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[ "Hi ! That's missing indeed. We'll try to implement this for the next version :)\r\n\r\nI guess we just need to implement #2564 first, and then we should be able to add support for splits combinations" ]

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[ "@lhoestq @albertvillanova - It might be a good idea to have a patch release after this gets merged (presumably tomorrow morning when you're around). The Colab issue linked to this PR is a pretty big blocker. " ]

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[ "The CI failure seems unrelated to this PR (probably has something to do with Transformers)." ]

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[ "I'm facing the same issue on Colab today too.\r\n\r\n```\r\nModuleNotFoundError Traceback (most recent call last)\r\n<ipython-input-4-5833ac0f5437> in <module>()\r\n 3 \r\n 4 from ray import tune\r\n----> 5 from datasets import DatasetDict, Dataset\r\n 6 from datasets import load_dataset, load_metric\r\n 7 from dataclasses import dataclass\r\n\r\n7 frames\r\n/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()\r\n 25 import posixpath\r\n 26 import requests\r\n---> 27 from tqdm.contrib.concurrent import thread_map\r\n 28 \r\n 29 from .. import __version__, config, utils\r\n\r\nModuleNotFoundError: No module named 'tqdm.contrib.concurrent'\r\n\r\n---------------------------------------------------------------------------\r\nNOTE: If your import is failing due to a missing package, you can\r\nmanually install dependencies using either !pip or !apt.\r\n\r\nTo view examples of installing some common dependencies, click the\r\n\"Open Examples\" button below.\r\n---------------------------------------------------------------------------\r\n```", "@phosseini \r\nI think it is related to [1.10.0](https://github.com/huggingface/datasets/actions/runs/1052653701) release done 3 hours ago. (cc: @lhoestq )\r\nFor now I just downgraded to 1.9.0 and it is working fine.", "> @phosseini\r\n> I think it is related to [1.10.0](https://github.com/huggingface/datasets/actions/runs/1052653701) release done 3 hours ago. (cc: @lhoestq )\r\n> For now I just downgraded to 1.9.0 and it is working fine.\r\n\r\nSame here, downgraded to 1.9.0 for now and works fine.", "Hi, \r\n\r\nupdating tqdm to the newest version resolves the issue for me. You can do this as follows in Colab:\r\n```\r\n!pip install tqdm --upgrade\r\n```", "Hi @bayartsogt-ya and @phosseini, thanks for reporting.\r\n\r\nWe are fixing this critical issue and making an urgent patch release of the `datasets` library today.\r\n\r\nIn the meantime, as pointed out by @mariosasko, you can circumvent this issue by updating the `tqdm` library: \r\n```\r\n!pip install -U tqdm\r\n```" ]

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[ "Hi @severo, thanks for reporting.\r\n\r\nHowever I have not been able to reproduce this issue. Could you please confirm if the problem persists for you?\r\n\r\nMaybe Dropbox (where the data source is hosted) was temporarily unavailable when you tried.", "Hmmm, the file (https://www.dropbox.com/s/dl/12h3qqog6q4bjve/panx_dataset.tar) really seems to be unavailable... I tried from various connexions and machines and got the same 404 error. Maybe the dataset has been loaded from the cache in your case?", "Yes @severo, weird... I could access the file when I answered to you, but now I cannot longer access it either... Maybe it was from the cache as you point out.\r\n\r\nAnyway, I have opened an issue in the GitHub repository responsible for the original dataset: https://github.com/afshinrahimi/mmner/issues/4\r\nI have also contacted the maintainer by email.\r\n\r\nI'll keep you informed with their answer.", "Reply from the author/maintainer: \r\n> Will fix the issue and let you know during the weekend.", "The author told that apparently Dropbox has changed their policy and no longer allow downloading the file without having signed in first. The author asked Hugging Face to host their dataset." ]

