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[ "Same here !", "This is the only fix I could come up with without touching the repo's code.\r\n```python\r\nfrom nlp.builder import FORCE_REDOWNLOAD\r\ndataset = load_dataset('csv', data_file='./a.csv', download_mode=FORCE_REDOWNLOAD, version='0.0.1')\r\n```\r\nYou'll have to change the version each time you want to load a different csv file.\r\nIf you're willing to add a ```print```, you can go to ```nlp.load``` and add ```print(builder_instance.cache_dir)``` right before the ```return ds``` in the ```load_dataset``` method. It'll print the cache folder, and you'll just have to erase it (and then you won't need the change here above)." ]

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[ "This seems to be fixed in a non-released version. \r\n\r\nInstalling nlp from source\r\n```\r\ngit clone https://github.com/huggingface/nlp\r\ncd nlp\r\npip install .\r\n```\r\nsolves the issue. " ]

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[ "This one is ready for review now", "I changed to using features only, instead of info.\r\nLet mw know if it sounds good to you now @thomwolf " ]

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[ "`DPRContextEncoder` and `DPRContextEncoderTokenizer` will be available in the next release of `transformers`.\r\n\r\nRight now you can experiment with it by installing `transformers` from the master branch.\r\nYou can also check the docs of DPR [here](https://huggingface.co/transformers/master/model_doc/dpr.html).\r\n\r\nMoreover all the indexing features will also be available in the next release of `nlp`.", "@lhoestq Thanks for the info ", "@lhoestq I tried installing transformer from the master branch. Python imports for DPR again didnt' work. Anyways, Looking forward to trying it in the next release of nlp ", "@nsankar have you tried with the latest version of the library?", "@yjernite it worked. Thanks" ]

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[ "Thanks Sam, we now have a very detailed tutorial and template on how to add a new dataset to the library. It typically take 1-2 hours to add one. Do you want to give it a try ?\r\nThe tutorial on writing a new dataset loading script is here: https://huggingface.co/nlp/add_dataset.html\r\nAnd the part on how to share a new dataset is here: https://huggingface.co/nlp/share_dataset.html", "Hi @sshleifer, I'm trying to add IWSLT using the link you provided but the download urls are not working. Only `[en, de]` pair is working. For others language pairs it throws a `404` error.\r\n\r\n" ]

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[ "There is an interesting design question here (cc @lhoestq).\r\n\r\nI guess the labels form a closed set so we could also use a [nlp.ClassLabel](https://huggingface.co/nlp/package_reference/main_classes.html#nlp.ClassLabel) instead of a string. The differences will be mainly that:\r\n- the labels are stored as integers and thus ready for training a model\r\n- the string to int conversion methods are handled by the `nlp.ClassLabel` feature (see the [doc](https://huggingface.co/nlp/package_reference/main_classes.html#nlp.ClassLabel) and [here](https://huggingface.co/nlp/features.html) and [here](https://huggingface.co/nlp/quicktour.html#fine-tuning-a-deep-learning-model)).\r\n\r\nIn my opinion, storing the labels as integers instead of strings makes it:\r\n- slightly less readable when accessing a dataset example (e.g. with `dataset[0]`)\r\n- force you with a specific mapping from string to integers\r\n- more clear that there is a fixed and predefined list of labels\r\n- easier to list all the labels (directly visible in the features).\r\n\r\n=> overall I'm pretty neutral about using one or the other option (`nlp.string` or `nlp.ClassLabel`).\r\n\r\nNote that we can now rather easily convert from one to the other with the map function and something like:\r\n```python\r\ndataset = dataset.map(lambda x: x, features=nlp.Features({'labels': nlp.ClassLabel(MY_LABELS_NAMES)}))\r\ndataset = dataset.map(lambda x: {'labels': dataset.features['labels'].int2str(x['labels'])}, features=nlp.Features({'labels': nlp.Value('string')}))\r\n```\r\n^^ this could probably be made even simpler (in particular for the second case)", "I see. This is an interesting question.\r\nMaybe as the dataset doesn't provide the mapping we shouldn't force an arbitrary one, and keep them as strings ?\r\nMoreover for NER the labels are often different from a dataset to the other so it's probably good to keep strings (there is no conventional mapping).\r\nAlso as the column is called \"ner_tags\" (or \"langs\"), you can already assume that there is a fixed and predefined list of labels.", "Yes sounds good to me.\r\nThis make me wonder if we don’t want to have a default identity function in `map` so this method could also be used to easily cast features. What do you think?", "Yes sounds good. I also noticed that people use map with identity to write a dataset into a specified cache file." ]

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[ "Indeed, we’ll make a new PyPi release next week to solve this. Cc @lhoestq ", "+1! this is the reason our tests are failing at [TextAttack](https://github.com/QData/TextAttack) \r\n\r\n(Though it's worth noting if we fixed the version number of pyarrow to 0.16.0 that would fix our problem too. But in this case we'll just wait for you all to update)", "Came to raise this issue, great to see other already have and it's being fixed so soon!\r\n\r\nAs an aside, since no one wrote this already, it seems like the version check only looks at the second part of the version number making sure it is >16, but pyarrow newest version is 1.0.0 so the second past is 0!", "> Indeed, we’ll make a new PyPi release next week to solve this. Cc @lhoestq\r\n\r\nYes definitely", "please fix this on pypi! @lhoestq ", "Is this issue fixed ?", "We’ll release the new version later today. Apologies for the delay.", "I just pushed the new version on pypi :)", "Thanks for the update." ]

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[ "This was fixed in #434 \r\nWe'll do a release later this week to include this fix.\r\nThanks for reporting", "I dont know if the fix was made but the problem is still present : \r\nInstaled with pip : NLP 0.3.0 // pyarrow 1.0.0 \r\nOS : archlinux with kernel zen 5.8.5", "Yes it was fixed in `nlp>=0.4.0`\r\nYou can update with pip", "Sorry, I didn't got the updated version, all is now working perfectly thanks" ]

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[ "Great, thanks!" ]

