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[ "Hi @BramVanroy, thanks for reporting.\r\n\r\nFirst, note that `\"min\"` is not an allowed value for `average`. According to scikit-learn [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html), `average` can only take the values: `{\"micro\", \"macro\", \"samples\", \"weighted\", \"binary\"} or None, default=\"binary\"`.\r\n\r\nSecond, you should take into account that all additional metric-specific argument should be passed in the method `compute` (and not in the method `load_metric`). You can find more information in our documentation: https://huggingface.co/docs/datasets/using_metrics.html#computing-the-metric-scores\r\n\r\nSo for example, if you would like to calculate the macro-averaged F1 score, you should use:\r\n```python\r\nimport datasets\r\n\r\nf1 = datasets.load_metric(\"f1\", keep_in_memory=True)\r\nf1.add_batch(predictions=[0,2,3], references=[1, 2, 3])\r\nf1.compute(average=\"macro\")\r\n```", "Thanks, that was it. A bit strange though, since `load_metric` had an argument `metric_init_kwargs`. I assume that that's for specific initialisation arguments whereas `average` is for the function itself." ]

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[ "Added the missing label classes and their explanations (to the best of my understanding)", "Thanks a lot ! Once the last comment about the label names is addressed we can merge :)" ]

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[ "The current CI failure is due to modifications in the dataset script.", "Merging since the CI is only failing because of dataset card issues, which is unrelated to this PR" ]

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[ "Hi @stas00, \r\nI want to work on this issue and I was thinking why don't we use `imap` [in this loop](https://github.com/huggingface/datasets/blob/440b14d0dd428ae1b25881aa72ba7bbb8ad9ff84/src/datasets/io/json.py#L99)? This way, using offset (which is being used to slice the pyarrow table) we can convert pyarrow table to `json` using multiprocessing. I've a small code snippet for some clarity:\r\n```\r\nresult = list(\r\n pool.imap(self._apply_df, [(offset, batch_size) for offset in range(0, len(self.dataset), batch_size)])\r\n )\r\n```\r\n`_apply_df` is a function which will return `batch.to_pandas().to_json(path_or_buf=None, orient=\"records\", lines=True)` which is basically json version of the batched pyarrow table. Later on we can concatenate it to form json file? \r\n\r\nI think the only downside here is to write file from `imap` output (output would be a list and we'll need to iterate over it and write in a file) which might add a little overhead cost. What do you think about this?", "Followed up in https://github.com/huggingface/datasets/pull/2747" ]

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[ "This is ready for review now :)\r\n\r\nI would love to have some feedback on the changes in load.py @albertvillanova. There are many changes so if you have questions let me know, especially on the `resolve_data_files` functions and on the changes in `prepare_module`.\r\n\r\nAnd @thomwolf if you want to take a look at the documentation, feel free to share your suggestions :)", "I took your comments into account thanks !\r\nAnd I made `aiohttp` a required dependency :)", "Just updated the documentation :)\r\n[share_datasets.html](https://45532-250213286-gh.circle-artifacts.com/0/docs/_build/html/share_dataset.html)\r\n\r\nLet me know if you have some comments", "Merging this one :) \r\n\r\nWe can try to integrate the changes in the docs to #2718 @stevhliu !", "Baked this into the [docs](https://44335-250213286-gh.circle-artifacts.com/0/docs/_build/html/loading.html#hugging-face-hub) already, let me know if there is anything else I should add! :)" ]

