__version__}'); import datasets; print(f'dvers={datasets.__version__}')\"\r\nhbvers=0.0.8\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\n File \"/opt/conda/lib/python3.6/site-packages/datasets/__init__.py\", line 37, in <module>\r\n from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n File \"/opt/conda/lib/python3.6/site-packages/datasets/builder.py\", line 44, in <module>\r\n from .data_files import DataFilesDict, _sanitize_patterns\r\n File \"/opt/conda/lib/python3.6/site-packages/datasets/data_files.py\", line 122, in <module>\r\n allowed_extensions: Optional[list] = None,\r\nAttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'\r\n````\r\nNote that pip reports the latest `datasets` version as \r\n```bash\r\n pip show datasets\r\nName: datasets\r\nVersion: 1.14.0\r\n```\r\nHowever, if I downgrade datasets with `pip install datasets==1.11.0`, things now work\r\n```bash\r\npython -c \"import huggingface_hub; print(f'hbvers={huggingface_hub.__version__}'); import datasets; print(f'dvers={datasets.__version__}')\"\r\nhbvers=0.0.8\r\ndvers=1.11.0\r\n````", "> Hi @JTWang2000, thanks for reporting.\r\n> \r\n> However, I cannot reproduce your reported bug:\r\n> \r\n> ```python\r\n> >>> from datasets import load_dataset\r\n> \r\n> >>> dataset = load_dataset(\"sst\", \"default\")\r\n> >>> dataset\r\n> DatasetDict({\r\n> train: Dataset({\r\n> features: ['sentence', 'label', 'tokens', 'tree'],\r\n> num_rows: 8544\r\n> })\r\n> validation: Dataset({\r\n> features: ['sentence', 'label', 'tokens', 'tree'],\r\n> num_rows: 1101\r\n> })\r\n> test: Dataset({\r\n> features: ['sentence', 'label', 'tokens', 'tree'],\r\n> num_rows: 2210\r\n> })\r\n> })\r\n> ```\r\n> \r\n> Maybe, the cause is that you have a quite old version of `huggingface_hub`. Could you please try to update it and confirm if the problem persists?\r\n> \r\n> ```\r\n> pip install -U huggingface_hub\r\n> ```\r\n\r\nMy problem solved after updating huggingface hub command. Thanks a lot and sorry for the late reply. ", "@tjruwase, please note that versions of `datsets` and `huggingface_hub` must be compatible one with each other:\r\n- In `datasets` version `1.11.0`, the requirement on `huggingface_hub` was: `huggingface_hub<0.1.0`\r\n https://github.com/huggingface/datasets/blob/2cc00f372a96133e701275eec4d6b26d15257289/setup.py#L90\r\n - Therefore, your installed `huggingface_hub` version `0.0.8` was compatible\r\n- In `datasets` version `1.12.0`, the requirement on `huggingface_hub` was: `huggingface_hub>=0.0.14,<0.1.0`\r\n https://github.com/huggingface/datasets/blob/6c766f9115d686182d76b1b937cb27e099c45d68/setup.py#L104\r\n - Therefore, your installed `huggingface_hub` version `0.0.8` was no longer compatible \r\n- Currently, in `datasets` version `1.15.1`, the requirement on `huggingface_hub` is: `huggingface_hub>=0.1.0,<1.0.0`\r\n https://github.com/huggingface/datasets/blob/018100679d21cf27136f0eccb1c50e3a9c968ce2/setup.py#L102\r\n\r\n@JTWang2000, thanks for your answer. I close this issue then." ]

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[ "Thanks for reporting, @VictorSanh.\r\n\r\nI'm fixing it." ]

