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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi @osanseviero I would like to add this dataset.", "Hey @nandwalritik! Thanks for offering to help!\r\n\r\nThis dataset might be somewhat complex and I'm concerned about it being 65 TB, which would be quite expensive to host. @lhoestq @mariosasko I would love your input if you think it's worth adding this dataset.", "Thanks for proposing this interesting dataset, @osanseviero.\r\n\r\nPlease note that the data files are already hosted in a third-party server: e.g. the index of data files for entity \"6795937\" is at https://fb-baas-f32eacb9-8abb-11eb-b2b8-4857dd089e15.s3.amazonaws.com/MugsyDataRelease/v0.0/identities/6795937/index.html \r\n- audio.tar: https://fb-baas-f32eacb9-8abb-11eb-b2b8-4857dd089e15.s3.amazonaws.com/MugsyDataRelease/v0.0/identities/6795937/audio.tar\r\n- ...\r\n\r\nTherefore, in principle, we don't need to host them on our Hub: it would be enough to just implement a loading script in the corresponding Hub dataset repo, e.g. \"facebook/multiface\"..." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "IMO it would make more sense to add a \"Create image dataset\" page with two main sections - a no-code approach with `imagefolder` + metadata (preferred way), and with a loading script (advanced). It should be clear when to choose which. If we leave this as-is, the user who jumps straight to the Vision section could be under the impression that writing a loading script is the preferred way to share a vision dataset due to how this subsection starts:\r\n```\r\nWrite a dataset loading script to share a dataset.\r\n```\r\n \r\nAlso, I think a note explaining how to make a dataset gated/disable the viewer to hide the data would be beneficial (it's pretty common to require submitting a form to access a CV dataset).", "Great suggestion @mariosasko! I added your suggestions, let me know what you think. For gated dataset access, I just added a tip referring users to the relevant docs since it's more of a Hub feature than `datasets` feature.", "Thanks, looks much better now :). I would also move the sections explaining how to create an `imagefolder` for the specific task from the [loading page](https://raw.githubusercontent.com/huggingface/datasets/main/docs/source/image_load.mdx) to this one. IMO it makes more sense to have the basic info (imagefolder structure + `load_dataset` call) there + a link to this page for info on how to create an image folder dataset.", "Good idea! Moved everything about `imagefolder` + metadata to the create an image dataset section since the `load_dataset` call is the same for different computer vision tasks. ", "Thanks for all the feedbacks! 🥰\r\n\r\nWhat do you think about creating how to share an `ImageFolder` dataset in a separate PR? I think we should create a new section under `Vision` for how to share an image dataset.", "I love it thanks ! I think moving forward we can use CSV instead of JSON Lines in the docs ;)" ]