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[ "I make a summary about our discussion with @lewtun and @Narsil on the agreed schema for this dataset and the additional steps required to generate the 2D array labels:\r\n- The labels for this dataset are a 2D array:\r\n Given an example:\r\n ```python\r\n {\"record_id\": record_id, \"file\": file, \"start\": start, \"end\": end, \"speakers\": [...]}\r\n ```\r\n the labels are a 2D array of shape `(num_frames, num_speakers)` where `num_frames = end - start` and `num_speakers = 2`.\r\n- In order to avoid a too large dataset (too large disk space), `datasets` does not store the 2D array label. Instead, we store a compact form:\r\n ```\r\n \"speakers\": [\r\n {\"speaker_id\": speaker_0_id, \"start\": start_0_speaker_0, \"end\": end_0_speaker_0},\r\n {\"speaker_id\": speaker_0_id, \"start\": start_1_speaker_0, \"end\": end_1_speaker_0},\r\n {\"speaker_id\": speaker_1_id, \"start\": start_0_speaker_1, \"end\": end_0_speaker_1},\r\n ],\r\n ```\r\n - Once loaded the dataset, an additional step is required to generate the 2D array label from this compact form\r\n - This additional step should be a modified version of the s3prl method `_get_labeled_speech`:\r\n - Original s3prl `_get_labeled_speech` includes 2 functionalities: reading the audio file and transforming it into an array, and generating the label 2D array; I think we should separate these 2 functionalities\r\n - Original s3prl `_get_labeled_speech` performs 2 steps to generate the labels:\r\n - Transform start/end seconds (float) into frame numbers (int): I have already done this step to generate the dataset\r\n - Generate the 2D array label from the frame numbers\r\n\r\nI also ping @osanseviero and @lhoestq to include them in the loop.", "Here I would like to discuss (and agree) one of the decisions I made, as I'm not completely satisfied with it: to transform the seconds (float) into frame numbers (int) to generate this dataset.\r\n\r\n- A priori, the most natural and general choice would be to preserve the seconds (float), because:\r\n - this is the way the raw data comes from\r\n - the transformation into frame numbers depends on the sample rate, frame_shift and subsampling\r\n\r\nHowever, I finally decided to transform seconds into frame numbers because:\r\n- for SUPERB, sampling rate, frame_shift and subsampling are fixed (`rate = 16_000`, `frame_shift = 160`, `subsampling = 1`)\r\n- it makes easier the post-processing, as labels are generated from sample numbers: labels are a 2D array of shape `(num_frames, num_speakers)`\r\n- the number of examples depends on the number of frames:\r\n - if an example has more than 2_000 frames, then it is split into 2 examples. This is the case for `record_id = \"7859-102521-0017_3983-5371-0014\"`, which has 2_452 frames and it is split into 2 examples:\r\n ```\r\n {\"record_id\": \"7859-102521-0017_3983-5371-0014\", \"start\"= 0, \"end\": 2_000,...},\r\n {\"record_id\": \"7859-102521-0017_3983-5371-0014\", \"start\"= 2_000, \"end\": 2_452,...},\r\n ```\r\n\r\nAs I told you, I'm not totally convinced of this decision, and I would really appreciate your opinion.\r\n\r\ncc: @lewtun @Narsil @osanseviero @lhoestq ", "It makes total sense to prepare the data to be in a format that can actually be used for model training and evaluation. That's one of the roles of this lib :)\r\n\r\nSo for me it's ok to use frames as a unit instead of seconds. Just pinging @patrickvonplaten in case he has ever played with such audio tasks and has some advice. For the context: the task is to classify which speaker is speaking, let us know if you are aware of any convenient/standard format for this.\r\n\r\nAlso I'm not sure why you have to split an example if it's longer that 2,000 frames ?", "> Also I'm not sure why you have to split an example if it's longer that 2,000 frames ?\r\n\r\nIt is a convention in SUPERB benchmark.", "Note that if we agree to leave the dataset as it is now, 2 additional custom functions must be used:\r\n- one to generate the 2D array labels\r\n- one to load the audio file into an array, but taking into account start/end to cut the audio\r\n\r\nIs there a way we can give these functions ready to be used? Or should we leave this entirely to the end user? This is not trivial...", "You could add an example of usage in the dataset card, as it is done for other audio datasets", "@albertvillanova this simple function can be edited simply to add the start/stop cuts \r\n\r\nhttps://github.com/huggingface/transformers/blob/master/src/transformers/pipelines/automatic_speech_recognition.py#L29 ", "Does this function work on windows ?", "Windows ? What is it ? (Not sure not able to test, it's directly calling ffmpeg binary, so depending on the setup it could but can't say for sure without testing)\r\n", "It's one of the OS we're supposed to support :P (for the better and for the worse)", "> Note that if we agree to leave the dataset as it is now, 2 additional custom functions must be used:\r\n> \r\n> * one to generate the 2D array labels\r\n> * one to load the audio file into an array, but taking into account start/end to cut the audio\r\n> \r\n> Is there a way we can give these functions ready to be used? Or should we leave this entirely to the end user? This is not trivial...\r\n\r\n+1 on providing the necessary functions on the dataset card. aside from that, the current implementation looks great from my perspective!" ]

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[ "@lhoestq This one has been open for a while. Could you please take a look?", "@lhoestq Ready for the final review!", "I forgot to update the signature of `DatasetDict.map`, so did that now." ]

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[ "This is not the default in pandas right ?\r\nWe try to align our CSV loader with the pandas API.\r\n\r\nMoreover according to their documentation, the python parser is used when sep is None, which might not be the fastest one.\r\n\r\nMaybe users could just specify `sep=None` themselves ?\r\nIn this case we should add some documentation about this" ]

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[ "Hi! I was looking into this and hope you can clarify a point. Your my_dataset variable would be of type DatasetDict which means the alternative you've described (dict comprehension) is what makes sense. \r\nIs there a reason why you wouldn't want to convert my_dataset to a pandas df if you'd like to use it like one? Please let me know if I'm missing something.", "Hi! Sorry for the delay.\r\n\r\nIn this case, the dataset would be a `datasets.Dataset` and we want to select multiple columns, the `idx` and `label` columns for example.\r\n\r\nMy issue is that my dataset is too big for memory if I load everything into pandas." ]

