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[ "The parameter can also be named `split_by` with values \"line\", \"paragraph\" or \"document\" (no 's' at the end)", "> The parameter can also be named `split_by` with values \"line\", \"paragraph\" or \"document\" (no 's' at the end)\r\n\r\n@lhoestq @mariosasko I would avoid the term `split` in this context and keep it only for \"train\", \"validation\" and \"test\" splits.\r\n- https://huggingface.co/docs/datasets/process.html#split\r\n > datasets.Dataset.train_test_split() creates train and test splits, if your dataset doesn’t already have them.\r\n- https://huggingface.co/docs/datasets/process.html#process-multiple-splits\r\n > Many datasets have splits that you can process simultaneously with datasets.DatasetDict.map().\r\n\r\nPlease note that in the documentation, one of the terms more frequently used in this context is **\"row\"**:\r\n- https://huggingface.co/docs/datasets/access.html#features-and-columns\r\n > A dataset is a table of rows and typed columns.\r\n\r\n > Return the number of rows and columns with the following standard attributes:\r\n > dataset.num_columns\r\n > 4\r\n > dataset.num_rows\r\n > 3668\r\n\r\n- https://huggingface.co/docs/datasets/access.html#rows-slices-batches-and-columns\r\n > Get several rows of your dataset at a time with slice notation or a list of indices:\r\n- https://huggingface.co/docs/datasets/process.html#map\r\n > This function can even create new rows and columns.\r\n\r\nOther of the terms more frequently used in the docs (in the code as well) is **\"example\"**:\r\n- https://huggingface.co/docs/datasets/process.html#map\r\n > It allows you to apply a processing function to each example in a dataset, independently or in batches.\r\n- https://huggingface.co/docs/datasets/process.html#batch-processing\r\n > datasets.Dataset.map() also supports working with batches of examples.\r\n- https://huggingface.co/docs/datasets/process.html#split-long-examples\r\n > When your examples are too long, you may want to split them\r\n- https://huggingface.co/docs/datasets/process.html#data-augmentation\r\n > With batch processing, you can even augment your dataset with additional examples.\r\n\r\nLess frequently used: **\"item\"**:\r\n- https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.add_item\r\n > Add item to Dataset.\r\n\r\nOther term used in the docs (although less frequently) is **\"sample\"**. The advantage of this word is that it is also a verb, so we can use the parameter: \"sample_by\" (if you insist on using a verb instead of a noun).\r\n\r\nIn summary, these proposals:\r\n- config.row\r\n- config.example\r\n- config.item\r\n- config.sample\r\n- config.sample_by", "I like `sample_by`. Another idea I had was `separate_by`.\r\n\r\nIt could also be `sampling`, `sampling_method`, `separation_method`.\r\n\r\nNot a big fan of the proposed nouns alone since they are very generic, that's why I tried to have something more specific.\r\n\r\nI also agree that we actually should avoid `split` to avoid any confusion", "Thanks for the analysis of the used terms. I also like `sample_by` (`separate_by` is good too).", "Thank you !! :D " ]

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[ "I'll take this one if no one hasn't yet!" ]

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[ "Hi ! What version of `datasets` are you using ? Can you also provide the logs you get before it raises the error ?" ]

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[ "Awesome thanks a lot @mariosasko " ]

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[ "seems like a very very low-prio improvement :)", "@albertvillanova thanks for the feedback! I removed the formatting altogether since I think this is a bit simpler tor read than non-rendered Markdown" ]

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[ "I wanted to flag a change from since we discussed this: I initially wrote a function for using the Wikimedia APIs to collect namespace aliases, but decided that adding in more http requests to the script wasn't a great idea so instead used that code to build a static list that I just added directly to the code.\r\n\r\nAlso, an FYI that python library dependencies weren't working on my local end so I wasn't able to directly test the code. I tested a copy with the problematic elements stripped (beam etc.) that worked fine, but someone with a working local copy may want to test just to make sure I didn't accidentally break anything.", "Also, while I would argue more strongly for some of the changes in this code, they are five distinct changes so not so hard to remove one or two if other folks think they aren't worth the overhead etc." ]

