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[ "I think this is a duplicate of [#3190](https://github.com/huggingface/datasets/issues/3190)?", "Upgrading the datasets version as per #3190 fixes this bug. \r\nI'm Marking as closed." ]

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[ "CI is failing just because of empty sections - merging" ]

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[ "This dataset has files in ASF/AMC format. [ The skeleton file is the ASF file (Acclaim Skeleton File). The motion file is the AMC file (Acclaim Motion Capture data). ] \r\n\r\nSome questions : \r\n1. How do we go about representing these features using datasets.Features and generate examples ?\r\n2. The dataset download link for ASF/AMC files does not have metadata information, for eg : category and subcategory information. We will need to crawl the website for this information. The authors mention \"Please don't crawl this database for all motions.\" Can we mail the authors for this information ?\r\nThe dataset structure is as follows : \r\n```\r\nsubjects\r\n\t- 01\r\n\t\t- 01_01.amc\r\n\t\t- 01_02.amc\r\n\t\t.\r\n\t\t.\r\n\t\t.\r\n\t\t- 01.asf\r\n\t- 02\r\n\t\t- 02_01.amc\r\n\t\t- 02_02.amc\r\n\t\t.\r\n\t\t.\r\n\t\t.\r\n\t\t- 02.asf\r\n```\r\nThere is no metadata regarding the category, sub-category and motion description.\r\n\r\nNeed your inputs. @mariosasko / @lhoestq \r\nThank you.\r\n", "Hi @dnaveenr! Thanks for working on this!\r\n\r\n1. We can use the `Sequence(Value(\"string\"))` feature type for the subject's AMC files and `Value(\"string\")` for the subject's ASF file (`Value(\"string\")` represents the file paths) + the types for categories/subcategories and descriptions.\r\n2. We can use this URL to download the motion descriptions: http://mocap.cs.cmu.edu/search.php?subjectnumber=<subject_number>&motion=%%%&maincat=%&subcat=%&subtext=yes where `subject_number` is the number between 1 and 144. And to get categories/subcategories, feel free to contact the authors (they state in the FAQ they are happy to help) and ask them if they can provide the mapping from categories/subcategories to the AMC files to avoid crawling. You can also mention that your goal is to make their dataset more accessible by adding its loading script to the Hub.\r\n\r\nThe AMC files are also available in the tvd, c3d, mpg and avi formats (the links are in the [FAQ](http://mocap.cs.cmu.edu/faqs.php) section), so it would be nice to have one config for each of these additional formats. \r\n\r\nAnd additionally, we can add a `Data Preprocessing` section to the card where we explain how to load/process the files. I can help with that.", "Hi @mariosasko ,\r\n\r\n1. Thanks for this, so we can add the file paths.\r\n2. Yes, I had already mailed the authors a couple of days back actually, asking for the metadata details[ i.e category, sub-category and motion description] . They are yet to respond though, I will wait for a couple of days and try to follow up with them again. :) Else we can use the workaround solution.\r\n\r\nYes. Supporting all the formats would be helpful. \r\n\r\n> And additionally, we can add a Data Preprocessing section to the card where we explain how to load/process the files. I can help with that.\r\n\r\nOkay. Got it." ]

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[ "Hi ! I don't think this would solve this issue.\r\nCurrently it looks like there's a bug that converts the list `[\"hello it's nice\"]` to a string `'[\"hello it's nice\"]'` since this is what the metric expects as input. The conversion is done before the data are passed to `_compute()`.\r\n\r\nThis is `Value(\"string\").encode_example` that is called to do the conversion. Since `str()` encoding is too permissive we should consider raising an error if the example is not a string (even though it can be converted to string). ", "> called\r\n\r\nAh yeah you're right", "I just opened https://github.com/huggingface/datasets/pull/3460 to fix that. It now raises an error instead of computing the wrong WER", "Thank you - that should be good enough!" ]

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[ "Hi ! I guess you are talking about the dataset cards that are in this repository on github ?\r\n\r\nI think github allows to submit a PR even for 1 line though the `Edit file` button on the page of the dataset card.\r\n\r\nMaybe let's mention this in `CONTRIBUTING.md` ?" ]