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[ "#self-assign", "I understand it already raises a `NotImplementedError` exception, eg:\r\n\r\n```\r\n>>> dataset = load_dataset(\"journalists_questions\", name=\"plain_text\", split=\"train\", streaming=True)\r\n\r\n[...]\r\nNotImplementedError: Extraction protocol for file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet\r\n```\r\n" ]

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[ "Note that this subset requires us to:\r\n\r\n* generate the LibriMix corpus from LibriSpeech\r\n* prepare the corpus for diarization\r\n\r\nAs suggested by @lhoestq we should perform these steps locally and add the prepared data to this public repo on the Hub: https://huggingface.co/datasets/superb/superb-data\r\n\r\nThen we can use the URLs for the files to load the data in `superb`'s dataset loading script.\r\n\r\nFor consistency, I suggest we name the folders in `superb-data` in the same way as the configs in the dataset loading script - e.g. use `sd` for speech diarization in both places :)", "@lewtun @lhoestq: \r\n\r\nI have already generated the LibriMix corpus and prepared the corpus for diarization. The output is 3 dirs (train, dev, test), each one containing 6 files: reco2dur rttm segments spk2utt utt2spk wav.scp\r\n\r\nNext steps:\r\n- Upload these files to the superb-data repo\r\n- Transcribe the corresponding s3prl processing of these files into our superb loading script\r\n\r\nNote that processing of these files is a bit more intricate than usual datasets: https://github.com/s3prl/s3prl/blob/master/s3prl/downstream/diarization/dataset.py#L233\r\n\r\n" ]

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[ "Hi,\r\n\r\nyou can suppress progress bars by patching logging as follows:\r\n```python\r\nimport datasets\r\nimport logging\r\ndatasets.logging.get_verbosity = lambda: logging.NOTSET\r\n# map call ...\r\n```", "Thank you, it worked :)", "See https://github.com/huggingface/datasets/issues/2528 for reference", "Note also that you can disable the progress bar with\r\n\r\n```python\r\nfrom datasets.utils import disable_progress_bar\r\ndisable_progress_bar()\r\n```\r\n\r\nSee https://github.com/huggingface/datasets/blob/8814b393984c1c2e1800ba370de2a9f7c8644908/src/datasets/utils/tqdm_utils.py#L84", "Now the library officially recommends `set_progress_bar_enabled(False)`\r\n\r\n```py\r\nfrom datasets.utils import set_progress_bar_enabled\r\n\r\nset_progress_bar_enabled(False)\r\n```\r\n\r\nsource:\r\n\r\nhttps://github.com/huggingface/datasets/blob/1fd47120ace13626c528367787ffa13e1a26e6c0/src/datasets/utils/tqdm_utils.py#L83-L88\r\n\r\n" ]

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[ "I need the same feature for distributed training" ]

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[ "#take" ]

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[ "Hi ! I just tested and it worked fine today for me.\r\n\r\nI think this is because the dataset is stored on Google Drive which has a quota limit for the number of downloads per day, see this similar issue https://github.com/huggingface/datasets/issues/996 \r\n\r\nFeel free to try again today, now that the quota was reset" ]

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[ "Hi ! It looks like an issue with pytorch.\r\n\r\nCould you try to run `import torch` and see if it raises an error ?", "> Hi ! It looks like an issue with pytorch.\r\n> \r\n> Could you try to run `import torch` and see if it raises an error ?\r\n\r\nIt works. Thank you!" ]

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[ "Hi ! Thanks for reporting. Indeed it looks like type inference makes it fail. We should probably just ignore this step until a non-empty batch is passed.", "Sounds good! Do you want me to propose a PR? I'm quite busy right now, but if it's not too urgent I could take a look next week.", "Sure if you're interested feel free to open a PR :)\r\n\r\nYou can also ping me anytime if you have questions or if I can help !", "Sorry to ping you, @lhoestq, did you have a chance to take a look at the proposed PR? Thank you!", "Yes and it's all good, thank you :)\r\n\r\nFeel free to close this issue if it's good for you", "Everything's good, thanks!" ]