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[ "Thanks for reporting @conceptofmind.\r\n\r\nCould you please give details about your environment? \r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n```", "Hi @albertvillanova ,\r\n\r\nHere is the environment information:\r\n```\r\n- `datasets` version: 2.3.2\r\n- Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.12\r\n- PyArrow version: 7.0.0\r\n- Pandas version: 1.4.2\r\n```\r\nThanks,\r\n\r\nEnrico", "I think this issue is solved here https://discuss.huggingface.co/t/minhash-deduplication/19992/12?u=loubnabnl, this only happens for very large datasets we will update it in CodeParrot code", "Hi @loubnabnl,\r\n\r\nYes, the issue is solved in the discussion thread.\r\n\r\nI will close this issue.\r\n\r\nThank you again for all of your help.\r\n\r\nEnrico", "Thanks @loubnabnl for pointing out the solution to this issue." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Why don't we deprecate label renaming already instead ?", "I think it'll break a lot of workflows if we deprecate it now! There isn't really a non-deprecated workflow yet - once we've added the `auto_rename_labels` option, then we can have `prepare_tf_dataset` on the `transformers` side use that, and then we can consider setting the default option to `False`, or beginning to deprecate it somehow.", "I'm worried it's a bit of a waste of time to continue working on this behavior that shouldn't be here in the first place. Do you have a plan in mind ?", "@lhoestq Broadly! The plan is:\r\n\r\n1) Create the `auto_rename_labels` flag with this PR and skip label renaming if it isn't set. Leave it as `True` for backward compatibility.\r\n2) Add the label renaming logic to `model.prepare_tf_dataset` in `transformers`. That method calls `to_tf_dataset()` right now. Once the label renaming logic is moved there, `model.prepare_tf_dataset` will set `auto_rename_labels=False` when calling `to_tf_dataset()`, and do label renaming itself.\r\n\r\nAfter step 2, `auto_rename_labels` is now only necessary for backward compatibility when users use `to_tf_dataset` directly. I want to leave it alone for a while because the `model.prepare_tf_dataset` workflow is very new. However, once it is established, we can deprecate `auto_rename_labels` and then finally remove it from the `datasets` code and keep it in `transformers` where it belongs.", "I see ! Could it be possible to not add `auto_rename_labels` at all, since you want to remove it at the end ? Something roughly like this:\r\n1. show a warning in `to_tf_dataset` whevener a label is renamed automatically, saying that in the next major release this will be removed\r\n1. add the label renaming logic in `transformers` (to not have the warning)\r\n1. after some time, do a major release 3.0.0 and remove label renaming completely in `to_tf_dataset`\r\n\r\nWhat do you think ? cc @LysandreJik in case you have an opinion on this process.", "@lhoestq I think that plan is mostly good, but if we make the change to `datasets` first then all users will keep getting deprecation warnings until we update the method in `transformers` and release a new version. \r\n\r\nI think we can follow your plan, but make the change to `transformers` first and wait for a new release before changing `datasets` - that way there are no visible warnings or API changes for users using `prepare_tf_dataset`. It also gives us more time to update the docs and try to move people to `prepare_tf_dataset` so they aren't confused by this!", "Sounds good to me ! To summarize:\r\n1. add the label renaming logic in `transformers` + release\r\n1. show a warning in `to_tf_dataset` whevener a label is renamed automatically, saying that in the next major release this will be removed + minor release\r\n1. after some time, do a major release 3.0.0 and remove label renaming completely in `to_tf_dataset`", "Yep, that's the plan! ", "@lhoestq Are you okay with me merging this for now? ", "Can you remove `auto_rename_labels` ? I don't think it's a good idea to add it if the plan is to remove it later", "Right now, the `auto_rename_labels` behaviour happens in all cases! Making it an option is the first step in the process of disabling it (and moving the functionality to `transformers`) and then finally deprecating it." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4778). All of your documentation changes will be reflected on that endpoint.", "I would rather have a section in the docs that explains how to modify the script of an existing dataset (`inspect_dataset` + modification + `load_dataset`) instead of focusing on the GH datasets bundled with the source (only applicable for devs).", "Good idea! I went with @mariosasko's suggestion to use `inspect_dataset` instead of cloning a dataset repository since it's a good opportunity to show off more of the library's lesser-known functions if that's ok with everyone :)", "One advantage of cloning the repo is that it fetches potential data files referenced inside a script using relative paths, so if we decide to use `inspect_dataset`, we should at least add a tip to explain this limitation and how to circumvent it.", "Oh you're right. Calling `load_dataset` on the modified script without having the files that come with it is not ideal. I agree it should be `git clone` instead - and inspect is for inspection only ^^'" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Requiring torchaudio<0.12.0 isn't really a viable solution because that implies torch<0.12.0 which means no sm_86 CUDA support which means no RTX 3090 support in PyTorch.\r\n\r\nBut in my case, the error only occurs if `_fallback_load` resolves to `_fail_load` inside torchaudio 0.12.0 which is only the case if FFMPEG initialization failed: https://github.com/pytorch/audio/blob/b1f510fa5681e92ee82bdc6b2d1ed896799fc32c/torchaudio/backend/sox_io_backend.py#L36-L47\r\n\r\nThat means the proper solution for torchaudio>=0.12.0 is to check `torchaudio._extension._FFMPEG_INITIALIZED` and if it is False, then we need to remind the user to install a dynamically linked ffmpeg 4.1.8 and then maybe call `torchaudio._extension._init_ffmpeg()` to force a user-visible exception showing the missing ffmpeg dynamic library name.\r\n\r\nOn my system, installing \r\n\r\n- libavcodec.so.58 \r\n- libavdevice.so.58 \r\n- libavfilter.so.7 \r\n- libavformat.so.58 \r\n- libavutil.so.56 \r\n- libswresample.so.3 \r\n- libswscale.so.5\r\n\r\nfrom ffmpeg 4.1.8 made HF datasets 2.3.2 work just fine with torchaudio 0.12.1+cu116:\r\n\r\n```python3\r\nimport sox, torchaudio, datasets\r\nprint('torchaudio', torchaudio.__version__)\r\nprint('datasets', datasets.__version__)\r\ntorchaudio._extension._init_ffmpeg()\r\nprint(torchaudio._extension._FFMPEG_INITIALIZED)\r\nwaveform, sample_rate = torchaudio.load('/workspace/.cache/huggingface/datasets/downloads/extracted/8e5aa88585efa2a4c74c6664b576550d32b7ff9c3d1d17cc04f44f11338c3dc6/cv-corpus-8.0-2022-01-19/en/clips/common_voice_en_100038.mp3', format='mp3')\r\nprint(waveform.shape)\r\n```\r\n\r\n```\r\ntorchaudio 0.12.1+cu116\r\ndatasets 2.3.2\r\nTrue\r\ntorch.Size([1, 369792])\r\n```", "Related: https://github.com/huggingface/datasets/issues/4889" ]

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[ "Thanks for reporting @NitishkKarra.\r\n\r\nThe root source of the issue is that streaming mode is not supported out-of-the-box for that dataset, because it contains a TAR file.\r\n\r\nWe have opened a discussion in the corresponding Hub dataset page, pointing out this issue: https://huggingface.co/datasets/Theivaprakasham/wildreceipt/discussions/1\r\n\r\nI'm closing this issue here, so this discussion is transferred there instead." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks for the feedback!\r\n\r\nI agree that adding it to `load_hub.mdx` is probably a bit too specific, especially for beginners reading the tutorials. Since this clarification is closely related to loading from the Hub (the only difference being the presence/absence of a loading script), I think it makes the most sense to keep it somewhere in `loading.mdx`. What do you think about adding a Warning in Loading >>> Hugging Face Hub that explains the difference between relative/absolute paths when there is a script?", "What about updating the section about \"manual download\" ? I think it goes there no ?\r\n\r\nhttps://huggingface.co/docs/datasets/v2.4.0/en/loading#manual-download", "Updated the manual download section :)", "Thanks ! Pinging @albertvillanova to review this change, and then I think we're good to merge" ]

