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[ "> Hi @tianjianjiang, thanks for the fix.\r\n> The links should also be updated in the `dataset_infos.json` file.\r\n> The failing tests are due to the missing tag in the header of the `README.md` file:\r\n\r\nHi @albertvillanova, thank you for the info! My apologies for the messy PR.\r\n" ]

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[ "I created a GitHub project to keep track of what needs to be done:\r\nhttps://github.com/huggingface/datasets/projects/3\r\n\r\nI also store my code in a (private for now) repository at https://github.com/huggingface/mirror_canonical_datasets_on_hub" ]

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[ "This might be related to https://github.com/tensorflow/tensorflow/issues/38762 , what do you think @Rocketknight1 ?\r\n> Dataset.from_generator is expected to not work with TPUs as it uses py_function underneath which is incompatible with Cloud TPU 2VM setup. If you would like to read from large datasets, maybe try to materialize it on disk and use TFRecordDataest instead.", "Hi @lhoestq @nbroad1881, I think it's very similar, yes. Unfortunately `to_tf_dataset` uses `tf.numpy_function` which can't be compiled - this is a necessary evil to load from the underlying Arrow dataset. We need to update the notebooks/examples to clarify that this won't work, or to identify a workaround. You may be able to get it to work on an actual cloud TPU VM, but those are quite new and we haven't tested it yet. ", "Thank you for the explanation. I didn't realize the nuances of `tf.numpy_function`. In this scenario, would it be better to use `export(format='tfrecord')` ? It's not quite the same, but for very large datasets that don't fit in memory it looks like it is the only option. I haven't used `export` before, but I do recall reading that there are suggestions for how big and how many tfrecords there should be to not bottleneck the TPU. It might be nice if there were a way for the `export` method to split the files up into appropriate chunk sizes depending on the size of the dataset and the number of devices. And if that is too much, it would be nice to be able to specify the number of files that would be created when using `export`. Well... maybe the user should just do the chunking themselves and call `export` a bunch of times. Whatever the case, you have been helpful. Thanks Tensorflow boy ;-) ", "Yeah, this is something we really should have a proper guide on. I'll make a note to test some things and make a 'TF TPU best practices' notebook at some point, but in the meantime I think your solution of exporting TFRecords will probably work. ", "Also: I knew that tweet would haunt me" ]

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[ "I manage to remove the mentions of ellipsis in the code by launching the command as follows:\r\n\r\n```\r\npython -m doctest -v docs/source/load_hub.rst -o=ELLIPSIS\r\n```\r\n\r\nThe way you put your ellipsis will only work on mac, I've adapted it for linux as well with the following:\r\n\r\n```diff\r\n >>> from datasets import load_dataset_builder\r\n >>> dataset_builder = load_dataset_builder('imdb')\r\n- >>> print(dataset_builder.cache_dir) #doctest: +ELLIPSIS\r\n- /Users/.../.cache/huggingface/datasets/imdb/plain_text/1.0.0/...\r\n+ >>> print(dataset_builder.cache_dir)\r\n+ /.../.cache/huggingface/datasets/imdb/plain_text/1.0.0/...\r\n```\r\n\r\nThis passes on my machine:\r\n\r\n```\r\nTrying:\r\n print(dataset_builder.cache_dir)\r\nExpecting:\r\n /.../.cache/huggingface/datasets/imdb/plain_text/1.0.0/...\r\nok\r\n```\r\n\r\nI'm getting a last error:\r\n\r\n```py\r\nExpected:\r\n DatasetDict({\r\n train: Dataset({\r\n features: ['sentence1', 'sentence2', 'label', 'idx'],\r\n num_rows: 3668\r\n })\r\n validation: Dataset({\r\n features: ['sentence1', 'sentence2', 'label', 'idx'],\r\n num_rows: 408\r\n })\r\n test: Dataset({\r\n features: ['sentence1', 'sentence2', 'label', 'idx'],\r\n num_rows: 1725\r\n })\r\n })\r\nGot:\r\n DatasetDict({\r\n train: Dataset({\r\n features: ['idx', 'label', 'sentence1', 'sentence2'],\r\n num_rows: 3668\r\n })\r\n validation: Dataset({\r\n features: ['idx', 'label', 'sentence1', 'sentence2'],\r\n num_rows: 408\r\n })\r\n test: Dataset({\r\n features: ['idx', 'label', 'sentence1', 'sentence2'],\r\n num_rows: 1725\r\n })\r\n })\r\n```\r\n\r\nBut this is due to `doctest` looking for an exact match and the list having an unordered print order. I wish `doctest` would be a bit more flexible with that." ]

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[ "Hi ! Thanks for the suggestion, I didn't know about this decorator.\r\n\r\nIf you are interesting in contributing, feel free to open a pull request to add the overload methods for each typing combination :) To assign you to this issue, you can comment `#self-assign` in this thread.\r\n\r\n`Dataset.__getitem__` is defined right here: https://github.com/huggingface/datasets/blob/e6f1352fe19679de897f3d962e616936a17094f5/src/datasets/arrow_dataset.py#L1840", "#self-assign" ]