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[ "I'm running into this as well. (Thank you so much for reporting @jorgeecardona — was staring at this massive stack trace and unsure what exactly was wrong!)", "Hi ! Thanks for reporting :)\r\n\r\nUntil this is fixed on `dill`'s side, we could implement a custom saving in our Pickler indefined in utils.py_utils.py\r\nThere is already a suggestion in this message about how to do it:\r\nhttps://github.com/uqfoundation/dill/issues/250#issuecomment-852566284\r\n\r\nLet me know if such a workaround could help, and feel free to open a PR if you want to contribute !", "I have the same bug.\r\nthe code is as follows:\r\n\r\nthe error is: \r\n\r\n\r\nLook for the solution for this bug.", "Hi ! I think your RecursionError comes from a different issue @BitcoinNLPer , could you open a separate issue please ?\r\n\r\nAlso which dataset are you using ? I tried loading `CodedotAI/code_clippy` but I get a different error\r\n```python\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/load.py\", line 1615, in load_dataset\r\n **config_kwargs,\r\n File \"/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/load.py\", line 1446, in load_dataset_builder\r\n builder_cls = import_main_class(dataset_module.module_path)\r\n File \"/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/load.py\", line 101, in import_main_class\r\n module = importlib.import_module(module_path)\r\n File \"/Users/quentinlhoest/.virtualenvs/hf-datasets/lib/python3.7/importlib/__init__.py\", line 127, in import_module\r\n return _bootstrap._gcd_import(name[level:], package, level)\r\n File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\r\n File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n File \"<frozen importlib._bootstrap>\", line 677, in _load_unlocked\r\n File \"<frozen importlib._bootstrap_external>\", line 728, in exec_module\r\n File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n File \"/Users/quentinlhoest/.cache/huggingface/modules/datasets_modules/datasets/CodedotAI___code_clippy/d332f69d036e8c80f47bc9a96d676c3fa30cb50af7bb81e2d4d12e80b83efc4d/code_clippy.py\", line 66, in <module>\r\n url_elements = results.find_all(\"a\")\r\nAttributeError: 'NoneType' object has no attribute 'find_all'\r\n```" ]

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[ "Hi ! This is a great idea :)\r\nI think we could have something similar to what we have in `datasets.Dataset.map`, i.e. a `num_proc` parameter that tells how many processes to spawn to parallelize the data processing. \r\n\r\nRegarding AUTOTUNE, this could be a nice feature as well, we could see how to add it in a second step" ]

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[ "Hi! It's probably because this dataset is stored on google drive and it has a per day quota limit. It should work if you retry, I was able to initiate the download.\r\n\r\nSimilar issue [here](https://github.com/huggingface/datasets/issues/2646)", "Hi ! Loading the dataset works on my side as well.\r\nFeel free to try again and let us know if it works for you know", "Thank you! I've been trying periodically for the past month, and no luck yet with this particular dataset. Just tried again and still hitting the checksum error.\r\n\r\nCode:\r\n\r\n`dataset = load_dataset(\"financial_phrasebank\", \"sentences_allagree\") `\r\n\r\nTraceback:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError Traceback (most recent call last)\r\n<ipython-input-2-55cc2144f31e> in <module>\r\n----> 1 dataset = load_dataset(\"financial_phrasebank\", \"sentences_allagree\")\r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n 859 ignore_verifications=ignore_verifications,\r\n 860 try_from_hf_gcs=try_from_hf_gcs,\r\n--> 861 use_auth_token=use_auth_token,\r\n 862 )\r\n 863 \r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n 582 if not downloaded_from_gcs:\r\n 583 self._download_and_prepare(\r\n--> 584 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 585 )\r\n 586 # Sync info\r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 642 if verify_infos:\r\n 643 verify_checksums(\r\n--> 644 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n 645 )\r\n 646 \r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n 38 if len(bad_urls) > 0:\r\n 39 error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n 41 logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n 42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip']\r\n```" ]

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[ "A note is that I think we should add a few indicator of status (as mentioned by @stas00 in #2649), probably at the (1) downloading, (2) extracting and (3) reading steps. In particular when loading many very large files it's interesting to know a bit where we are in the process.", "I tested locally, and the builtin `json` loader is 4x slower than `pyarrow.json`. Thanks for the comment @albertvillanova !\r\n\r\nTherefore I switched back to using `pyarrow.json`, but only on the batch that is read. This way we don't have to deal with its `block_size`, and it only loads in memory one batch at a time." ]

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[ "same" ]

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[ "Awesome!", "Thanks for adding a new task template - great work @nateraw 🚀 !" ]

