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[ "Hi @stas00, thanks for pointing out this issue with logging.\r\n\r\nI agree that `datasets` can sometimes be too verbose... I can create a PR and we could discuss there the choice of the log levels for different parts of the code." ]

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[ "very much related: https://github.com/huggingface/datasets/pull/2333", "Hi @VictorSanh, thank you for reporting this issue with duplicated keys.\r\n\r\n- The issue with \"adversarial_qa\" was fixed 23 days ago: #2433. Current version of `datasets` (1.8.0) includes the patch.\r\n- I am investigating the issue with `drop`. I'll ping you to keep you informed.", "Hi @VictorSanh, the issue is already fixed and merged into master branch and will be included in our next release version 1.9.0.", "thank you!" ]

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[ "The CI is failing because the dataset tags for `discofuse` are missing. I'm merging this PR since this is unrelated to this PR, but feel free to open another PR to add the tags here if you have some time:\r\n\r\nhttps://github.com/huggingface/datasets/blob/19408f9fab85c79b966085574cd2da3b90959179/datasets/discofuse/README.md#L1-L5\r\n\r\nThe missing tags are:\r\n```\r\n'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'pretty_name', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'\r\n```\r\nThanks again !" ]

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[ "Hi ! I'm sorry to hear that.\r\nThough we are not redistributing the dataset, we just provide a python script that downloads and process the dataset from its original source hosted at https://www.cl.cam.ac.uk\r\n\r\nTherefore I'm not sure what's the issue with licensing. What do you mean exactly ?", "I think that the main issue is that the licesenses of the data are not made clear in the huggingface hub – other people wrongly assumed that the data was license-free, which resulted in commercial use, which is against the licenses.\r\nIs it possible to add the licenses from the original download to huggingface? that would help clear any confusion (licenses can be found here: https://www.cl.cam.ac.uk/research/nl/bea2019st/data/wi+locness_v2.1.bea19.tar.gz)", "Thanks for the clarification @SimonHFL \r\nYou're completely right, we need to show the licenses.\r\nI just added them here: https://huggingface.co/datasets/wi_locness#licensing-information", "Hi guys, I'm one of the authors of this dataset. \r\n\r\nTo clarify, we're happy for you to keep the data in the repo on 2 conditions:\r\n1. You don't host the data yourself.\r\n2. You make it clear that anyone who downloads the data via HuggingFace should read and abide by the license. \r\n\r\nI think you've now met these conditions, so we're all good, but I just wanted to make it clear in case there are any issues in the future. Thanks again to @aseifert for bringing this to our attention! :)", "Thanks for your message @chrisjbryant :)\r\nI'm closing this PR then.\r\n\r\nAnd thanks for reporting @aseifert" ]