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[ "Hi @ArneBinder, we have a few \"generic\" datasets which are intended to load data files with a predefined format:\r\n- csv: https://github.com/huggingface/nlp/tree/master/datasets/csv\r\n- json: https://github.com/huggingface/nlp/tree/master/datasets/json\r\n- text: https://github.com/huggingface/nlp/tree/master/datasets/text\r\n\r\nYou can find more details about this way to load datasets here in the documentation: https://huggingface.co/nlp/loading_datasets.html#from-local-files\r\n\r\nMaybe your brat loading script could be shared in a similar fashion?", "> Maybe your brat loading script could be shared in a similar fashion?\r\n\r\n@thomwolf that was also my first idea and I think I will tackle that in the next days. I separated the code and created a real abstract class `AbstractBrat` to allow to inherit from that (I've just seen that the dataset_loader loads the first non abstract class), now `Brat` is very similar in its functionality to https://github.com/huggingface/nlp/tree/master/datasets/text but inherits from `AbstractBrat`.\r\n\r\nHowever, it is still not clear to me how to add a specific dataset (as explained in https://huggingface.co/nlp/add_dataset.html) to your repo that uses this format/abstract class, i.e. re-using the `features` entry of the `DatasetInfo` object and `_generate_examples()`. Again, by doing so, the only remaining entries/functions to define would be `_DESCRIPTION`, `_CITATION`, `homepage` and `_URL` (which is all copy-paste stuff) and `_split_generators()`.\r\n \r\nIn a lack of better ideas, I tried sth like below, but of course it does not work outside `nlp` (`AbstractBrat` is currently defined in [datasets/brat.py](https://github.com/ArneBinder/nlp/blob/5e81fb8710546ee7be3353a7f02a3045e9a8351e/datasets/brat/brat.py)):\r\n```python\r\nfrom __future__ import absolute_import, division, print_function\r\n\r\nimport os\r\n\r\nimport nlp\r\n\r\nfrom datasets.brat.brat import AbstractBrat\r\n\r\n_CITATION = \"\"\"\r\n@inproceedings{lauscher2018b,\r\n title = {An argument-annotated corpus of scientific publications},\r\n booktitle = {Proceedings of the 5th Workshop on Mining Argumentation},\r\n publisher = {Association for Computational Linguistics},\r\n author = {Lauscher, Anne and Glava\\v{s}, Goran and Ponzetto, Simone Paolo},\r\n address = {Brussels, Belgium},\r\n year = {2018},\r\n pages = {40–46}\r\n}\r\n\"\"\"\r\n\r\n_DESCRIPTION = \"\"\"\\\r\nThis dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing \r\nfine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific \r\npublications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of \r\nscientific writing.\r\n\"\"\"\r\n\r\n_URL = \"http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip\"\r\n\r\n\r\nclass Sciarg(AbstractBrat):\r\n\r\n VERSION = nlp.Version(\"1.0.0\")\r\n\r\n def _info(self):\r\n\r\n brat_features = super()._info().features\r\n return nlp.DatasetInfo(\r\n # This is the description that will appear on the datasets page.\r\n description=_DESCRIPTION,\r\n # nlp.features.FeatureConnectors\r\n features=brat_features,\r\n # If there's a common (input, target) tuple from the features,\r\n # specify them here. They'll be used if as_supervised=True in\r\n # builder.as_dataset.\r\n #supervised_keys=None,\r\n # Homepage of the dataset for documentation\r\n homepage=\"https://github.com/anlausch/ArguminSci\",\r\n citation=_CITATION,\r\n )\r\n\r\n def _split_generators(self, dl_manager):\r\n \"\"\"Returns SplitGenerators.\"\"\"\r\n # TODO: Downloads the data and defines the splits\r\n # dl_manager is a nlp.download.DownloadManager that can be used to\r\n # download and extract URLs\r\n dl_dir = dl_manager.download_and_extract(_URL)\r\n data_dir = os.path.join(dl_dir, \"compiled_corpus\")\r\n print(f'data_dir: {data_dir}')\r\n return [\r\n nlp.SplitGenerator(\r\n name=nlp.Split.TRAIN,\r\n # These kwargs will be passed to _generate_examples\r\n gen_kwargs={\r\n \"directory\": data_dir,\r\n },\r\n ),\r\n ]\r\n``` \r\n\r\nNevertheless, many thanks for tackling the dataset accessibility problem with this great library!", "As temporary fix I've created [ArneBinder/nlp-formats](https://github.com/ArneBinder/nlp-formats) (contributions welcome)." ]

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[ "Ok for this but I think we may want to use the general `filelock` method we are using at other places in the library instead of filecmp (in particular `filelock` take care of being an atomic operation which is safer for concurrent processes)", "Ok I see.\r\nWhy not use filelock in this case then ?", "I think we should 🙂", "Thanks for approving my patch.\n\nI agree that if copying is needed then some locking mechanism should be put in place. But, I don't think a file should be needlessly copied without a check. So I guess the flow should be, lock => copy if needed => unlock, and add locks wherever else that file is being accessed.\n\nI'll also add that my personal experience with filelock on a different project hasn't been that great, and on some occasions a process somehow got through the lock -- I've never gotten to the bottom of that but it tainted my view of that module. Perhaps it's been fixed (or I just miss used it), but thought you should know to take steps to test it." ]

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[ "I was using a hack in `wiki_dpr` to download the index from GCS even for the configurations without the embeddings.\r\nHowever as GCS is something internal, I changed the logic to add a download step for indexes directly in the dataset script, using the `DownloadManager`.\r\n\r\nThis change was directly linked to the changes I did to take into account the split name in the post processing, so I included this change in this PR too.\r\n\r\nTo summarize:\r\n\r\nDataset builders can now implement\r\n- `_post_processing_resources(split)`: return a dict `resource_name -> resource_file_name`. It defines the additional resources such as indexes or arrow files that you need in post processing\r\n- `_download_post_processing_resources(split, resource_name, dl_manager))`: if some resources can be downloaded, you can use the download_manager to download them\r\n- `_post_process(dataset, resources_path)`: (main function for post processing) given a dataset, you can apply dataset transforms or add indexes. For resources that have been downloaded, you can load them. For the others, you can generate and save them. The paths to load/save resources are in `resources_path` which is a dictionary `resource_name -> resource_path`\r\n\r\nAbout the CI:\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_dataset_common.py::AWSDatasetTest::test_load_dataset_wiki_dpr\r\n```\r\nIt fails because I changed the input of post processing functions (to include the split name)", "I started to add metadata in the DatasetInfo.\r\nNote that because there are new fields, **ALL the dataset_info[s].json generated after these changes won't be loadable from older versions of the lib**\r\n\r\nRight now it looks like this:\r\n```json\r\n \"post_processing_resources_checksums\": {\r\n \"train\": {\r\n \"embeddings_index\": {\r\n \"num_bytes\": 30720045,\r\n \"checksum\": \"b04fb4f4f3ab83b9d1b9f6f9eb236f1c04a9fd61bef7cee16b12df8ac911766a\"\r\n }\r\n }\r\n },\r\n \"post_processing_size\": 30720045,\r\n```", "Good point. Should we anticipate already that we may add other fields in the future and change the code to support the addition of new fields without breaking backward compatibility in the future?", "I added:\r\n- post processing features (inside a PostProcessedInfo object)\r\n- backward compatibility for dataset info\r\n- post processing tests (as_dataset and download_and_prepare) for map (change features), select (change number of elements) and add_faiss_index (add indexes)\r\nAnd I fixed a bug in `map` that I found thanks to the new tests\r\n\r\nNow I just have to move `post_processing_resources_checksums` to PostProcessedInfo as well and everything should be good :)\r\nEdit: done" ]