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[ "Sure @stas00, it is still a draft pull request. :)", "Yes, I noticed it after reviewing - my apologies.", "The problem with this approach is that it also deletes the downloaded files (if they need not be extracted). 😟 ", "> The problem with this approach is that it also deletes the downloaded files (if they need not be extracted). worried\r\n\r\nRight! These probably should not be deleted by default, but having an option for those users who are tight on disc space?", "> Right! These probably should not be deleted by default, but having an option for those users who are tight on disc space?\r\n\r\nI propose leaving that for another PR, and leave this one handling only with \"extracted\" files. Is it OK for you? :) ", "Awesome thanks !\r\nI just have one question: what about image/audio datasets for which we store the path to the extracted file on the arrow data ?\r\nIn this case the default should be to keep the extracted files.\r\n\r\nSo for now I would just make `keep_extracted=True` by default until we have a way to separate extracted files that can be deleted and extracted files that are actual resources of the dataset.", "@lhoestq, current implementation only deletes extracted \"files\", not extracted \"directories\", as it uses: `os.remove(path)`. I'm going to add a filter on files, so that this line does not throw an exception when passed a directory.\r\n\r\nFor audio datasets, the audio files are inside the extracted \"directory\", so they are not deleted.", "I'm still more in favor of having `keep_extracted=True` by default:\r\n- When working with a dataset, you call `load_dataset` many times. By default we want to keep objects extracted to not extract them over and over again (it can take a long time). Then once you know what you're doing and you want to optimize disk space, you can do `keep_extracted=False`. Deleting the extracted files by default is a regression that can lead to slow downs for people calling `load_dataset` many times, which is common when experimenting\r\n- This behavior doesn't sound natural as a default behavior. In the rest of the library, things are cached and not removed unless you explicitly say do (`map` caching for example). Moreover the function in the download manager is called `download_and_extract`, not `download_and_extract_and_remove_extracted_files`\r\n\r\nLet me know what you think !", "I think the main issue is that after doing some work users typically move on to other datasets and the amount of disc space used keeps on growing. So your logic is very sound and perhaps what's really needed is a cleansweep function that can go through **all** datasets and clean them up to the desired degree:\r\n\r\n- delete all extracted files\r\n- delete all sources\r\n- delete all caches\r\n- delete all caches that haven't been accessed in 6 months\r\n- delete completely old datasets that haven't been accessed in 6 months\r\n- more?\r\n\r\nSo a user can launch a little application, choose what they want to clean up and voila they have just freed up a huge amount of disc space. Makes me think of Ubuntu Tweak's Janitor app - very useful.\r\n\r\nAt the moment, this process of linting is very daunting and error-prone, especially due to all those dirs/files with hash names.", "@stas00 I've had the same idea. Instead of the full-fledged app, a simpler approach would be to add a new command to the CLI.", "oh, CLI would be perfect. I didn't mean to request a GUI-one specifically, was just using it as an example.\r\n\r\nOne could even do a crontab to delete old datasets that haven't been accesses in X months.", "@lhoestq I totally agree with you. I'm addressing that change.\r\n\r\n@stas00, @mariosasko, that could eventually be addressed in another pull request. The objective of this PR is:\r\n- add an option to pass to `load_dataset`, so that extracted files are deleted\r\n- do this deletion file per file, once the file has been already used to generate the cache Arrow file", "I also like the idea of having a CLI tool to help users clean their cache and save disk space, good idea !" ]

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[ "To add my experience when trying to debug this issue:\r\n\r\nSeems like previously the workaround given [here](https://github.com/tqdm/tqdm/issues/485#issuecomment-473338308) worked around this issue. But with the latest version of jupyter/tqdm I still get terminal warnings that IPython tried to send a message from a forked process.", "Hi @mludv, thanks for the hint!!! :) \r\n\r\nWe will definitely take it into account to try to fix this issue... It seems somehow related to `multiprocessing` and `tqdm`..." ]

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[ "This is so cool, let us know if we can help with anything on the hub side (@Pierrci @elishowk) 🎉 " ]

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[ "Thanks @thomas-happify for reporting and thanks @mariosasko for the fix." ]

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

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[ "Hi,\r\n\r\nyou can define your own custom formatting with `Dataset.set_transform()` and then run the tokenizer with the batches of augmented data as follows:\r\n```python\r\ndset = load_dataset(\"imdb\", split=\"train\") # Let's say we are working with the IMDB dataset\r\ndset.set_transform(lambda ex: {\"text\": augly_text_augmentation(ex[\"text\"])}, columns=\"text\", output_all_columns=True)\r\ndataloader = torch.utils.data.DataLoader(dset, batch_size=32)\r\nfor epoch in range(5):\r\n for batch in dataloader:\r\n tokenizer_output = tokenizer(batch.pop(\"text\"), padding=True, truncation=True, return_tensors=\"pt\")\r\n batch.update(tokenizer_output)\r\n output = model(**batch)\r\n ...\r\n```" ]

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[ "Does this mean the `FileNotFoundError` that avoids infinite loop can be removed?", "Yes, I think so...", "Or maybe we could leave it in case a relative path exceeds the MAX_PATH limit?", " > Or maybe we could leave it in case a relative path exceeds the MAX_PATH limit?\r\n\r\nWhat about converting relative paths to absolute?", "Nice ! Have you had a chance to test it on a windows machine with the max path limit enabled ? Afaik the CI doesn't have the path limit", "Sure @lhoestq: I've tested on my machine... And this fixes most of the tests... 😅 " ]

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[ "Are there any `task_categories:automatic-speech-recognition` dataset for which we should update the tags ?", "> Are there any `task_categories:automatic-speech-recognition` dataset for which we should update the tags ?\r\n\r\nYes there's a few - I'll fix them tomorrow :)" ]