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[ "Hi ! `load_dataset` accepts as input either a local dataset directory or a dataset name from the Hugging Face Hub.\r\n\r\nSo here you are getting this error the second time because it tries to load the local `wiki_dpr` directory, instead of `wiki_dpr` from the Hub. It doesn't work since it's a **cache** directory, not a **dataset** directory in itself.\r\n\r\nTo fix that you can use another cache directory like `cache_dir=\"/data2/whr/lzy/open_domain_data/retrieval/cache\"`", "thx a lot" ]

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[ "Closing this PR for now due to https://github.com/huggingface/datasets/issues/3145#issuecomment-993664104.", "Okay, after some more thinking, I'm re-opening this PR for three reasons:\r\n* This feature will allow us to remove the `image_file_path`/`audio_file_path` columns in our vision/audio datasets. Currently, it makes sense to keep those columns because it's not obvious how to access the underlying path information of the Image/Audio feature. However, if the user is not aware and does `dataset[0][\"image_file_path\"]` on our vision/audio datasets, this will be costly because the image/audio file data has to be decoded first (stored in `dataset[0][\"image\"]`/`dataset[0][\"audio\"]`)\r\n* In CV, we often work with the so-called \"half life\" datasets (RedCaps, WIT, ...) that only provide image URLs and not actual image data, and some of these image URLs even go down. Our solution to this problem is to give a note on how to efficiently download the image data using `map` in the datasets cards of such datasets. This feature will remove the need for a separate `image_url` column of type `Value(\"string\")` in such datasets. Instead, we will be able to use the `image` column of type `Image()` (the image feature knows how to decode image URLs using `xopen`), disable decoding and use `requests.get` for download, which I expect to be faster than `xopen`.\r\n* This feature should help us in implementing `push_to_hub` for the Image/Audio where we transfer actual image/audio data and not paths", "> This feature will allow us to remove the image_file_path/audio_file_path columns in our vision/audio datasets. Currently, it makes sense to keep those columns because it's not obvious how to access the underlying path information of the Image/Audio feature. However, if the user is not aware and does dataset[0][\"image_file_path\"] on our vision/audio datasets, this will be costly because the image/audio file data has to be decoded first (stored in dataset[0][\"image\"]/dataset[0][\"audio\"])\r\n\r\nThat makes sense !\r\n\r\n> Instead, we will be able to use the image column of type Image() (the image feature knows how to decode image URLs using xopen), disable decoding and use requests.get for download, which I expect to be faster than xopen.\r\n\r\nI feel like it's a bit convoluted compared to having the `image_url` column as string, and say to users to `map` using `requests.get` with `image_url`.\r\n\r\nMoreover I'm not 100% sure that we should have `Image` features with both local paths and URLs, since this behavior is a bit hidden the users and they don't give the same performance at all.\r\n\r\n> This feature should help us in implementing push_to_hub for the Image/Audio where we transfer actual image/audio data and not paths\r\n\r\nCool !", "Thanks, @lhoestq.\r\n\r\n> I feel like it's a bit convoluted compared to having the image_url column as string, and say to users to map using requests.get with image_url.\r\n\r\nYes, that makes sense. \r\n\r\n>Moreover I'm not 100% sure that we should have Image features with both local paths and URLs, since this behavior is a bit hidden the users and they don't give the same performance at all.\r\n\r\nDo you mean we should remove support for URLs in the Image feature? Because this is what we get for free by adding support for streaming (by using `xopen` instead of `open`) and this is also what the Audio feature does.", "> Do you mean we should remove support for URLs in the Image feature? Because this is what we get for free by adding support for streaming (by using xopen instead of open) and this is also what the Audio feature does.\r\n\r\nI think it might not be ideal to have URLs in an `Image` type column for a dataset in **non-streaming** mode, since you'd expect to have everything locally. But for a streaming dataset it must use `xopen` indeed", "Yes, I agree. Let's have the `image_url` columns as `Value(\"string\")` + a note with the map function to download images for local datasets and a note with `cast_column` (which is requested in https://github.com/huggingface/datasets/issues/3369) for streamed datasets (`ds.cast_column(\"image_url\", Image())`)." ]