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[ "I did the changes in the docstrings and I added a type check in each `DatasetDict` method to make sure all values are of type `Dataset`", "Awesome, do you mind adding these in the doc as well?", "I added it to the docs (processing + main classes)", "I'm trying to follow along with the following about datasets from the docs:\r\n\r\nhttps://huggingface.co/nlp/loading_datasets.html\r\nhttps://huggingface.co/nlp/processing.html\r\n\r\nHowever the train_test_split method no longer works as it is expecting a dataset, rather than a datsetdict. How would I got about splitting a CSV into a train and test set? \r\n\r\nI'm trying to utilize the Trainer() class, but am having trouble converting my data from a csv into dataset objects to pass in." ]

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[ "Thanks @RachelKer for this PR.\r\n\r\nI think the dummy_data structure does not also match. In the `_split_generator` you have something like `os.path.join(downloaded_files[\"validation\"], lang+'_val.jsonl')` but in you dummy_data you have `os.path.join(downloaded_files[\"validation\"], lang+\"_val.zip\", lang+'_val.jsonl')`. I think ` jsonl` files should be directly in the `dummy_data` folder without the sub-folder \r\n\r\n@lhoestq ", "Hi @RachelKer :)\r\nThanks for adding MLSUM !\r\n\r\nTo fix the CI I think you just have to rebase from master", "Great, I think it is working now. Thanks :)", "It looks like your PR does tons of changes in other datasets. \r\nMaybe this is because of the merge from master ?", "Hmm, I see, sorry I messed up somewhere. Maybe it's easier if we close the pull request and I do another one ?", "Yea if it's easier for you feel free to re-open a PR" ]

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

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[ "Thanks for noticing ! This looks more reasonable indeed.\r\nFeel free to open a PR", "Hi @lhoestq \r\nI made the proposed changes to the `xtreme.py` script. I noticed that I also need to change the schema in the `dataset_infos.json` file. More specifically the `\"features\"` part of the PAN-X.LANG dataset:\r\n\r\n```json\r\n\"features\":{\r\n \"word\":{\r\n \"dtype\":\"string\",\r\n \"id\":null,\r\n \"_type\":\"Value\"\r\n },\r\n \"ner_tag\":{\r\n \"dtype\":\"string\",\r\n \"id\":null,\r\n \"_type\":\"Value\"\r\n },\r\n \"lang\":{\r\n \"dtype\":\"string\",\r\n \"id\":null,\r\n \"_type\":\"Value\"\r\n }\r\n}\r\n```\r\nTo fit the code above the fields `\"word\"`, `\"ner_tag\"`, and `\"lang\"` would become `\"words\"`, `ner_tags\"` and `\"langs\"`. In addition the `dtype` should be changed from `\"string\"` to `\"list\"`.\r\n\r\n I made this changes but when trying to test this locally with `dataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='./data')` I face the issue that the `dataset_info.json` file is always overwritten by a downloaded version with the old settings, which then throws an error because the schema does not match. This makes it hard to test the changes locally. Do you have any suggestions on how to deal with that?\r\n", "Hi !\r\n\r\nYou have to point to your local script.\r\nFirst clone the repo and then:\r\n\r\n```python\r\ndataset = load_dataset(\"./datasets/xtreme\", \"PAN-X.en\")\r\n```\r\nThe \"xtreme\" directory contains \"xtreme.py\".\r\n\r\nYou also have to change the features definition in the `_info` method. You could use:\r\n\r\n```python\r\nfeatures = nlp.Features({\r\n \"words\": [nlp.Value(\"string\")],\r\n \"ner_tags\": [nlp.Value(\"string\")],\r\n \"langs\": [nlp.Value(\"string\")],\r\n})\r\n```\r\n\r\nHope this helps !\r\nLet me know if you have other questions.", "Thanks, I am making progress. I got a new error `NonMatchingSplitsSizesError ` (see traceback below), which I suspect is due to the fact that number of rows in the dataset changed (one row per word --> one row per sentence) as well as the number of bytes due to the slightly updated data structure. \r\n\r\n```python\r\nNonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=1756492, num_examples=80536, dataset_name='xtreme'), 'recorded': SplitInfo(name='validation', num_bytes=1837109, num_examples=10000, dataset_name='xtreme')}, {'expected': SplitInfo(name='test', num_bytes=1752572, num_examples=80326, dataset_name='xtreme'), 'recorded': SplitInfo(name='test', num_bytes=1833214, num_examples=10000, dataset_name='xtreme')}, {'expected': SplitInfo(name='train', num_bytes=3496832, num_examples=160394, dataset_name='xtreme'), 'recorded': SplitInfo(name='train', num_bytes=3658428, num_examples=20000, dataset_name='xtreme')}]\r\n```\r\nI can fix the error by replacing the values in the `datasets_infos.json` file, which I tested for English. However, to update this for all 40 datasets manually is slightly painful. Is there a better way to update the expected values for all datasets?", "You can update the json file by calling\r\n```\r\nnlp-cli test ./datasets/xtreme --save_infos --all_configs\r\n```", "One more thing about features. I mentioned\r\n\r\n```python\r\nfeatures = nlp.Features({\r\n \"words\": [nlp.Value(\"string\")],\r\n \"ner_tags\": [nlp.Value(\"string\")],\r\n \"langs\": [nlp.Value(\"string\")],\r\n})\r\n```\r\n\r\nbut it's actually not consistent with the way we write datasets. Something like this is simpler to read and more consistent with the way we define datasets:\r\n\r\n```python\r\nfeatures = nlp.Features({\r\n \"words\": nlp.Sequence(nlp.Value(\"string\")),\r\n \"ner_tags\": nlp.Sequence(nlp.Value(\"string\")),\r\n \"langs\": nlp.Sequence(nlp.Value(\"string\")),\r\n})\r\n```\r\n\r\nSorry about that", "Closing this since PR #437 fixed the problem and has been merged to `master`. " ]

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[ "I had to make `SplitDict` serializable to be able to copy `DatasetInfo` objects properly.\r\nSerialization was also asked in #389 ", "One thing I forgot to say here, is that we also want to use the features arguments of `load_dataset` (which goes in the builder’s config) to override the default features of a dataset script." ]