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[ "cc @Rocketknight1 ", "Hi @lehrig, this is caused by the data collator renaming \"label\" to \"labels\". If you set `label_cols=[\"labels\"]` in the call it will work correctly. However, I agree that the cause of the bug is not obvious, so I'll see if I can make a PR to clarify things when the collator renames columns.", "Thanks - and wow, that appears like a strange side-effect of the data collator. Is that really needed?\r\n\r\nWhy not make it more explicit? For example, extend `DefaultDataCollator` with an optional property `label_col_name` to be used as label column; only when it is not provided default to `labels` (and document that this happens) for backwards-compatibility? ", "Haha, I honestly have no idea why our data collators rename `\"label\"` (the standard label column name in our datasets) to `\"labels\"` (the standard label column name input to our models). It's been a pain point when I design TF data pipelines, though, because I don't want to hardcode things like that - especially in `datasets`, because the renaming is something that happens purely at the `transformers` end. I don't think I could make the change in the data collators themselves at this point, because it would break backward compatibility for everything in PyTorch as well as TF.\r\n\r\nIn the most recent version of `transformers` we added a [prepare_tf_dataset](https://huggingface.co/docs/transformers/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset) method to our models which takes care of these details for you, and even chooses appropriate columns and labels for the model you're using. In future we might make that the officially recommended way to convert HF datasets to `tf.data.Dataset`.", "Interesting, that'd be great especially for clarity. https://huggingface.co/docs/datasets/use_with_tensorflow#data-loading already improved clarity, yet, all those options will still confuse people. Looking forward to those advances in the hope there'll be only 1 way in the future ;)\r\n\r\nAnyways, I am happy for the time being with the work-around you provided. Thank you!" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "good catch thanks ! Can you check if the same typo is also present in `add_elasticsearch_index` ? It has a very similar signature", "> good catch thanks ! Can you check if the same typo is also present in `add_elasticsearch_index` ? It has a very similar signature\r\n\r\nfixed" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Thanks for reporting, @cheaterHy.\r\n\r\nThe cause of this issue is a misalignment between the names of the repo (`goes-mrms`, with hyphen) and its Python loading scrip file (`goes_mrms.py`, with underscore).\r\n\r\nI've opened an Issue discussion in their repo: https://huggingface.co/datasets/openclimatefix/goes-mrms/discussions/1" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Just took your comment into account @mariosasko , let me know if it's good for you now :)" ]