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[ "@anton-l ping", "@lhoestq \r\nHi Quentin! Thank you for your feedback and suggestions! 🤗\r\n\r\nYes, that was actually what I wanted to do next - I mean the steaming stuff :)\r\nAlso, I need to make some changes to the readme (to account for the updated features set).\r\n\r\nHopefully, I will be done by tomorrow afternoon if that's ok. \r\n", "@lhoestq Hi Quentin!\r\n\r\nI've implemented (hopefully, correctly) the streaming compatibility but the problem with the current approach is that we first need to iterate over the full archive anyway to get the list of filenames for train and validation sets (see [this](https://github.com/huggingface/datasets/pull/3335/files#diff-aeea540d136025e30a842856779e9c6485a5dc6fc9eb7fd6d3be2acd2f49b8e3R186), the same approach is implemented in TFDS version). Only after that, we can generate examples, so we cannot stream the dataset before the first iteration ends and it takes some time. It's probably not the most effective way. \r\n\r\nIf the streaming mode is turned off, this approach (with two iterations) is actually slower than the previous implementation (with archive extraction). \r\n\r\nMy suggestion is to host separate archives for each split prepared in advance. That way there would be no need for iterating over the common archive to collect train and validation filenames. @anton-l suggested to make AWS mirrors for them. I've prepared these archives, for now you can take a look at them [here](https://drive.google.com/drive/folders/1oMrZHzPgHAKprKJuvih91CM8KMSzh_pL?usp=sharing). I simplified their structure a bit so if we switch to using them, the code then should be changed (and simplified) a bit too.\r\n", "Hi ! Thanks for the changes :)\r\n\r\n> My suggestion is to host separate archives for each split prepared in advance. That way there would be no need for iterating over the common archive to collect train and validation filenames. @anton-l suggested to make AWS mirrors for them. I've prepared these archives, for now you can take a look at them here. I simplified their structure a bit so if we switch to using them, the code then should be changed (and simplified) a bit too.\r\n\r\nI agree, I just uploaded them on AWS\r\n\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.01/v0.01_test.tar.gz\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.01/v0.01_train.tar.gz\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.01/v0.01_validation.tar.gz\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.02/v0.02_test.tar.gz\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.02/v0.02_validation.tar.gz\r\n\r\nNote that in the future we can move those files to actual repositories on the Hugging Face Hub, since we are migrating the datasets from this repository to the Hugging Face Hub (as mirrors), to make them more accessible to the community.", "@lhoestq Thank you! Gonna look at this tomorrow :)", "@lhoestq I've modified the code to fit new data format, now it works for v0.01 but doesn't work for v0.02 as the training archive is missing. Could you please create a mirror for that one too? You can find it [here](https://drive.google.com/file/d/1mPjnVMYb-VhPprGlOX8v9TBT1GT-rtcp/view?usp=sharing)\r\n\r\nAnd when it's done I'll need to regenerate all the meta / dummy stuff, and this version will be ready for a review :)", "Here you go :)\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.02/v0.02_train.tar.gz", "FYI I juste merged a fix for the Windows CI error on `master`, feel free to merge `master` again into your branch", "All green ! I had to fix some minor stuff in the CI but it's good now\r\n\r\nNext step is to mark it as ready for review, and I think it's all good so we can merge 🚀 ", "@lhoestq 🤗", ":tada: " ]

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[ "If possible, a neat API could be something like `Dataset.to_polars()`, as well as `Dataset.set_format(\"polars\")`", "Note they use a \"custom\" implementation of Arrow: [Arrow2](https://github.com/jorgecarleitao/arrow2)." ]