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[ "maybe related to https://github.com/huggingface/datasets/issues/2859\r\n", "It looks like it's currently calling `HfFileSystem.ls()` ~8 times at the root and for each subdirectory:\r\n- \"\"\r\n- \"en.noblocklist\"\r\n- \"en.noclean\"\r\n- \"en\"\r\n- \"multilingual\"\r\n- \"realnewslike\"\r\n\r\nCurrently `ls` is slow because it iterates on all the files inside the repository.\r\n\r\nAn easy optimization would be to cache the result of each call to `ls`.\r\nWe can also optimize `ls` by using a tree structure per directory instead of a list of all the files.\r\n", "ok\r\n" ]

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[ "Cool ! If you want you can include `dataset_infos.json` but only for the main configurations. That's what we do for example for translation datasets when there are too many configs" ]

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[ "Hi ! Thanks for reporting :)\r\n\r\nWhen using a PyTorch's data loader with `num_workers>1` and an iterable dataset, each worker streams the exact same data by default, resulting in duplicate data when iterating using the data loader.\r\n\r\nWe can probably fix this in `datasets` by checking `torch.utils.data.get_worker_info()` which gives the worker id if it happens.", "> Hi ! Thanks for reporting :)\r\n> \r\n> When using a PyTorch's data loader with `num_workers>1` and an iterable dataset, each worker streams the exact same data by default, resulting in duplicate data when iterating using the data loader.\r\n> \r\n> We can probably fix this in `datasets` by checking `torch.utils.data.get_worker_info()` which gives the worker id if it happens.\r\nHi ! Thanks for reply\r\n\r\nDo u have some plans to fix the problem?\r\n", "Isn’t that somehow a bug on PyTorch side? (Just asking because this behavior seems quite general and maybe not what would be intended)", "From PyTorch's documentation [here](https://pytorch.org/docs/stable/data.html#dataset-types):\r\n\r\n> When using an IterableDataset with multi-process data loading. The same dataset object is replicated on each worker process, and thus the replicas must be configured differently to avoid duplicated data. See [IterableDataset](https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset) documentations for how to achieve this.\r\n\r\nIt looks like an intended behavior from PyTorch\r\n\r\nAs suggested in the [docstring of the IterableDataset class](https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset), we could pass a `worker_init_fn` to the DataLoader to fix this. It could be called `streaming_worker_init_fn` for example.\r\n\r\nHowever, while this solution works, I'm worried that many users simply don't know about this parameter and just start their training with duplicate data without knowing it. That's why I'm more in favor of integrating the check on the worker id directly in `datasets` in our implementation of `IterableDataset.__iter__`." ]

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[ "Hi ! I wasn't able to reproduce your error.\r\n\r\nCan you try to clear your cache at `~/.cache/huggingface/modules` and try again ?" ]

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[ "Hi @albertvillanova we've made a major overhaul of the loading script including all configurations we're making available. Could you please review it again?", "@albertvillanova :ping_pong: " ]

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[ "> Thanks a lot for adding this dataset ! Good job with the dataset card and the dataset scripts - they're really good :)\r\n> \r\n> I just added minor changes\r\n\r\nThanks for fixing typos!" ]

