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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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[ "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" ]

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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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[ "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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[ "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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[ "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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[ "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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[ "@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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[ "#take" ]

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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 @profsatwinder.\r\n\r\nIt looks like you are using an old version of `datasets`. Please update it with `pip install -U datasets` and indicate if the problem persists.", "@albertvillanova Thanks for the information. I updated it to 1.9.0 and the issue is resolved. Thanks again. " ]

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[ "Hi! I'm transferring this issue over to `datasets`", "I'm using the datasets concatenate dataset to combine the datasets and then train.\r\ntrain_dataset = concatenate_datasets([dataset1, dataset2, common_voice_train])\r\n\r\n", "Hi @BirgerMoell.\r\n\r\nYou have several options:\r\n- to set caching to be stored on a different path location, other than the default one (`~/.cache/huggingface/datasets`):\r\n - either setting the environment variable `HF_DATASETS_CACHE` with the path to the new cache location\r\n - or by passing it with the parameter `cache_dir` when loading each of the datasets: `dataset = load_dataset(..., cache_dir=your_new_location)`\r\n\r\n You can get all the information in the docs: https://huggingface.co/docs/datasets/loading_datasets.html#cache-directory\r\n- I wouldn't recommend disabling caching, because current implementation generates cache files anyway, although in a temporary directory and they are deleted when the session closes. See details here: https://huggingface.co/docs/datasets/processing.html#enable-or-disable-caching\r\n- You could alternatively load the datasets in streaming mode. This is a new feature which allows loading the datasets without downloading the entire files. More information here: https://huggingface.co/docs/datasets/dataset_streaming.html", "Hi @BirgerMoell,\r\n\r\nWe are planning to add a new feature to datasets, which could be interesting in your case: Add the option to delete temporary files (decompressed files) from the cache directory (see: #2481, #2604).\r\n\r\nWe will ping you once this feature is implemented, so that the size of your cache directory will be considerably reduced." ]

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[ "Hi ! Thanks for the fix :)\r\n\r\nIf I understand correctly, `OptionalSequence` doesn't have an associated arrow type that we know in advance unlike the other feature types, because it depends on the type of the examples.\r\n\r\nFor example, I tested this and it raises an error:\r\n```python\r\nimport datasets as ds\r\nimport pyarrow as pa\r\n\r\nfeatures = ds.Features({\"a\": ds.features.OptionalSequence(ds.Value(\"int32\"))})\r\nbatch = {\"a\": [[0]]}\r\n\r\nwriter = ds.ArrowWriter(features=features, stream=pa.BufferOutputStream())\r\nwriter.write_batch(batch)\r\n# ArrowInvalid: Could not convert [0] with type list: tried to convert to int\r\n```\r\nThis error happens because `features.type` is `StructType(struct<a: int32>)`.\r\n\r\nAnother way to add support for multilabel would be to have several configurations for these metrics. By default it would set the features without sequences, and for the multi label configuration it would use features with sequences. Let me know what you think", "Hi @lhoestq, thanks for your feedback :)\r\n\r\nDefinitely, your suggested approach is simpler. I am going to refactor all my PR unless we could envision some other use cases where an OptionalSequence might be convenient, but for now I can't think of any..." ]

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[ "Hi @mmajurski, thanks for reporting this issue.\r\n\r\nIndeed this misalignment arises because the source dataset context field contains leading blank spaces (and these are counted within the answer_start), while our datasets loading script removes these leading blank spaces.\r\n\r\nI'm going to fix our script so that all leading blank spaces in the source dataset are kept, and there is no misalignment between the answer text and the answer_start within the context.", "If you are going to be altering the data cleaning from the source Squad dataset, here is one thing to consider.\r\nThere are occasional double spaces separating words which it might be nice to get rid of. \r\n\r\nEither way, thank you." ]