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[ "Hi ! The message you're getting is not an error. It simply says that your JSON dataset is being prepared to a location in `/root/.cache/huggingface/datasets`", "> \r\n\r\nbut I want to load local JSON file by command\r\n`python3 run.py --do_train --task qa --dataset squad-retrain-data/train-v2.0.json --output_dir ./re_trained_model/`\r\n\r\n**squad-retrain-data/train-v2.0.json** is the local JSON file, how to load it and map it to a special structure?", "You can load it with `dataset = datasets.load_dataset('json', data_files=args.dataset)` as you said.\r\nThen if you need to apply additional processing to map it to a special structure, you can use rename columns or use `dataset.map`. For more information, you can check the documentation here: https://huggingface.co/docs/datasets/process.html\r\n\r\nAlso feel free to share your `run.py` code so we can take a look", "```\r\n# Dataset selection\r\n if args.dataset.endswith('.json') or args.dataset.endswith('.jsonl'):\r\n dataset_id = None\r\n # Load from local json/jsonl file\r\n dataset = datasets.load_dataset('json', data_files=args.dataset)\r\n # By default, the \"json\" dataset loader places all examples in the train split,\r\n # so if we want to use a jsonl file for evaluation we need to get the \"train\" split\r\n # from the loaded dataset\r\n eval_split = 'train'\r\n else:\r\n default_datasets = {'qa': ('squad',), 'nli': ('snli',)}\r\n dataset_id = tuple(args.dataset.split(':')) if args.dataset is not None else \\\r\n default_datasets[args.task]\r\n # MNLI has two validation splits (one with matched domains and one with mismatched domains). Most datasets just have one \"validation\" split\r\n eval_split = 'validation_matched' if dataset_id == ('glue', 'mnli') else 'validation'\r\n # Load the raw data\r\n dataset = datasets.load_dataset(*dataset_id)\r\n```\r\n\r\nI want to load JSON squad dataset instead `dataset = datasets.load_dataset('squad')` to retrain the model. \r\n", "If your JSON has the same format as the SQuAD dataset, then you need to pass `field=\"data\"` to `load_dataset`, since the SQuAD format is one big JSON object in which the \"data\" field contains the list of questions and answers.\r\n```python\r\ndataset = datasets.load_dataset('json', data_files=args.dataset, field=\"data\")\r\n```\r\n\r\nLet me know if that helps :)\r\n\r\n", "Yes, code works. but the format is not as expected.\r\n```\r\ndataset = datasets.load_dataset('json', data_files=args.dataset, field=\"data\")\r\n```\r\n```\r\npython3 run.py --do_train --task qa --dataset squad --output_dir ./re_trained_model/\r\n```\r\n************ train_dataset: Dataset({\r\n features: ['id', 'title', 'context', 'question', 'answers'],\r\n num_rows: 87599\r\n})\r\n\r\n\r\n```\r\npython3 run.py --do_train --task qa --dataset squad-retrain-data/train-v2.0.json --output_dir ./re_trained_model/\r\n```\r\n************ train_dataset: Dataset({\r\n features: ['title', 'paragraphs'],\r\n num_rows: 442\r\n})\r\n\r\nI want the JSON to have the same format as before features. https://github.com/huggingface/datasets/blob/master/datasets/squad_v2/squad_v2.py is the script dealing with **squad** but how can I apply it by using JSON? ", "Ok I see, you have the paragraphs so you just need to process them to extract the questions and answers. I think you can process the SQuAD-like data this way:\r\n```python\r\ndef process_squad(articles):\r\n out = {\r\n \"title\": [],\r\n \"context\": [],\r\n \"question\": [],\r\n \"id\": [],\r\n \"answers\": [],\r\n }\r\n for title, paragraphs in zip(articles[\"title\"], articles[\"paragraphs\"]):\r\n for paragraph in paragraphs:\r\n for qa in paragraph[\"qas\"]:\r\n out[\"title\"].append(title)\r\n out[\"context\"].append(paragraph[\"context\"])\r\n out[\"question\"].append(qa[\"question\"])\r\n out[\"id\"].append(qa[\"id\"])\r\n out[\"answers\"].append({\r\n \"answer_start\": [answer[\"answer_start\"] for answer in qa[\"answers\"]],\r\n \"text\": [answer[\"text\"] for answer in qa[\"answers\"]],\r\n })\r\n return out\r\n\r\ndataset = dataset.map(process_squad, batched=True, remove_columns=[\"paragraphs\"])\r\n```\r\n\r\nI adapted the code from [squad.py](https://github.com/huggingface/datasets/blob/master/datasets/squad/squad.py). The code takes as input a batch of articles (title + paragraphs) and gets all the questions and answers from the JSON structure.\r\n\r\nThe output is a dataset with `features: ['answers', 'context', 'id', 'question', 'title']`\r\n\r\nLet me know if that helps !\r\n", "Yes, this works. But how to get the training output during training the squad by **Trainer** \r\nfor example https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/trainer_qa.py \r\nI want the training inputs, labels, outputs for every epoch and step to produce the training dynamic graph", "I think you may need to implement your own Trainer, from the `QuestionAnsweringTrainer` for example.\r\nThis way you can have the flexibility of saving all the inputs/output used at each step", "does there have any function to be overwritten to do this?", "> does there have any function to be overwritten to do this?\r\n\r\nok, I overwrote the compute_loss, thank you.", "Hi, I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs\r\n\r\n```\r\n*********************** inputs: {'attention_mask': tensor([[1, 1, 1, ..., 0, 0, 0],\r\n [1, 1, 1, ..., 0, 0, 0],\r\n [1, 1, 1, ..., 0, 0, 0],\r\n ...,\r\n [1, 1, 1, ..., 0, 0, 0],\r\n [1, 1, 1, ..., 0, 0, 0],\r\n [1, 1, 1, ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25, 97, 93, 44, 25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390, ..., 0, 0, 0],\r\n [ 101, 2054, 2515, ..., 0, 0, 0],\r\n [ 101, 2054, 2106, ..., 0, 0, 0],\r\n ...,\r\n [ 101, 2339, 2001, ..., 0, 0, 0],\r\n [ 101, 2054, 2515, ..., 0, 0, 0],\r\n [ 101, 2054, 2003, ..., 0, 0, 0]], device='cuda:0'), 'start_positions': tensor([ 20, 90, 89, 41, 25, 96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0, ..., 0, 0, 0],\r\n [0, 0, 0, ..., 0, 0, 0],\r\n [0, 0, 0, ..., 0, 0, 0],\r\n ...,\r\n [0, 0, 0, ..., 0, 0, 0],\r\n [0, 0, 0, ..., 0, 0, 0],\r\n [0, 0, 0, ..., 0, 0, 0]], device='cuda:0')} \r\n```\r\n\r\n```\r\n# This function preprocesses a question answering dataset, tokenizing the question and context text\r\n# and finding the right offsets for the answer spans in the tokenized context (to use as labels).\r\n# Adapted from https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py\r\ndef prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):\r\n questions = [q.lstrip() for q in examples[\"question\"]]\r\n max_seq_length = tokenizer.model_max_length\r\n # tokenize both questions and the corresponding context\r\n # if the context length is longer than max_length, we split it to several\r\n # chunks of max_length\r\n tokenized_examples = tokenizer(\r\n questions,\r\n examples[\"context\"],\r\n truncation=\"only_second\",\r\n max_length=max_seq_length,\r\n stride=min(max_seq_length // 2, 128),\r\n return_overflowing_tokens=True,\r\n return_offsets_mapping=True,\r\n padding=\"max_length\"\r\n )\r\n\r\n # Since one example might give us several features if it has a long context,\r\n # we need a map from a feature to its corresponding example.\r\n sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n # The offset mappings will give us a map from token to character position\r\n # in the original context. This will help us compute the start_positions\r\n # and end_positions to get the final answer string.\r\n offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n tokenized_examples[\"start_positions\"] = []\r\n tokenized_examples[\"end_positions\"] = []\r\n\r\n tokenized_examples[\"example_id\"] = []\r\n\r\n for i, offsets in enumerate(offset_mapping):\r\n input_ids = tokenized_examples[\"input_ids\"][i]\r\n # We will label features not containing the answer the index of the CLS token.\r\n cls_index = input_ids.index(tokenizer.cls_token_id)\r\n sequence_ids = tokenized_examples.sequence_ids(i)\r\n # from the feature idx to sample idx\r\n sample_index = sample_mapping[i]\r\n # get the answer for a feature\r\n answers = examples[\"answers\"][sample_index]\r\n\r\n tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n if len(answers[\"answer_start\"]) == 0:\r\n tokenized_examples[\"start_positions\"].append(cls_index)\r\n tokenized_examples[\"end_positions\"].append(cls_index)\r\n else:\r\n # Start/end character index of the answer in the text.\r\n start_char = answers[\"answer_start\"][0]\r\n end_char = start_char + len(answers[\"text\"][0])\r\n\r\n # Start token index of the current span in the text.\r\n token_start_index = 0\r\n while sequence_ids[token_start_index] != 1:\r\n token_start_index += 1\r\n\r\n # End token index of the current span in the text.\r\n token_end_index = len(input_ids) - 1\r\n while sequence_ids[token_end_index] != 1:\r\n token_end_index -= 1\r\n\r\n # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n if not (offsets[token_start_index][0] <= start_char and\r\n offsets[token_end_index][1] >= end_char):\r\n tokenized_examples[\"start_positions\"].append(cls_index)\r\n tokenized_examples[\"end_positions\"].append(cls_index)\r\n else:\r\n # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n # Note: we could go after the last offset if the answer is the last word (edge case).\r\n while token_start_index < len(offsets) and \\\r\n offsets[token_start_index][0] <= start_char:\r\n token_start_index += 1\r\n tokenized_examples[\"start_positions\"].append(\r\n token_start_index - 1)\r\n while offsets[token_end_index][1] >= end_char:\r\n token_end_index -= 1\r\n tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n return tokenized_examples\r\n```" ]