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[ "What about the other PR #419 ?", "Oh this is the PR where I ran make quality and make style and some previous files from master were changed", "Oh right ! Let me fix the style myself if you don't mind" ]

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[ "I think the problem is the way you wrote your urls. Try the following structure to see `https://drive.google.com/uc?export=download&id=your_file_id` . \r\n\r\n@lhoestq ", "Oh sorry, I think `_get_drive_url` is doing that. \r\n\r\nHave you tried to use `dl_manager.download_and_extract(_get_drive_url(_TRAIN_URL)`? it should work with google drive links.\r\n", "Yes it worked, thank you!" ]

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[ "great, I think I did not download the data the way you do, but yours is more reasonable." ]

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[ "`from_dict` was added in #350 that was unfortunately not included in the 0.3.0 release. It's going to be included in the next release that will be out pretty soon though.\r\nRight now if you want to use `from_dict` you have to install the package from the master branch\r\n```\r\npip install git+https://github.com/huggingface/nlp.git\r\n```", "> `from_dict` was added in #350 that was unfortunately not included in the 0.3.0 release. It's going to be included in the next release that will be out pretty soon though.\r\n> Right now if you want to use `from_dict` you have to install the package from the master branch\r\n> \r\n> ```\r\n> pip install git+https://github.com/huggingface/nlp.git\r\n> ```\r\nOK, thank you.\r\n" ]

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[ "Unfortunately it's not possible to download only the dev set of NQ.\r\n\r\nI think we could add a way to download only the test set by adding a custom configuration to the processing script though.", "Ok, got it. I think this could be a valuable feature - especially for large datasets like NQ, but potentially also others. \r\nFor us, it will in this case make the difference of using the library or keeping the old downloads of the raw dev datasets. \r\nHowever, I don't know if that fits into your plans with the library and can also understand if you don't want to support this.", "I don't think we could force this behavior generally since the dataset script authors are free to organize the file download as they want (sometimes the mapping between split and files can be very much nontrivial) but we can add an additional configuration for Natural Question indeed as @lhoestq indicate." ]

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[ "Hi @lewtun, you have to provide the full path to the downloaded file for example `/home/lewtum/..`", "I was able to repro. Opening a PR to fix that.\r\nThanks for reporting this issue !", "Thanks for the rapid fix @lhoestq!" ]

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[ "It was fixed in 2ddd18d139d3047c9c3abe96e1e7d05bb360132c.\r\nCould you pull the latest changes from master @morganmcg1 ?", "Thanks @lhoestq, works fine now!" ]

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[ "Fixed. Could you try again @mitchellgordon95 ?\r\nIt was due a file not being updated on S3.\r\n\r\nWe need to make sure all the datasets scripts get updated properly @julien-c ", "Works for me! Thanks.", "I found the same issue with almost any language other than English. (For English, it works). Will someone need to update the file on S3 again?", "This is because only some languages are already preprocessed (en, de, fr, it) and stored on our google storage.\r\nWe plan to have a systematic way to preprocess more wikipedia languages in the future.\r\n\r\nFor the other languages you have to process them on your side using apache beam. That's why the lib asks for a Beam runner." ]

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[ "I think the slowness here probably come from the fact that we are copying from and to python.\r\n\r\n@lhoestq for all the `select`-based methods I think we should stay in Arrow format and update the writer so that it can accept Arrow tables or batches as well. What do you think?", "> @lhoestq for all the `select`-based methods I think we should stay in Arrow format and update the writer so that it can accept Arrow tables or batches as well. What do you think?\r\n\r\nI just tried with `writer.write_table` with tables of 1000 elements and it's slower that the solution in #405 \r\n\r\nOn my side (select 10 000 examples):\r\n- Original implementation: 12s\r\n- Batched solution: 100ms\r\n- solution using arrow tables: 350ms\r\n\r\nI'll try with arrays and record batches to see if we can make it work.", "I tried using `.take` from pyarrow recordbatches but it doesn't improve the speed that much:\r\n```python\r\nimport nlp\r\nimport numpy as np\r\n\r\ndset = nlp.Dataset.from_file(\"dummy_test_select.arrow\") # dummy dataset with 100000 examples like {\"a\": \"h\"*512}\r\nindices = np.random.randint(0, 100_000, 1000_000)\r\n```\r\n\r\n```python\r\n%%time\r\nbatch_size = 10_000\r\nwriter = ArrowWriter(schema=dset.schema, path=\"dummy_path\",\r\n writer_batch_size=1000, disable_nullable=False)\r\nfor i in tqdm(range(0, len(indices), batch_size)):\r\n table = pa.concat_tables(dset._data.slice(int(i), 1) for i in indices[i : min(len(indices), i + batch_size)])\r\n batch = table.to_pydict()\r\n writer.write_batch(batch)\r\nwriter.finalize()\r\n# 9.12s\r\n```\r\n\r\n\r\n```python\r\n%%time\r\nbatch_size = 10_000\r\nwriter = ArrowWriter(schema=dset.schema, path=\"dummy_path\", \r\n writer_batch_size=1000, disable_nullable=False)\r\nfor i in tqdm(range(0, len(indices), batch_size)):\r\n batch_indices = indices[i : min(len(indices), i + batch_size)]\r\n # First, extract only the indices that we need with a mask\r\n mask = [False] * len(dset)\r\n for k in batch_indices:\r\n mask[k] = True\r\n t_batch = dset._data.filter(pa.array(mask))\r\n # Second, build the list of indices for the filtered table, and taking care of duplicates\r\n rev_positions = {}\r\n duplicates = 0\r\n for i, j in enumerate(sorted(batch_indices)):\r\n if j in rev_positions:\r\n duplicates += 1\r\n else:\r\n rev_positions[j] = i - duplicates\r\n rev_map = [rev_positions[j] for j in batch_indices]\r\n # Third, use `.take` from the combined recordbatch\r\n t_combined = t_batch.combine_chunks() # load in memory\r\n recordbatch = t_combined.to_batches()[0]\r\n table = pa.Table.from_arrays(\r\n [recordbatch[c].take(pa.array(rev_map)) for c in range(len(dset._data.column_names))],\r\n schema=writer.schema\r\n )\r\n writer.write_table(table)\r\nwriter.finalize()\r\n# 3.2s\r\n```\r\n", "Shuffling is now significantly faster thanks to #513 \r\nFeel free to play with it now :)\r\n\r\nClosing this one, but feel free to re-open if you have other questions" ]

