Datasets:

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anna-bozhenko

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github-issues

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[ "Hi ! the `desc` parameter is only available for Dataset / DatasetDict for the progress bar of `map()``\n\nSince IterableDataset only runs the map functions when you iterate over the dataset, there is no progress bar and `desc` is useless. We could still add the argument for parity but it wouldn't be used for anything", "I think you should add it. It doesn't hurt. The reason I ran into it was because I re-wrote a pipeline to use either a stream or a fully loaded dataset. Of course I can simply remove it but it is nice to have on the memory loaded dataset. " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7398). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "Hi ! feel free to uplad the CSV on https://huggingface.co/datasets :)\r\n\r\nwe don't store the datasets' data in this github repository" ]

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[ "I transferred to the `datasets` repository. Is there any retry mechanism in `datasets` @lhoestq ?\n\nAnother solution @hotchpotch if you want to get your dataset pushed to the Hub in a robust way is to save it to a local folder first and then use `huggingface-cli upload-large-folder` (see https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-large-folder). It has better retry mechanism in case of failure.", "There is no retry mechanism for `api.preupload_lfs_files` in `push_to_hub()` but we can definitely add one here\n\nhttps://github.com/huggingface/datasets/blob/de062f0552a810c52077543c1169c38c1f0c53fc/src/datasets/arrow_dataset.py#L5372", "@Wauplin \n\nThank you! I believe that to use load_dataset() to read data from Hugging Face, we need to first save the markdown metadata and parquet files in our local filesystem, then upload them using upload-large-folder. If you know how to do this, could you please let me know?\n\n", "@lhoestq \n\nI see, so adding a retry mechanism there would solve it. If I continue to have issues, I'll consider implementing that kind of solution." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7396). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7395). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7393). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "See also <https://discuss.huggingface.co/t/datasetdict-push-to-hub-failing-with-payload-to-large/140083/8>\n" ]

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[ "You can actually track a running sum in map() or filter() :)\n\n```python\nnum_filtered = 0\n\ndef f(x):\n global num_filtered\n condition = len(x[\"text\"]) < 1000\n if not condition:\n num_filtered += 1\n return condition\n\nds = ds.filter(f)\nprint(num_filtered)\n```\n\nand if you want to use multiprocessing, make sure to use a variable that is shared across processes\n\n\n```python\nfrom multiprocess import Manager\n\nmanager = Manager()\nnum_filtered = manager.Value('i', 0)\n\ndef f(x):\n global num_filtered\n condition = len(x[\"text\"]) < 1000\n if not condition:\n num_filtered.value += 1\n return condition\n\nds = ds.filter(f, num_proc=4)\nprint(num_filtered.value)\n```\n\nPS: `datasets` uses `multiprocess` instead of the `multiprocessing` package to support lambda functions in map() and filter()", "Oh that's great to know!\n\nI guess this value would not be exactly synced with the batch in cases of pre-fetch and shuffle buffers and so on, but that's probably fine. Thanks!" ]

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[ "You can probably copy the dataset in your HF account and rename the files (without having to download them to your disk). Or alternatively feel free to open a Pull Request to this dataset with the renamed file", "Thank you, that will help me work around this problem" ]

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[ "You mean based on a condition that has to be checked on-the-fly during training ? Otherwise if you know in advance after how many samples you need to change the sampling you can simply concatenate the two mixes", "Yes, like during training, if one data sample's prediction is consistently wrong, its sampling weight gets higher and higher, and if one data sample's prediction is already correct, then we rarely sample it", "it's not possible to use `interleave_datasets()` and modify the probabilities while iterating on the dataset at the moment, so you'd have to implement your own torch `Sampler` or your own`IterableDataset` to implement this logic" ]

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[ "On second thought, probably not a good idea." ]