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[ "Hi ! It's simply not added yet :)\r\n\r\nIf someone wants to contribute to add the `stratify` parameter I'd be happy to give some pointers.\r\n\r\nIn the meantime, I guess you can use `sklearn` or other tools to do a stratified train/test split over the **indices** of your dataset and then do\r\n```\r\ntrain_dataset = dataset.select(train_indices)\r\ntest_dataset = dataset.select(test_indices)\r\n```", "Hi @lhoestq I would like to add `stratify` parameter, can you give me some pointers for adding the same ?", "Hi ! Sure :)\r\n\r\nThe `train_test_split` method is defined here: \r\n\r\nhttps://github.com/huggingface/datasets/blob/dc62232fa1b3bcfe2fbddcb721f2d141f8908943/src/datasets/arrow_dataset.py#L3253-L3253\r\n\r\nand inside `train_test_split ` we need to create the right `train_indices` and `test_indices` that are passed here to `.select()`:\r\n\r\nhttps://github.com/huggingface/datasets/blob/dc62232fa1b3bcfe2fbddcb721f2d141f8908943/src/datasets/arrow_dataset.py#L3450-L3464\r\n\r\nFor example if your dataset is like\r\n| | label |\r\n|---:|--------:|\r\n| 0 | 1 |\r\n| 1 | 1 |\r\n| 2 | 0 |\r\n| 3 | 0 |\r\n\r\nand the user passes `stratify=dataset[\"label\"]`, then you should get indices that look like this\r\n```\r\ntrain_indices = [0, 2]\r\ntest_indices = [1, 3]\r\n```\r\n\r\nthese indices will be passed to `.select` to return the stratified train and test splits :)\r\n\r\nFeel free to îng me if you have any question !", "@lhoestq \r\nI just added the implementation for `stratify` option here #4322 " ]

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[ "do keep us updated on how it's going in staging! cc @SBrandeis ", "Sure ! For now it works smoothly. We'll also do a new release today.\r\n\r\nI can send you some repos to explore on staging, in case you want to see how they look like after being updated.\r\nFor example [swahili_news](https://moon-staging.huggingface.co/datasets/swahili_news/tree/main)" ]

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[ "Hi ! This is an issue with `datasets` 1.12. Sorry for the inconvenience. Can you update to `>=1.13` ?\r\nsee https://github.com/huggingface/datasets/issues/3190\r\n\r\nMaybe we should also backport the bug fix to `1.12` (in a new version `1.12.2`)" ]

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[ "I was going through the codebase, and I believe the implementation of __add__() and __iadd__() will be similar to concatenate_datasets() after the elimination of code for arguments other than the list of datasets (info, split, axis). \r\n(Assuming elimination of axis means concatenating over axis 1.)" ]

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[ "Hi ! I think the issue comes from the dataset_infos.json file: it has the \"flat\" field twice.\r\n\r\nCan you try deleting this file and regenerating it please ?", "Thanks! That fixed that, but now I am getting:\r\nServer Error\r\nStatus code: 400\r\nException: KeyError\r\nMessage: 'feature'\r\n\r\nI checked the dataset_infos.json and pubmed_neg.py script, I don't use 'feature' anywhere as a key. Is the dataset viewer expecting that I do?", "It seems that the `feature` key is missing from some feature type definition in your dataset_infos.json:\r\n```json\r\n\t\t\t\"tokens\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n\t\t\t},\r\n\t\t\t\"tags\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n\t\t\t}\r\n```\r\nThey should be\r\n```json\r\n\t\t\t\"tokens\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n \"feature\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"}\r\n\t\t\t},\r\n\t\t\t\"tags\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\",\r\n \"feature\": {\"num_classes\": 5, \"names\": [\"-\", \"S\", \"H\", \"N\", \"C\"], \"names_file\": null, \"id\": null, \"_type\": \"ClassLabel\"}\r\n\t\t\t}\r\n```\r\n\r\nNote that you can generate the dataset_infos.json automatically to avoid mistakes:\r\n```bash\r\ndatasets-cli test ./path/to/dataset --save_infos\r\n```" ]