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[ "What does the `nlp-cli dummy_data` command returns ?", "`test.json` -> `test` \r\nand \r\n`train.json` -> `train`\r\n\r\nas shown by the `nlp-cli dummy_data` command ;-)", "LGTM for merge @lhoestq - I let you merge if you want to." ]

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[ "Thanks!" ]

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[ "Do you mind adding a link to the much more extended pages on adding and sharing a dataset in the new documentation?", "Sure, I will do that too" ]

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[ "Looks cool :)\r\n\r\nI feel like \r\n```python\r\nconcatenated_dataset = dataset1.concatenate(dataset2)\r\n```\r\ncould be more natural. What do you think ?\r\n\r\nAlso could you also concatenate the `nlp.Dataset._data_files` ?\r\n```python\r\nreturn cls(table, info=info, split=split, data_files=self._data_files + other_dataset._data_files)\r\n```", "I feel like \"WikiBooks\" would be a multi task dataset that could fit in the #217 discussion.\r\nNot sure concatenate should be the solution for a multi task dataset.", "Thanks for the suggestion! `dset1.concatenate(dset2)` does feel more natural. Although this seems to be a different \"class\" of transformation function than map() or filter(), acting on two datasets rather than on one. I would prefer the function signature treat both datasets symmetrically.\r\n\r\nPython lists have `list1 + list2` or `list1.extend(list2)`.\r\nNumPy has `np.concatenate((arr1, arr2))`.\r\nPandas has `pd.join((df1, df2))`.\r\nPyTorch has `ConcatDataset((dset1, dset2))`.\r\n\r\nGiven the symmetrical treatment and clear communication that this creates a new object, rather than a simple chaining on the first, my preference is now for `nlp.concatenate((dset1, dset2))`. This would place the function in the same API class as `nlp.load_dataset`. Does that work?", "The multi-task discussion is interesting, thanks for pointing me to that! I'll be focusing on T5 in a few weeks, so I'm sure I'll have many opinions then :). For now, I think a simple concatenate feature is important and orthogonal to that discussion. For example, a user may want to create a custom dataset that joins Wikipedia with their own custom text.", "> Given the symmetrical treatment and clear communication that this creates a new object, rather than a simple chaining on the first, my preference is now for `nlp.concatenate((dset1, dset2))`. This would place the function in the same API class as `nlp.load_dataset`. Does that work?\r\n\r\nYep I like this idea. Maybe `nlp.concatenate_datasets()` ?\r\n\r\n> For now, I think a simple concatenate feature is important and orthogonal to that discussion. For example, a user may want to create a custom dataset that joins Wikipedia with their own custom text.\r\n\r\nI agree :)", "Great, just updated!" ]

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

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[ "similar slow download speed here for nlp.load_dataset('wmt14', 'fr-en')\r\n`\r\nDownloading: 100%|██████████████████████████████████████████████████████████| 658M/658M [1:00:42<00:00, 181kB/s]\r\nDownloading: 100%|██████████████████████████████████████████████████████████| 918M/918M [1:39:38<00:00, 154kB/s]\r\nDownloading: 2%|▉ | 40.9M/2.37G [04:48<5:03:06, 128kB/s]\r\n`\r\nCould we just download a specific subdataset in 'wmt14', such as 'newstest14'? ", "> The code runs but the download speed is extremely slow, the same behaviour is not observed on wmt16 and wmt18\r\n\r\nThe original source for the files may provide slow download speeds.\r\nWe can probably host these files ourselves.\r\n\r\n> When trying to download wmt17 zh-en, I got the following error:\r\n> ConnectionError: Couldn't reach https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-zh.tar.gz\r\n\r\nLooks like the file`UNv1.0.en-zh.tar.gz` is missing, or the url changed. We need to fix that\r\n\r\n> Could we just download a specific subdataset in 'wmt14', such as 'newstest14'?\r\n\r\nRight now I don't think it's possible. Maybe @patrickvonplaten knows more about it\r\n", "Yeah, the download speed is sadly always extremely slow :-/. \r\nI will try to check out the `wmt17 zh-en` bug :-) ", "Maybe this can be used - https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00 " ]

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[ "To convert from arrow type we have three options: to_numpy, to_pandas and to_pydict/to_pylist.\r\n\r\n- to_numpy and to_pandas return numpy arrays instead of lists but are very fast.\r\n- to_pydict/to_pylist can be 100x slower and become the bottleneck for reading data, but at least they return lists.\r\n\r\nMaybe we can have to_pydict/to_pylist as the default and use to_numpy or to_pandas when the format (set by `set_format`) is 'numpy' or 'pandas'" ]

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[ "I just copied the files that are on google storage to follow the new `_relative_data_dir ` format. It should be good to merge now :)\r\n\r\nWell actually it seems there are some merge conflicts to fix first" ]