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[ "Hi,\r\n\r\nthe fix was merged and will be available in the next release of `datasets`.\r\nIn the meantime, you can use it by installing `datasets` directly from master as follows:\r\n```\r\npip install git+https://github.com/huggingface/datasets.git\r\n```" ]

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[ "Hi, thanks for reporting.\r\n\r\nIt would be really helpful if you could provide the actual code of the `text_numbers_to_int` function so we can reproduce the error.", "```\r\ndef text_numbers_to_int(text, column=\"\"):\r\n \"\"\"\r\n Convert text numbers to int.\r\n\r\n :param text: text numbers\r\n :return: int\r\n \"\"\"\r\n try:\r\n numbers = find_numbers(text)\r\n if not numbers:\r\n return text\r\n result = \"\"\r\n i, j = 0, 0\r\n while i < len(text):\r\n if j < len(numbers) and i == numbers[j][1]:\r\n n = int(numbers[j][0]) if numbers[j][0] % 1 == 0 else float(numbers[j][0])\r\n result += str(n)\r\n i = numbers[j][2] #end\r\n j += 1\r\n else:\r\n result += text[i]\r\n i += 1\r\n if column:\r\n return{column: result}\r\n else:\r\n return {column: result}\r\n except Exception as e:\r\n print(e)\r\n return {column: result}\r\n```", "Maybe this is because of the `return text` line ? I think it should return a dictionary rather than a string", "Yes that was it, good catch! Thanks" ]

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[ "#3323 " ]

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[ "Closes #3327 " ]

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[ "<s>To be more precise: `os.path.join` is replaced on-the-fly by `xjoin` anyway with patching, to extend it to remote files</s>", "Oh actually just ignore what I said: they were used to concatenate URLs, which is not recommended. Let me fix that again by appending using `+`" ]

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[ "Looks good to me! :)\r\n\r\nMaybe we can mention that users can also set the `private` argument if they want to keep their dataset private? It would lead nicely into the next section on Privacy.", "Thanks for your comments, I fixed the capitalization for consistency and added an passage to mention the `private` parameter and to have a nice transition to the Privacy section :)\r\n\r\nI also added the login instruction that was missing before the user can actually upload a dataset." ]

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[ "Hi,\r\n\r\n`Dataset.map` allows more generic transforms compared to `Dataset.filter`, which purpose is very specific (to filter examples based on a condition). That's why I don't think we need the `desc` parameter there for consistency. #3196 has added descriptions to the `Dataset` methods that call `.map` internally, but not for the `filter` method, so we should do that.\r\n\r\nDo you have a description in mind? Maybe `\"Filtering the dataset\"` or `\"Filtering the indices\"`? If yes, feel free to open a PR.", "I'm personally ok with adding the `desc` parameter actually. Let's say you have different filters, it can be nice to differentiate between the different filters when they're running no ?", "@mariosasko the use case is filtering of a dataset prior to tokenization and subsequent training. As the dataset is huge it's just a matter of giving a user (model trainer) some feedback on what's going on. Otherwise, feedback is given for all steps in training preparation and not for filtering and the filtering in my use case lasts about 4-5 minutes. And yes, if there are more filtering stages, as @lhoestq pointed out, it would be nice to give some feedback. I thought desc is there already and got confused when I got the script error. ", "I don't have a strong opinion on that, so having `desc` as a parameter is also OK." ]

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[ "IMO it makes more sense to have `ignore_url_params` as an attribute of `DownloadConfig` to avoid defining a new argument in `DownloadManger`'s methods.", "@mariosasko that would make sense to me too, but it seems like `DownloadConfig` wasn't intended to be modified from a dataset loading script. @lhoestq wdyt?", "We can expose `DownloadConfig` as a property of `DownloadManager`, and then in the script before the download call we could do: `dl_manager.download_config.ignore_url_params = True`. But yes, let's hear what Quentin thinks.", "Oh indeed that's a great idea. This parameter is similar to others like `download_config.use_etag` that defines the behavior of the download and caching, so it's better if we have it there, and expose the `download_config`", "Implemented it via `dl_manager.download_config.ignore_url_params` now, and also added a usage example above :) " ]