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[ "And I don't see any speed up when increasing the number of GPUs while calling `get_nearest_examples_batch`.", "Hi ! Yes search_batch uses FAISS search which happens in parallel across the GPUs\r\n\r\n> And I don't see any speed up when increasing the number of GPUs while calling get_nearest_examples_batch.\r\n\r\nThat's unexpected, can you share the code you're running ?", "here is the code snippet\r\n\r\n```python\r\n\r\n# add faiss index\r\nsource_dataset = load_dataset(source_path)\r\nqueries = load_dataset(query_path)\r\ngpu = [0,1,2,3]\r\nsource_dataset.add_faiss_index(\r\n \"embedding\",\r\n device=gpu,\r\n )\r\n\r\n\r\n# batch query\r\nbatch_size = 32\r\nfor i in tqdm(range(0, len(queries), batch_size)):\r\n if i + batch_size >= len(queries):\r\n batched_queries = queries[i:]\r\n else:\r\n batched_queries = queries[i:i+batch_size]\r\n\r\n batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)\r\n scores, candidates = source_dataset.get_nearest_examples_batch(\r\n \"embedding\",\r\n batched_query_embeddings,\r\n k=5\r\n )\r\n```", "My version of datasets is `2.4.1.dev0`.", "The code looks all good to me, do you see all the GPUs being utilized ? What version of faiss are you using ?", "I can see the memory usage of all the GPUs.\r\nMy version of `faiss-gpu` is `1.7.2`", "It looks all good to me then ^^ though you said you didn't experienced speed improvements by adding more GPUs ? What size is your source dataset and what time differences did you experience ?", "query set: 1e6\r\nsource dataset: 1e6\r\nembedding size: 768\r\nindex: Flat\r\ntopk: 20\r\nGPU: V100\r\n\r\nThe time taken to traverse the query set once is about 1.5h, which is almost not influenced by the value of query batch size or the number of GPUs according to my experiments.", "Hmmm the number of GPUs should divide the time, something is going wrong. Can you check that adding more GPU does divide the memory used per GPU ? Maybe it can be worth looking at similar issues in the FAISS repository or create a noew issue over there to understand what's going on", "> Can you check that adding more GPU does divide the memory used per GPU \r\n\r\nThe memory used per GPU is unchanged while adding more GPU. Is this unexpected?\r\n\r\nI used to think that every GPU loads all the source vectors and the data parallelism is at the query level. 😆 ", "> I used to think that every GPU loads all the source vectors and the data parallelism is at the query level. 😆\r\n\r\nOh indeed that's possible, I wasn't sure. Anyway you can check that calling get_nearest_examples_batch simply calls search under the hood: \r\n\r\nhttps://github.com/huggingface/datasets/blob/f90f71fbbb33889fe75a3ffc101cdf16a88a3453/src/datasets/search.py#L375", "Here is a runnable script. \r\nMulti-GPU searching still does not work in my experiments.\r\n\r\n\r\n```python\r\nimport os\r\nfrom tqdm import tqdm\r\nimport numpy as np\r\nimport datasets\r\nfrom datasets import Dataset\r\n\r\nclass DPRSelector:\r\n\r\n def __init__(self, source, target, index_name, gpu=None):\r\n self.source = source\r\n self.target = target\r\n self.index_name = index_name\r\n\r\n cache_path = 'embedding.faiss'\r\n\r\n if not os.path.exists(cache_path):\r\n self.source.add_faiss_index(\r\n column=\"embedding\",\r\n index_name=index_name,\r\n device=gpu,\r\n )\r\n self.source.save_faiss_index(index_name, cache_path)\r\n else:\r\n self.source.load_faiss_index(\r\n index_name,\r\n cache_path,\r\n device=gpu\r\n )\r\n print('index builded!')\r\n\r\n def build_dataset(self, top_k, batch_size):\r\n print('start search')\r\n\r\n for i in tqdm(range(0, len(self.target), batch_size)):\r\n if i + batch_size >= len(self.target):\r\n batched_queries = self.target[i:]\r\n else:\r\n batched_queries = self.target[i:i+batch_size]\r\n\r\n\r\n batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)\r\n search_res = self.source.get_nearest_examples_batch(\r\n self.index_name,\r\n batched_query_embeddings,\r\n k=top_k\r\n )\r\n \r\n print('finish search')\r\n\r\n\r\ndef get_pseudo_dataset():\r\n pseudo_dict = {\"embedding\": np.zeros((1000000, 768), dtype=np.float32)}\r\n print('generate pseudo data')\r\n\r\n dataset = Dataset.from_dict(pseudo_dict)\r\n def list_to_array(data):\r\n return {\"embedding\": [np.array(vector, dtype=np.float32) for vector in data[\"embedding\"]]} \r\n dataset.set_transform(list_to_array, columns='embedding', output_all_columns=True)\r\n\r\n print('build dataset')\r\n return dataset\r\n\r\n\r\n\r\nif __name__==\"__main__\":\r\n\r\n np.random.seed(42)\r\n\r\n\r\n source_dataset = get_pseudo_dataset()\r\n target_dataset = get_pseudo_dataset()\r\n\r\n gpu = [0,1,2,3,4,5,6,7]\r\n selector = DPRSelector(source_dataset, target_dataset, \"embedding\", gpu=gpu)\r\n\r\n selector.build_dataset(top_k=20, batch_size=32)\r\n```", "@lhoestq Hi, could you please test the code above if you have time? 😄 ", "Maybe @albertvillanova you can take a look ? I won't be available in the following days", "@albertvillanova Hi, can you help with this issue?", "Hi @xwwwwww I'm investigating it, but I'm not an expert in Faiss. In principle, it is weird that your code does not work properly because it seems right...", "Have you tried passing `gpu=-1` and check if there is a speedup?", "> Have you tried passing `gpu=-1` and check if there is a speedup?\r\n\r\nyes, there is a speed up using GPU compared with CPU. ", "When passing `device=-1`, ALL existing GPUs are used (multi GPU): this is the maximum speedup you can get. To know the number of total GPUs:\r\n```\r\nimport faiss\r\n\r\nngpus = faiss.get_num_gpus()\r\nprint(ngpus)\r\n```\r\n\r\nWhen passing a list of integers to `device`, then only that number of GPUs are used (multi GPU as well)\r\n- the speedup should be proportional (more or less) to the ratio of the number of elements passed to `device` over `ngpus`\r\n- if this is not the case, then there is an issue in the implementation of this use case (however, I have reviewed the code and in principle I can't find any evident bug)\r\n\r\nWhen passing a positive integer to `device`, then only a single GPU is used.\r\n- this time should be more or less proportional to the time when passing `device=-1` over `ngpus`", "Thanks for your help!\r\nHave you run the code and replicated the same experimental results (i.e., no speedup while increasing the number of GPUs)?", "@albertvillanova @lhoestq Sorry for the bother, is there any progress on this issue? 😃 ", "I can confirm `add_faiss_index` calls `index = faiss.index_cpu_to_gpus_list(index, gpus=list(device))`.\r\n\r\nCould this be an issue with your environment ? Could you try running with 1 and 8 GPUs with a code similar to[ this one from the FAISS examples](https://github.com/facebookresearch/faiss/blob/main/tutorial/python/5-Multiple-GPUs.py) but using `gpu_index = faiss.index_cpu_to_gpus_list(cpu_index, gpus=list(device))`, and see if the speed changes ?", "Hi, I test the FAISS example and the speed indeed changes. I set `nb=1000000`, `nq=1000000` and `d=64`\r\n\r\n| num GPUS | time cost |\r\n| -------- | --------- |\r\n| 1 | 28.53 |\r\n| 5 | 7.16 |\r\n\r\n\r\n\r\n", "Ok the benchmark is great, not sure why it doesn't speed up the index in your case though. You can try running the benchmark with the same settings as your actual dataset\r\n```\r\nquery set: 1e6\r\nsource dataset: 1e6\r\nembedding size: 768\r\nindex: Flat\r\ntopk: 20\r\nGPU: V100\r\n```\r\n\r\nNote that you can still pass a FAISS index you built yourself to a dataset using https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index_from_external_arrays", "> Here is a runnable script. Multi-GPU searching still does not work in my experiments.\r\n> \r\n> ```python\r\n> import os\r\n> from tqdm import tqdm\r\n> import numpy as np\r\n> import datasets\r\n> from datasets import Dataset\r\n> \r\n> class DPRSelector:\r\n> \r\n> def __init__(self, source, target, index_name, gpu=None):\r\n> self.source = source\r\n> self.target = target\r\n> self.index_name = index_name\r\n> \r\n> cache_path = 'embedding.faiss'\r\n> \r\n> if not os.path.exists(cache_path):\r\n> self.source.add_faiss_index(\r\n> column=\"embedding\",\r\n> index_name=index_name,\r\n> device=gpu,\r\n> )\r\n> self.source.save_faiss_index(index_name, cache_path)\r\n> else:\r\n> self.source.load_faiss_index(\r\n> index_name,\r\n> cache_path,\r\n> device=gpu\r\n> )\r\n> print('index builded!')\r\n> \r\n> def build_dataset(self, top_k, batch_size):\r\n> print('start search')\r\n> \r\n> for i in tqdm(range(0, len(self.target), batch_size)):\r\n> if i + batch_size >= len(self.target):\r\n> batched_queries = self.target[i:]\r\n> else:\r\n> batched_queries = self.target[i:i+batch_size]\r\n> \r\n> \r\n> batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)\r\n> search_res = self.source.get_nearest_examples_batch(\r\n> self.index_name,\r\n> batched_query_embeddings,\r\n> k=top_k\r\n> )\r\n> \r\n> print('finish search')\r\n> \r\n> \r\n> def get_pseudo_dataset():\r\n> pseudo_dict = {\"embedding\": np.zeros((1000000, 768), dtype=np.float32)}\r\n> print('generate pseudo data')\r\n> \r\n> dataset = Dataset.from_dict(pseudo_dict)\r\n> def list_to_array(data):\r\n> return {\"embedding\": [np.array(vector, dtype=np.float32) for vector in data[\"embedding\"]]} \r\n> dataset.set_transform(list_to_array, columns='embedding', output_all_columns=True)\r\n> \r\n> print('build dataset')\r\n> return dataset\r\n> \r\n> \r\n> \r\n> if __name__==\"__main__\":\r\n> \r\n> np.random.seed(42)\r\n> \r\n> \r\n> source_dataset = get_pseudo_dataset()\r\n> target_dataset = get_pseudo_dataset()\r\n> \r\n> gpu = [0,1,2,3,4,5,6,7]\r\n> selector = DPRSelector(source_dataset, target_dataset, \"embedding\", gpu=gpu)\r\n> \r\n> selector.build_dataset(top_k=20, batch_size=32)\r\n> ```\r\n\r\nBy the way, have you run this toy example and replicated my experiment results? I think it is a more direct way to figure this out :)" ]