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[ "We can probably scan the dataset scripts with a regexpr to try to identify this pattern cc @patrickvonplaten maybe", "@mariamabarham This script should work. I tested it for a couple of datasets. There might be exceptions where the script breaks - did not test everything.\r\n\r\n```python\r\n#!/usr/bin/env python3\r\n\r\nfrom nlp import prepare_module, DownloadConfig, import_main_class, hf_api\r\nimport tempfile\r\n\r\n\r\ndef scan_for_nested_unnecessary_dict(dataset_name):\r\n\r\n def load_builder_class(dataset_name):\r\n module_path = prepare_module(dataset_name, download_config=DownloadConfig(force_download=True))\r\n return import_main_class(module_path)\r\n\r\n def load_configs(dataset_name):\r\n builder_cls = load_builder_class(dataset_name)\r\n if len(builder_cls.BUILDER_CONFIGS) == 0:\r\n return [None]\r\n return builder_cls.BUILDER_CONFIGS\r\n\r\n def scan_features_for_nested_dict(features):\r\n is_sequence = False\r\n if hasattr(features, \"_type\"):\r\n if features._type != 'Sequence':\r\n return False\r\n else:\r\n is_sequence = True\r\n features = features.feature\r\n\r\n if isinstance(features, list):\r\n for value in features:\r\n if scan_features_for_nested_dict(value):\r\n return True\r\n return False\r\n\r\n elif isinstance(features, dict):\r\n for key, value in features.items():\r\n if is_sequence and len(features.keys()) == 1 and hasattr(features[key], \"_type\") and features[key]._type != \"Sequence\":\r\n return True\r\n if scan_features_for_nested_dict(value):\r\n return True\r\n return False\r\n elif hasattr(features, \"_type\"):\r\n return False\r\n else:\r\n raise ValueError(f\"{features} should be either a list, a dict or a feature\")\r\n\r\n configs = load_configs(dataset_name)\r\n\r\n for config in configs:\r\n with tempfile.TemporaryDirectory() as processed_temp_dir:\r\n # create config and dataset\r\n dataset_builder_cls = load_builder_class(dataset_name)\r\n name = config.name if config is not None else None\r\n dataset_builder = dataset_builder_cls(name=name, cache_dir=processed_temp_dir)\r\n\r\n is_nested_dict_in_dataset = scan_features_for_nested_dict(dataset_builder._info().features)\r\n if is_nested_dict_in_dataset:\r\n print(f\"{dataset_name} with {name} needs refactoring\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n scan_for_nested_unnecessary_dict(\"race\") # prints True\r\n scan_for_nested_unnecessary_dict(\"mlqa\") # prints True\r\n scan_for_nested_unnecessary_dict(\"squad\") # prints Nothing\r\n\r\n # ran the following lines for 1min and seems to work -> didn't check for all datasets though\r\n# api = hf_api.HfApi()\r\n# all_datasets = [x.id for x in api.dataset_list(with_community_datasets=False)]\r\n# for dataset in all_datasets:\r\n# scan_for_nested_unnecessary_dict(dataset)\r\n```", "> @mariamabarham This script should work. I tested it for a couple of datasets. There might be exceptions where the script breaks - did not test everything.\r\n> \r\n> ```python\r\n> #!/usr/bin/env python3\r\n> \r\n> from nlp import prepare_module, DownloadConfig, import_main_class, hf_api\r\n> import tempfile\r\n> \r\n> \r\n> def scan_for_nested_unnecessary_dict(dataset_name):\r\n> \r\n> def load_builder_class(dataset_name):\r\n> module_path = prepare_module(dataset_name, download_config=DownloadConfig(force_download=True))\r\n> return import_main_class(module_path)\r\n> \r\n> def load_configs(dataset_name):\r\n> builder_cls = load_builder_class(dataset_name)\r\n> if len(builder_cls.BUILDER_CONFIGS) == 0:\r\n> return [None]\r\n> return builder_cls.BUILDER_CONFIGS\r\n> \r\n> def scan_features_for_nested_dict(features):\r\n> is_sequence = False\r\n> if hasattr(features, \"_type\"):\r\n> if features._type != 'Sequence':\r\n> return False\r\n> else:\r\n> is_sequence = True\r\n> features = features.feature\r\n> \r\n> if isinstance(features, list):\r\n> for value in features:\r\n> if scan_features_for_nested_dict(value):\r\n> return True\r\n> return False\r\n> \r\n> elif isinstance(features, dict):\r\n> for key, value in features.items():\r\n> if is_sequence and len(features.keys()) == 1 and hasattr(features[key], \"_type\") and features[key]._type != \"Sequence\":\r\n> return True\r\n> if scan_features_for_nested_dict(value):\r\n> return True\r\n> return False\r\n> else:\r\n> raise ValueError(f\"{features} should be either a list of a dict\")\r\n> \r\n> configs = load_configs(dataset_name)\r\n> \r\n> for config in configs:\r\n> with tempfile.TemporaryDirectory() as processed_temp_dir:\r\n> # create config and dataset\r\n> dataset_builder_cls = load_builder_class(dataset_name)\r\n> name = config.name if config is not None else None\r\n> dataset_builder = dataset_builder_cls(name=name, cache_dir=processed_temp_dir)\r\n> \r\n> is_nested_dict_in_dataset = scan_features_for_nested_dict(dataset_builder._info().features)\r\n> if is_nested_dict_in_dataset:\r\n> print(f\"{dataset_name} with {name} needs refactoring\")\r\n> \r\n> \r\n> if __name__ == \"__main__\":\r\n> scan_for_nested_unnecessary_dict(\"race\") # prints True\r\n> scan_for_nested_unnecessary_dict(\"mlqa\") # prints True\r\n> scan_for_nested_unnecessary_dict(\"squad\") # prints Nothing\r\n> \r\n> # ran the following lines for 1min and seems to work -> didn't check for all datasets though\r\n> # api = hf_api.HfApi()\r\n> # all_datasets = [x.id for x in api.dataset_list(with_community_datasets=False)]\r\n> # for dataset in all_datasets:\r\n> # scan_for_nested_unnecessary_dict(dataset)\r\n> ```\r\n\r\nGreat, I will try it", "I'm not sure the work on this PR was finished @lhoestq cc @mariamabarham @patrickvonplaten ", "Sorry for that, apparently there are other datasets that could have unnecessary nested dicts.\r\nWe can have another PR to scan and fix the other datasets.\r\n" ]