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[ "Hi ! It's slower indeed because a datasets on which `select`/`shard`/`train_test_split`/`shuffle` has been called has to do additional steps to retrieve the data of the dataset table in the right order.\r\n\r\nIndeed, if you call `dataset.select([0, 5, 10])`, the underlying table of the dataset is not altered to keep the examples at index 0, 5, and 10. Instead, an indices mapping is added on top of the table, that says that the first example is at index 0, the second at index 5 and the last one at index 10.\r\n\r\nTherefore accessing the examples of the dataset is slower because of the additional step that uses the indices mapping.\r\n\r\nThe step that takes the most time is to query the dataset table from a list of indices here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/047dc756ed20fbf06e6bcaf910464aba0e20610a/src/datasets/formatting/formatting.py#L61-L63\r\n\r\nIn your case it can be made significantly faster by checking if the indices are contiguous. If they're contiguous, we could pass a python `slice` or `range` instead of a list of integers to `_query_table`. This way `_query_table` will do only one lookup to get the queried batch instead of `batch_size` lookups.\r\n\r\nGiven that calling `select` with contiguous indices is a common use case I'm in favor of implementing such an optimization :)\r\nLet me know what you think", "Hi, thanks for the response!\r\nI still don't understand why it is so much slower than iterating and saving:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal = load_dataset(\"squad\", split=\"train\")\r\noriginal.to_json(\"from_original.json\") # Takes 0 seconds\r\n\r\nselected_subset1 = original.select([i for i in range(len(original))])\r\nselected_subset1.to_json(\"from_select1.json\") # Takes 99 seconds\r\n\r\nselected_subset2 = original.select([i for i in range(int(len(original) / 2))])\r\nselected_subset2.to_json(\"from_select2.json\") # Takes 47 seconds\r\n\r\nselected_subset3 = original.select([i for i in range(len(original)) if i % 2 == 0])\r\nselected_subset3.to_json(\"from_select3.json\") # Takes 49 seconds\r\n\r\nimport json\r\nimport time\r\ndef fast_to_json(dataset, path):\r\n start = time.time()\r\n with open(path, mode=\"w\") as f:\r\n for example in dataset:\r\n f.write(json.dumps(example, separators=(',', ':')) + \"\\n\")\r\n end = time.time()\r\n print(f\"Saved {len(dataset)} examples to {path} in {end - start} seconds.\")\r\n\r\nfast_to_json(original, \"from_original_fast.json\")\r\nfast_to_json(selected_subset1, \"from_select1_fast.json\")\r\nfast_to_json(selected_subset2, \"from_select2_fast.json\")\r\nfast_to_json(selected_subset3, \"from_select3_fast.json\")\r\n```\r\n```\r\nSaved 87599 examples to from_original_fast.json in 8 seconds.\r\nSaved 87599 examples to from_select1_fast.json in 10 seconds.\r\nSaved 43799 examples to from_select2_fast.json in 6 seconds.\r\nSaved 43800 examples to from_select3_fast.json in 5 seconds.\r\n```", "There are slight differences between what you're doing and what `to_json` is actually doing.\r\nIn particular `to_json` currently converts batches of rows (as an arrow table) to a pandas dataframe, and then to JSON Lines. From your benchmark it looks like it's faster if we don't use pandas.\r\n\r\nThanks for investigating, I think we can optimize `to_json` significantly thanks to your test.", "Thanks for your observations, @eladsegal! I spent some time with this and tried different approaches. Turns out that https://github.com/huggingface/datasets/blob/bb13373637b1acc55f8a468a8927a56cf4732230/src/datasets/io/json.py#L100 is giving the problem when we use `to_json` after `select`. This is when `indices` parameter in `query_table` is not `None` (if it is `None` then `to_json` should work as expected)\r\n\r\nIn order to circumvent this problem, I found out instead of doing Arrow Table -> Pandas-> JSON we can directly go to JSON by using `to_pydict()` which is a little slower than the current approach but at least `select` works properly now. Lmk what you guys think of it @lhoestq, @eladsegal?", "Sounds good to me ! Feel free to also share your benchmarks for reference @bhavitvyamalik ", "Posting it in @eladsegal's format:\r\n\r\nFor `squad`:\r\nSaving examples using current `to_json` in 3.63 secs\r\nSaving examples to `from_select1_fast.json` in 5.00 secs\r\nSaving examples to `from_select2_fast.json` in 2.45 secs\r\nSaving examples to `from_select3_fast.json` in 2.50 secs\r\n\r\nFor `squad_v2`:\r\nSaving examples using current `to_json` in 5.26 secs\r\nSaving examples to `from_select1_fast.json` in 7.54 secs\r\nSaving examples to `from_select2_fast.json` in 3.80 secs\r\nSaving examples to `from_select3_fast.json` in 3.67 secs" ]