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[ "Hi @SaulLu, thanks for reporting.\r\n\r\nI think offline mode is not supported for datasets containing only data files (without any loading script). I'm having a look into this...", "Thanks for your feedback! \r\n\r\nTo give you a little more info, if you don't set the offline mode flag, the script will load the cache. I first noticed this behavior with the `evaluate` library, and while trying to understand the downloading flow I realized that I had a similar error with datasets.", "This is an issue we have to fix.", "This is related to https://github.com/huggingface/datasets/issues/3547" ]

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[ "I refreshed the dataset viewer manually, it's fixed now. Sorry for the inconvenience.\r\n<img width=\"1557\" alt=\"Capture d’écran 2022-07-28 à 09 17 39\" src=\"https://user-images.githubusercontent.com/1676121/181514666-92d7f8e1-ddc1-4769-84f3-f1edfdb902e8.png\">\r\n\r\n" ]

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[]

[ "I've built a minimal example that shows this bug without `n_proc`. It seems like it's a problem any way of using **tokenizers, `overflow_to_sample_mapping`, and Dataset.map, with a small batch size**:\r\n\r\n```\r\nimport datasets\r\nimport transformers\r\npretrained = 'deepset/tinyroberta-squad2'\r\ntokenizer = transformers.AutoTokenizer.from_pretrained(pretrained)\r\n\r\nquestions = ['Can you tell me why?', 'What time is it?']\r\ncontexts = ['This is context zero', 'Another paragraph goes here'] \r\n\r\ndef tok(questions, contexts):\r\n return tokenizer(text=questions,\r\n text_pair=contexts,\r\n truncation='only_second',\r\n return_overflowing_tokens=True,\r\n )\r\nprint(tok(questions, contexts)['overflow_to_sample_mapping'])\r\nassert tok(questions, contexts)['overflow_to_sample_mapping'] == [0, 1] # PASSES\r\n\r\ndef tok2(d):\r\n return tok(d['question'], d['context'])\r\n\r\ndef tok2(d):\r\n return tok(d['question'], d['context'])\r\n\r\nds = datasets.Dataset.from_dict({'question': questions, 'context': contexts})\r\ntokens = ds.map(tok2, batched=True, batch_size=1)\r\nprint(tokens['overflow_to_sample_mapping'])\r\nassert tokens['overflow_to_sample_mapping'] == [0, 1] # FAILS produces [0,0]\r\n```\r\n\r\nNote that even if the batch size would be larger, there will be instances where we will not have a lot of data, and end up using small batches. This can occur e.g. if `n_proc` causes batches to be underfill. I imagine it can also occur in other ways, e.g. the final leftover batch at the end.", "A larger batch size does _not_ have this behavior:\r\n\r\n```\r\ndef tok2(d):\r\n return tok(d['question'], d['context'])\r\n\r\nds = datasets.Dataset.from_dict({'question': questions, 'context': contexts})\r\ntokens = ds.map(tok2, batched=True, batch_size=2)\r\nprint(tokens['overflow_to_sample_mapping'])\r\nassert tokens['overflow_to_sample_mapping'] == [0, 1] # PASSES\r\n```" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "I've narrowed down the issue to the `dataset_module_factory` which already creates a `dataset_infos.json` file down in the `.cache/modules/dataset_modules/..` folder. That JSON file already contains the wrong task_templates for `unfiltered`.", "Ugh. Found the issue: apparently `datasets` was reusing the already existing `dataset_infos.json` that is inside `datasets/datasets/hebban-reviews`! Is this desired behavior?\r\n\r\nPerhaps when `--save_infos` and `--all_configs` are given, an existing `dataset_infos.json` file should first be deleted before continuing with the test? Because that would assume that the user wants to create a new infos file for all configs anyway.", "Hi! I think this is a reasonable solution. Would you be interested in submitting a PR?" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi ! I think the simplest is to copy paste the `_split_generators` code from the other datasets and do a bunch of if-else, as in the glue dataset: https://huggingface.co/datasets/glue/blob/main/glue.py#L467", "Ok, I see. Thank you" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "This is ready for review cc @mariosasko :) please let me know what you think !" ]