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[ "I am checking the details of the CI log for the failed test, but I don't see how the error relates to the code I added; the error is coming from a config builder different than the `LinceConfig`, and it crashes when `self.config.data_files` because is self.config is None. I would appreciate if someone could help me find out where I could have messed things up :)\r\n\r\nAlso, the real and dummy data tests passed before committing and pushing my changes.\r\n\r\nThanks a lot in advance!\r\n\r\n```\r\n=================================== FAILURES ===================================\r\n____________________ AWSDatasetTest.test_load_dataset_text _____________________\r\n\r\nself = <tests.test_dataset_common.AWSDatasetTest testMethod=test_load_dataset_text>\r\ndataset_name = 'text'\r\n\r\n def test_load_dataset(self, dataset_name):\r\n configs = self.dataset_tester.load_all_configs(dataset_name)[:1]\r\n> self.dataset_tester.check_load_dataset(dataset_name, configs)\r\n\r\ntests/test_dataset_common.py:243: \r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\ntests/test_dataset_common.py:137: in check_load_dataset\r\n try_from_hf_gcs=False,\r\n../.local/lib/python3.6/site-packages/nlp/builder.py:432: in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n../.local/lib/python3.6/site-packages/nlp/builder.py:466: in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\nself = <nlp.datasets.text.bf5568367c6707640e5601a44ed0af98f40a8483db81a7db99b85fab6606fc8b.text.Text object at 0x7efa744ffb70>\r\ndl_manager = <nlp.utils.mock_download_manager.MockDownloadManager object at 0x7efb304c52b0>\r\n\r\n def _split_generators(self, dl_manager):\r\n \"\"\" The `datafiles` kwarg in load_dataset() can be a str, List[str], Dict[str,str], or Dict[str,List[str]].\r\n \r\n If str or List[str], then the dataset returns only the 'train' split.\r\n If dict, then keys should be from the `nlp.Split` enum.\r\n \"\"\"\r\n if isinstance(self.config.data_files, (str, list, tuple)):\r\n # Handle case with only one split\r\n files = self.config.data_files\r\n if isinstance(files, str):\r\n files = [files]\r\n return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={\"files\": files})]\r\n else:\r\n # Handle case with several splits and a dict mapping\r\n splits = []\r\n for split_name in [nlp.Split.TRAIN, nlp.Split.VALIDATION, nlp.Split.TEST]:\r\n> if split_name in self.config.data_files:\r\nE TypeError: argument of type 'NoneType' is not iterable\r\n\r\n../.local/lib/python3.6/site-packages/nlp/datasets/text/bf5568367c6707640e5601a44ed0af98f40a8483db81a7db99b85fab6606fc8b/text.py:24: TypeError\r\n=============================== warnings summary ===============================\r\n... \r\n=========================== short test summary info ============================\r\nFAILED tests/test_dataset_common.py::AWSDatasetTest::test_load_dataset_text\r\n====== 1 failed, 963 passed, 532 skipped, 5 warnings in 166.33s (0:02:46) ======\r\n\r\nExited with code exit status 1\r\n```", "@lhoestq Hi Quentin, I was wondering if you could give some feedback on this error from the `run_dataset_script_tests` script. It seems that's coming from a different config builder than the one I added, so I am not sure why this error would occur. Thanks in advance!", "Awesome! Thank you for all your comments! 👌 I will update the PR in a bit with all the required changes 🙂 \r\n\r\nLet me just provide a bit of context for my changes:\r\n\r\nI was referring to the GLUE, XTREME and WNUT_17 dataset scripts to build mine (not sure if the new documentation was available last week). This is where I took the naming convention for the citation and description variables. Also, these scripts didn't have the `BUILDER_CONFIG_CLASS = LinceConfig` line so I commented this out thinking I didn't need that; I tried this line in my attempts to make the real and dummy data tests pass but it was not helping. \r\n\r\nThe problem I was facing was that the tests were passing a default `BuilderConfig` (i.e., `self.config.name` property was set to `'default'` and my custom properties were not available). This means, for example, that within the `def _info(...)` method, I was not able to access the specific fields of my `LinceConfig` class (which is why I have now a global variable `_LINCE_CITATIONS`, to detach the individual citations from the corresponding LinceConfig objects, as well as I am constructing manually the feature infos). This default `BuilderConfig` is why I added the `if not isinstance(self.config, LinceConfig): return []` statement. Otherwise, accessing custom properties like `self.config.colnames` was failing the test because such properties did not exist in the default config (i.e., it was not a `LinceConfig`).\r\n\r\nI will update the PR and see if these problems happen in the CI tests.\r\n\r\nThanks again for the follow-up! @lhoestq ", "Ok I see !\r\n\r\nTo give you more details: the line `BUILDER_CONFIG_CLASS = LinceConfig` tells the tests how to instantiate a config for this dataset. Therefore if you have this line you should have all the fields of your config available.\r\n\r\nTo fix the errors you get you'll have to, first, have the `BUILDER_CONFIG_CLASS = LinceConfig` line, and second, add default values for the parameters of your config (or the tests functions will be unable to instantiate it by calling `LinceConfig()`.\r\n\r\nAn example of dataset with a custom config with additional filed like this one is [biomrc](https://github.com/huggingface/nlp/blob/master/datasets/biomrc/biomrc.py).\r\nFeel free to give a look at it if you want.", "Thanks for the reference!\r\n\r\nI just updated the PR with the suggested changes. It seems the CI failed on the same test you said we could ignore, so I guess it's okay :) \r\n\r\nPlease let me know if there is something else I may need to change." ]

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[ "Same for the RACE dataset: https://github.com/huggingface/nlp/blob/master/datasets/race/race.py\r\n\r\nShould we scan all the datasets to remove this pattern of un-necessary nesting?", "You're right, I think we don't need to use the nested dictionary. \r\n" ]

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[ "**Edit**: other topic previously in this message moved to a new issue: https://github.com/huggingface/nlp/issues/387", "Could you try to update pyarrow to >=0.17.0 ? It should fix the `to_pandas` bug\r\n\r\nAlso I'm not sure that structures like list<struct> are fully supported in the lib (none of the datasets use that).\r\nIt can cause issues when using dataset transforms like `filter` for example" ]

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[ "I am not able to reproduce this issue on my side.\r\nCould you give us more details about the inputs you used ?\r\n\r\nI do get another error though:\r\n```\r\n~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/bert_score/utils.py in bert_cos_score_idf(model, refs, hyps, tokenizer, idf_dict, verbose, batch_size, device, all_layers)\r\n 371 return sorted(list(set(l)), key=lambda x: len(x.split(\" \")))\r\n 372 \r\n--> 373 sentences = dedup_and_sort(refs + hyps)\r\n 374 embs = []\r\n 375 iter_range = range(0, len(sentences), batch_size)\r\n\r\nValueError: operands could not be broadcast together with shapes (0,) (2,)\r\n```\r\nThat's because it gets numpy arrays as input and not lists. See #387 ", "The other issue was fixed by #403 \r\n\r\nDo you still get this issue @willywsm1013 ?\r\n" ]

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[ "I changed the `wiki_dpr` script to ignore the last 24 examples for now. Hopefully we'll have the full version soon.\r\nThe datasets_infos.json and the data on GCS are updated.\r\n\r\nAnd I also added a check to make sure we don't have post processing resources in sub-directories.", "I added a dummy config that can be loaded with:\r\n```python\r\nwiki = load_dataset(\"wiki_dpr\", \"dummy_psgs_w100_no_embeddings\", with_index=True, split=\"train\")\r\n```\r\nIt's only 6MB of arrow files and 30MB of index" ]