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[ "Hi ! Indeed it says \"downloading and preparing\" but in your case it didn't need to download anything since you used local files (it would have thrown an error otherwise). I think we can improve the logging to make it clearer in this case", "@lhoestq Thank you for explaining. I am sorry but I was not clear about my intention. I didn't want to kill internet traffic; I wanted to kill all write activity. In other words, you can imagine that my storage has only read access but crashes on write.\r\n\r\nWhen run_clm.py is invoked with the same parameters, the hash in the cache directory \"datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/...\" doesn't change, and my job can load cached data properly. This is great.\r\n\r\nUnfortunately, when params change (which happens sometimes), the hash changes and the old cache is invalid. datasets builder would create a new cache directory with the new hash and create JSON builder there, even though every JSON builder is the same. I didn't find a way to avoid such behavior.\r\n\r\nThis problem can be resolved when using datasets.map() for tokenizing and grouping text. This function allows me to specify output filenames with --cache_file_names, so that the cached files are always valid.\r\n\r\nThis is the code that I used to freeze cache filenames for tokenization. I wish I could do the same to datasets.load_dataset()\r\n```\r\n tokenized_datasets = raw_datasets.map(\r\n tokenize_function,\r\n batched=True,\r\n num_proc=data_args.preprocessing_num_workers,\r\n remove_columns=column_names,\r\n load_from_cache_file=not data_args.overwrite_cache,\r\n desc=\"Running tokenizer on dataset\",\r\n cache_file_names={k: os.path.join(model_args.cache_dir, f'{k}-tokenized') for k in raw_datasets},\r\n )\r\n```", "Hi ! `load_dataset` may re-generate your dataset if some parameters changed indeed. If you want to freeze a dataset loaded with `load_dataset`, I think the best solution is just to save it somewhere on your disk with `.save_to_disk(my_dataset_dir)` and reload it with `load_from_disk(my_dataset_dir)`. This way you will be able to reload the dataset without having to run `load_dataset`" ]

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[ "Cool, I'm in favor of this PR. Our official examples in speech already make use of `\"audio\"` so no need to change anything there. It would be great if we could prominently feature how one can get the audio path without decoding in the docs.", "@patrickvonplaten Yes, I agree.\r\n\r\ncc @stevhliu we should add an example where decoding is disabled (to read paths) to [this section](https://github.com/huggingface/datasets/blob/master/docs/source/audio_process.rst#audio-datasets) in the docs and remove the mentions of `path`/`file` (if we merge this PR)." ]

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[ "Hi ! What's your question ?" ]