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[ "@lhoestq Hi again~ Just circling back on this\r\nWondering if there’s anything I can do to help move this forward. 🤗 \r\nThanks!", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7385). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7384). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "example of what you can do with it:\r\n\r\n```python\r\nimport aiohttp\r\nfrom huggingface_hub import get_token\r\n\r\nfrom datasets import Dataset\r\n\r\n\r\nAPI_URL = \"https://api-inference.huggingface.co/models/microsoft/Phi-3-mini-4k-instruct/v1/chat/completions\"\r\nPROMPT = \"What is this text mainly about ? Here is the text:\\n\\n```\\n{Problem}\\n```\\n\\nReply in one or two words.\"\r\n\r\nasync def query(example):\r\n headers = {\"Authorization\": f\"Bearer {get_token()}\", \"Content-Type\": \"application/json\"}\r\n json = {\"messages\": [{\"role\": \"user\", \"content\": PROMPT.format(Problem=example[\"Problem\"])}], \"max_tokens\": 20, \"seed\": 42}\r\n async with aiohttp.ClientSession() as session, session.post(API_URL, headers=headers, json=json) as response:\r\n output = await response.json()\r\n return {\"output\": output[\"choices\"][0][\"message\"][\"content\"]}\r\n\r\nds = Dataset.from_dict({\"Problem\": [\"1 + 1\"] * 10})\r\nds = ds.map(query)\r\nprint(ds[0])\r\n# {'Problem': '1 + 1', 'output': 'Arithmetic'}\r\n```" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7382). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]

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[ "I'd be in favor of that ! I saw many people implementing their own iterables that wrap a dataset just to iterate on a single column, that would make things more practical.\n\nKinda related: https://github.com/huggingface/datasets/issues/5847", "(For anyone's information, I'm going on vacation for the next 3 weeks, so the work is postponed. If anyone can implement this feature within the next 4 weeks, go ahead :) )\n\nUPD from 04/06/25:\nI'm planning to start work on the feature in early May." ]

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[ "`datasets` doesn't support `dill` 0.3.9 yet afaik since `dill` made some changes related to the determinism of dumps\r\n\r\nIt would be cool to investigate (maybe run the `datasets` test) with recent `dill` to see excactly what breaks and if we can make `dill` 0.3.9 work with `datasets`" ]

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[ "Hi ! This sounds reasonable to me, feel free to open a PR :)" ]

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[ "Hi ! Unfortunately the Sparse Tensor structure in Arrow is not part of the Arrow format (yes it's confusing...), so it's not possible to use it in `datasets`. It's a separate structure that doesn't correspond to any type or extension type in Arrow.\n\nThe Arrow community recently added an extension type for fixed shape tensors at https://arrow.apache.org/docs/format/CanonicalExtensions.html#fixed-shape-tensor, it should be possible to contribute an extension type for sparse tensors as well." ]

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[ "Make sure you have installed a recent version of `soundfile`" ]

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[ "\n\n\n\nAdding a img from memray\nhttps://gist.github.com/sam-hey/00c958f13fb0f7b54d17197fe353002f", "I'm having the same issue where concatenation seems to use a huge amount of RAM.\n\n```python\n# Load all chunks and concatenate them into a final dataset.\n chunk_datasets = [\n Dataset.load_from_disk(file, keep_in_memory=False)\n for file in tqdm(chunk_files, desc=\"Loading chunk datasets\")\n ]\n logging.info(\"Concatenating chunk datasets...\")\n final_dataset = concatenate_datasets(chunk_datasets)\n```\n\nThis is a real issue for me as the final dataset is a few terabytes in size. I'm using datasets version `3.1.0`. Also tested with version `3.4.1`", "I did have a short look, the error seems to be from `memory_map` and the stream not being closed. \n\nhttps://github.com/huggingface/datasets/blob/5f8d2ad9a1b0bccfd962d998987228addfd5be9f/src/datasets/table.py#L48-L50\n\n\nDid not have the time to test jet: https://github.com/sam-hey/datasets/tree/fix/concatenate_datasets\n\nI will probably have a better look in a couple of days. \n\n" ]