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[ "The dataset is empty, as far as I can tell: there are no files in the repository at https://huggingface.co/datasets/yanekyuk/wikikey/tree/main\r\n\r\nMaybe the viewer can display a better message for empty datasets", "OK. Closing as it's not an error. We will work on making the error message a lot clearer." ]

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[ "Thanks for opening this issue :)\r\n\r\nIf it can help, I think you can already use `huggingface_hub` to achieve this:\r\n```python\r\n>>> from huggingface_hub import HfApi\r\n>>> [ds_info.id for ds_info in HfApi().list_datasets(use_auth_token=token) if ds_info.private]\r\n['bigscience/xxxx', 'bigscience-catalogue-data/xxxxxxx', ... ]\r\n```\r\n\r\n---------\r\n\r\nThough the latest versions of `huggingface_hub` that contain this feature are not available on python 3.6, so maybe we should first drop support for python 3.6 (see #4460) to update `list_datasets` in `datasets` as well (or we would have to copy/paste some `huggingface_hub` code)", "Great, thanks @lhoestq the workaround works! I think it would be intuitive to have the support directly in `datasets` but it makes sense to wait given that the workaround exists :)", "i also think that going forward we should replace more and more implementations inside datasets with the corresponding ones from `huggingface_hub` (same as we're doing in `transformers`)" ]

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[ "Note that for me personally, conceptually all the dummy data (even for \"canonical\" datasets) should be superseded by `datasets-server`, which performs some kind of CI/CD of datasets (including the canonical ones)", "I totally agree: next step should be rethinking if dummy data makes sense for canonical datasets (once we have datasets-server) and eventually remove it.\r\n\r\nBut for now, we could at least start by removing the indication to generate dummy data from our docs." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi @BramVanroy, thanks for reporting.\r\n\r\nFirst of all, please note that you do not need the dummy data: this was the case when we were adding datasets to the `datasets` library (on this GitHub repo), so that we could test the correct loading of all datasets with our CI. However, this is no longer the case for datasets on the Hub.\r\n- We should definitely update our docs.\r\n\r\nSecond, the dummy data is generated locally:\r\n- in your case, the dummy data will be generated inside the directory: `./datasets/hebban-reviews/dummy`\r\n- please note the preceding `./datasets` directory: the reason for this is that the command to generate the dummy data was specifically created for our `datasets` library, and therefore assumes our directory structure: commands are run from the root directory of our GitHub repo, and datasets scripts are under `./datasets` \r\n\r\n\r\n ", "I have opened an Issue to update the instructions on dummy data generation:\r\n- #4744" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq as a workaround to preserve previous behavior, the parameter `multiprocessing_min_length=16` is passed from `download` to `map_nested`, so that multiprocessing is only used if at least 16 files to be downloaded.\r\n\r\nNote that there is a small breaking change (I think previously it was unintended behavior, so that I have fixed it):\r\n- Before (with default `num_proc=16`) if there were 16 files to be downloaded, multiprocessing was not used\r\n- Now (with default `num_proc=16`) if there are 16 files to be downloaded, multiprocessing is used", "Thanks for the workaround !" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I mark this as Draft because the deprecated version number needs being updated after the latest release.", "Perhaps now is the time to also update the `inspect_metric` from `evaluate` with the changes introduced in https://github.com/huggingface/datasets/pull/4433 (cc @lvwerra) ", "What do you think of including what changes users have to do to switch to `evaluate` in the warning message ?\r\n(basically replace `datasets.load_metric` by `evaluate.load`)\r\n\r\nI think it can help users migrate to `evaluate` and silence the warnings" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I think this PR can be merged. Willing to see it in action.\r\n\r\nCC: @lhoestq " ]

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[ "Hi! The server with the data seems to be down. I've reported this issue (https://github.com/CSAILVision/sceneparsing/issues/34) in the dataset repo. ", "The URL seems to work now, and therefore the script as well." ]

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[ "Thanks for reporting. You're right, workers were under-provisioned due to a manual error, and the job queue was full. It's fixed now." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "We have added a comment on an existing issue opened in their repo: https://github.com/google-research/google-research/issues/1212#issuecomment-1192267130\r\n- https://github.com/google-research/google-research/issues/1212" ]

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[ "Fixed by:\r\n- #4735" ]