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[ "I've seen this sort of thing before -- it might help to delete the directory -- I've also noticed that there is an error with the json Dataloader for any data I've tried to load. I've replaced it with this, which skips over the data feature population step:\r\n\r\n\r\n```python\r\nimport os\r\n\r\nimport pyarrow.json as paj\r\n\r\nimport nlp as hf_nlp\r\n\r\nfrom nlp import DatasetInfo, BuilderConfig, SplitGenerator, Split, utils\r\nfrom nlp.arrow_writer import ArrowWriter\r\n\r\n\r\nclass JSONDatasetBuilder(hf_nlp.ArrowBasedBuilder):\r\n BUILDER_CONFIG_CLASS = BuilderConfig\r\n\r\n def _info(self):\r\n return DatasetInfo()\r\n\r\n def _split_generators(self, dl_manager):\r\n \"\"\" We handle string, list and dicts in datafiles\r\n \"\"\"\r\n if isinstance(self.config.data_files, (str, list, tuple)):\r\n files = self.config.data_files\r\n if isinstance(files, str):\r\n files = [files]\r\n return [SplitGenerator(name=Split.TRAIN, gen_kwargs={\"files\": files})]\r\n splits = []\r\n for split_name in [Split.TRAIN, Split.VALIDATION, Split.TEST]:\r\n if split_name in self.config.data_files:\r\n files = self.config.data_files[split_name]\r\n if isinstance(files, str):\r\n files = [files]\r\n splits.append(SplitGenerator(name=split_name, gen_kwargs={\"files\": files}))\r\n return splits\r\n\r\n def _prepare_split(self, split_generator):\r\n fname = \"{}-{}.arrow\".format(self.name, split_generator.name)\r\n fpath = os.path.join(self._cache_dir, fname)\r\n\r\n writer = ArrowWriter(path=fpath)\r\n\r\n generator = self._generate_tables(**split_generator.gen_kwargs)\r\n for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n writer.write_table(table)\r\n num_examples, num_bytes = writer.finalize()\r\n\r\n split_generator.split_info.num_examples = num_examples\r\n split_generator.split_info.num_bytes = num_bytes\r\n\r\n def _generate_tables(self, files):\r\n for i, file in enumerate(files):\r\n pa_table = paj.read_json(\r\n file\r\n )\r\n yield i, pa_table\r\n\r\n```", "Yes, deleting the directory solves the error whenever I try to rerun.\r\n\r\nBy replacing the json-loader, you mean the cached file in my `site-packages` directory? e.g. `/home/XXX/.cache/lib/python3.7/site-packages/nlp/datasets/json/(...)/json.py` \r\n\r\nWhen I was testing this out before the #372 PR was merged I had issues installing it properly locally. Since the `json.py` script was downloaded instead of actually using the one provided in the local install. Manually updating that file seemed to solve it, but it didn't seem like a proper solution. Especially when having to run this on a remote compute cluster with no access to that directory.", "I see, diving in the JSON file for SQuAD it's a pretty complex structure.\r\n\r\nThe best solution for you, if you have a dataset really similar to SQuAD would be to copy and modify the SQuAD data processing script. We will probably add soon an option to be able to specify file path to use instead of the automatic URL encoded in the script but in the meantime you can:\r\n- copy the [squad script](https://github.com/huggingface/nlp/blob/master/datasets/squad/squad.py) in a new script for your dataset\r\n- in the new script replace [these `urls_to_download `](https://github.com/huggingface/nlp/blob/master/datasets/squad/squad.py#L99-L102) by `urls_to_download=self.config.data_files`\r\n- load the dataset with `dataset = load_dataset('path/to/your/new/script', data_files={nlp.Split.TRAIN: \"./datasets/train-v2.0.json\"})`\r\n\r\nThis way you can reuse all the processing logic of the SQuAD loading script.", "This seems like a more sensible solution! Thanks, @thomwolf. It's been a little daunting to understand what these scripts actually do, due to the level of abstraction and central documentation.\r\n\r\nAm I correct in assuming that the `_generate_examples()` function is the actual procedure for how the data is loaded from file? Meaning that essentially with a file containing another format, that is the only function that requires re-implementation? I'm working with a lot of datasets that, due to licensing and privacy, cannot be published. As this library is so neatly integrated with the transformers library and gives easy access to public sets such as SQUAD and increased performance, it is very neat to be able to load my private sets as well. As of now, I have just been working on scripts for translating all my data into the SQUAD-format before using the json script, but I see that it might not be necessary after all. ", "Yes `_generate_examples()` is the main entry point. If you change the shape of the returned dictionary you also need to update the `features` in the `_info`.\r\n\r\nI'm currently writing the doc so it should be easier soon to use the library and know how to add your datasets.\r\n", "Could you try to update pyarrow to >=0.17.0 @vegarab ?\r\nI don't have any segmentation fault with my version of pyarrow (0.17.1)\r\n\r\nI tested with\r\n```python\r\nimport nlp\r\ns = nlp.load_dataset(\"json\", data_files=\"train-v2.0.json\", field=\"data\", split=\"train\")\r\ns[0]\r\n# {'title': 'Normans', 'paragraphs': [{'qas': [{'question': 'In what country is Normandy located?', 'id':...\r\n```", "Also if you want to have your own dataset script, we now have a new documentation !\r\nSee here:\r\nhttps://huggingface.co/nlp/add_dataset.html", "@lhoestq \r\nFor some reason, I am not able to reproduce the segmentation fault, on pyarrow==0.16.0. Using the exact same environment and file.\r\n\r\nAnyhow, I discovered that pyarrow>=0.17.0 is required to read in a JSON file where the pandas structs contain lists. Otherwise, pyarrow complains when attempting to cast the struct:\r\n```py\r\nimport nlp\r\n>>> s = nlp.load_dataset(\"json\", data_files=\"datasets/train-v2.0.json\", field=\"data\", split=\"train\")\r\nUsing custom data configuration default\r\n>>> s[0]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py\", line 558, in __getitem__\r\n format_kwargs=self._format_kwargs,\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py\", line 498, in _getitem\r\n outputs = self._unnest(self._data.slice(key, 1).to_pandas().to_dict(\"list\"))\r\n File \"pyarrow/array.pxi\", line 559, in pyarrow.lib._PandasConvertible.to_pandas\r\n File \"pyarrow/table.pxi\", line 1367, in pyarrow.lib.Table._to_pandas\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/pyarrow/pandas_compat.py\", line 766, in table_to_blockmanager\r\n blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes)\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/pyarrow/pandas_compat.py\", line 1101, in _table_to_blocks\r\n list(extension_columns.keys()))\r\n File \"pyarrow/table.pxi\", line 881, in pyarrow.lib.table_to_blocks\r\n File \"pyarrow/error.pxi\", line 105, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: Not implemented type for Arrow list to pandas: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>\r\n>>> s\r\nDataset(schema: {'title': 'string', 'paragraphs': 'list<item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>>'}, num_rows: 35)\r\n```\r\n\r\nUpgrading to >=0.17.0 provides the same dataset structure, but accessing the records is possible without the same exception. \r\n\r\n", "Very happy to see some extended documentation! ", "#376 seems to be reporting the same issue as mentioned above. ", "This issue helped me a lot, thanks.\r\nHope this issue will be fixed soon." ]

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[ "Thanks for the fast fix!" ]

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[ "Looks like a flaky CI, failed download from S3." ]

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[]

[ "I am able to reproduce this with the official SQuAD `train-v2.0.json` file downloaded directly from https://rajpurkar.github.io/SQuAD-explorer/", "I am facing this issue in transformers library 3.0.2 while reading a csv using datasets.\r\nIs this fixed in latest version? \r\nI updated the latest version 4.0.1 but still getting this error. What could cause this error?" ]

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