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[ "cc @anton-l @albertvillanova ", "Thanks for reporting, @patrickvonplaten.\r\n\r\nI think the issue is related to mp3 resampling, not to `cast_column`.\r\n\r\nYou can check that `cast_column` works OK with non-mp3 audio files:\r\n```python\r\nfrom datasets import load_dataset\r\nimport datasets\r\nds = load_dataset(\"arabic_speech_corpus\", split=\"train\")\r\nds = ds.cast_column(\"audio\", datasets.features.Audio(sampling_rate=16_000))\r\nprint(ds[0][\"audio\"])\r\n```\r\n\r\nI'm fixing it." ]

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[ "Hi, \r\n\r\neven Pandas, which is less strict compared to PyArrow when it comes to reading JSON, doesn't support different orderings:\r\n```python\r\nimport io\r\nimport pandas as pd\r\n\r\ns = \"\"\"\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n\"\"\"\r\n\r\nbuffer = io.StringIO(s)\r\ndf = pd.read_json(buffer, lines=True)\r\n\r\nprint(df.shape[0]) # 0\r\n```\r\n\r\nSo we can't even fall back to Pandas in such cases.\r\n\r\nIt seems the only option is a script that recursively re-orders fields to enforce deterministic order:\r\n```python\r\nwith open(\"train.json\", \"r\") as fin:\r\n with open(\"train_reordered.json\", \"w\") as fout:\r\n for line in fin:\r\n obj_jsonl = json.loads(line.strip())\r\n fout.write(json.dumps(obj_jsonl, sort_keys=True) + \"\\n\")\r\n```" ]

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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!" ]