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[ "Wait until #2620 is merged before pushing the README tags in this PR", "> Thanks!\r\n> \r\n> One question: aren't you adding `task_templates` to the `_info` method (and to the `dataset_infos.json`?\r\n\r\ngreat catch! i've now added the asr task template (along with a mapping from superb task -> template) and updated the `dataset_infos.json` :) ", "> Good!\r\n> \r\n> I have a suggested refactoring... Tell me what you think! :)\r\n\r\nyour approach is much more elegant - i've included your suggestions 🙏 " ]

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[ "Hi @liyucheng09, thanks for reporting.\r\n\r\nApparently this issue has to do with your environment setup. One question: is your data in an NFS share? Some people have reported this error when using `fcntl` to write to an NFS share... If this is the case, then it might be that your NFS just may not be set up to provide file locks. You should ask your system administrator, or try these commands in the terminal:\r\n```shell\r\nsudo systemctl enable rpc-statd\r\nsudo systemctl start rpc-statd\r\n```" ]

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[ "@lhoestq maybe we could also use (if available) the ETag of the remote file in `create_config_id`?", "> @lhoestq maybe we could also use (if available) the ETag of the remote file in `create_config_id`?\r\n\r\nSure ! We can get the ETag with\r\n```python\r\nheaders = get_authentication_headers_for_url(url, use_auth_token=use_auth_token) # auth for private repos\r\netag = http_head(url, headers=headers).headers.get(\"ETag\")\r\n```\r\n\r\nSince the computation of the `config_id` is done in the `DatasetBuilder.__init__`, then this means that we need to add a new parameter `use_auth_token` in `DatasetBuilder.__init__`\r\n\r\nDoes that sound good ? We can add this in a following PR" ]

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[ "Thanks for reporting @TevenLeScao! I'm having a look...", "(not sure what just happened on the assignations sorry)", "For some reason this happens (both `datasets` version are on master) only on Python 3.6 and not Python 3.8.", "@TevenLeScao we are using `pandas` to serialize the dataset to JSON Lines. So it must be due to pandas. Could you please check the pandas version causing the issue?", "@TevenLeScao I have just checked it: this was a bug in `pandas` and it was fixed in version 1.2: https://github.com/pandas-dev/pandas/pull/36898", "Thanks ! I'm creating a PR", "Well I though it was me who has taken on this issue... 😅 ", "Sorry, I was also talking to teven offline so I already had the PR ready before noticing x)", "I was also already working in my PR... Nevermind. Next time we should pay attention if there is somebody (self-)assigned to an issue and if he/she is still working on it before overtaking it... 😄 ", "The fix is available on `master` @TevenLeScao , thanks for reporting" ]

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[ "I opened an issue on the `sklearn` repo to understand why `numpy.float64` is the default: https://github.com/scikit-learn/scikit-learn/discussions/20490", "It could be surprising at first to use `tolist()` on numpy scalars but it works ^^", "did the same for Pearsonr here: https://github.com/huggingface/datasets/pull/2614" ]

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[ "Finally, I'm splitting this PR." ]

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[ "Updating to pyarrow-4.0.1 didn't fix the issue", "Here is an exemple dataset with 2 of these compressed JSON files: https://huggingface.co/datasets/thomwolf/github-python", "Hi @thomwolf, thanks for reporting.\r\n\r\nIt seems this might be due to the fact that the JSON Dataset builder uses `pyarrow.json` (`paj.read_json`) to read the data without using the Python standard `open(file,...` (which is the one patched with `xopen` to work in streaming mode).\r\n\r\nThis has to be fixed.", "Sorry for reopening this, but I'm having the same issue as @thomwolf when streaming a gzipped JSON Lines file from the hub. Or is that just not possible by definition?\r\nI installed `datasets`in editable mode from source (so probably includes the fix from #2608 ?): \r\n```\r\n>>> datasets.__version__\r\n'1.9.1.dev0'\r\n```\r\n\r\n```\r\n>>> msmarco = datasets.load_dataset(\"webis/msmarco\", \"corpus\", streaming=True)\r\nUsing custom data configuration corpus-174d3b7155eb68db\r\n>>> msmarco_iter = iter(msmarco['train'])\r\n>>> print(next(msmarco_iter))\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/media/ssd/TREC/msmarco/datasets/src/datasets/iterable_dataset.py\", line 338, in __iter__\r\n for key, example in self._iter():\r\n File \"/media/ssd/TREC/msmarco/datasets/src/datasets/iterable_dataset.py\", line 335, in _iter\r\n yield from ex_iterable\r\n File \"/media/ssd/TREC/msmarco/datasets/src/datasets/iterable_dataset.py\", line 78, in __iter__\r\n for key, example in self.generate_examples_fn(**self.kwargs):\r\n File \"/home/christopher/.cache/huggingface/modules/datasets_modules/datasets/msmarco/eb63dff8d83107168e973c7a655a6082d37e08d71b4ac39a0afada479c138745/msmarco.py\", line 96, in _generate_examples\r\n with gzip.open(file, \"rt\", encoding=\"utf-8\") as f:\r\n File \"/usr/lib/python3.6/gzip.py\", line 53, in open\r\n binary_file = GzipFile(filename, gz_mode, compresslevel)\r\n File \"/usr/lib/python3.6/gzip.py\", line 163, in __init__\r\n fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')\r\nFileNotFoundError: [Errno 2] No such file or directory: 'https://huggingface.co/datasets/webis/msmarco/resolve/main/msmarco_doc_00.gz'\r\n```\r\n\r\nLoading the dataset without streaming set to True, works fine.", "Hi ! To make the streaming work, we extend `open` in the dataset builder to work with urls.\r\n\r\nTherefore you just need to use `open` before using `gzip.open`:\r\n```diff\r\n- with gzip.open(file, \"rt\", encoding=\"utf-8\") as f:\r\n+ with gzip.open(open(file, \"rb\"), \"rt\", encoding=\"utf-8\") as f:\r\n```\r\n\r\nYou can see that it is the case for oscar.py and c4.py for example:\r\n\r\nhttps://github.com/huggingface/datasets/blob/8814b393984c1c2e1800ba370de2a9f7c8644908/datasets/oscar/oscar.py#L358-L358\r\n\r\nhttps://github.com/huggingface/datasets/blob/8814b393984c1c2e1800ba370de2a9f7c8644908/datasets/c4/c4.py#L88-L88\r\n\r\n", "@lhoestq Sorry I missed that. Thank you Quentin!" ]