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[ "EDIT: seems to be all good now. I'll add a comment if the error happens again within the next 48 hours. If it doesn't, I'll just close the topic." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7370). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "merging this and will make some docs and communications around using polars for optimizing data processing :)" ]

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[ "I'd prefer even more verbose errors; like `\"file123.mp3\" is referenced in metadata.csv, but not found in the data directory '/path/to/audiofolder' ! (and 100+ more missing files)` Or something along those lines." ]

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[ "Can you check this out, @lhoestq?", "cc @lhoestq @albertvillanova ", "@lhoestq\r\n", "@lhoestq\r\n", "@lhoestq", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7368). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@lhoestq ", "@lhoestq please...", "Thank you so much for reviewing this PR! Have a great weekend~" ]

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[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]

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[ "Looks like a [similar feature request](https://github.com/huggingface/huggingface_hub/issues/1198) was made to the HF Hub team. Is handling this at the Hub level more appropriate?\r\n\r\n(As an aside, I've gotten the [HTTP-based solution](https://github.com/huggingface/huggingface_hub/issues/1198#issuecomment-1905774983) proposed in that forum to work for simple cases.)", "yes it's more for https://github.com/huggingface/huggingface_hub cc @hanouticelina ", "yes i think @Wauplin's comment on that thread is still what we recommend" ]

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[ "what's your `pip show Pillow` output", "same issue.. my pip show Pillow output as below:\n\n```\nName: pillow\nVersion: 11.1.0\nSummary: Python Imaging Library (Fork)\nHome-page: https://python-pillow.github.io/\nAuthor: \nAuthor-email: \"Jeffrey A. Clark\" <[email protected]>\nLicense: MIT-CMU\nLocation: [/opt/homebrew/lib/python3.10/site-packages](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/lib/python3.10/site-packages)\nRequires: \nRequired-by:\n```", "I encountered the same problem on Ubuntu system, my pip show Pillow output as below:\n\n```\nName: pillow\nVersion: 10.4.0\nSummary: Python Imaging Library (Fork)\nHome-page: https://python-pillow.org/\nAuthor: \nAuthor-email: \"Jeffrey A. Clark\" <[[email protected]](mailto:[email protected])>\nLicense: HPND\nLocation: /home/shunying/.local/lib/python3.8/site-packages\nRequires: \nRequired-by: \n```\n\nWell, solved this by specifying the pip version to my conda virtual environment :)" ]

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[ "I got the same error and was able to resolve it by upgrading from 2.15.0 to 3.2.0.", "> I got the same error and was able to resolve it by upgrading from 2.15.0 to 3.2.0.\r\n\r\nWhat is needed is upgrading `huggingface-hub==0.27.1`. `datasets` does not appear to have anything to do with the error. The upgrade is a workaround, if the workaround works for your use case. Otherwise, this issue breaks all existing Python clients not using some minimum version of `huggingface-hub`. ", "Correct, this has to do with `huggingface_hub`, not `datasets`. Some old versions of `huggingface_hub` are unfortunately not robust to recent changes on HF. Updating `huggingface_hub` fixes the issue :)\r\n\r\nClosing this issue since it's not directly related to `datasets`" ]

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[ "Hi ! I couldn't reproduce on my side, can you try deleting your cache at `~/.cache/huggingface/modules/datasets_modules/datasets/InstaDeepAI--nucleotide_transformer_downstream_tasks_revised` and try again ? For some reason `datasets` wasn't able to find the DatasetBuilder class in the python script of this dataset", "I've met the same problem when importing [LongBench-v1](https://github.com/THUDM/LongBench/blob/main/LongBench/README.md). the debugger reports `dataset_module.builder_configs_parameters.builder_configs` as `None` so that no `builder_cls` gets created:\n\n<img width=\"711\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/b62bdea7-442b-47dc-b892-87f4d235e324\" />\n\ndoes this mean that I need to downgrade `datasets`?", "I tried downgrading `datasets` to v2.20.0 and it works fine now...\n\nI think there might be some compatibility issues during code updates between `v2.20.0` and `v3.0.0` 🤔 \n\nalso I suggest @nanu23333 to see if downgrading works.", "Found the same problem. When I tried to downgrade the datasets to version below v3.0.0, another problem was raised: `UnicodeDecodeError: 'utf-8' codec can't decode byte 0xb5 in position 1: invalid start byte`", "\nwhen I use the pip install datasets==3.3, I come across the error。Then I \n```\npip uninstall datasets\npip install datasets==2.21.0\n```\nIt is OK now" ]