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[ "Thanks for all the comments and for the corrections in the docs !\r\n\r\nAbout all the points you mentioned:\r\n\r\n> * the code samples assume the expected libraries have already been installed. Maybe add a section at start, or add it to every code sample. Something like `pip install datasets transformers torch 'datasets[streaming]'` (maybe just link to https://huggingface.co/docs/datasets/installation.html + a one-liner that installs all the requirements / alternatively a requirements.txt file)\r\n\r\nYes good idea\r\n\r\n> * \"If you’d like to play with the examples, you must install it from source.\" in https://huggingface.co/docs/datasets/installation.html: it's not clear to me what this means (what are these \"examples\"?)\r\n\r\nIt refers to examples scripts inside the git repository of the library, see the `examples` folder in the `transformers` repo.\r\nWe don't have examples yet in the git repo of `datasets` as in transformers. So currently there are no examples. Maybe we can just remove this sentence from the docs for now\r\n\r\n> * in https://huggingface.co/docs/datasets/loading_datasets.html: \"or AWS bucket if it’s not already stored in the library\". It's the only place in the doc (aside from the docstring https://huggingface.co/docs/datasets/package_reference/loading_methods.html?highlight=aws bucket#datasets.list_datasets) where the \"AWS bucket\" is mentioned. It's not easy to understand what this means. Maybe explain more, and link to https://s3.amazonaws.com/datasets.huggingface.co and/or https://huggingface.co/docs/datasets/filesystems.html.\r\n\r\nThis is outdated and must be replaced by\r\n```\r\nor from the Hugging Face Hub if it’s not already stored in the library\r\n```\r\n\r\n> * example in https://huggingface.co/docs/datasets/loading_datasets.html#manually-downloading-files is obsoleted by [Enable auto-download for PAN-X / Wikiann domain in XTREME #2326](https://github.com/huggingface/datasets/pull/2326). Also: see [xtreme / pan-x cannot be downloaded #2691](https://github.com/huggingface/datasets/issues/2691) for a bug on this specific dataset.\r\n\r\nWe can replace the `XTREME` `PANX` dataste by `matinf` instead for example\r\n\r\n> * in https://huggingface.co/docs/datasets/loading_datasets.html#manually-downloading-files the doc says \"After you’ve downloaded the files, you can point to the folder hosting them locally with the data_dir argument as follows:\", but the following example does not show how to use `data_dir`\r\n\r\nLet's add `data_dir=\"path/to/your/downloaded/data\"` for example\r\n\r\n> * in https://huggingface.co/docs/datasets/loading_datasets.html#csv-files, it would be nice to have an URL to the csv loader reference (but I'm not sure there is one in the API reference). This comment applies in many places in the doc: I would want the API reference to contain doc for all the code/functions/classes... and I would want a lot more links inside the doc pointing to the API entries.\r\n\r\nCurrently there's no documentation for the CSV loader config. Maybe we can add the docstrings to the `CsvConfig` class to explain the parameters and how it works, and then redirect to the doc of this class in this section of the documentation.\r\n\r\n> * in the API reference (docstrings) I would prefer \"SOURCE\" to link to github instead of a copy of the code inside the docs site (eg. https://github.com/huggingface/datasets/blob/master/src/datasets/load.py#L711 instead of https://huggingface.co/docs/datasets/_modules/datasets/load.html#load_dataset)\r\n\r\nThis is the same as in `transformers`, not sure if this is a big issue\r\n\r\n> * it seems like not all the API is exposed in the doc. For example, there is no doc for [`disable_progress_bar`](https://github.com/huggingface/datasets/search?q=disable_progress_bar), see https://huggingface.co/docs/datasets/search.html?q=disable_progress_bar, even if the code contains docstrings. Does it mean that the function is not officially supported? (otherwise, maybe it also deserves a mention in https://huggingface.co/docs/datasets/package_reference/logging_methods.html)\r\n\r\nThe function `disable_progress_bar` should definitely be in the docs, thanks. We can add it to the logging methods\r\n\r\n> * in https://huggingface.co/docs/datasets/loading_datasets.html?highlight=most%20efficient%20format%20have%20json%20files%20consisting%20multiple%20json%20objects#json-files, \"The most efficient format is to have JSON files consisting of multiple JSON objects, one per line, representing individual data rows:\", maybe link to https://en.wikipedia.org/wiki/JSON_streaming#Line-delimited_JSON and give it a name (\"line-delimited JSON\"? \"JSON Lines\" as in https://huggingface.co/docs/datasets/processing.html#exporting-a-dataset-to-csv-json-parquet-or-to-python-objects ?)\r\n\r\nYes good idea !\r\n\r\n> * in https://huggingface.co/docs/datasets/loading_datasets.html, for the local files sections, it would be nice to provide sample csv / json / text files to download, so that it's easier for the reader to try to load them (instead: they won't try)\r\n\r\nSure why not. Moreover the csv loader now supports remote files so you could just run the code pass an an URL to the sample csv file.\r\n\r\n> * the doc explains how to shard a dataset, but does not explain why and when a dataset should be sharded (I have no idea... for [parallelizing](https://huggingface.co/docs/datasets/processing.html#multiprocessing)?). It does neither give an idea of the number of shards a dataset typically should have and why.\r\n\r\nThis can be used for distributed processing or just to use a percentage of the data. We can definitely give example of use cases\r\n\r\n> * the code example in https://huggingface.co/docs/datasets/processing.html#mapping-in-a-distributed-setting does not work, because `training_args` has not been defined before in the doc.\r\n\r\n`training_args` comes from `transformers`, it's a practical way to define all your arguments to train a model. Maybe we can just import it from `transformers` and use it with the default values\r\n\r\n" ]