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[ "Hi ! `load_dataset` downloads the full dataset once and caches it, so that subsequent calls to `load_dataset` just reloads the dataset from your disk.\r\nThen when you specify a `split` in `load_dataset`, it will just load the requested split from the disk. If your specified split is a sliced split (e.g. `\"train[:10]\"`), then it will load the 10 first rows of the train split that you have on disk.\r\n\r\nTherefore, as long as you don't delete your cache, all your calls to `load_dataset` will be very fast. Except the first call that downloads the dataset of course ^^", "That’s a use case for the new streaming feature, no?", "Hi @reachtarunhere.\r\n\r\nBesides the above insights provided by @lhoestq and @thomwolf, there is also a Dataset feature in progress (I plan to finish it this week): #2249, which will allow you, when calling `load_dataset`, to pass the option to download/preprocess/cache only some specific split(s), which will definitely speed up your workflow.\r\n\r\nIf this feature is interesting for you, I can ping you once it will be merged into the master branch.", "Thanks all for responding.\r\n\r\nHey @albertvillanova \r\n\r\nThanks. Yes, I would be interested.\r\n\r\n@lhoestq I think even if a small split is specified it loads up the full dataset from the disk (please correct me if this is not the case). Because it does seem to be slow to me even on subsequent calls. There is no repeated downloading so it seems that the cache is working.\r\n\r\nI am not aware of the streaming feature @thomwolf mentioned. So I might need to read up on it.", "@reshinthadithyan I use the .select function to have a fraction of indices.", "If I want to create a dataset, containing only the 10 elements of a given dataset (slice it), how do I do that?", "```python \r\nsmall_ds = ds.select(range(10))\r\n```", "\r\n\r\n> ```python\r\n> small_ds = ds.select(range(10))\r\n> ```\r\n\r\nThanks, but this doesn't help me to save time during initial loading, right?", "Indeed by default load_dataset would download and prepare everything as Arrow files. And passing `split=train[:10]` memory maps only the beginning of the full dataset that has been prepared on disk.\r\n\r\nIf you don't want to download everything, you can use streaming : \r\n```python \r\nids = load_dataset(..., streaming=True)\r\nfirst_samples = list(ids[\"train\"].take(10))\r\n```\r\n\r\nTo get a Dataset you can use \r\n```python \r\nds = Dataset.from_generator(ids.take(10).__iter__)\r\n```\r\n\r\nedit: fixed small bug", "Thanks @lhoestq, but I don't think it is 100% accurate, as it doesn't keep the dataset structure exactly the same.\r\nTo load the full dataset, I do:\r\n```\r\ndata = load_dataset(\"json\", data_files=\"a.json\")\r\ntrain_data = data[\"train\"].shuffle()\r\n```\r\n\r\nBut when I am changing it as per your instructions: \r\n```\r\nids = load_dataset(\"json\", data_files=\"a.json\", streaming=True)\r\ndata = Dataset.from_generator(ids[\"train\"].take(1).__iter__)\r\ntrain_data = data[\"train\"].shuffle()\r\n```\r\nIt throws KeyError.\r\nI need a simple way, like you suggested, to have a subset of a Dataset, which exactly the same attributes.\r\n", "Whoops I fixed my code sorry\r\n```diff\r\n- ds = Dataset.from_generator(ids[\"train\"].take(10).__iter__)\r\n+ ds = Dataset.from_generator(ids.take(10).__iter__)\r\n```\r\n\r\nin your case that means running\r\n```python\r\ntrain_data = data.shuffle()\r\n```\r\n\r\nwithout `[\"train\"]`" ]

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[ "`pyarrow` 1.0.0 doesn't support some types in parquet, we'll have to bump its minimum version.\r\n\r\nAlso I still need to add dummy data to test the parquet builder.", "I had to bump the minimum pyarrow version to 3.0.0 to properly support parquet.\r\n\r\nEverything is ready for review now :)\r\nI reused pretty much the same tests we had for CSV", "Done !\r\nNow we're still allowing pyarrow>=1.0.0, but when users want to use parquet features they're asked to update to pyarrow>=3.0.0" ]

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[ "I'm just retaking and working on #2324. 😉 ", "Resolved via https://github.com/huggingface/datasets/pull/4006." ]

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[ "Nice thanks ! I think there are other places with\r\n```python\r\nnot_verbose = bool(logger.getEffectiveLevel() > WARNING)\r\n```\r\n\r\nCould you replace them as well ?", "Sure @lhoestq! I was not sure if this change should only be circumscribed to `http_get`..." ]

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[ "@SBrandeis @lhoestq i've integrated your suggestions, so this is ready for another review :)", "Merging if it's good for you @lewtun :)" ]

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[ "Hi @jerryIsHere, thanks for reporting the issue. But are you sure this is a bug in HuggingFace **Datasets**?", "> Hi @jerryIsHere, thanks for reporting the issue. But are you sure this is a bug in HuggingFace **Datasets**?\r\n\r\nOh, I am sorry\r\nI would reopen the post on huggingface/transformers" ]