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[ "Hi ! This is because you first need to format the dataset for pytorch:\r\n\r\n```python\r\n>>> import torch\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset('oscar', \"unshuffled_deduplicated_en\", split='train', streaming=True)\r\n>>> torch_iterable_dataset = dataset.with_format(\"torch\")\r\n>>> assert isinstance(torch_iterable_dataset, torch.utils.data.IterableDataset)\r\n>>> dataloader = torch.utils.data.DataLoader(torch_iterable_dataset, batch_size=4)\r\n>>> next(iter(dataloader))\r\n{'id': tensor([0, 1, 2, 3]), 'text': ['Mtendere Village was inspired...]}\r\n```\r\n\r\nThis is because the pytorch dataloader expects a subclass of `torch.utils.data.IterableDataset`. Since you can't pass an arbitrary iterable to a pytorch dataloader, you first need to build an object that inherits from `torch.utils.data.IterableDataset` using `with_format(\"torch\")` for example.\r\n", "Thank you for that and the example! \r\n\r\nWhat you said makes total sense; I just somehow missed that and assumed HF IterableDataset was a subclass of Torch IterableDataset. " ]

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[ "@lhoestq looks good. I tried with https://huggingface.co/datasets/vblagoje/wikipedia_snippets_streamed and it worked nicely. I would add more unit tests for edge cases. What happens if the n is larger than the total number of samples? Just to make sure these cases are handled properly. ", "Yup I'll add the tests thanks ;)\r\n\r\nMoreover, I just noticed something in your wiki snippets code. FYI you're using `++passage_counter ` at https://huggingface.co/datasets/vblagoje/wikipedia_snippets_streamed/blob/main/wikipedia_snippets_streamed.py#L102 but in python this doesn't increment the value @vblagoje ", "Thanks @lhoestq - not easy to convert after 10+ years of Java" ]