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[ "# Steps to reproduce\r\n\r\nTo reproduce:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('jnlpba')\r\n\r\ndataset['train'].features['ner_tags']\r\n```\r\nOutput:\r\n```python\r\nSequence(feature=ClassLabel(num_classes=3, names=['O', 'B', 'I'], names_file=None, id=None), length=-1, id=None)\r\n```\r\n\r\n", "Since I cannot create a branch here is the updated code:\r\n\r\n```python\r\n\r\n# coding=utf-8\r\n# Copyright 2020 HuggingFace Datasets Authors.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n\r\n# Lint as: python3\r\n\"\"\"Introduction to the Bio-Entity Recognition Task at JNLPBA\"\"\"\r\n\r\nimport os\r\n\r\nimport datasets\r\n\r\n\r\nlogger = datasets.logging.get_logger(__name__)\r\n\r\n\r\n_CITATION = \"\"\"\\\r\n@inproceedings{kim2004introduction,\r\n title={Introduction to the bio-entity recognition task at JNLPBA},\r\n author={Kim, Jin-Dong and Ohta, Tomoko and Tsuruoka, Yoshimasa and Tateisi, Yuka and Collier, Nigel},\r\n booktitle={Proceedings of the international joint workshop on natural language processing in biomedicine and its applications},\r\n pages={70--75},\r\n year={2004},\r\n organization={Citeseer}\r\n}\r\n\"\"\"\r\n\r\n_DESCRIPTION = \"\"\"\\\r\nThe data came from the GENIA version 3.02 corpus (Kim et al., 2003). This was formed from a controlled search\r\non MEDLINE using the MeSH terms \u0018human\u0019, \u0018blood cells\u0019 and \u0018transcription factors\u0019. From this search 2,000 abstracts\r\nwere selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification.\r\nAmong the classes, 36 terminal classes were used to annotate the GENIA corpus.\r\n\"\"\"\r\n\r\n_HOMEPAGE = \"http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004\"\r\n_TRAIN_URL = \"http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Train/Genia4ERtraining.tar.gz\"\r\n_VAL_URL = 'http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Evaluation/Genia4ERtest.tar.gz'\r\n\r\n\r\n_URLS = {\r\n \"train\": _TRAIN_URL,\r\n \"val\": _VAL_URL,\r\n}\r\n\r\n_TRAIN_DIRECTORY = \"Genia4ERtraining\"\r\n_VAL_DIRECTORY = \"Genia4ERtest\"\r\n\r\n_TRAIN_FILE = \"Genia4ERtask1.iob2\"\r\n_VAL_FILE = \"Genia4EReval1.iob2\"\r\n\r\n\r\nclass JNLPBAConfig(datasets.BuilderConfig):\r\n \"\"\"BuilderConfig for JNLPBA\"\"\"\r\n\r\n def __init__(self, **kwargs):\r\n \"\"\"BuilderConfig for JNLPBA.\r\n Args:\r\n **kwargs: keyword arguments forwarded to super.\r\n \"\"\"\r\n super(JNLPBAConfig, self).__init__(**kwargs)\r\n\r\n\r\nclass JNLPBA(datasets.GeneratorBasedBuilder):\r\n \"\"\"JNLPBA dataset.\"\"\"\r\n\r\n BUILDER_CONFIGS = [\r\n JNLPBAConfig(name=\"jnlpba\", version=datasets.Version(\"1.0.0\"), description=\"JNLPBA dataset\"),\r\n ]\r\n\r\n def _info(self):\r\n return datasets.DatasetInfo(\r\n description=_DESCRIPTION,\r\n features=datasets.Features(\r\n {\r\n \"id\": datasets.Value(\"string\"),\r\n \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n \"ner_tags\": datasets.Sequence(\r\n datasets.features.ClassLabel(\r\n names=[\r\n 'O',\r\n 'B-DNA',\r\n 'I-DNA', \r\n 'B-RNA',\r\n 'I-RNA',\r\n 'B-cell_line',\r\n 'I-cell_line',\r\n 'B-cell_type',\r\n 'I-cell_type',\r\n 'B-protein',\r\n 'I-protein',\r\n ]\r\n )\r\n ),\r\n }\r\n ),\r\n supervised_keys=None,\r\n homepage=_HOMEPAGE,\r\n citation=_CITATION,\r\n )\r\n\r\n def _split_generators(self, dl_manager):\r\n downloaded_files = dl_manager.download_and_extract(_URLS)\r\n \r\n return [\r\n datasets.SplitGenerator(name=datasets.Split.TRAIN, \r\n gen_kwargs={\"filepath\": os.path.join(downloaded_files['train'], _TRAIN_FILE)}),\r\n datasets.SplitGenerator(name=datasets.Split.VALIDATION, \r\n gen_kwargs={\"filepath\": os.path.join(downloaded_files['val'], _VAL_FILE)})\r\n ]\r\n \r\n\r\n def _generate_examples(self, filepath):\r\n logger.info(\"⏳ Generating examples from = %s\", filepath)\r\n with open(filepath, encoding=\"utf-8\") as f:\r\n guid = 0\r\n tokens = []\r\n ner_tags = []\r\n for line in f:\r\n if line.startswith('###'):\r\n continue\r\n if line == '' or line == '\\n':\r\n if tokens:\r\n yield guid, {\r\n \"id\": str(guid),\r\n \"tokens\": tokens,\r\n \"ner_tags\": ner_tags,\r\n }\r\n guid += 1\r\n tokens = []\r\n ner_tags = []\r\n else:\r\n # tokens are tab separated\r\n splits = line.split(\"\\t\")\r\n #print(splits)\r\n #print(len(splits))\r\n if len(splits) < 2:\r\n splits = splits[0].split()\r\n tokens.append(splits[0])\r\n ner_tags.append(splits[1].rstrip())\r\n # last example\r\n yield guid, {\r\n \"id\": str(guid),\r\n \"tokens\": tokens,\r\n \"ner_tags\": ner_tags,\r\n }\r\n```" ]

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[ "I just opened a PR and I verified that the code you provided doesn't show any deprecation warning :)" ]

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[ "This seems to be an issue with importing PyArrow so I posted the problem [here](https://issues.apache.org/jira/browse/ARROW-14323), and I'm closing this issue.\r\n" ]

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[ "Hi @zixiliuUSC, \r\n\r\nAs explained in the documentation (https://huggingface.co/docs/datasets/loading.html#json), we support loading any dataset in JSON (as well as CSV, text, Parquet) format:\r\n```python\r\nds = load_dataset('json', data_files='my_file.json')\r\n```" ]