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[ "@lhoestq, can you please review this PR?\r\nWhat exactly is the problem in the test case? Should it matter?", "Hi ! Thanks for fixing this :)\r\n\r\nThe CI fails for two reasons:\r\n- the `pretty_name` tag is missing in yaml tags in ./datasets/turkish_product_reviews/README.md. You can fix that by adding this in the yaml tags:\r\n```yaml\r\npretty_name: Turkish Product Reviews\r\n```\r\n- The test that runs the turkish_product_reviews.py file on the dummy_data.zip data returned 0 examples. Indeed it looks like you changed dummy_data.zip file and now it is an empty zip file. I think you can fix that by reverting your change to the dummy_data.zip file", "> Hi ! Thanks for fixing this :)\r\n> \r\n> The CI fails for two reasons:\r\n> \r\n> * the `pretty_name` tag is missing in yaml tags in ./datasets/turkish_product_reviews/README.md. You can fix that by adding this in the yaml tags:\r\n> \r\n> \r\n> ```yaml\r\n> pretty_name: Turkish Product Reviews\r\n> ```\r\n> \r\n> * The test that runs the turkish_product_reviews.py file on the dummy_data.zip data returned 0 examples. Indeed it looks like you changed dummy_data.zip file and now it is an empty zip file. I think you can fix that by reverting your change to the dummy_data.zip file\r\n\r\nMany thanks for the quick feedback.\r\nI made the relevant fixes but still got the error :(", "> Thanks !\r\n> The CI was failing because of the dataset card that was missing some sections. I fixed that.\r\n> \r\n> It's all good now\r\n\r\nSuper. Thanks for the support." ]

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[ "> Nice thanks !\r\n> Could you just move the test outside of the BaseDatasetTest class please ? Otherwise it will unnecessarily be run twice.\r\n\r\nsure, on it! thanks for the explanations about the `self._to` method :)", "@lhoestq i've moved all the task template tests outside of `BaseDatasetTest` and collected them in their dedicated test case. (at some point i'll revisit this so we can just use `pytest` natively, but the PR is already getting out-of-scope :))" ]

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[ "Hi @joshzwiebel, thanks for reporting. We are going to align with `transformers`." ]