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[ "> What if people want to run some tests without having zstandard ?\r\n> Usually what we do is add a decorator @require_zstandard for example\r\n\r\n@lhoestq I think I'm missing something here...\r\n\r\nTests are a *development* tool (to ensure we deliver a good quality lib), not something we offer to the end users of the lib. Users of the lib just `pip install datasets` and no tests are delivered with the lib (`tests` directory is outside the `src` code dir). \r\n\r\nOn the contrary, developers (contributors) of the lib do need to be able to run tests (TDD). And because of that, they are required to install datasets differently: `pip install -e .[dev]`, so that all required developing (and testing) dependencies are properly installed (included `zstandard`).\r\n\r\nApart from `zsatandard`, there are many other dev/test required dependencies for running tests, and we do not have a `@require_toto` for each and every of these dependencies in our tests: \r\n- `pytest` and `absl-py` (they are not dependencies in install_requires, but only in TEST_REQUIRE extras_require), \r\n- `boto3` (in test_filesystem.py), \r\n- `seqeval` (in test_metric_common.py), \r\n- `bs4` (used by eli5 and tested in test_hf_gcp.py)\r\n- ...\r\n\r\nSo IMHO, to run tests you should previously install datasets with dev or tests dependencies: either `pip install -e .[dev]` or `pip install -e .[tests]` (the latter to be used in CI testing-only part of the development cycle). And the tests should be written accordingly, assuming all tests dependencies are installed.", "Hi !\r\nI was saying that because the other dependencies you mentioned are only required for _some_ tests. While here zstd is required for _all_ tests since it's imported in the conftest.py\r\nFeel free to keep it as it is right now, or maybe move the fixture to test_file_utils.py to allow users without zstd to run tests for their builders, dataset card etc. without issues", "Thank you ! I think we can merge now", "@lhoestq does this mean that the pile could have streaming support in the future? Afaik streaming doesnt support zstandard compressed type", "> @lhoestq does this mean that the pile could have streaming support in the future? Afaik streaming doesnt support zstandard compressed type\r\n\r\njust for reference, i tried to stream one of the `.zst` files from [the pile](https://the-eye.eu/public/AI/pile/) using\r\n\r\n```python\r\ndata_files = [\"https://the-eye.eu/public/AI/pile/train/00.jsonl.zst\"]\r\nstreamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True)\r\n```\r\n\r\nand got the following error:\r\n\r\n```\r\nUsing custom data configuration default-4e71acadc389c254\r\n---------------------------------------------------------------------------\r\nNotImplementedError Traceback (most recent call last)\r\n/tmp/ipykernel_1187680/10848115.py in <module>\r\n 1 data_files = [\"https://the-eye.eu/public/AI/pile/train/00.jsonl.zst\"]\r\n 2 \r\n----> 3 streamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True)\r\n 4 \r\n\r\n~/miniconda3/envs/hf/lib/python3.8/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 835 # this extends the open and os.path.join functions for data streaming\r\n 836 extend_module_for_streaming(builder_instance.__module__, use_auth_token=use_auth_token)\r\n--> 837 return builder_instance.as_streaming_dataset(\r\n 838 split=split,\r\n 839 use_auth_token=use_auth_token,\r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/builder.py in as_streaming_dataset(self, split, base_path, use_auth_token)\r\n 922 data_dir=self.config.data_dir,\r\n 923 )\r\n--> 924 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}\r\n 925 # By default, return all splits\r\n 926 if split is None:\r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py in _split_generators(self, dl_manager)\r\n 50 if not self.config.data_files:\r\n 51 raise ValueError(f\"At least one data file must be specified, but got data_files={self.config.data_files}\")\r\n---> 52 data_files = dl_manager.download_and_extract(self.config.data_files)\r\n 53 if isinstance(data_files, (str, list, tuple)):\r\n 54 files = data_files\r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in download_and_extract(self, url_or_urls)\r\n 140 \r\n 141 def download_and_extract(self, url_or_urls):\r\n--> 142 return self.extract(self.download(url_or_urls))\r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in extract(self, path_or_paths)\r\n 115 \r\n 116 def extract(self, path_or_paths):\r\n--> 117 urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)\r\n 118 return urlpaths\r\n 119 \r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types)\r\n 202 num_proc = 1\r\n 203 if num_proc <= 1 or len(iterable) <= num_proc:\r\n--> 204 mapped = [\r\n 205 _single_map_nested((function, obj, types, None, True))\r\n 206 for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/utils/py_utils.py in <listcomp>(.0)\r\n 203 if num_proc <= 1 or len(iterable) <= num_proc:\r\n 204 mapped = [\r\n--> 205 _single_map_nested((function, obj, types, None, True))\r\n 206 for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n 207 ]\r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/utils/py_utils.py in _single_map_nested(args)\r\n 141 # Singleton first to spare some computation\r\n 142 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 143 return function(data_struct)\r\n 144 \r\n 145 # Reduce logging to keep things readable in multiprocessing with tqdm\r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _extract(self, urlpath)\r\n 119 \r\n 120 def _extract(self, urlpath):\r\n--> 121 protocol = self._get_extraction_protocol(urlpath)\r\n 122 if protocol is None:\r\n 123 # no extraction\r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _get_extraction_protocol(self, urlpath)\r\n 137 elif path.endswith(\".zip\"):\r\n 138 return \"zip\"\r\n--> 139 raise NotImplementedError(f\"Extraction protocol for file at {urlpath} is not implemented yet\")\r\n 140 \r\n 141 def download_and_extract(self, url_or_urls):\r\n\r\nNotImplementedError: Extraction protocol for file at https://the-eye.eu/public/AI/pile/train/00.jsonl.zst is not implemented yet\r\n```\r\n\r\ni'm not sure whether @Shashi456 is referring to a fundamental limitation with \"streaming\" zstandard compression files or simply that we need to support the protocol in the streaming api of `datasets`\r\n\r\n", "@lewtun our streaming mode patches the Python `open` function. I could have a look tomorrow if it is easily implementable for this case.", "@lewtun, I have tested and yes, it is easily implementable. I've created a draft Pull Request with an implementation proposal: #2786.", "thanks a lot @albertvillanova - now i can stream the pile :)" ]

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[ "I think the failing test has nothing to do with my PR..." ]

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