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[ "Hi, thanks for reporting! This is a duplicate of https://github.com/huggingface/datasets/issues/3240. We are working on a fix.\r\n\r\n" ]

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[ "I think it might come from two different issues:\r\n1. Google Drive is an unreliable host, mainly because of quota limitations\r\n2. the staging environment can sometimes raise some errors\r\n\r\nFor Google Drive tests we could set up some retries with backup URLs if necessary I guess.\r\nFor staging on the other hand, I guess we can investigate what causes this and discuss with the back-end team" ]

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[ "@LysandreJik not so sure who to @\r\nCould you help?", "Hi @hyusterr, I believe it is @wlhgtc from https://github.com/huggingface/transformers/pull/9887" ]

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[ "Fixed, thanks\r\n<img width=\"661\" alt=\"Capture d’écran 2021-12-22 à 12 02 30\" src=\"https://user-images.githubusercontent.com/1676121/147082881-cf700e8d-0511-4431-b214-d6cf8137db10.png\">\r\n" ]

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[ "Cool thanks :) Feel free to merge if it's all good for you" ]

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[ "Hi ! Can you try updating `fsspec` ? The minimum version is `2021.05.0`", "hey @lhoestq. I'm using `fsspec-2021.11.1` but still getting that error.", "Maybe this discussion can help:\r\n\r\nhttps://github.com/fsspec/filesystem_spec/issues/597#issuecomment-958646964", "Thanks @lhoestq. Downgrading `fsspec and s3fs` to `2021.10` fixed this issue!" ]

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[ "Thanks! See https://public.paws.wmcloud.org/User:Isaac_(WMF)/HuggingFace%20Wikipedia%20Processing.ipynb for more implementation details / some data around the overhead induced by adding the extra preprocessing steps (stripping link prefixes and magic words)" ]

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[ "See notebook by @geohci: https://public.paws.wmcloud.org/User:Isaac_(WMF)/HuggingFace%20Wikisource%20Processing.ipynb" ]