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[ "I'm currently adding it, the entire dataset is quite big around 30 GB so I add splits separately. You can take a look here https://huggingface.co/datasets/merve/coco", "I talked to @lhoestq and it's best if I download this dataset through TensorFlow datasets instead, so I'll be implementing that one really soon.\r\n@NielsRogge ", "I started adding COCO, will be done tomorrow EOD\r\nmy work so far https://github.com/merveenoyan/datasets (my fork)", "Hi Merve @merveenoyan , thank you so much for your great contribution! May I ask about the current progress of your implementation? Cuz I see the pull request is still in progess here. Or can I just run the COCO scripts in your fork repo?", "Hello @yixuanren I had another prioritized project about to be merged, but I'll start continuing today will finish up soon. ", "> Hello @yixuanren I had another prioritized project about to be merged, but I'll start continuing today will finish up soon.\r\n\r\nIt's really nice of you!! I see you've commited another version just now", "@yixuanren we're working on it, will be available soon, thanks a lot for your patience", "Hi @NielsRogge and @merveenoyan, did you find a way to load a dataset with COCO annotations to HF's hub?\r\nI have a panoptic segmentation dataset in COCO format and would like to share it with the community.\r\nThanks in advance :)", "The COCO format is not supported out of the box in the HF's hub - you'd need to reformat it to an [ImageFolder](https://huggingface.co/docs/datasets/image_dataset#imagefolder) with metadata format, or write a [loading script](https://huggingface.co/docs/datasets/image_dataset#loading-script)", "> The COCO format is not supported out of the box in the HF's hub - you'd need to reformat it to an [ImageFolder](https://huggingface.co/docs/datasets/image_dataset#imagefolder) with metadata format, or write a [loading script](https://huggingface.co/docs/datasets/image_dataset#loading-script)\r\n\r\nHi @lhoestq , thank you for your quick reply.\r\nI've correctly created a metadata.jsonl file for a dataset with instance segmentation annotations [here](https://huggingface.co/datasets/lombardata/data_2017)\r\nbut do not understand how I can integrate panoptic annotations with the metadata format of ImageFolder datasets. The \"problem\" with panoptic annotations is that we have a folder with images, a json file with annotations and another folder with png annotations.\r\n\r\nI checked between all the datasets already published on HuggingFace and, the only one who has uploaded a correct panoptic dataset is @NielsRogge [here](https://huggingface.co/datasets/nielsr/coco-panoptic-val2017) and [here](https://huggingface.co/datasets/nielsr/ade20k-panoptic-demo). Indeed he accomplished to have three fields : \r\n1.image (image)\r\n2.label (image)\r\n3.segments_info (list)\r\nbut I not find the corresponding code that allows to upload a panoptic dataset from this 3 sources.\r\nCan you please share an example code?\r\nThanks !", "Both were uploaded using `ds.push_to_hub()` :)\r\n\r\nYou can get a Dataset from a python dictionary using `ds = Dataset.from_dict(...)` and casts the paths to images to the `Image()` type using `ds = ds.cast_column(\"image\", Image())`.\r\n\r\n```python\r\nfrom datasets import Dataset, Image\r\n\r\nds = Dataset.from_dict(...)\r\nds = ds.cast_column(\"image\", Image())\r\nds = ds.cast_column(\"label\", Image())\r\nds.push_to_hub(...)\r\n```", "> Both were uploaded using `ds.push_to_hub()` :)\r\n> \r\n> You can get a Dataset from a python dictionary using `ds = Dataset.from_dict(...)` and casts the paths to images to the `Image()` type using `ds = ds.cast_column(\"image\", Image())`.\r\n> \r\n> ```python\r\n> from datasets import Dataset, Image\r\n> \r\n> ds = Dataset.from_dict(...)\r\n> ds = ds.cast_column(\"image\", Image())\r\n> ds = ds.cast_column(\"label\", Image())\r\n> ds.push_to_hub(...)\r\n> ```\r\n\r\nThank you very much @lhoestq , I succesfully created a hf dataset [here](https://huggingface.co/datasets/lombardata/panoptic_2023_06_21) with the two fields :\r\n1.image (image)\r\n2.label (image)\r\nfollowing your suggestions. Now still remain the problem of uploading **segments_info** information to the dataset.\r\nThere is a function that easily imports the _panoptic_coco_annotation.json_ file to a segment_info field?\r\nI think we must define a **list_of_segment**, i.e. a list of lists of this type : \r\n```python\r\n[ { \"area\": 214858, \"bbox\": [ 0, 0, 511, 760 ], \"category_id\": 0, \"id\": 7895160, \"iscrowd\": 0 }, { \"area\": 73067, \"bbox\": [ 98, 719, 413, 253 ], \"category_id\": 3, \"id\": 3289680, \"iscrowd\": 0 }, { \"area\": 832, \"bbox\": [ 53, 0, 101, 16 ], \"category_id\": 5, \"id\": 5273720, \"iscrowd\": 0 }, { \"area\": 70668, \"bbox\": [ 318, 60, 191, 392 ], \"category_id\": 8, \"id\": 15132390, \"iscrowd\": 0 }, { \"area\": 32696, \"bbox\": [ 0, 100, 78, 872 ], \"category_id\": 18, \"id\": 472063, \"iscrowd\": 0 }, { \"area\": 76045, \"bbox\": [ 42, 48, 264, 924 ], \"category_id\": 37, \"id\": 16713830, \"iscrowd\": 0 }, { \"area\": 27103, \"bbox\": [ 288, 482, 216, 306 ], \"category_id\": 47, \"id\": 16753408, \"iscrowd\": 0 } ]\r\n```\r\nand then apply again the **cast_column** function [here](https://github.com/huggingface/datasets/blob/2.13.0/src/datasets/arrow_dataset.py#L2060) but with a list as a second argument, like : \r\n```python\r\nfrom datasets import Dataset, Image\r\nds = ds.cast_column(\"image\", Image())\r\nds = ds.cast_column(\"label\", Image())\r\nds = ds.cast_column(\"segments_info\", list)\r\n```\r\nbut I do not see how to transfer the information of the _panoptic_coco_annotation.json_ to a list of lists of this type : \r\n```python\r\n[ { \"area\": 214858, \"bbox\": [ 0, 0, 511, 760 ], \"category_id\": 0, \"id\": 7895160, \"iscrowd\": 0 }, { \"area\": 73067, \"bbox\": [ 98, 719, 413, 253 ], \"category_id\": 3, \"id\": 3289680, \"iscrowd\": 0 }, { \"area\": 832, \"bbox\": [ 53, 0, 101, 16 ], \"category_id\": 5, \"id\": 5273720, \"iscrowd\": 0 }, { \"area\": 70668, \"bbox\": [ 318, 60, 191, 392 ], \"category_id\": 8, \"id\": 15132390, \"iscrowd\": 0 }, { \"area\": 32696, \"bbox\": [ 0, 100, 78, 872 ], \"category_id\": 18, \"id\": 472063, \"iscrowd\": 0 }, { \"area\": 76045, \"bbox\": [ 42, 48, 264, 924 ], \"category_id\": 37, \"id\": 16713830, \"iscrowd\": 0 }, { \"area\": 27103, \"bbox\": [ 288, 482, 216, 306 ], \"category_id\": 47, \"id\": 16753408, \"iscrowd\": 0 } ]\r\n```\r\nlike @NielsRogge has done [here](https://huggingface.co/datasets/nielsr/coco-panoptic-val2017) and [here](https://huggingface.co/datasets/nielsr/ade20k-panoptic-demo).\r\nThank you again for your help and have a good day !", "You can pass this data in .from_dict() - no need to cast anything for this column\r\n\r\n```python\r\nds = Dataset.from_dict({\r\n \"image\": [...],\r\n \"label\": [...],\r\n \"segments_info\": [...],\r\n)}\r\n```\r\n\r\nwhere `segments_info` is the list of the segment_infos of all the examples in the dataset, and therefore is a list of lists of dicts.", "> You can pass this data in .from_dict() - no need to cast anything for this column\r\n> \r\n> ```python\r\n> ds = Dataset.from_dict({\r\n> \"image\": [...],\r\n> \"label\": [...],\r\n> \"segments_info\": [...],\r\n> )}\r\n> ```\r\n> \r\n> where `segments_info` is the list of the segment_infos of all the examples in the dataset, and therefore is a list of lists of dicts.\r\n\r\nThank you for the quick reply @lhoestq , but then how to generate the `segments_info` list of lists of dicts starting from a _panoptic_coco_annotation.json_ file ?\r\n\r\n\r\n", "You read the JSON file and transform the data yourself. I don't think there's an automatic converter somewhere", "> You read the JSON file and transform the data yourself. I don't think there's an automatic converter somewhere\r\n\r\nPerfect, I've done it and succesfully uploaded a new dataset [here](https://huggingface.co/datasets/lombardata/panoptic_2023_06_22), but I've (I hope) a last problem.\r\nThe dataset has currently 302 images and, when I upload it to the hub, only the first page of images is correctly uploaded.\r\nWhen I try to see the second/third/fourth page of items of my dataset, I can see that the fields **segments_info** and **image_name** are correctly uploaded, while the images are not (the \"null\" string is shown everywhere).\r\n\r\nI've checked the path of images that are not uploaded and they exists, is there a problem with the size of the dataset ?\r\nHow can I upload the whole dataset to the hub ?\r\nThank you again @lhoestq and have a good day !", "Awesome ! Your dataset looks all good 🤗 \r\n\r\nThe `null` in the viewer is a bug on our side, let me investigate" ]