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[ "Related to https://github.com/embeddings-benchmark/mteb/issues/1714" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7358). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The issue seems to come from `pyarrow`, I opened an issue on their side at https://github.com/apache/arrow/issues/45214" ]

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[ "@lhoestq \r\nIf it's okay with you, can I work on this?", "Hi ! Can you give an example of what it would look like to use this new feature ?\r\n\r\nNote that currently you can already do\r\n\r\n```python\r\nds[\"train\"] = ds[\"train\"].map(process_train)\r\nds[\"test\"] = ds[\"test\"].map(process_test)\r\n```", "@lhoestq \nThanks for the response! \nLet me clarify what I'm looking for with an example:\n\nCurrently, we need to write separate processing functions or call .map() separately:\n```python\n# Current approach\ndef process_train(example):\n # Training-specific processing\n return example\n\ndef process_valid(example):\n # Validation-specific processing\n return example\n\nds[\"train\"] = ds[\"train\"].map(process_train)\nds[\"valid\"] = ds[\"valid\"].map(process_valid)\n```\n\nWhat I'm proposing is to have a single processing function that knows which split it's processing:\n\n```python\n# Proposed feature\ndef process(example, split_key):\n if split_key == \"train\":\n # Training-specific processing\n elif split_key == \"valid\":\n # Validation-specific processing\n return example\n\n# Using with_key=True to pass the split information\nds = ds.map(process, with_key=True)\n```\n\nThis becomes particularly useful when:\n1. The processing logic is heavily shared between splits but needs minor adjustments\n2. You want to maintain the processing logic in one place for better maintainability\n3. The processing function is complex and you want to avoid duplicating code\n\nSo I wanted to request this feature to achieve this kind of functionality. \nI've created a draft PR implementing this: https://github.com/huggingface/datasets/pull/7240/files\n", "I see ! I think it makes sense, and it's more readable than doing something like this:\r\n```python\r\nfrom functools import partial\r\nds = DatasetDict({key: ds[key].map(partial(process, split_key=key)) for key in ds})\r\n```\r\n\r\nPS: you named the argument `with_key` in your example, but it might be even clearer with it's named `with_split` maybe no ?", "@lhoestq I agree. \nIt seems better to use `with_split`.\nSo can I open a PR with this change?", "Sure !" ]

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[ "It looks like an issue with `numba` which can't be installed on 3.13 ? `numba` is a dependency of `librosa`, used to decode audio files" ]

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[ "recreated .venv and run this: pip install diffusers[training]==0.11.1" ]