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[ "I think this problem is already fixed:\r\n```python\r\nIn [4]: import fsspec\r\n ...:\r\n ...: url = \"https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes\"\r\n ...:\r\n ...: fsspec.open(url).open()\r\nOut[4]: <File-like object HTTPFileSystem, https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattribu\r\n```", "No I'm still having this issue on my windows, and so does the CI" ]

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[ "@lhoestq, @albertvillanova can we have `**tqdm_kwargs` in `map`? If there are any fields that are important to our tqdm (like iterable or unit), we can pop them before initialising the tqdm object so as to avoid duplicity.", "Hi ! Sounds like a good idea :)\r\n\r\nAlso I think it would be better to have this as an actual parameters instead of kwargs to make it clearer" ]

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[ "Hi @RylanSchaeffer, thanks for reporting.\r\n\r\nI'm sorry, but I was not able to reproduce your problem.\r\n\r\nNormally, the reason for this type of error is that, during your download of the data files, this was not fully complete.\r\n\r\nCould you please try to load the dataset again but forcing its redownload? Please use:\r\n```python\r\ndataset = load_dataset(\"openwebtext\", download_mode=\"FORCE_REDOWNLOAD\")\r\n```\r\n\r\nLet me know if the problem persists.", "I close this issue for the moment. Feel free to re-open it again if the problem persists." ]

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[ "Hi ! I think this issue is more related to the `transformers` project. Could you open an issue on https://github.com/huggingface/transformers ?\r\n\r\nAnyway I think the issue could be that both wikipedia and bookcorpusopen have an additional \"title\" column, contrary to wikitext which only has a \"text\" column. After calling `load_dataset`, can you try doing `dataset = dataset.remove_columns(\"title\")` ?", "Removing the \"title\" column works! Thanks for your advice.\r\n\r\nMaybe I should still create an issue to `transformers' to mark this solution?" ]

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[ "Hi @tidhamecha2, thanks for reporting.\r\n\r\nIndeed, we fixed this issue just one week ago: #3008\r\n\r\nThe fix will be included in our next version release.\r\n\r\nIn the meantime, you can incorporate the fix by installing `datasets` 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```" ]

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[ "I encountered the same bug using different datasets.\r\nany suggestions?", "+1, can reproduce here!" ]

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[ "Issue was environment inconsistency, updating packages did the trick\r\n\r\n`conda install -c huggingface -c conda-forge datasets`\r\n\r\n> Collecting package metadata (current_repodata.json): done\r\n> Solving environment: |\r\n> The environment is inconsistent, please check the package plan carefully\r\n> The following packages are causing the inconsistency:\r\n> \r\n> - conda-forge/noarch::datasets==1.12.1=pyhd8ed1ab_1\r\n> - conda-forge/win-64::multiprocess==0.70.12.2=py38h294d835_0\r\n> done\r\n> \r\n> Package Plan\r\n> \r\n> environment location: C:\\xxx\\anaconda3\\envs\\UnBias-94-1\r\n> \r\n> added / updated specs:\r\n> - datasets\r\n> \r\n> \r\n> The following NEW packages will be INSTALLED:\r\n> \r\n> dill conda-forge/noarch::dill-0.3.4-pyhd8ed1ab_0\r\n> \r\n> The following packages will be UPDATED:\r\n> \r\n> ca-certificates pkgs/main::ca-certificates-2021.9.30-~ --> conda-forge::ca-certificates-2021.10.8-h5b45459_0\r\n> certifi pkgs/main::certifi-2021.5.30-py38haa9~ --> conda-forge::certifi-2021.10.8-py38haa244fe_0\r\n> \r\n> The following packages will be SUPERSEDED by a higher-priority channel:\r\n> " ]