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[ "Hi ! Could you clarify what it fixes exactly and give more details please ? Especially why this is related to the windows hanging error ?", "This has already been merged, but I'll clarify the idea of this PR. Before this merge, FileLock was the only component affected by the max path limit on Windows (that came to my notice) because of its infinite loop that would suppress errors. So instead of suppressing the `FileNotFoundError` that is thrown by `os.open` if the file name is longer than the max allowed path length, this PR reraises it to notify the user." ]

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[ "Hi,\r\n\r\nthis issue has been already reported in the dataset repo (https://github.com/dair-ai/emotion_dataset/issues/2), so this is a bug on their side.", "The documentation has another bug in the dataset card [here](https://huggingface.co/datasets/emotion). \r\n\r\nIn the dataset summary **six** emotions are mentioned: *\"six basic emotions: anger, fear, joy, love, sadness, and surprise\"*, however, in the datafields section we have only **five**:\r\n```\r\nlabel: a classification label, with possible values including sadness (0), joy (1), love (2), anger (3), fear (4).\r\n```", "@GDGauravDutta the dataset author replied in their issue: https://github.com/dair-ai/emotion_dataset/issues/2\r\n> The dataset released is a preprocessed six emotion variant released for educational and research purposes.\r\n\r\n@albertovilla the dataset card was fixed with 6 emotions." ]

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[ "The `task_templates` API is deprecated in favor of the `train-eval-index` YAML field, so I'm closing this issue." ]