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[ "Yes I agree, these should be as aligned as possible. Maybe we can also check the feedback in the survey at http://hf.co/oss-survey and see if people mentioned related things on the API (in particular if we go the breaking change way, it would be good to be sure we are taking the right direction for the community).", "I like this proposal.\r\n\r\n> There is also an important difference in terms of behavior:\r\nDataset.map adds new columns (with dict.update)\r\nBUT\r\nIterableDataset discards previous columns (it overwrites the dict)\r\nIMO the two methods should have the same behavior. This would be an important breaking change though.\r\n\r\n> The main breaking change would be the change of behavior of IterableDataset.map, because currently it discards all the previous columns instead of keeping them.\r\n\r\nYes, this behavior of `IterableDataset.map` was surprising to me the first time I used it because I was expecting the same behavior as `Dataset.map`, so I'm OK with the breaking change here.\r\n\r\n> IterableDataset only supports \"torch\" (it misses tf, jax, pandas, arrow) and is missing the parameters: columns, output_all_columns and format_kwargs\r\n\r\n\\+ it's also missing the actual formatting code (we return unformatted tensors)\r\n> We could have a completely aligned map method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that.\r\n\r\n> For information, TFDS does lazy map by default, and has an additional .cache() method.\r\n\r\nIf I understand this part correctly, the idea would be for `Dataset.map` to behave similarly to `Dataset.with_transform` (lazy processing) and to have an option to cache processed data (with `.cache()`). This idea is really nice because it can also be applied to `IterableDataset` to fix https://github.com/huggingface/datasets/issues/3142 (again we get the aligned APIs). However, this change would break a lot of things, so I'm still not sure if this is a step in the right direction (maybe it's OK for Datasets 2.0?) \r\n> If the two APIs are more aligned it would be awesome for the examples in transformers, and it would create a satisfactory experience for users that want to switch from one mode to the other.\r\n\r\nYes, it would be amazing to have an option to easily switch between these two modes.\r\n\r\nI agree with the rest.\r\n", "> If I understand this part correctly, the idea would be for Dataset.map to behave similarly to Dataset.with_transform (lazy processing) and to have an option to cache processed data (with .cache()). This idea is really nice because it can also be applied to IterableDataset to fix #3142 (again we get the aligned APIs). However, this change would break a lot of things, so I'm still not sure if this is a step in the right direction (maybe it's OK for Datasets 2.0?)\r\n\r\nYea this is too big of a change in my opinion. Anyway it's fine as it is right now with streaming=lazy and regular=eager.", "Hi, IterableDataset is also missing set_format.", "Yes indeed, thanks. I added it to the list of methods to align in the first post", "I just encountered the problem of the missing `fn_kwargs` parameter in the `map` method. I am commenting to give a workaround in case someone has the same problem and does not find a solution.\r\nYou can wrap your function call inside a class that contains the other parameters needed by the function called by map, like this:\r\n\r\n```python\r\ndef my_func(x, y, z):\r\n # Do things\r\n\r\nclass MyFuncWrapper:\r\n def __init__(self, y, z):\r\n self.y = y\r\n self.z = z\r\n\r\n def __call__(self, x):\r\n return my_func(x, self.y, self.z)\r\n```\r\n\r\nThen, give an instance of the `MyFuncWrapper` to the map function." ]

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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.", "I also add a comment by @geohci in the Issue page:\r\n> 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)", "Hi ! Thanks a lot, this is very cool ! Note that unfortunately if we change the processing right now, users won't be able to load the \"big\" languages like english anymore, because it requires an Apache Beam runtime to process them. Some Wikipedia dumps have been processed by Hugging Face so that users don't need to run Apache Beam stuff.\r\n\r\nTherefore, we can merge this change after we have processed dumps using this new processing, and host them on the Hugging Face google storage.\r\n\r\nI think we can take care of this and let you know once this is ready ? What do you think @albertvillanova ?\r\n\r\nThis is also an opportunity to have the latest dumps ready, the current ones are from 2020", "Related PR on updating to the latest dates: https://github.com/huggingface/datasets/pull/3612", "@lhoestq if the additional processing steps are validated, we could go on generating the processed datasets for the big languages.\r\n\r\nThe only thing before doing that is that we should also validate other change (so that we include it also in the processed datasets):\r\n- #3398 ", "> @lhoestq if the additional processing steps are validated, we could go on generating the processed datasets for the big languages.\r\n\r\nCool ! Looking forward to it :)\r\n\r\n> The only thing before doing that is that we should also validate other change (so that we include it also in the processed datasets):\r\n> \r\n> https://github.com/huggingface/datasets/issues/3398\r\n\r\nSounds good ! We can definitely add the URL as asked by the Wikipedia to provide credits to the authors.", "@geohci I do not have push rights to this PR. See: [Enabling repository maintainer permissions on existing pull requests](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/allowing-changes-to-a-pull-request-branch-created-from-a-fork#enabling-repository-maintainer-permissions-on-existing-pull-requests).\r\n\r\nI would like to merge the master branch so that all tests pass. Once done, I will be able approve this PR.", "> @geohci I do not have push rights to this PR. See: [Enabling repository maintainer permissions on existing pull requests](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/allowing-changes-to-a-pull-request-branch-created-from-a-fork#enabling-repository-maintainer-permissions-on-existing-pull-requests).\r\n> \r\n> I would like to merge the master branch so that all tests pass. Once done, I will be able approve this PR.\r\n\r\n@albertvillanova the `Allow edits by maintainers` box was already checked (what your instructions indicated) and indicates `If checked, users with write access to huggingface/datasets can add new commits to your wikipedia-updates branch. You can always change this setting later.` so you should have permissions already. If there's something else I'm missing or can do, please let me know. If it's not easy to resolve, I am plenty comfortable with you creating a new PR with these changes under your account too." ]