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[ "Some commits seem to be there twice (made the mistake of rebasing because I wasn't sure whether the doc had changed), is this an issue @lhoestq ?" ]

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[ "Hi ! You're completely right, this must be mentioned in the dataset card.\r\nIf you're interesting in contributing, feel free to open a pull request to mention this in the `trivia_qa` dataset card in the \"Licensing Information\" section at https://github.com/huggingface/datasets/blob/master/datasets/trivia_qa/README.md" ]

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[ "To fix the CI, feel free to run the `make style` command to format the code.\r\n\r\nThen it also looks like the dummy_data.zip archives are all empty, which makes the tests fail. Can you try regenerating them ? They should have one file inside which is a dummy version of the file at https://bl.iro.bl.uk/downloads/36c7cd20-c8a7-4495-acbe-469b9132c6b1?locale=en", "@lhoestq, thanks for that feedback. \r\n\r\nI should have made most of these changes now. The `--auto_generate` flag wasn't working because the file wasn't downloaded with a `.csv` extension. I used `--match_text_files \"*\"` to get around this. Because there is a lot of data that isn't annotated using the default line number for the dummy data causes the `annotated_raw` and the `title_genre_classifiction` configs to fail because they don't generate any examples — bumping the line numbers to `250` fixes this. This does make the dummy data a bit bigger, though. \r\n\r\nThe total directory size for the dataset is now `150kb`. Is this okay, or do you want me to generate the dummy data manually instead? ", "Hi ! yes 150kB is fine :)\r\nFeel free to push your new dummy_data.zip files (I think the current one are still the empty ones)", "@lhoestq I've pushed those dummy files now and added your other suggestions.", "The CI failure is unrelated to this PR, merging :)", "@lhoestq, thanks for all your help with this pull request 😀" ]

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[ "Hi ! Are you having this issue only with this specific dataset, or it also happens with other ones like `squad` ?", "@lhoestq It happens also on `squad`. It successfully downloads the whole dataset and then crashes on: \r\n\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\nI tested it on Ubuntu and its working OK. Didn't test on non-preview version of Windows 11, `Windows-10-10.0.22504-SP0` is a preview version, not sure if this is causing it.", "I see the same error in Windows-10.0.19042 as of a few days ago:\r\n\r\n`Fatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS`\r\n\r\npython 3.8.12 h7840368_2_cpython conda-forge\r\nboto3 1.20.11 pyhd8ed1ab_0 conda-forge\r\nbotocore 1.23.11 pyhd8ed1ab_0 conda-forge\r\n\r\n...but I am not using `datasets` (although I might take a look now that I know about it!)\r\n\r\nThe error has occurred a few times over the last two days, but not consistently enough for me to get it with DEBUG. If there is any interest I can report back here, but it seems not unique to `datasets`.", "I'm not sure what `datasets` has to do with a crash that seems related to `aws-c-io`, could it be an issue with your environment ?", "> I'm not sure what `datasets` has to do with a crash that seems related to `aws-c-io`, could it be an issue with your environment ?\r\n\r\nAgreed, this issue is not likely a bug in datasets, since I get the identical error without datasets installed.", "Will close this issue. Bug in `aws-c-io` shouldn't be in `datasets` repo. Nevertheless, it can be useful to know that it happens. Thanks @leehaust @lhoestq ", "I have also had this issue since a few days, when running scripts using PyCharm in particular, but it does not seem to affect the script from running, only reporting this error at the end of the run.", "I also get this issue, It appears after my script has finished running. I get the following error message\r\n```\r\nFatal error condition occurred in /home/conda/feedstock_root/build_artifacts/aws-c-io_1637179816120/work/source/event_loop.c:72: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n################################################################################\r\nStack trace:\r\n################################################################################\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_backtrace_print+0x59) [0x2aabe0479579]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_fatal_assert+0x48) [0x2aabe04696c8]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././././libaws-c-io.so.1.0.0(+0x13ad3) [0x2aabe0624ad3]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././././libaws-c-io.so.1.0.0(+0x113ca) [0x2aabe06223ca]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-crt-cpp.so(_ZN3Aws3Crt2Io15ClientBootstrapD1Ev+0x3a) [0x2aabe041cf5a]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././libaws-cpp-sdk-core.so(+0x5f570) [0x2aabe00eb570]\r\n/lib64/libc.so.6(+0x39ce9) [0x2aaaab835ce9]\r\n/lib64/libc.so.6(+0x39d37) [0x2aaaab835d37]\r\n/lib64/libc.so.6(__libc_start_main+0xfc) [0x2aaaab81e55c]\r\npython(+0x1c721d) [0x55555571b21d]\r\nAborted\r\n```\r\nI don't get this issue when running my code in a container, and it seems more relevant to PyArrow but thought a more complete stack trace might be helpful to someone\r\n", "I created an issue on JIRA:\r\nhttps://issues.apache.org/jira/browse/ARROW-15141", "@CallumMcMahon Do you have a small reproducer for this problem on Linux? I can reproduce this on Windows but sadly not with linux." ]