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[ "Looks like this change\r\n```\r\npa_table[pa_table.column_names[0]].to_pandas(types_mapper=pandas_types_mapper)\r\n```\r\ndoesn't return a Series with the correct type.\r\nThis is related to https://issues.apache.org/jira/browse/ARROW-9664\r\n\r\nSince the types_mapper isn't taken into account, the ArrayXD types are not converted to the correct pandas extension dtype", "@lhoestq I think I found a workaround... 😉 ", "For some reason the benchmarks are not run Oo", "Anyway, merging.\r\nWe'll see on master how much speed ups we got" ]

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[ "Just tested TydiQA and it works fine :)" ]

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[ "Hi ! `map` calls `__getstate__` using `dill` to hash your map function. This is used by the caching mechanism to recover previously computed results. That's why you don't see any `__setstate__` call.\r\n\r\nWhy do you change an attribute of your tokenizer when `__getstate__` is called ?", "@lhoestq because if I try to pickle my custom tokenizer (it contains a pure python pretokenization step in an otherwise rust backed tokenizer) I get\r\n\r\n> Exception: Error while attempting to pickle Tokenizer: Custom PreTokenizer cannot be serialized\r\n\r\nSo I remove the Custom PreTokenizer in `__getstate__` and then restore it in `__setstate__` (since it doesn't contain any state). This is what my `__getstate__` / `__setstate__` looks like:\r\n\r\n def __getstate__(self):\r\n \"\"\"\r\n Removes pre_tokenizer since it cannot be pickled\r\n \"\"\"\r\n logger.debug(\"Copy state dict\")\r\n out = self.__dict__.copy()\r\n logger.debug(\"Detaching pre_tokenizer\")\r\n out['_tokenizer'].pre_tokenizer = tokenizers.pre_tokenizers.Sequence([]) \r\n return out\r\n\r\n def __setstate__(self, d):\r\n \"\"\"\r\n Reinstates pre_tokenizer\r\n \"\"\"\r\n logger.debug(\"Reattaching pre_tokenizer\")\r\n self.__dict__ = d\r\n self.backend_tokenizer.pre_tokenizer = self._pre_tokenizer()\r\n\r\nIf this is the case can you think of another way of avoiding my issue?", "Actually, maybe I need to deep copy `self.__dict__`? That way `self` isn't modified. That was my intention and I thought it was working - I'll double-check after the weekend.", "Doing a deep copy results in the warning:\r\n\r\n> 06/20/2021 16:02:15 - WARNING - datasets.fingerprint - Parameter 'function'=<function tokenize_function at 0x7f1e95f05d40> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\n\r\n```\r\ndef __getstate__(self):\r\n \"\"\"\r\n Removes pre_tokenizer since it cannot be pickled\r\n \"\"\"\r\n logger.debug(\"Copy state dict\")\r\n out = copy.deepcopy(self.__dict__)\r\n logger.debug(\"Detaching pre_tokenizer\")\r\n out['_tokenizer'].pre_tokenizer = tokenizers.pre_tokenizers.Sequence([]) \r\n return out\r\n```", "Looks like there is still an object that is not pickable in your `tokenize_function` function.\r\n\r\nYou can test if an object can be pickled and hashed by using \r\n```python\r\nfrom datasets.fingerprint import Hasher\r\n\r\nHasher.hash(my_object)\r\n```\r\n\r\nUnder the hood it pickles the object to compute its hash, so it calls `__getstate__` when applicable.", "I figured it out, the problem is deep copy itself uses pickle (unless you implement `__deepcopy__`). So when I changed `__getstate__` it started throwing an error.\r\n\r\nI'm sure there's a better way of doing this, but in order to return the `__dict__` without the non-pikelable pre-tokeniser and without modifying self I removed the pre-tokenizers, did a deep copy and then re-generated it.\r\n\r\nIt does work - although I noticed Hasher doesn't call `__hash__` if the object being hashed implements it which I feel it should? If it did I could return a hash of the tokenizers.json file instead.\r\n\r\n```\r\n def __getstate__(self):\r\n \"\"\"\r\n Removes pre_tokenizer since it cannot be pickled\r\n \"\"\"\r\n logger.debug(\"Copy state dict\")\r\n self.backend_tokenizer.pre_tokenizer = tokenizers.pre_tokenizers.Sequence([])\r\n out = copy.deepcopy(self.__dict__) #self.__dict__.copy()\r\n self.backend_tokenizer.pre_tokenizer = self._pre_tokenizer()\r\n\r\n return out\r\n```\r\n", "I'm glad you figured something out :)\r\n\r\nRegarding hashing: we're not using hashing for the same purpose as the python `__hash__` purpose (which is in general for dictionary lookups). For example it is allowed for python hashing to not return the same hash across sessions, while our hashing must return the same hashes across sessions for the caching to work properly." ]