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[ "\r\n> Also, maybe calling the dataset as \"bnl_historical_newspapers\" and setting \"processed\" as one configuration name?\r\n\r\nThis sounds like a good idea but my only question around this is how easy it would be to use the same approach for processing the other newspaper collections [https://data.bnl.lu/data/historical-newspapers/](). \r\n\r\nFor example, the \"BIG DATA PACK\" is `257GB` of ALTO XML. This format is slightly more annoying to process because the metadata and text are contained in different files but the bigger issue might be that processing this XML using the Python XML libraries will probably be quite slow? I had thought for those larger datasets it might be more appropriate to use the Beam datasets? I don't have any experience using Beam so I'm not sure what that would involve and there is a reason to not include it in a dataset script alongside a non Beam dataset? \r\n\r\nIf there isn't an issue with potentially later adding other datasets (which may require Beam) into the same script I'll add one config for the processed version now which leaves open the option for later adding the other datasets. If this makes sense I'll also change the name as you suggest. \r\n\r\nThere is another dataset that could be a good candidate for inclusion here is the \"Monograph Text pack\" which is also processed into a simpler XML format however as the name suggests this isn't newspapers so might be confusing to include under a 'newspapers' script. One option would be to put everything under a `BNL` collection but it might be better to keep the monographs separate if they are added as a dataset so a single script doesn't end up including too much variety of content types? \r\n\r\n\r\n\r\n", "> My initial idea was to contribute the script also as \"community\" datasets (instead of canonical), i.e. in this case, pushing the script to the repo [huggingface.co/datasets/bigscience-catalogue-data/bnl_historical_newspapers](https://huggingface.co/datasets/bigscience-catalogue-data/bnl_historical_newspapers)\r\n\r\nSorry to respond to this late - happy for this to go in the community datasets. I think it would be nice to include in the canonical datasets at some point but since there is less urgency with this I could try and first work on improving the Datacard before doing that (i.e. make this a draft PR) - let me know if you think that makes more sense? \r\n\r\n\r\n", "> My initial idea was to contribute the script also as \"community\" datasets (instead of canonical), i.e. in this case, pushing the script to the repo https://huggingface.co/datasets/bigscience-catalogue-data/bnl_historical_newspapers\r\n> One of the advantages is that no dummy data is required, so the addition can be made faster\r\n> On the other hand, one disadvantage is that contributions cannot be made through PRs\r\n> Therefore, we should use the Issue page for discussions, reviews, decisions,...\r\n\r\nSure we can use the issues to discuss/review community datasets. Maybe let's have an issue template for that ?\r\nFor this dataset in particular I'll let @albertvillanova decide whether it's best as community dataset or not. IMO both are fine :)\r\n\r\n> I had thought for those larger datasets it might be more appropriate to use the Beam datasets? I don't have any experience using Beam so I'm not sure what that would involve and there is a reason to not include it in a dataset script alongside a non Beam dataset?\r\n\r\nBeam is nice to process a dataset once and for all and store the resulting processed data on the Hugging Face Hub or elsewhere. However for big datasets it must run on a distributed processing runtime like Google DataFlow, which is often inconvenient for many users. We've been using it though for datasets like Wikipedia and sharing the processed data in a GCS bucket.\r\n\r\nSo feel free to use the tools you like to process the datasets, but in the end I think we just need to host the processed data in a convenient format on the Hugging Face Hub to share it with the community. The processing script you used can also be shared with the community for reproducibility and documentation. But maybe @albertvillanova already has something in mind", "> > My initial idea was to contribute the script also as \"community\" datasets (instead of canonical), i.e. in this case, pushing the script to the repo [huggingface.co/datasets/bigscience-catalogue-data/bnl_historical_newspapers](https://huggingface.co/datasets/bigscience-catalogue-data/bnl_historical_newspapers)\r\n> > One of the advantages is that no dummy data is required, so the addition can be made faster\r\n> > On the other hand, one disadvantage is that contributions cannot be made through PRs\r\n> > Therefore, we should use the Issue page for discussions, reviews, decisions,...\r\n> \r\n> Sure we can use the issues to discuss/review community datasets. Maybe let's have an issue template for that ? For this dataset in particular I'll let @albertvillanova decide whether it's best as community dataset or not. IMO both are fine :)\r\n\r\nThanks, I'll hold off and let @albertvillanova decide best place for this. \r\n\r\n> > I had thought for those larger datasets it might be more appropriate to use the Beam datasets? I don't have any experience using Beam so I'm not sure what that would involve and there is a reason to not include it in a dataset script alongside a non Beam dataset?\r\n> \r\n> Beam is nice to process a dataset once and for all and store the resulting processed data on the Hugging Face Hub or elsewhere. However for big datasets it must run on a distributed processing runtime like Google DataFlow, which is often inconvenient for many users. We've been using it though for datasets like Wikipedia and sharing the processed data in a GCS bucket.\r\n> \r\n> So feel free to use the tools you like to process the datasets, but in the end I think we just need to host the processed data in a convenient format on the Hugging Face Hub to share it with the community. The processing script you used can also be shared with the community for reproducibility and documentation. But maybe @albertvillanova already has something in mind\r\n\r\nThat's useful, my own 2 cents are that it would make sense to do as @albertvillanova suggested and:-\r\n\r\n- rename the dataset to 'bnl_newspapers' \r\n- make the 'processed dataset' a config \r\n\r\nI won't try and include all the other datasets now but this leaves open the option of adding those later. The actual ALTO processing should be okay to do but I think it makes sense to do this as a one-off process and make the plain text + some associated metadata available elsewere so the dataset script can be kept simple and the processing doesn't get done multiple times. \r\n\r\n@albertvillanova if that sounds okay I'll update pull request to include those changes. \r\n", "@albertvillanova I've now created a config (currently with only one option) and renamed the dataset. This should keep the option to add other configs based on different bnl newspapers in the future. \r\n" ]

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[ "https://huggingface.co/datasets/projecte-aina/parlament_parla -> works (but we still have to show an audio player)\r\n\r\nhttps://huggingface.co/datasets/openslr -> another issue: `Message: [Errno 2] No such file or directory: '/home/hf/datasets-preview-backend/zip:/asr_javanese/data/00/00004fe6aa.flac'`" ]