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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())`).", "I fixed the merge conflicts and small bugs in nested decoding introduced by #3575. Additionally, I addressed https://github.com/huggingface/datasets/issues/3473 by adding the `_iter` method to `Dataset` (inspired by the `_getitem` method). For `iter(dset)` I set `_iter(dset, decoded=True)` to enable decoding and for `map` `_iter(dset, decoded=False)` to make it lazy." ]

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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", "@lhoestq I've added an example that uses `map` to download the images." ]

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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__`.", "Fixed by https://github.com/huggingface/datasets/pull/4375", "> Fixed by #4375\r\n\r\nThanks!" ]

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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: ", "Thanks @lhbonifacio for adding this dataset.\r\nHi there, i got an error about mmarco:\r\nConnectionError: Couldn't reach 'unicamp-dl/mmarco' on the Hub (ConnectionError)\r\ncode:\r\n`from datasets import list_datasets, load_dataset\r\ndataset = load_dataset('unicamp-dl/mmarco', language='portuguese')`\r\n\r\nAny help will be appreciated!", "Hi @catqaq, we updated the loading script. Now you can load the datasets with:\r\n\r\n```python\r\ndataset = load_dataset('unicamp-dl/mmarco', 'portuguese')\r\n```\r\n\r\nYou can check the list of supported languages and usage examples in [this link](https://huggingface.co/datasets/unicamp-dl/mmarco). Feel free to contact us if you have any issues.", "\r\n\r\n\r\n> \r\n\r\n\r\n\r\n> Hi @catqaq, we updated the loading script. Now you can load the datasets with:\r\n> \r\n> ```python\r\n> dataset = load_dataset('unicamp-dl/mmarco', 'portuguese')\r\n> ```\r\n> \r\n> You can check the list of supported languages and usage examples in [this link](https://huggingface.co/datasets/unicamp-dl/mmarco). Feel free to contact us if you have any issues.\r\n\r\nThanks for your quick updates. So, how can i get the fixed version, install from the source? It seems that the merging is blocked.", "@catqaq you can load mMARCO using the namespace `unicamp-dl/mmarco` while this PR remains under review." ]

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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", "Closed by:\r\n- #3982" ]

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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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[ "@lhoestq Hi~, does this support that `datasets.map(func, batched=True, batch_size, num_proc>1, new_fingerprint=\"func_v1\")` even if `func` can't pickle. I also notice that you said \"Unfortunately you need picklable mapping functions to make multiprocessing work :confused: Also feel free to open an issue or send me a dm if you are in a situation where the caching fails. I can help you with that :slight_smile:\" in [here](https://discuss.huggingface.co/t/how-to-deal-with-unpickable-objects-in-map/1547/8). So, I want to ask that is there a way for users to use multiprocessing in `datasets.map` when the `func` can't pickle? \r\nThanks in advance!", "Yea you need your function to be picklable for multiprocessing, otherwise the main process is not able to pass your function to the child processes." ]

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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)", "Closed by:\r\n- #3435" ]