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[ "Hi ! That's because you are trying to overwrite a file that is already open and being used.\r\nIndeed `foo/dataset.arrow` is open and used by your `dataset` object.\r\n\r\nWhen you do `rename_column`, the resulting dataset reads the data from the same arrow file.\r\nIn other cases like when using `map` on the other hand, the resulting dataset reads the data from another arrow file that is the result of the map transform.\r\n\r\nTherefore overwriting a dataset after `rename_column` is not possible, but it is possible after `map`, since `rename_column` doesn't switch to using another arrow file (the actual data stay the same).", "Ok, thanks for clearing it up :)" ]

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[ "Hi @eyaler, thanks for reporting.\r\n\r\nWhile you are true with respect the Hebrew language tag (\"iw\" is deprecated and \"he\" is the preferred value), in the \"mc4\" dataset (which is a derived dataset) we have kept the language tags present in the original dataset: [Google C4](https://www.tensorflow.org/datasets/catalog/c4).", "For discoverability on the website I updated the YAML tags at the top of the mC4 dataset card https://github.com/huggingface/datasets/commit/38288087b1b02f97586e0346e8f28f4960f1fd37\r\n\r\nOnce the website is updated, mC4 will be listed in https://huggingface.co/datasets?filter=languages:he\r\n\r\n" ]

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[ "Normally, I'm expecting errors from the validation of the README file... 😅 ", "That is:\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_dataset_cards.py::test_changed_dataset_card[blog_authorship_corpus]\r\n==== 1 failed, 3182 passed, 2763 skipped, 16 warnings in 201.23s (0:03:21) =====\r\n```", "@lhoestq, apart from the dataset card, everything is OK with this PR: I tested it locally." ]

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

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[ "Hi @izaskr, thanks for reporting.\r\n\r\nHowever the traceback you joined does not correspond to the codec error message: it is about other error `NonMatchingSplitsSizesError`. Maybe you missed some important part of your traceback...\r\n\r\nI'm going to have a look at the dataset anyway...", "Hi @izaskr, thanks again for having reported this issue.\r\n\r\nAfter investigation, I have created a Pull Request (#2685) to fix several issues with this dataset:\r\n- the `NonMatchingSplitsSizesError`\r\n- the `UnicodeDecodeError`\r\n\r\nOnce the Pull Request merged into master, you will be able to load this dataset if you install `datasets` from our GitHub repository master branch. Otherwise, you will be able to use it after our next release, by updating `datasets`: `pip install -U datasets`.", "@albertvillanova \r\nCan you shed light on how this fix works?\r\n\r\nWe're experiencing a similar issue. \r\n\r\nIf we run several runs (eg in a Wandb sweep) the first run \"works\" but then we get `NonMatchingSplitsSizesError`\r\n\r\n| run num | actual train examples # | expected example # | recorded example # |\r\n| ------- | -------------- | ----------------- | -------- |\r\n| 1 | 100 | 100 | 100 |\r\n| 2 | 102 | 100 | 102 |\r\n| 3 | 100 | 100 | 202 | \r\n| 4 | 40 | 100 | 40 |\r\n| 5 | 40 | 100 | 40 |\r\n| 6 | 40 | 100 | 40 | \r\n\r\n\r\nThe second through the nth all crash with \r\n\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=19980970, num_examples=100, dataset_name='cies'), 'recorded': SplitInfo(name='train', num_bytes=40163811, num_examples=202, dataset_name='cies')}]\r\n\r\n```" ]