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[ "Hi ! For now this is probably the best option.\r\nWe might add a feature like this in the feature as well.\r\n\r\nDo you know any deduplication method that works on arbitrary big datasets without filling up RAM ?\r\nOtherwise we can have do the deduplication in memory like pandas but I feel like this is going to be limiting for some cases", "Yes, I'd like to work on this feature once I'm done with #2500, but first I have to do some research, and see if the implementation wouldn't be too complex.\r\n\r\nIn the meantime, maybe [this lib](https://github.com/TomScheffers/pyarrow_ops) can help. However, note that this lib operates directly on pyarrow tables and relies only on `hash` to find duplicates (e.g. `-1` and `-2` have the same hash in Python 3, so this lib will treat them as duplicates), which doesn't make much sense.", "> Hi ! For now this is probably the best option.\r\n> We might add a feature like this in the feature as well.\r\n> \r\n> Do you know any deduplication method that works on arbitrary big datasets without filling up RAM ?\r\n> Otherwise we can have do the deduplication in memory like pandas but I feel like this is going to be limiting for some cases\r\n\r\nGreat if this is can be done. Thanks!!\r\n\r\nNot sure if you are asking me. In any case I don't know of any unfortunately :( in practice if data is really large we normally do it with spark (only for info. I understand this is not useful in developing this library..)", "Hello,\r\n\r\nI'm also interested in this feature.\r\nHas there been progress on this issue?\r\n\r\nCould we use a similar trick as above, but with a better hashing algorithm like SHA?\r\n\r\nWe could also use a [bloom filter](https://en.wikipedia.org/wiki/Bloom_filter), should we care a lot about collision in this case?", "For reference, we can get a solution fairly easily if we assume that we can hold in memory all unique values. \r\n\r\n```python\r\nfrom datasets import Dataset\r\nfrom itertools import cycle\r\nfrom functools import partial\r\n\r\nmemory = set()\r\ndef is_unique(elem:Any , column: str, memory: set) -> bool:\r\n if elem[column] in memory:\r\n return False\r\n else:\r\n memory.add(elem[column])\r\n return True\r\n\r\n# Example dataset\r\nds = Dataset.from_dict({\"col1\" : [sent for i, sent in zip(range(10), cycle([\"apple\", \"orange\", \"pear\"]))],\r\n \"col2\": [i % 5 for i in range(10)]})\r\n\r\n# Drop duplicates in `ds` on \"col1\"\r\nds2 = ds.filter(partial(is_unique, column=\"col1\", memory=memory))\r\n```\r\n\r\nOf course, we can improve the API so that we can introduce `Dataset.drop_duplicates`.\r\nFor the parallel version, we can use a shared memory set.", "An approach that works assuming you can hold the all the unique document hashes in memory:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndef get_hash(example):\r\n \"\"\"Get hash of content field.\"\"\"\r\n return {\"hash\": hash(example[\"content\"])} # can use any hashing function here\r\n \r\ndef check_uniques(example, uniques):\r\n \"\"\"Check if current hash is still in set of unique hashes and remove if true.\"\"\"\r\n if example[\"hash\"] in uniques:\r\n uniques.remove(example[\"hash\"])\r\n return True\r\n else:\r\n return False\r\n\r\nds = load_dataset(\"some_dataset\")\r\nds = ds.map(get_hash)\r\nuniques = set(ds.unique(\"hash\"))\r\nds_filter = ds.filter(check_uniques, fn_kwargs={\"uniques\": uniques})\r\n```\r\nIf the `uniques` could be stored in arrow then no additional memory would used at all but I don't know if this is possible.\r\n", "@lvwerra hey, could you tell me how reliable is this deduplication method. i am currently using the same deduplication strategy to deduplicate a large text corpus to pretrain LLMs ~ 11B to 20B. just needed to ensure if this strategy would be fine on large datasets for LLMs pretraining. ", "Hi @StephennFernandes I'm also trying to pretrain an llm, and need to do deduplication for my dataset, \r\nwhich method you applied please?", "Hey @Manel-Hik \n\nThe following is a simpler yet really effective deduplication code that i has used in the past. \n\ngiven that I have limited training corpus for the languages I wanted to train i relied on this code. https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned/blob/main/deduplicate.py\n\n\nfor more robust and stronger deduplication, refer to this huggingface repo, that's newly released: https://github.com/huggingface/datatrove\n\n\n\n", "Thanks a lot Sure I will check it @StephennFernandes " ]