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[ "Thanks for the feedback!\r\n\r\nI think since this issue is closely related to loading, I can add a clearer explanation under [Load > local loading script](https://huggingface.co/docs/datasets/main/en/loading#local-loading-script).", "That makes sense but I think having a line about it under https://huggingface.co/docs/datasets/installation#source the \"source\" header here would be useful. My mental model of `pip install -e .` does not include the fact that the source files aren't actually being used. ", "Thanks for sharing your perspective. I think the `load_dataset` function is the only one that pulls from GitHub, and since this use-case is very specific, I don't think we need to include such a broad clarification in the Installation section.\r\n\r\nFeel free to check out the linked PR and let me know if it needs any additional explanation 😊" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "My bad, `482 * 418 * 50000 * 3 / 1000000 = 30221 MB` ( https://stackoverflow.com/a/42979315 ).\r\n\r\nMeanwhile `256 * 256 * 50000 * 3 / 1000000 = 9830 MB`. We are loading the non-cropped images and that is why we take so much RAM." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi @rohitvincent, thanks for reporting.\r\n\r\nUnfortunately I'm not able to reproduce your issue:\r\n```python\r\nIn [2]: from datasets import load_dataset, DownloadMode\r\n ...: load_dataset(path='financial_phrasebank',name='sentences_allagree', download_mode=\"force_redownload\")\r\nDownloading builder script: 6.04kB [00:00, 2.87MB/s] \r\nDownloading metadata: 13.7kB [00:00, 7.24MB/s] \r\nDownloading and preparing dataset financial_phrasebank/sentences_allagree (download: 665.91 KiB, generated: 296.26 KiB, post-processed: Unknown size, total: 962.17 KiB) to .../.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141...\r\nDownloading data: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 682k/682k [00:00<00:00, 7.66MB/s]\r\nDataset financial_phrasebank downloaded and prepared to .../.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 918.80it/s]\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence', 'label'],\r\n num_rows: 2264\r\n })\r\n})\r\n```\r\n\r\nAre you able to access the link? https://www.researchgate.net/profile/Pekka-Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip", "Yes was able to download from the link manually. But still, get the same error when I use load_dataset.", "Fixed once data files are hosted on the Hub:\r\n- #4598" ]

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[ "The preview is working OK:\r\n\r\n\r\n\r\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Thanks for reporting, @TrentBrick. We are addressing the change with their data host server.\r\n\r\nOn the meantime, if you would like to work with your fixed local copy of the_pile script, you should use:\r\n```python\r\nload_dataset(\"path/to/your/local/the_pile/the_pile.py\",...\r\n```\r\ninstead of just `load_dataset(\"the_pile\",...`.\r\n\r\nThe latter downloads a copy of `the_pile.py` from our GitHub, caches it locally (inside `~/.cache/huggingface/modules`) and uses that.", "@TrentBrick, I have checked the URLs and both hosts work, the original (https://the-eye.eu/) and the mirror (https://mystic.the-eye.eu/). See e.g.:\r\n- https://mystic.the-eye.eu/public/AI/pile/\r\n- https://mystic.the-eye.eu/public/AI/pile_preliminary_components/\r\n\r\nPlease, let me know if you still find any issue loading this dataset by using current server URLs.", "Great this is working now. Re the download from GitHub... I'm sure thought went into doing this but could it be made more clear maybe here? https://huggingface.co/docs/datasets/installation for example under installing from source? I spent over an hour questioning my sanity as I kept trying to edit this file, uninstall and reinstall the repo, git reset to previous versions of the file etc.", "Thanks for the quick reply and help too\r\n", "Thanks @TrentBrick for the suggestion about improving our docs: we should definitely do this if you find they are not clear enough.\r\n\r\nCurrently, our docs explain how to load a dataset from a local loading script here: [Load > Local loading script](https://huggingface.co/docs/datasets/loading#local-loading-script)\r\n\r\nI've opened an issue here:\r\n- #4732\r\n\r\nFeel free to comment on it any additional explanation/suggestion/requirement related to this problem." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Added some docs for dask and took your comments into account\r\n\r\ncc @philschmid if you also want to take a look :)", "Just noticed that it would be more convenient to pass the output dir to download_and_prepare directly, to bypass the caching logic which prepares the dataset at `<cache_dir>/<name>/<version>/<hash>/`. And this way the cache is only used for the downloaded files. What do you think ?\r\n\r\n```python \r\n\r\nbuilder = load_datadet_builder(\"squad\")\r\n# or with a custom cache\r\nbuilder = load_datadet_builder(\"squad\", cache_dir=\"path/to/local/cache/for/downloaded/files\")\r\n\r\n# download and prepare to s3\r\nbuilder.download_and_prepare(\"s3://my_bucket/squad\")\r\n```", "Might be of interest: \r\nPyTorch and AWS introduced better support for S3 streaming in `torchtext`. \r\n\r\n", "Having thought about it a bit more, I also agree with @philschmid in that it's important to follow the existing APIs (pandas/dask), which means we should support the following at some point:\r\n\r\n* remote data files resolution for the packaged modules to support `load_dataset(\"<format>\", data_files=\"<fs_url>\")`\r\n* `to_<format>(\"<fs_url>\")`\r\n* `load_from_disk` and `save_to_disk` already expose the `fs` param, but it would be cool to support specifying `fsspec` URLs directly as the source/destination path (perhaps we can then deprecate `fs` to be fully aligned with pandas/dask)\r\n\r\nIMO these are the two main issues with the current approach:\r\n* relying on the builder API to generate the formatted files results in a non-friendly format due to how our caching works (a lot of nested subdirectories)\r\n* this approach still downloads the files needed to generate a dataset locally. Considering one of our goals is to align the streaming API with the non-streaming one, this could be avoided by running `to_<format>` on streamed/iterable datasets", "Alright I did the last change I wanted to do, here is the final API:\r\n\r\n```python\r\nbuilder = load_dataset_builder(...)\r\nbuilder.download_and_prepare(\"s3://...\", storage_options={\"token\": ...})\r\n```\r\n\r\nand it creates the arrow files directly in the specified directory, not in a nested subdirectory structure as we do in the cache !\r\n\r\n> this approach still downloads the files needed to generate a dataset locally. Considering one of our goals is to align the streaming API with the non-streaming one, this could be avoided by running to_<format> on streamed/iterable datasets\r\n\r\nYup this can be explored in some future work I think. Though to keep things simple and clear I would keep the streaming behaviors only when you load a dataset in streaming mode, and not include it in `download_and_prepare` (because it wouldn't be aligned with the name of the function, which imply to 1. download and 2. prepare ^^). Maybe an API like that can make sense for those who need full streaming\r\n\r\n```python\r\nds = load_dataset(..., streaming=True)\r\nds.to_parquet(\"s3://...\")\r\n```", "totally agree with your comment on the meaning of \"loading\", I'll update the docs", "I took your comments into account and reverted all the changes related to `cache_dir` to keep the support for remote `cache_dir` for beam datasets. I also updated the wording in the docs to not use \"load\" when it's not appropriate :)" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi ! It looks like a bug in `pyarrow`. If you manage to end up with only one chunk per parquet file it should workaround this issue.\r\n\r\nTo achieve that you can try to lower the value of `max_shard_size` and also don't use `map` before `push_to_hub`.\r\n\r\nDo you have a minimum reproducible example that we can share with the Arrow team for further debugging ?", "> If you manage to end up with only one chunk per parquet file it should workaround this issue.\r\n\r\nYup, I did not encounter this bug when I was testing my script with a slice of <1000 samples for my dataset.\r\n\r\n> Do you have a minimum reproducible example...\r\n\r\nNot sure if I can get more minimal than the script I shared above. Are you asking for a sample json file?\r\nJust generate a random manifest list, I can add that to the above script if that's what you mean?\r\n", "Actually this is probably linked to this open issue: https://issues.apache.org/jira/browse/ARROW-5030.\r\n\r\nsetting `max_shard_size=\"2GB\"` should do the job (or `max_shard_size=\"1GB\"` if you want to be on the safe side, especially given that there can be some variance in the shard sizes if the dataset is not evenly distributed)" ]