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[ "This looks like an issue with PyArrow. Did you try reinstalling it ?", "@lhoestq I did, and then let pip handle the installation in `pip import datasets`. I also tried using conda but it gives the same error.\r\n\r\nEdit: pyarrow version on kaggle is 4.0.0, it gets replaced with 4.0.1. So, I don't think uninstalling will change anything.\r\n```\r\nInstall Trace of datasets:\r\n\r\nCollecting datasets\r\n Downloading datasets-1.9.0-py3-none-any.whl (262 kB)\r\n |████████████████████████████████| 262 kB 834 kB/s eta 0:00:01\r\nRequirement already satisfied: dill in /opt/conda/lib/python3.7/site-packages (from datasets) (0.3.4)\r\nCollecting pyarrow!=4.0.0,>=1.0.0\r\n Downloading pyarrow-4.0.1-cp37-cp37m-manylinux2014_x86_64.whl (21.8 MB)\r\n |████████████████████████████████| 21.8 MB 6.2 MB/s eta 0:00:01\r\nRequirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from datasets) (3.4.0)\r\nRequirement already satisfied: huggingface-hub<0.1.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (0.0.8)\r\nRequirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from datasets) (1.2.4)\r\nRequirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (2.25.1)\r\nRequirement already satisfied: fsspec>=2021.05.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (2021.6.1)\r\nRequirement already satisfied: multiprocess in /opt/conda/lib/python3.7/site-packages (from datasets) (0.70.12.2)\r\nRequirement already satisfied: packaging in /opt/conda/lib/python3.7/site-packages (from datasets) (20.9)\r\nCollecting xxhash\r\n Downloading xxhash-2.0.2-cp37-cp37m-manylinux2010_x86_64.whl (243 kB)\r\n |████████████████████████████████| 243 kB 23.7 MB/s eta 0:00:01\r\nRequirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.7/site-packages (from datasets) (1.19.5)\r\nRequirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.7/site-packages (from datasets) (4.61.1)\r\nRequirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from huggingface-hub<0.1.0->datasets) (3.0.12)\r\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (1.26.5)\r\nRequirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (2.10)\r\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (2021.5.30)\r\nRequirement already satisfied: chardet<5,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (4.0.0)\r\nRequirement already satisfied: typing-extensions>=3.6.4 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->datasets) (3.7.4.3)\r\nRequirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->datasets) (3.4.1)\r\nRequirement already satisfied: pyparsing>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging->datasets) (2.4.7)\r\nRequirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets) (2.8.1)\r\nRequirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets) (2021.1)\r\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\r\nInstalling collected packages: xxhash, pyarrow, datasets\r\n Attempting uninstall: pyarrow\r\n Found existing installation: pyarrow 4.0.0\r\n Uninstalling pyarrow-4.0.0:\r\n Successfully uninstalled pyarrow-4.0.0\r\nSuccessfully installed datasets-1.9.0 pyarrow-4.0.1 xxhash-2.0.2\r\nWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\r\n```", "You may need to restart your kaggle notebook after installing a newer version of `pyarrow`.\r\n\r\nIf it doesn't work we'll probably have to create an issue on [arrow's JIRA](https://issues.apache.org/jira/projects/ARROW/issues/), and maybe ask kaggle why it could fail", "> You may need to restart your kaggle notebook before after installing a newer version of `pyarrow`.\r\n> \r\n> If it doesn't work we'll probably have to create an issue on [arrow's JIRA](https://issues.apache.org/jira/projects/ARROW/issues/), and maybe ask kaggle why it could fail\r\n\r\nIt works after restarting.\r\nMy bad, I forgot to restart the notebook. Sorry for the trouble!" ]

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[ "Hi Thanks for reporting !\r\nIt looks like these files are not correctly reported in the list of expected files to download, let me fix that ;)", "Alright this is fixed now. We'll do a new release soon to make the fix available.\r\n\r\nIn the meantime feel free to simply pass `ignore_verifications=True` to `load_dataset` to skip this error", "@lhoestq thank you for such a quick feedback!" ]

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[ "It'll be new pull request with new commits." ]

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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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