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[ "I got the same error for another dataset (`multi_woz_v22`):\r\n\r\n```\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_001.json']\r\n```", "I'm seeing the same issue as @RylanSchaeffer:\r\nPython 3.7.11, macOs 11.4\r\ndatasets==1.14.0\r\n\r\nfails on:\r\n```python\r\ndataset = datasets.load_dataset(\"multi_woz_v22\")\r\n```" ]

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[ "Independently of this I think it raises the need to allow multiprocessing during streaming so that we get samples from multiple shards in one batch." ]

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[ "This has been fixed in 1.15, let me know if you still have this issue" ]

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[ "Following the discussion in #3045 if would be nice to have a way to let users have a nice experience with caching even if the function is not hashable.\r\n\r\nCurrently a workaround is to make the function picklable. This can be done by implementing a callable class instead, that can be pickled using by implementing a custom `__getstate__` method for example.\r\n\r\nHowever it sounds pretty complicated for a simple thing. Maybe one idea would be to have something similar to streamlit: they allow users to register the hashing of their own objects.\r\n\r\nSee the documentation about their `hash_funcs` here: https://docs.streamlit.io/library/advanced-features/caching#the-hash_funcs-parameter\r\n\r\nHere is the example they give:\r\n\r\n```python\r\nclass FileReference:\r\n def __init__(self, filename):\r\n self.filename = filename\r\n\r\ndef hash_file_reference(file_reference):\r\n filename = file_reference.filename\r\n return (filename, os.path.getmtime(filename))\r\n\r\[email protected](hash_funcs={FileReference: hash_file_reference})\r\ndef func(file_reference):\r\n ...\r\n```", "My solution was to generate a custom hash, and use the hash as a `new_fingerprint` argument to the `map()` method to enable caching. This works, but is quite hacky.\r\n\r\n@lhoestq, this approach is very neat, this would make the whole caching mechanic more explicit. I don't have so much time to look into this right now, but I might give it a try in the future. " ]

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[ "Hi,\r\n\r\nthe `save_to_disk` docstring explains that `flatten_indices` has to be called on a dataset before saving it to save only the shard/slice of the dataset.", "That works! Thansk!\r\n\r\nMight be worth doing that automatically actually in case the `save_to_disk` is called on a dataset that has an indices mapping :-)", "I agree with @patrickvonplaten: this issue is reported recurrently, so better if we implement the `.flatten_indices()` automatically?", "That would be great indeed - I don't really see a use case where one would not like to call `.flatten_indices()` before calling `save_to_disk`", "+1 on this !" ]

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[ "It would be nice to protect the master from direct commits, but still having a way to merge our own PRs when no review is required (for example when updating a dataset_infos.json file, or minor bug fixes - things that happen quite often actually).\r\nDo you know if there's a way ?", "you can if you're an admin of the repo", "This is done. Now the master branch is protected:\r\n- [x] Require a pull request before merging: all commits must be made to a non-protected branch and submitted via a pull request\r\n - Required number of approvals before merging: 1 \r\n- [x] Require linear history: prevent merge commits from being pushed\r\n- [x] These requirements are not enforced for administrators\r\n- [x] Additionally, the master branch is also protected against deletion and force pushes\r\n\r\nCC: @lhoestq @julien-c @thomwolf " ]

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[ "Hi ! This is not a bug, this is simply not implemented.\r\n`save_to_disk` is for on-disk serialization and was not made compatible for the Hub.\r\nThat being said, I agree we actually should make it work with the Hub x)", "cc @LysandreJik maybe we can solve this at the same time as adding `push_to_hub`" ]

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[ "We'll do a release tomorrow or on wednesday to make the fix available :)\r\n\r\nThanks for reproting !" ]