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[ "It seems like the list of splits could not be obtained:\r\n\r\n```python\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names(\"shamikbose89/lancaster_newsbooks\", \"default\")\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 354, in get_dataset_config_info\r\n for split_generator in builder._split_generators(\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/shamikbose89--lancaster_newsbooks/2d1c63d269bf7b9342accce0a95960b1710ab4bc774248878bd80eb96c1afaf7/lancaster_newsbooks.py\", line 73, in _split_generators\r\n data_dir = dl_manager.download_and_extract(_URL)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 916, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 879, in extract\r\n urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 348, in map_nested\r\n return function(data_struct)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 884, in _extract\r\n protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 388, in _get_extraction_protocol\r\n return _get_extraction_protocol_with_magic_number(f)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 354, in _get_extraction_protocol_with_magic_number\r\n f.seek(0)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py\", line 684, in seek\r\n raise ValueError(\"Cannot seek streaming HTTP file\")\r\nValueError: Cannot seek streaming HTTP file\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 404, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 359, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\nping @huggingface/datasets ", "Oh, I removed the 'split' key from `kwargs`. I put it back in, but there's still the same error", "It looks like the data host doesn't support http range requests, which is necessary to glob inside a ZIP archive in streaming mode. Can you try hosting the dataset elsewhere ? Or download each file separately from https://ota.bodleian.ox.ac.uk/repository/xmlui/handle/20.500.12024/2531 ?", "@lhoestq Thanks! That seems to have solved it. I can get the splits with the `get_dataset_split_names()` function. The dataset viewer is still not loading properly, though. The new error is\r\n```\r\nStatus code: 400\r\nException: BadZipFile\r\nMessage: File is not a zip file\r\n```\r\n\r\nPS. The dataset loads properly and can be accessed" ]

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[ "I am also getting a ValueError: 'Couldn't cast' at the bottom. Is this because of some delimiter issue? My dataset is on the Huggingface Hub. If you could look at it, that would be greatly appreciated.", "Hi @thenerd31, thanks for reporting.\r\n\r\nPlease note that your issue is not caused by the Hugging Face Datasets library, but it has to do with the specific implementation of your dataset on the Hub.\r\n\r\nTherefore, I'm transferring this discussion to your own dataset Community tab: https://huggingface.co/datasets/TheNoob3131/mosquito-data/discussions/1" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "It's currently working, as far as I understand\r\n\r\nhttps://huggingface.co/datasets/LawalAfeez/englishreview-ds-mini/viewer/LawalAfeez--englishreview-ds-mini/train\r\n\r\n<img width=\"1556\" alt=\"Capture d’écran 2022-07-19 à 09 24 01\" src=\"https://user-images.githubusercontent.com/1676121/179761130-2d7980b9-c0f6-4093-8b1d-f0a3872fef3f.png\">\r\n\r\n---\r\n\r\nWhat was your issue?" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "IMO `DatasetMetadata` shouldn't crash with attributes that it doesn't know, btw", "Yea this PR is mostly to have a validation that this field contains a list of strings.\r\n\r\nRegarding unknown fields, the tagging app currently returns an error if a field is unknown using the `DatasetMetadata`. We can change that though" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "CI failures are about missing content in the dataset cards or bad tags, and this is unrelated to this PR. Merging :)" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "hahaha what a timing, I added my comment right after you merged x)\r\n\r\nyou can ignore my (nit), it's fine", "Sorry, just too sync... :sweat_smile: " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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