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[ "I see it creates some errors in the tests.\r\n\r\nAnother solution if needed is to add something like `data_files = list(set(data_files))` after [this line](https://github.com/huggingface/datasets/blob/8555197a3fe826e98bd0206c2d031c4488c53c5c/src/datasets/data_files.py#L511)", "Hi ! Thanks for the correction :)\r\n\r\nYour change seems right, let me look at the errors and try to fix this", "Not sure if it's due to this change but I tested `load_dataset('dalle-mini/encoded-vqgan_imagenet_f16_16384', streaming=True)` and the `validation` set is empty.", "So indeed there was an issue with the patterns `*` and `**/*` that would return some files twice. This issue came from the fact that we were not using the right `glob`.\r\n\r\nIndeed we were using `Path.rglob` for local files and `Path.match` for remote files. Since these two methods don't have the same behavior for such patterns, I decided to change that.\r\n\r\nIn particular, we now use `glob.glob` (same as `fsspec` glob) as a reference for data files resolution from patterns. This is the same as dask for example.\r\n\r\n/!\\ Here are some behaviors specific to `glob.glob` that are different from Path.glob, Path.match or fnmatch:\r\n- '*' matches only first level files\r\n- '**/*' matches only at least second level files\r\n\r\nThis way we have a consistent behavior with respect to other python data libraries and there's no overlap anymore between the two patterns.\r\n\r\nSome implementations details:\r\n\r\nTo ensure that we have the same behavior for local files and for files in a remote dataset repository, I decided to use `fsspec` glob for both. This was made possible by implementing the `HfFileSystem` class as a `fsspec` filesystem.\r\n\r\nI pushed those changes directly to your PR - I hope you don't mind. I'm still fixing the remaining tests.\r\nPlease let me know if that solves your problem, and then we can merge !", "There's still an issue with fsspec's glob - I'll take a look this afternoon", "I just found out that actually glob.glob and fsspec glob are different haha\r\nglob.glob needs `**/*` and recursive=True to look into deep subdirectories, while fsspec only requires `**`\r\n\r\nI think we can go with fsspec glob for consistency with dask and since it's our main tool for filesystems management", "To recap:\r\n```\r\nWe use fsspec glob as a reference for data files resolution from patterns.\r\nThis is the same as dask for example.\r\n\r\n/!\\ Here are some behaviors specific to fsspec glob that are different from glob.glob, Path.glob, Path.match or fnmatch:\r\n- '*' matches only first level items\r\n- '**' matches all items\r\n- '**/*' matches all at least second level items\r\n\r\nMore generally:\r\n- `*`` matches any character except a forward-slash (to match just the file or directory name)\r\n- `**`` matches any character including a forward-slash /\r\n```", "lol Windows… Maybe `Pathlib` for the tests?\r\n\r\nI tested streaming a repo and it worked perfectly now!" ]

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[ "Hi ! Thanks for reporting :) \r\nWe can easily add the metadata for `chr_en` IMO, but for mC4 it will take more time, since it requires to count the number of examples in each language", "No problem. I am trying to do some analysis on the metadata of all available datasets. Is reading `metadata_infos.json` for each dataset the correct way to go? \r\n\r\nI noticed that the same information is also available as special variables inside .py file of each dataset. So, I was wondering if `metadata_infos.json` has been deprecated?\r\n\r\n\r\n", "The `dataset_infos.json` files have more information and are made to be used to analyze the datasets without having to run/parse the python scripts. Moreover some datasets on the Hugging face don't even have a python script, and for those ones we'll make tools to generate the JSON file automatically :)" ]

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[ "@lhoestq thanks for the review! I've modified the labels to follow other NLI datasets.\r\nPlease review my change and let me know if I miss anything." ]

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[ "knock knock", "Hi, thanks for reporting! I've linked a PR that should fix the issue.", "I've checked the PR and it looks great, thanks a lot!" ]

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[ "The issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.\r\n\r\n```\r\n# upgrade transformers and datasets to latest versions\r\n!pip install --upgrade transformers\r\n!pip install --upgrade datasets\r\n```\r\n\r\nRegards!" ]

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[ "\r\n> \r\n> Input:\r\n> \r\n> ```\r\n> tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n> data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\r\n> ```\r\n> \r\n> Output:\r\n> \r\n> ```\r\n> TypeError Traceback (most recent call last)\r\n> /tmp/ipykernel_42/1563280798.py in <module>\r\n> 1 checkpoint = 'bert-base-uncased'\r\n> 2 tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n> ----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"pt\")\r\n> TypeError: __init__() got an unexpected keyword argument 'return_tensors'\r\n> ```\r\n> \r\n\r\nThe issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.\r\n\r\n`# upgrade transformers and datasets to latest versions`\r\n`!pip install --upgrade transformers`\r\n`!pip install --upgrade datasets`\r\n\r\nCheers!" ]