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[ "Awesome @lhoestq I like the proposal and it works great on my JSON community dataset. Here is the [log](https://gist.github.com/vblagoje/714babc325bcbdd5de579fd8e1648892). ", "From my discussion with @borisdayma it would be more general the files match if their paths contains the split name - not only if the filename contains the split name. For example for a dataset like this:\r\n```\r\ntrain/\r\n└── data.csv\r\ntest/\r\n└── data.csv\r\n```\r\n\r\nBut IMO the default should be \r\n```\r\ndata/\r\n├── train.csv\r\n└── test.csv\r\n```\r\nbecause it allows people to have other directories if they have different subsets of their data (different configurations, not splits)", "I just created a PR for this at https://github.com/huggingface/datasets/pull/3221, let me know what you think :)" ]

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[ "@lhoestq I would like to draw your attention to the proposed API by @lewtun, using `data_dir` to pass the ZIP URL.\r\n\r\nI'm not totally convinced with this... What do you think?\r\n\r\nMaybe we could discuss other approaches...\r\n\r\nOne brainstorming idea: what about using URL chaining with the hop operator in `data_files`?", "`data_dir` is currently exclusively used for manually downloaded data.\r\n\r\nMaybe we can have an API that only uses data_files as you are suggesting, using URL chaining ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nurl = \"https://domain.org/filename.zip\"\r\ndata_files = {\"train\": \"zip://train_filename.csv::\" + url, \"test\": \"zip://test_filename.csv::\" + url}\r\ndataset = load_dataset(\"csv\", data_files=data_files)\r\n```\r\n\r\nURL chaining is used by `fsspec` to get access to files in nested filesystems of any kind. Since `fsspec` is being used by `pandas`, `dask` and also extensively by `datasets` I think it would be nice to use it here too", "URL chaining sounds super nice to me! And it's also a nice way to leverage the same concepts we currently have in the docs around `fsspec` :)" ]

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[ "To keeps things simple maybe we should use `load_dataset_builder` in `get_dataset_infos`.\r\n`load_dataset_builder` instantiates a builder and runs the _infos() method in order to give you the most up-to-date infos, even if the dataset_infos.json is outdated or missing." ]

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[ "Yes I think it should raise an error. Currently it looks like it instantiates a custom configuration with the name given by the user:\r\nhttps://github.com/huggingface/datasets/blob/ba27ce33bf568374cf23a07669fdd875b5718bc2/src/datasets/builder.py#L391-L397" ]

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

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[ "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" ]

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[ "Hi @marshmellow77, thanks for reporting.\r\n\r\nThat issue is fixed since `datasets` version 1.9.0 (see 16bc665f2753677c765011ef79c84e55486d4347).\r\n\r\nPlease, update `datasets` with: `pip install -U datasets`", "Thanks. Please note that the dataset explorer (https://huggingface.co/datasets/viewer/?dataset=turkish_product_reviews) still shows the incorrect state. The sentiment for the first few customer reviews is actually negative and should be labelled with \"0\", see screenshot:\r\n\r\n\r\n\r\n\r\n", "Thanks @marshmellow77, good catch! I'm transferring this issue to https://github.com/huggingface/datasets-viewer. " ]