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[ "I noticed that `Dataset.map` has a more complex output depending on `remove_columns`. In particular [this](https://github.com/huggingface/datasets/blob/6457be66e2ef88411281eddc4e7698866a3977f1/src/datasets/arrow_dataset.py#L3371) line removes columns from output if the input is being modified in place (i.e. `input_columns = None`). I tried to mimic this behaviour in `MappedExamplesIterable` by checking if the input and output point to the same dictionary object.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7353). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7352). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7350). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7349). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7348). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "Hi,\r\n\r\nI've downloaded an Arrow-formatted dataset offline using the hugggingface's datasets library by:\r\n\r\nimport json\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"pixparse/cc3m-wds\")\r\ndataset.save_to_disk(\"./cc3m_1\")\r\n\r\n\r\nnow I need to convert it to WebDataset's TAR format for offline data ingestion.\r\nIs there a straightforward method to achieve this conversion without an internet connection? Can I simply convert it by\r\n\r\ntar -cvf\r\n\r\n\r\nbtw, when I tried:\r\n\r\nimport webdataset as wds\r\nfrom huggingface_hub import get_token\r\nfrom torch.utils.data import DataLoader\r\n\r\nhf_token = get_token()\r\nurl = \"https://huggingface.co/datasets/timm/imagenet-12k-wds/resolve/main/imagenet12k-train-{{0000..1023}}.tar\"\r\nurl = f\"pipe:curl -s -L {url} -H 'Authorization:Bearer {hf_token}'\"\r\ndataset = wds.WebDataset(url).decode()\r\ndataset.save_to_disk(\"./cc3m_webdataset\")\r\n\r\n\r\nerror occured:\r\n\r\nAttributeError: 'WebDataset' object has no attribute 'save_to_disk'\r\n\r\n\r\nThanks a lot!\r\n\r\nMotivation\r\n\r\nConverting Arrow to WebDataset TAR Format\r\n\r\nYour contribution\r\n\r\nNo clue yet\r\n\r\n\r\nاحصل على Outlook لـ iOS<https://aka.ms/o0ukef>\r\n________________________________\r\nمن: katie312 ***@***.***>\r\n‏‏تم الإرسال: Friday, December 27, 2024 4:41:21 AM\r\nإلى: huggingface/datasets ***@***.***>\r\nنسخة: Subscribed ***@***.***>\r\n‏‏الموضوع: [huggingface/datasets] Converting Arrow to WebDataset TAR Format for Offline Use (Issue #7347)\r\n\r\n\r\nFeature request\r\n\r\nHi,\r\n\r\nI've downloaded an Arrow-formatted dataset offline using the hugggingface's datasets library by:\r\n\r\nimport json\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"pixparse/cc3m-wds\")\r\ndataset.save_to_disk(\"./cc3m_1\")\r\n\r\n\r\nnow I need to convert it to WebDataset's TAR format for offline data ingestion.\r\nIs there a straightforward method to achieve this conversion without an internet connection? Can I simply convert it by\r\n\r\ntar -cvf\r\n\r\n\r\nbtw, when I tried:\r\n\r\nimport webdataset as wds\r\nfrom huggingface_hub import get_token\r\nfrom torch.utils.data import DataLoader\r\n\r\nhf_token = get_token()\r\nurl = \"https://huggingface.co/datasets/timm/imagenet-12k-wds/resolve/main/imagenet12k-train-{{0000..1023}}.tar\"\r\nurl = f\"pipe:curl -s -L {url} -H 'Authorization:Bearer {hf_token}'\"\r\ndataset = wds.WebDataset(url).decode()\r\ndataset.save_to_disk(\"./cc3m_webdataset\")\r\n\r\n\r\nerror occured:\r\n\r\nAttributeError: 'WebDataset' object has no attribute 'save_to_disk'\r\n\r\n\r\nThanks a lot!\r\n\r\nMotivation\r\n\r\nConverting Arrow to WebDataset TAR Format\r\n\r\nYour contribution\r\n\r\nNo clue yet\r\n\r\n—\r\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/7347>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AQJDZ2X2RUIIULBJEF5R2HL2HSV4DAVCNFSM6AAAAABUH5QSLCVHI2DSMVQWIX3LMV43ASLTON2WKOZSG43DAMRYGIZTGOI>.\r\nYou are receiving this because you are subscribed to this thread.Message ID: ***@***.***>\r\n", "> now I need to convert it to WebDataset's TAR format for offline data ingestion.\r\n\r\nyou can directly download the .TAR files from HF using e.g. `huggingface-cli download` and load them in webdataset :)", "الفله سنه والطبقه يوم\r\n\r\nاحصل على Outlook لـ iOS<https://aka.ms/o0ukef>\r\n________________________________\r\nمن: Quentin Lhoest ***@***.***>\r\n‏‏تم الإرسال: Friday, December 27, 2024 4:14:43 PM\r\nإلى: huggingface/datasets ***@***.***>\r\nنسخة: hamad350 ***@***.***>; Comment ***@***.***>\r\n‏‏الموضوع: Re: [huggingface/datasets] Converting Arrow to WebDataset TAR Format for Offline Use (Issue #7347)\r\n\r\n\r\nnow I need to convert it to WebDataset's TAR format for offline data ingestion.\r\n\r\nyou can directly download the .TAR files from HF using e.g. huggingface-cli download and load them in webdataset :)\r\n\r\n—\r\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/7347#issuecomment-2563691570>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AQJDZ2R5M3Z7L2MZZYARYID2HVHEHAVCNFSM6AAAAABUH5QSLCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDKNRTGY4TCNJXGA>.\r\nYou are receiving this because you commented.Message ID: ***@***.***>\r\n", "> > now I need to convert it to WebDataset's TAR format for offline data ingestion.\r\n> \r\n> you can directly download the .TAR files from HF using e.g. `huggingface-cli download` and load them in webdataset :)\r\n\r\nThanks a lot! I completely forgot to use Hugging Face-CLI download. Thanks for the reminding!" ]