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[ "Hi !\r\nIf we want something more general, we could either\r\n1. delete the extracted files after the arrow data generation automatically, or \r\n2. delete each extracted file during the arrow generation right after it has been closed.\r\n\r\nSolution 2 is better to save disk space during the arrow generation. Is it what you had in mind ?\r\n\r\nThe API could look like\r\n```python\r\nload_dataset(..., delete_extracted_files_after_usage=True)\r\n```\r\n\r\nIn terms of implementation, here are some directions we could take for each solution:\r\n1. get the list of the extracted files from the DownloadManager and then delete them after the dataset is processed. This can be implemented in `download_and_prepare` I guess\r\n2. maybe wrap and mock `open` in the builder to make it delete the file when the file is closed.", "Also, if I delete the extracted files they need to be re-extracted again instead of loading from the Arrow cache files", "I think we already opened an issue about this topic (suggested by @stas00): duplicated of #2481?\r\n\r\nThis is in our TODO list... 😅 ", "I think the deletion of each extracted file could be implemented in our CacheManager and ExtractManager (once merged to master: #2295, #2277). 😉 ", "Oh yes sorry, I didn't check if this was a duplicate", "Nevermind @thomwolf, I just mentioned the other issue so that both appear linked in GitHub and we do not forget to close both once we make the corresponding Pull Request... That was the main reason! 😄 ", "Ok yes. I think this is an important feature to be able to use large datasets which are pretty much always compressed files.\r\n\r\nIn particular now this requires to keep the extracted file on the drive if you want to avoid reprocessing the dataset so in my case, this require using always ~400GB of drive instead of just 200GB (which is already significant). \r\n\r\nTwo nice features would be to:\r\n- allow to delete the extracted files without loosing the ability to load the dataset from the cached arrow-file\r\n- streamlined decompression when only the currently read file is extracted - this might require to read the list of files from the extracted archives before processing them?", "Here is a sample dataset with 2 such large compressed JSON files for debugging: https://huggingface.co/datasets/thomwolf/github-python", "Note that I'm confirming that with the current master branch of dataset, deleting extracted files (without deleting the arrow cache file) lead to **re-extracting** these files when reloading the dataset instead of directly loading the arrow cache file.", "Hi ! That's weird, it doesn't do that on my side (tested on master on my laptop by deleting the `extracted` folder in the download cache directory). You tested with one of the files at https://huggingface.co/datasets/thomwolf/github-python that you have locally ?", "Yes it’s when I load local compressed JSON line files with load_dataset(‘json’, data_files=…) ", "@thomwolf I'm sorry but I can't reproduce this problem. I'm also using: \r\n```python\r\nds = load_dataset(\"json\", split=\"train\", data_files=data_files, cache_dir=cache_dir)\r\n```\r\nafter having removed the extracted files:\r\n```python\r\nassert sorted((cache_dir / \"downloads\" / \"extracted\").iterdir()) == []\r\n```\r\n\r\nI get the logging message:\r\n```shell\r\nWARNING datasets.builder:builder.py:531 Reusing dataset json ...\r\n```", "Do you confirm the extracted folder stays empty after reloading?", "> \r\n> \r\n> Do you confirm the extracted folder stays empty after reloading?\r\n\r\nYes, I have the above mentioned assertion on the emptiness of the extracted folder:\r\n```python\r\nassert sorted((cache_dir / \"downloads\" / \"extracted\").iterdir()) == []\r\n```\r\n" ]