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

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[ "@geohci, I think the field \"url\" does not appear in the Wikimedia dumps. Therefore I guess we should generate it, using the \"title\" field and making some transformation of it (replacing spaces with underscores) and prepending the domain (created using the language)?", "Indeed:\r\n\r\n> To re-distribute text on Wikipedia in any form, provide credit to the authors either by including a) a [hyperlink](https://en.wikipedia.org/wiki/Hyperlink) (where possible) or [URL](https://en.wikipedia.org/wiki/URL) to the page or pages you are re-using, b) a hyperlink (where possible) or URL to an alternative, stable online copy which is freely accessible, which conforms with the license, and which provides credit to the authors in a manner equivalent to the credit given on this website, or c) a list of all authors. (Any list of authors may be filtered to exclude very small or irrelevant contributions.) This applies to text developed by the Wikipedia community. Text from external sources may attach additional attribution requirements to the work, which should be indicated on an article's face or on its talk page. For example, a page may have a banner or other notation indicating that some or all of its content was originally published somewhere else. Where such notations are visible in the page itself, they should generally be preserved by re-users.\r\n\r\nsource: https://en.wikipedia.org/wiki/Wikipedia:Copyrights\r\n\r\nI guess it's fine to add the URL field - it can be constructed easily from the title page IIRC.", "yep, sorry forgot that that wasn't already in the dumps. specifically `f\"https://{language}.wikipedia.org/wiki/{title.replace(' ', '_')}` should do it", "Thanks @geohci.\r\n\r\nI had already been looking for information about the conversion from title to URL and I found that apart from replacing blanks with underscores, some other special character must also be percent-encoded (e.g. `\"` to `%22`): https://meta.wikimedia.org/wiki/Help:URL\r\n\r\nTherefore, I have finally used `urllib.parse.quote` function. This additionally percent-encodes non-ASCII characters, but Wikimedia docs say these are equivalent:\r\n> For the other characters either the code or the character can be used in internal and external links, they are equivalent. The system does a conversion when needed.\r\n> [[%C3%80_propos_de_M%C3%A9ta]]\r\n> is rendered as [À_propos_de_Méta](https://meta.wikimedia.org/wiki/%C3%80_propos_de_M%C3%A9ta), almost like [À propos de Méta](https://meta.wikimedia.org/wiki/%C3%80_propos_de_M%C3%A9ta), which leads to this page on Meta with in the address bar the URL\r\n> [http://meta.wikipedia.org/wiki/%C3%80_propos_de_M%C3%A9ta](https://meta.wikipedia.org/wiki/%C3%80_propos_de_M%C3%A9ta)\r\n> while [http://meta.wikipedia.org/wiki/À_propos_de_Méta](https://meta.wikipedia.org/wiki/%C3%80_propos_de_M%C3%A9ta) leads to the same. ", "Closed by:\r\n- #3789 " ]

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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", "@mariosasko thanks for those suggestions ", "I just merged `master` into your branch to fix the CI :)", "@albertvillanova do you have additional comments ? Otherwise I think this PR is ready to merge :)", "> @davanstrien you did an awsome job!!! Thanks a lot!\r\n> \r\n> Just some very minor comments (mainly about the README documentation), and we merge this to master!\r\n\r\nThanks! Hopefully all addressed now. Thanks again for all the support with this pull request! " ]

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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'`", "Done", "https://huggingface.co/datasets/projecte-aina/parlament_parla/viewer/clean/train works\r\n\r\n<img width=\"1535\" alt=\"Capture d’écran 2022-04-12 à 13 58 47\" src=\"https://user-images.githubusercontent.com/1676121/162957855-cb3d9e2e-4b61-488c-99ca-8065cd8fe377.png\">\r\n", "But https://huggingface.co/datasets/openslr/viewer does not work\r\n\r\n<img width=\"678\" alt=\"Capture d’écran 2022-04-12 à 13 59 46\" src=\"https://user-images.githubusercontent.com/1676121/162958013-e31ef2ae-f886-47b7-9f27-664ed3d4b5a1.png\">\r\n\r\nSame issue as #4126:\r\n\r\n```\r\nStatus code: 400\r\nException: TypeError\r\nMessage: __init__() got an unexpected keyword argument 'audio_column'\r\n```", "Fixed:\r\n<img width=\"1561\" alt=\"Capture d’écran 2022-04-25 à 18 11 51\" src=\"https://user-images.githubusercontent.com/1676121/165129813-018ece9e-8b20-4544-844d-4e88148e738f.png\">\r\n" ]

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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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