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[ "wooo that was fast! thank you @lhoestq !\r\nit is able to process now, though it's ignoring all files and ending up with 0 examples now haha :/\r\n\r\nFor subset \"unfiltered\":\r\n```python\r\n>>> load_dataset(\"trivia_qa\", \"unfiltered\")\r\nDownloading and preparing dataset trivia_qa/unfiltered (download: 3.07 GiB, generated: 27.23 GiB, post-processed: Unknown size, total: 30.30 GiB) to /gpfsscratch/rech/six/commun/datasets/trivia_qa/unfiltered/1.1.0/910043a609bb2bdf62b4874f68e0c24fb648cf81e40a358f4bd54c919d72c9ab...\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1354.53it/s]\r\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 40.60it/s]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/load.py\", line 1198, in load_dataset\r\n use_auth_token=use_auth_token,\r\n File \"/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py\", line 647, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py\", line 748, in _download_and_prepare\r\n verify_splits(self.info.splits, split_dict)\r\n File \"/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/utils/info_utils.py\", line 74, in verify_splits\r\n raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=2906575347, num_examples=10832, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}, {'expected': SplitInfo(name='validation', num_bytes=3038966234, num_examples=11313, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='validation', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}]\r\n```\r\nFor subset \"rc\":\r\n```python\r\n>>> load_dataset(\"trivia_qa\", \"rc\")\r\nDownloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /gpfsscratch/rech/six/commun/datasets/trivia_qa/rc/1.1.0/910043a609bb2bdf62b4874f68e0c24fb648cf81e40a358f4bd54c919d72c9ab...\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 3806.08it/s]\r\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 51.57it/s]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/load.py\", line 1198, in load_dataset\r\n use_auth_token=use_auth_token,\r\n File \"/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py\", line 647, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/builder.py\", line 748, in _download_and_prepare\r\n verify_splits(self.info.splits, split_dict)\r\n File \"/gpfswork/rech/six/commun/conda/victor/lib/python3.7/site-packages/datasets/utils/info_utils.py\", line 74, in verify_splits\r\n raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=1577814583, num_examples=17210, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}, {'expected': SplitInfo(name='train', num_bytes=12750976012, num_examples=138384, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}, {'expected': SplitInfo(name='validation', num_bytes=1688535379, num_examples=18669, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='validation', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}]\r\n```\r\n\r\nCould you look into that when you get a chance?\r\nI wonder if it's not something they changed on the file to download? i couldn't find any information", "@VictorSanh have you tried passing `download_mode=\"force_redownload\"`?\r\n```python\r\nds = load_dataset(\"trivia_qa\", \"unfiltered\", download_mode=\"force_redownload\")\r\n```", "I aggressively rmed caches, especially rming the `datasets/downloads/extracted/c3d265fa20d99a147a76e4f5e...` solved the issue.\r\nthank you both!\r\n" ]

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[ "Hi ! Could you check the length of the `self.dataset` object (i.e. the Dataset object passed to the data loader) ? It looks like the dataset is empty.\r\nNot sure why the SWA optimizer would cause this though.", "Any updates on this? \r\nThe same error occurred to me too when running `cardiffnlp/twitter-roberta-base-sentiment` on a custom dataset. This happened when I tried to do `model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3])` without using sagemaker distribution. \r\nPython: 3.6.13\r\ndatasets: 1.6.2", "Hi @ruisi-su, do you have this issue while using SWA as well, or just data parallel ?\r\n\r\nIf you have a code example to reproduce this issue it would also be helpful", "@lhoestq I had this issue without SWA. I followed [this](https://github.com/huggingface/notebooks/blob/master/sagemaker/03_distributed_training_data_parallelism/sagemaker-notebook.ipynb) notebook to utilize multiple gpus on the [roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) model. This tutorial could only work if I am on `ml.p3.16xlarge`, which I don't have access to. So I tried using just `model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3]` before calling `trainer.fit()`. But maybe this is not the right way to do distributed training. I can provide a code example if that will be more helpful.", "It might be an issue with old versions of `datasets`, can you try updating `datasets` ?" ]

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[ "Hi @NielsRogge, thanks for reporting!\r\n\r\nIn `datasets`, we were handling N-dimensional arrays only when passed as an instance of `np.array`, not when passed as a list of `np.array`s.\r\n\r\nI'm fixing it." ]

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[ "Hi @zijwang, thanks for reporting this issue.\r\n\r\nYou did not mention which `datasets` version you are using, but looking at the code in the stack trace, it seems you are using an old version.\r\n\r\nCould you please update `datasets` (`pip install -U datasets`) and check if the problem persists?" ]