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[ "Hi ! In the next version of `datasets`, your train and test splits will be correctly separated (changes from #3027) if you create a dataset repository with only your CSV files.\r\n\r\nAlso it seems that you overwrite the `data_files` and `data_dir` arguments in your code, when you instantiate the AGNewsConfig objects. Those parameters are not necessary since you already know which files you want to load.\r\n\r\nYou can find an example on how to specify which file the dataset has to download in this [example script](https://huggingface.co/datasets/lhoestq/custom_squad/blob/main/custom_squad.py#L101-L107):\r\n```python\r\n_URLS = {\r\n \"train\": \"train-v1.1.json\", # you can use a URL or a relative path from the python script to your file in the repository\r\n \"dev\": \"dev-v1.1.json\",\r\n}\r\n```\r\n```python\r\n def _split_generators(self, dl_manager):\r\n downloaded_files = dl_manager.download_and_extract(_URLS)\r\n\r\n return [\r\n datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": downloaded_files[\"train\"]}),\r\n datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={\"filepath\": downloaded_files[\"dev\"]}),\r\n ]\r\n```", "Also I think the viewer will be updated when you fix the dataset script, let me know if it doesn't", "Hi @lhoestq,\r\n\r\nThanks a lot for the super quick answer!\r\n\r\nYour suggestion solves my issue. I am now able to load the dataset properly 🚀 \r\nHowever, the dataviewer is not working yet.\r\n\r\nReally, thanks a lot for your help and consideration!\r\n\r\nBest,\r\nPietro", "Great ! We'll take a look at the viewer to fix it", "@lhoestq I think I am having a related problem.\r\nMy call to load_dataset() looks like this:\r\n\r\n```\r\n datasets = load_dataset(\r\n os.path.abspath(layoutlmft.data.datasets.xfun.__file__),\r\n f\"xfun.{data_args.lang}\",\r\n additional_langs=data_args.additional_langs,\r\n keep_in_memory=True,\r\n )\r\n\r\n```\r\n\r\nMy _split_generation code is:\r\n\r\n```\r\n def _split_generators(self, dl_manager):\r\n \"\"\"Returns SplitGenerators.\"\"\"\r\n\r\n downloaded_file = dl_manager.download_and_extract(\"https://guillaumejaume.github.io/FUNSD/dataset.zip\")\r\n return [\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": f\"{downloaded_file}/dataset/training_data/\"}\r\n ),\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TEST, gen_kwargs={\"filepath\": f\"{downloaded_file}/dataset/testing_data/\"}\r\n ),\r\n ]\r\n\r\n```\r\nHowever I get the error \"TypeError: _generate_examples() got an unexpected keyword argument 'filepath'\"\r\nThe path looks right and I see the data in the path so I think the only problem I have is that it doesn't like the key \"filepath\". However, the documentation (example [here](https://huggingface.co/datasets/lhoestq/custom_squad/blob/main/custom_squad.py#L101-L107)) seems to show that this is the correct parameter. \r\n\r\nHere is the full stack trace:\r\n\r\n```\r\nDownloading and preparing dataset xfun/xfun.en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/caseygre/.cache/huggingface/datasets/xfun/xfun.en/0.0.0/96b8cb7c57f6f822f0ab37ae3be7b82d84ac57062e774c9361ccf0a4b9ef61cc...\r\nTraceback (most recent call last):\r\n File \"/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py\", line 574, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py\", line 652, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py\", line 975, in _prepare_split\r\n generator = self._generate_examples(**split_generator.gen_kwargs)\r\nTypeError: _generate_examples() got an unexpected keyword argument 'filepath'\r\npython-BaseException\r\n```", "Hi ! The `gen_kwargs` dictionary is passed to `_generate_examples`, so in your case it must be defined this way:\r\n```python\r\ndef _generate_examples(self, filepath):\r\n ...\r\n```\r\n\r\nAnd here is an additional tip: you can use `os.path.join(downloaded_file, \"dataset/testing_data\")` instead of `f\"downloaded_file}/dataset/testing_data/\"` to get compatibility with Windows and streaming.\r\n\r\nIndeed Windows uses a backslash separator, not a slash, and streaming uses chained URLs (like `zip://dataset/testing_data::https://https://guillaumejaume.github.io/FUNSD/dataset.zip` for example)", "Thanks for you quick reply @lhoestq and so sorry for my very delayed response.\r\nWe have gotten around the error another way but I will try to duplicate this when I can. We may have had \"filepaths\" instead of \"filepath\" in our def of _generate_examples() and not noticed the difference. If I find a more useful answer for others I will add to this ticket so they know what the issue was.\r\nNote: we do have our own _generate_examples() defined with the same def as Quentin has. (But one version does have \"filepaths\".)\r\n", "Fixed in the viewer: https://huggingface.co/datasets/pietrolesci/ag_news" ]

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[ "Hi ! Thanks for reporting\r\nWe've already fixed the code that generates the preview for this dataset, we'll release the fix soon :)", "Hi @lhoestq, thanks for your feedback!", "Fixed in the viewer.\r\n\r\nhttps://huggingface.co/datasets/ag_news" ]

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[ "Hi ! Thanks for reporting. Indeed this is a current limitation of the usage we have of `dill` in `datasets`. I'd suggest you use your workaround for now until we find a way to fix this. Maybe functions that are not coming from a module not installed with pip should be dumped completely, rather than only taking their locations into account", "I agree. Sounds like a solution for it would be pretty dirty, even [cloudpickle](https://stackoverflow.com/a/16891169) doesn't help in this case.\r\nIn the meanwhile I think that adding a warning and the workaround somewhere in the documentation can be helpful." ]

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[ "Hi ! Thanks for the fix :) \r\n\r\nI think this should be `extract_path_from_uri` 's responsibility to strip the extra `/` from a badly formatted path like `hdfs:///absolute/path` (or raise an error). Do you think you can simply do the changes in `extract_path_from_uri` ? This way this fix will be available for all the other parts of the lib that need to extract the inner path from an URI of a remote filesystem\r\n\r\nThen we can also keep your test cases but simply apply them to `extract_path_from_uri` instead", "Hi @lhoestq! No problem! Thanks for your interest! :)\r\n\r\nI think stripping the 3rd `/` in `hdfs:///absolute/path` inside `extract_path_from_uri` is not the solution. When I provide `hdfs:///absolute/path` to `extract_path_from_uri` we want `/absolute/path` to be returned, as it does now (at least in the case of URIs with `hdfs` schemas, for `s3` is different as it should start with a bucket name).\r\n\r\nThe problem comes in line 1041 in the original code below:\r\n\r\nhttps://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1038-L1042\r\n\r\nLets assume the following parameters for line 1041 after `extract_path_from_uri` has removed the `hdfs` schema part and the `://` from `hdfs:///absolute/path`, and `get_temporary_cache_files_directory()` returns `/tmp/a1b2b3c4`, as it is shown below: \r\n\r\n```python\r\nsrc_dataset_path = '/absolute/path'\r\ntmp_dir = '/tmp/a1b2b3c4'\r\ndataset_path = Path(tmp_dir, src_dataset_path)\r\n```\r\n\r\nAfter passing those paths to the `Path` object, `dataset_path` contains only `/absolute/path`; that is, it has lost the temporary directory path. This is because, when two (or more) absolute paths are passed to the `Path` function, only the last one is taken. However, if the contents of those variables are:\r\n\r\n```python\r\nsrc_dataset_path = 'relative/path'\r\ntmp_dir = '/tmp/a1b2b3c4'\r\ndataset_path = Path(tmp_dir, src_dataset_path)\r\n```\r\n\r\nthen `dataset_path` contains `/tmp/a1b2b3c4/relative/path` as expected.\r\n\r\nAbsolute paths are allowed in hdfs URIs, so that's why I added the extra function `build_local_temp_path` in the PR; so in case the second argument is an absolute path, it still will create the correct absolute path by concatenating the temp dir and the path passed by converting it to a relative path (and it also works for windows paths too.) It also allows to add the tests, checking that the main combinations are ok.\r\n\r\nI've checked all the places where the result of `extract_path_from_uri` is used, and as far as I've seen this is the only place where it is concatenated with another possible absolute path, so no need to add `build_local_temp_path` anywhere else. \r\n" ]