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[ "According to this [comment in the forum](https://discuss.huggingface.co/t/save-datasetdict-to-huggingface-hub/12075/8?u=lhoestq), using `push_to_hub` on a dataset with `ClassLabel` can also make the feature simply disappear when it's reloaded !", "Maybe we can also fix https://github.com/huggingface/datasets/issues/3035 while working on this because, as pointed out in my initial post, `save_to_disk` also saves the `dataset_info.json` file." ]

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[ "This issue was fixed by me calling `all_datasets.push_to_hub(\"hackernews_hiring_posts\")`.\r\n\r\nThe previous problems were from calling `all_datasets.save_to_disk` and then pushing with `my_repo.git_add` and `my_repo.push_to_hub`.\r\n" ]

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[ "duplicate of #2655" ]

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[ "Got solved it with push_to_hub, closing" ]

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[ "cc @juliensimon " ]

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[ "CI failed because of a server error - merging" ]

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[ "Hi ! Thanks for the suggestion :)\r\nIt makes sense to me, and it can surely be implemented by wrapping the user's function to make it a batched function. However I'm not a big fan of the inconsistency it would create with `map`: `with_transform` is batched by default while `map` isn't.\r\n\r\nIs there something you would like to contribute ? I can give you some pointers if you want" ]

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[ "Locally the `make quality` passes with the same dependencies. I would suggest upgrading flake8. (I can take care of it in another PR)\r\ncc @lhoestq ", "Thank you for fixing flake8! I think we are ready to merge then. " ]

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[ "Hi ! Feel free to access `batch[\"audio\"][\"array\"]` and `batch[\"audio\"][\"sampling_rate\"]` instead\r\n\r\n`datasets` 1.16 introduced some changes in `common_voice` and now the `path` field is no longer a path to a local file (but rather the path to the file in the archive it's extracted from)", "Thanks for the information. It works.", "Cool ! Closing this issue then" ]

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[ "Hello @lhoestq, thank you for your answer.\r\n\r\nI don't use pytest a lot so I think I might need some help on it :) but I tried some tests for `streaming_download_manager.py` only. I don't know how to test `download_manager.py` since we need to use local files.\r\n\r\n# Comments : \r\n* In **download_manager.py** I removed some unnecessary imports after the simplification of `_get_extraction_protocol_local`.\r\n* In **streaming_download_manager** I moved the raised Error as suggested.\r\n \r\n### I also started some tests on `StreamingDownloadManager()` :\r\n* Used an existing zipfile url and added a new one that has a folder and many files : \r\n```python\r\nTEST_GG_DRIVE_ZIPPED_URL = \"https://drive.google.com/uc?export=download&id=1k92sUfpHxKq8PXWRr7Y5aNHXwOCNUmqh\"\r\nTEST_GG_DRIVE2_ZIPPED_URL = \"https://drive.google.com/uc?export=download&id=1X4jyUBBbShyCRfD-vCO1ZvfqFXP3NEeU\"\r\n``` \r\n* **For now is being tested :**\r\n * Return type of the function : should be tuple\r\n * Files names\r\n * Files content\r\n * Added an `xfail` test for the gzip file, because I get a `zipfile.BadZipFile exception`.\r\n\r\n\r\n * And lastly, changed the test for `_get_extraction_protocol_throws` since it was moved to `_extract` : \r\n ```diff\r\n@pytest.mark.xfail(raises=NotImplementedError)\r\ndef test_streaming_dl_manager_get_extraction_protocol_throws(urlpath):\r\n- _get_extraction_protocol(urlpath)\r\n\r\n@pytest.mark.xfail(raises=NotImplementedError)\r\ndef test_streaming_dl_manager_get_extraction_protocol_throws(urlpath):\r\n+ StreamingDownloadManager()._extract(urlpath)\r\n```\r\n\r\n\r\n" ]