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[ "Whoever handles this just needs to: \r\n\r\n- [ ] fork the HuggingFace Datasets repo\r\n- [ ] update the [existing dataset script](https://github.com/huggingface/datasets/blob/master/datasets/openslr/openslr.py) to add SLR25. Lots of copypasting from other sections of the script should make that easy. \r\nAmharic URL: https://www.openslr.org/resources/25/data_readspeech_am.tar.bz2. \r\nSwahili URL: https://www.openslr.org/resources/25/data_broadcastnews_sw.tar.bz2, \r\nWolof URL: https://www.openslr.org/resources/25/data_readspeech_wo.tar.bz2\r\n- [ ] update the [data card](https://github.com/huggingface/datasets/blob/master/datasets/openslr/README.md) to include information about SLR25. There's lots of other examples to draw from. \r\n- [ ] add the appropriate language tags to the data card as well. https://www.w3.org/International/questions/qa-choosing-language-tags, or just use `sw`, `am`, and `wo` for consistency. \r\n- [ ] make a pull request to merge your changes back into HuggingFace's repo", "... also the example in \"use in datasets library\" should be updated. It currently says \r\n\r\nBut you actually have to specify a subset, e.g. \r\n```python\r\ndataset = load_dataset(\"openslr\", \"SLR32\")\r\n```", "\r\n\r\n" ]

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[ "Hi @asofiaoliveira, thanks for reporting.\r\n\r\nI'm fixing it." ]

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[ "Hey @albertvillanova could you provide more context, including extracts from the discussion we had ?\r\n\r\nLet's ping @Pierrci @julien-c and @n1t0 for their opinion about that", "@julien-c increased the huggingface.co production workers in order to see if it solve [the 502 you had this morning](https://app.circleci.com/pipelines/github/huggingface/datasets/7843/workflows/fc83fa32-18f5-4dc3-9e2f-ba277ae1af74)\r\n\r\nFor the decision process: be aware that moon-staging does not have persistent repos (they are deleted regularly). as a consequence, **if the moon-staging solution is validated**, you should consider a way to keep the repository that are loaded in tests. These are the ones I found : https://github.com/huggingface/datasets/blob/d488db2f64f312f88f72bbc57a09b7eddb329182/tests/test_load.py and https://github.com/huggingface/datasets/blob/40773111c3e7db8a992fa1c48af32d900a1018d6/tests/test_streaming_download_manager." ]

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[ "Hi @Valahaar, thanks for reporting and for your investigation about the source cause.\r\n\r\nYou are right and that commit prevents `pandas` from inferring the compression. On the other hand, @lhoestq did that change to support loading that dataset in streaming mode. \r\n\r\nI'm fixing it." ]

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[ "Hi @mskovalova, \r\n\r\nSome datasets have multiple configurations. Therefore, in order to load them, you have to specify both the *dataset name* and the *configuration name*.\r\n\r\nIn the error message you got, you have a usage example:\r\n- To load the 'wikitext-103-raw-v1' configuration of the 'wikitext' dataset, you should use: \r\n ```python\r\n load_dataset('wikitext', 'wikitext-103-raw-v1')\r\n ```\r\n\r\nIn your case, if you would like to load the 'wikitext-2-v1' configuration of the 'wikitext' dataset, please use:\r\n```python\r\nraw_datasets = load_dataset(\"wikitext\", \"wikitext-2-v1\")\r\n```" ]

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[ "Maybe we can change the disk space calculating API from `shutil.disk_usage` to `os.statvfs` in UNIX-like system, which can provide correct results.\r\n```\r\nstatvfs = os.statvfs('path')\r\navail_space_bytes = statvfs.f_frsize * statvfs.f_bavail\r\n```", "Hi @qqaatw, thanks for reporting.\r\n\r\nCould you please try:\r\n```python\r\ndataset = load_dataset(\"natural_questions\", cache_dir=os.path.abspath(args.dataset_cache_dir))\r\n```", "@albertvillanova it works! Thanks for your suggestion. Is that a bug of `DownloadConfig`?", "`DownloadConfig` only sets the location to download the files. On the other hand, `cache_dir` sets the location for both downloading and caching the data. You can find more information here: https://huggingface.co/docs/datasets/loading_datasets.html#cache-directory" ]