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[ "Hi ! Good catch and thanks for opening a PR :)\r\n\r\nI just responded in your PR" ]

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[ "Hi ! Indeed it looks like the code snippet on the Hugging face Hub doesn't show the second parameter\r\n\r\n\r\n\r\nThanks for reporting, I'm taking a look\r\n" ]

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[ "Reading FLAC (for `librispeech_asr`) works OK for me (`soundfile` version: `0.10.3`):\r\n```python\r\nIn [2]: ds = load_dataset(\"datasets/librispeech_asr/librispeech_asr.py\", \"clean\", streaming=True, split=\"train.100\")\r\n\r\nIn [3]: item = next(iter(ds))\r\n\r\nIn [4]: item.keys()\r\nOut[4]: dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])\r\n\r\nIn [5]: item[\"file\"]\r\nOut[5]: '374-180298-0000.flac'\r\n\r\nIn [6]: item[\"audio\"].keys()\r\nOut[6]: dict_keys(['path', 'array', 'sampling_rate'])\r\n\r\nIn [7]: item[\"audio\"][\"sampling_rate\"]\r\nOut[7]: 16000\r\n\r\nIn [8]: item[\"audio\"][\"path\"]\r\nOut[8]: '374-180298-0000.flac'\r\n\r\nIn [9]: item[\"audio\"][\"array\"].shape\r\nOut[9]: (232480,)\r\n```", "Oh cool ! I think this might have come from an issue with my local `soundfile` installation then", "I'll do `multilingual_librispeech` in a separate PR since it requires the data to be in another format (in particular separate the train/dev/test splits in different files)" ]

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[ "Hi ! Thanks for reporting :)\r\nI think this is because the dataset is behind an access page. We can fix the dataset viewer\r\n\r\nIf you also have this error when you use the `datasets` library in python, you should probably pass `use_auth_token=True` to the `load_dataset()` function to use your account to access the dataset.", "Ah ok, I didn't realise about the login page. I'll try `use_auth_token=True` and see if that solves it.\r\n\r\nRegards!", "Hi, \r\n\r\nUsing `use_auth_token=True` and downloading the credentials with `huggingface-cli login` (stored in .huggingface/token) solved the issue.\r\n\r\nShould I leave the issue open until you fix the Dataset viewer issue?", "Cool ! Yes let's keep this issue open until the viewer is fixed - I'll close it when this is fixed. Thanks", "The error I get when trying to load OSCAR 21.09 is this\r\n```\r\nConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py\r\n```\r\n\r\nThe URL I get in the browser is this\r\n```\r\nhttps://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py\r\n```\r\n\r\nMaybe URL is the issue? (resolve vs blob)", "> The error I get when trying to load OSCAR 21.09 is this\r\n> \r\n> ```\r\n> ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py\r\n> ```\r\n> \r\n> The URL I get in the browser is this\r\n> \r\n> ```\r\n> https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py\r\n> ```\r\n> \r\n> Maybe URL is the issue? (resolve vs blob)\r\n\r\nYou need to download your login credentials. See `huggingface-cli login` documentation and when loading the dataset use `use_auth_token=True`:\r\n`\r\nload_dataset(corpus, language, split=None, use_auth_token=True, cache_dir=cache_folder)`" ]

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[ "Hello @lhoestq, ```make style``` is applied as asked. :)" ]

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[ "The CI fail are caused by some missing sections or tags, which is unrelated to this PR. Merging !" ]

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[ "Hello, is this issue open for any contributor ? can I work on it ?\r\n\r\n", "Hi ! Sure this is open for any contributor. If you're interested feel free to self-assign this issue to you by commenting `#self-assign`. Then if you have any question or if I can help, feel free to ping me.\r\n\r\nTo begin with, feel free to take a look at both implementations of `iter_archive` for local downloads and for data streaming:\r\n\r\nIn the `DownloadManager` for local dowloads:\r\nhttps://github.com/huggingface/datasets/blob/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f/src/datasets/utils/download_manager.py#L218-L242\r\n\r\nIn the `StreamingDownloadManager` to stream the content of the archive directly from the remote file:\r\nhttps://github.com/huggingface/datasets/blob/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f/src/datasets/utils/streaming_download_manager.py#L502-L526\r\n\r\nNotice the call to `xopen` that opens and streams a file given either an URL or a local path :)", "Okay thank you for the information. I will work on this :) ", "#self-assign" ]

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