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[ "Hi @erikadistefano , thanks for reporting the issue.\r\n\r\nI have created a Pull Request that should fix it. \r\n\r\nOnce merged into master, feel free to update your installed `datasets` library (either by installing it from our GitHub master branch or waiting until our next release) to be able to load omp dataset." ]

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[ "Hi ! Do you have an example in mind that shows how this could be useful ?", "Example:\n\nMerge 2 datasets into one datasets\n\nLabel extraction from dataset\n\ndataset(text, label)\n —> dataset(text, newlabel)\n\nTextCleaning.\n\n\nFor image dataset, \nTransformation are easier (ie linear algebra).\n\n\n\n\n\n\n> On Jul 6, 2021, at 17:39, Quentin Lhoest ***@***.***> wrote:\n> \n> \n> Hi ! Do you have an example in mind that shows how this could be useful ?\n> \n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n", "There are already a few transformations that you can apply on a dataset using methods like `dataset.map()`.\r\nYou can find examples in the documentation here:\r\nhttps://huggingface.co/docs/datasets/processing.html\r\n\r\nYou can merge two datasets with `concatenate_datasets()` or do label extraction with `dataset.map()` for example", "Ok, sure.\n\nThanks for pointing on functional part.\nMy question is more\n“Philosophical”/Design perspective.\n\nThere are 2 perspetive:\n Add transformation methods to \n Dataset Class\n\n\n OR Create a Transformer Class\n which operates on Dataset Class.\n\nT(Dataset) —> Dataset\n\ndatasetnew = MyTransform.transform(dataset)\ndatasetNew.save(path)\n\n\nWhat would be the difficulty\nof implementing a Transformer Class\noperating at dataset level ?\n\n\nthanks\n\n\n\n\n\n\n\n\n\n> On Jul 6, 2021, at 22:00, Quentin Lhoest ***@***.***> wrote:\n> \n> \n> There are already a few transformations that you can apply on a dataset using methods like dataset.map().\n> You can find examples in the documentation here:\n> https://huggingface.co/docs/datasets/processing.html\n> \n> You can merge two datasets with concatenate_datasets() or do label extraction with dataset.map() for example\n> \n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n", "I can imagine that this would be a useful API to implement processing pipelines as transforms. They could be used to perform higher level transforms compared to the atomic transforms allowed by methods like map, filter, etc.\r\n\r\nI guess if you find any transform that could be useful for text dataset processing, image dataset processing etc. we could definitely start having such transforms :)", "Thanks for reply.\n\nWhat would be the constraints\nto have\nDataset —> Dataset consistency ?\n\nMain issue would be\nlarger than memory dataset and\nserialization on disk.\n\nTechnically,\none still process at atomic level\nand try to wrap the full results\ninto Dataset…. (!)\n\nWhat would you think ?\n\n\n\n\n\n\n\n\n> On Jul 7, 2021, at 16:51, Quentin Lhoest ***@***.***> wrote:\n> \n> \n> I can imagine that this would be a useful API to implement processing pipelines as transforms. They could be used to perform higher level transforms compared to the atomic transforms allowed by methods like map, filter, etc.\n> \n> I guess if you find any transform that could be useful for text dataset processing, image dataset processing etc. we could definitely start having such transforms :)\n> \n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n", "We can be pretty flexible and not impose any constraints for transforms.\r\n\r\nMoreover, this library is designed to support datasets bigger than memory. The datasets are loaded from the disk via memory mapping, without filling up RAM. Even processing functions like `map` work in a batched fashion to not fill up your RAM. So this shouldn't be an issue", "Ok thanks.\n\nBut, Dataset has various flavors.\nIn current design of Dataset,\n how the serialization on disk is done (?)\n\n\nThe main issue is serialization \nof newdataset= Transform(Dataset)\n (ie thats why am referring to Out Of memory dataset…):\n\n Should be part of Transform or part of dataset ?\n\n\n\n\nMaybe, not, since the output is aimed to feed model in memory (?)\n\n\n\n\n\n\n\n\n> On Jul 7, 2021, at 18:04, Quentin Lhoest ***@***.***> wrote:\n> \n> \n> We can be pretty flexible and not impose any constraints for transforms.\n> \n> Moreover, this library is designed to support datasets bigger than memory. The datasets are loaded from the disk via memory mapping, without filling up RAM. Even processing functions like map work in a batched fashion to not fill up your RAM. So this shouldn't be an issue\n> \n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n", "I'm not sure I understand, could you elaborate a bit more please ?\r\n\r\nEach dataset is a wrapper of a PyArrow Table that contains all the data. The table is loaded from an arrow file on the disk.\r\nWe have an ArrowWriter and ArrowReader class to write/read arrow tables on disk or in in-memory buffers." ]

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