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[ "Thanks for reporting, it looks like an issue with `pyarrow.ipc.open_stream`\r\n\r\nCan you try installing `datasets` from this pull request and see if it helps ? https://github.com/huggingface/datasets/pull/7348", "> Thanks for reporting, it looks like an issue with `pyarrow.ipc.open_stream`\r\n> \r\n> Can you try installing `datasets` from this pull request and see if it helps ? #7348\r\n\r\nThank you very much. Here, it also needed to be changed to `except (OSError, pa.lib.ArrowInvalid):`. And then the bug was fixed.\r\nhttps://github.com/huggingface/datasets/blob/2826a040a05e19fca894253b78a932d4fcb4a584/src/datasets/packaged_modules/arrow/arrow.py#L48", "Cool ! we will do a new release soon :) in the meantime you can use `datasets` from `main`" ]

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[ "Good catch ! Do you think you can open a PR to fix this issue ?" ]

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[ "Hi ! This is due to your old version of `datasets` which calls HF with `expand=True`, an option that is strongly rate limited.\r\n\r\nRecent versions of `datasets` don't rely on this anymore, you can fix your issue by upgrading `datasets` :)\r\n\r\n```\r\npip install -U datasets\r\n```\r\n\r\nYou can also get maximum HF availability on your compute nodes with HF Enterprise (see [network security features](https://huggingface.co/docs/hub/enterprise-hub-network-security))", "Upgrading fixed the issue for me. Thanks! " ]

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[ "Hi ! `data_files` with a list is equivalent to `data_files={\"train\": data_files}` with a train test only.\r\n\r\nWhen no split are specified, they are inferred based on file names, and files with no apparent split are ignored", "Thanks for your reply!\r\n`files with no apparent split are ignored`. Is there a option that I can choose to ignored it or not as I mention aboved? Thanks!", "To include all the files, the best way is to pass `data_files` yourself. There is no option to disable split detection at the moment", "Thanks! I hope you guys can consider adding this option in the future. :)" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7342). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7341). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7340). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7339). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "Hmmm I double checked in the source code and I found a contradiction: in the current implementation the metadata file is ignored if it's not in the same archive as the zip image somehow:\r\n\r\nhttps://github.com/huggingface/datasets/blob/caa705e8bf4bedf1a956f48b545283b2ca14170a/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L352-L353\r\n\r\nin the tests suite the metadata file is placed inside the archive:\r\n\r\nhttps://github.com/huggingface/datasets/blob/caa705e8bf4bedf1a956f48b545283b2ca14170a/tests/packaged_modules/test_imagefolder.py#L223-L223\r\n\r\nThanks for reporting this issue, it seems the documentation is wrong and we never implemented the support for zip + metadata outside zip. We might rewrite part of this code soon though to make it more flexible, it can be a good occasion to fix this. In the meantime feel free to open a PR to fix the documentation if you'd like" ]

[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7328). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]