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[ "@mariosasko I fixed couple of bugs", "TO-DO: \r\n- [x] Add unlabeled 2017 splits, train and validation splits of 2015\r\n- [x] Add Class Labels as list instead", "@mariosasko added fine & coarse grained labels, will fix the bugs (currently getting set up with VM, my internet is too slow to run the tests and download the data 🥲)", "migrated to here https://github.com/huggingface/datasets/tree/coco" ]

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[ "The CI error is due to missing tags in the CLUE dataset card - merging !" ]

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[ "I just tested and I think this only opens one file ? If there are several files in the ZIP, only the first one is opened. To open several files from a ZIP, one has to call `open` several times.\r\n\r\nWhat about updating the CSV loader to make it `download_and_extract` zip files, and open each extracted file ?", "I have implemented the glob of ZIP files in the packaged modules:\r\n- csv\r\n- json\r\n- text", "Also for streaming and non-streaming.", "In https://github.com/huggingface/datasets/pull/3375/commits/c10275fe36085601cb7bdb9daee9a8f1fc734f48, there were 3 failing tests, only on Linux:\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_streaming_download_manager.py::test_streaming_dl_manager_get_extraction_protocol[https://drive.google.com/uc?export=download&id=1k92sUfpHxKq8PXWRr7Y5aNHXwOCNUmqh-zip]\r\nFAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive - Fi...\r\nFAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped\r\n= 3 failed, 3553 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 192.79s (0:03:12) =\r\n```\r\n\r\nAfter re-running the CI in https://github.com/huggingface/datasets/pull/3375/commits/57bfe1f342cd3c59d2510b992d5f06a0761eb147, there was only 1 failing test:\r\n- On Linux:\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped\r\n= 1 failed, 3555 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 199.76s (0:03:19) =\r\n```\r\n- On Windows:\r\n```\r\n=========================== short test summary info ===========================\r\nFAILED tests/test_load.py::test_load_dataset_builder_for_community_dataset_without_script\r\n= 1 failed, 3551 passed, 2954 skipped, 2 xfailed, 1 xpassed, 121 warnings in 478.58s (0:07:58) =\r\n```\r\n\r\nThe test `tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped` passes locally.\r\n\r\nI guess the issue is caused by those tests and has nothing to do with this PR.", "@lhoestq my final proposed solution:\r\n- I have added the method `iter_files` to DownloadManager and StreamingDownloadManager\r\n- I use this in modules: \"csv\", \"json\", \"text\"\r\n- I test for CSV/JSONL/TXT zipped (and non-zipped) files, both in streaming and non-streaming modes", "> Note that at one point we might consider switching to using `iter_archive` for ZIP files in the json/text/csv loaders since it should be faster.\r\n\r\nAs far as the functionality is kept... ;)" ]

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[ "Seems like the issue still exists,:\r\n`Downloading and preparing dataset clue/chid (download: 127.15 MiB, generated: 259.71 MiB, post-processed: Unknown size, total: 386.86 MiB) to /mnt/cache/tanhaochen/.cache/huggingface/datasets/clue/chid/1.0.0/e55b490cb7809dcd8db31b9a87119f2e2ec87cdc060da8a9ac070b070ca3e379...\r\nTraceback (most recent call last):\r\n File \"/mnt/cache/tanhaochen/PromptCLUE/test_datasets.py\", line 3, in <module>\r\n cluewsc2020 = datasets.load_dataset(\"clue\",\"chid\")\r\n File \"/mnt/cache/tanhaochen/dependencies/datasets/src/datasets/load.py\", line 1667, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/mnt/cache/tanhaochen/dependencies/datasets/src/datasets/builder.py\", line 593, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/mnt/cache/tanhaochen/dependencies/datasets/src/datasets/builder.py\", line 663, in _download_and_prepare\r\n verify_checksums(\r\n File \"/mnt/cache/tanhaochen/dependencies/datasets/src/datasets/utils/info_utils.py\", line 40, in verify_checksums\r\n raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://storage.googleapis.com/cluebenchmark/tasks/chid_public.zip']\r\n`", "Hi,\r\n\r\nthe fix hasn't been merged yet (it should be merged early next week)." ]

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