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[ "Hi @colbym-MM, thanks for reporting. We are fixing it." ]

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[ "Sorry, I was using an old version of sequeval" ]

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[ "Update on this: I'm computing the checksums of the data files. It will be available soon", "Added in #2575 :)" ]

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[ "Windows, why are you doing this to me ?", "Thanks @lhoestq, I'm starting reviewing this PR.", "Yea issues on windows are about long paths, not long filenames.\r\nWe can make sure the lock filenames are not too long, but not for the paths", "Took your suggestions into account @albertvillanova :)" ]

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[ "Hi ! Is this folder structure a standard, a bit like imagenet ?\r\nIn this case maybe we can consider having a dataset loader for cifar-like, imagenet-like, squad-like, conll-like etc. datasets ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nmy_custom_cifar = load_dataset(\"cifar_like\", data_dir=\"path/to/data/dir\")\r\n```\r\n\r\nLet me know what you think", "Yep that would be sweet - closing for now as we found a workaround. ", "@lhoestq I think we'll want a generic `image-folder` dataset (same as 'imagenet-like'). This is like `torchvision.datasets.ImageFolder`, and is something vision folks are used to seeing.", "Opening this back up, since I'm planning on tackling this. Already posted a quick version of it on my account on the hub.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('nateraw/image-folder', data_files='PetImages/')\r\n```", "Bumping this one following our recent discussion @mariosasko @nateraw :)" ]

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[ "Looks like we have a nice speed up in some benchmarks. For example:\r\n- `read_formatted numpy 5000`: 4.584777 sec -> 0.487113 sec\r\n- `read_formatted torch 5000`: 4.565676 sec -> 1.289514 sec", "Can we convert this draft to PR @lhoestq ?", "Ready for review ! cc @vblagoje", "@lhoestq I tried the branch and it works for me. Although performance trace now shows a speedup, the overall pre-training speed up is minimal. But that's on my plate to explore further. ", "Thanks for investigating @vblagoje \r\n\r\n@albertvillanova , do you have any comments on this PR ? Otherwise I think we can merge it" ]

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[ "Hi @arnaudstiegler, thanks for reporting. I'm investigating it.", "@arnaudstiegler I have just checked that these mismatches are already present in the original dataset: https://github.com/megagonlabs/SubjQA\r\n\r\nWe are going to contact the dataset owners to report this.", "I have:\r\n- opened an issue in their repo: https://github.com/megagonlabs/SubjQA/issues/3\r\n- written an email to all the paper authors", "Please [see my response](https://github.com/megagonlabs/SubjQA/issues/3#issuecomment-905160010). There will be a fix in a couple of days." ]

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[ "Hi @mariosasko, thanks for taking on this issue.\r\n\r\nJust a few logistic suggestions, as you are one of our most active contributors ❤️ :\r\n- When you start working on an issue, you can self-assign it to you by commenting on the issue page with the keyword: `#self-assign`; we have implemented a GitHub Action to take care of that... 😉 \r\n- When you are still working on your Pull Request, instead of using the `[WIP]` in the PR name, you can instead create a *draft* pull request: use the drop-down (on the right of the *Create Pull Request* button) and select **Create Draft Pull Request**, then click **Draft Pull Request**.\r\n\r\nI hope you find these hints useful. 🤗 ", "@albertvillanova Thanks for the tips. When creating this PR, it slipped my mind that this should be a draft. GH has an option to convert already created PRs to draft PRs, but this requires write access for the repo, so maybe you can help.", "Ready for the review!\r\n\r\nOne additional change. I've modified the `camelcase_to_snakecase`/`snakecase_to_camelcase` conversion functions to fix conversion of the names with 2 or more underscores (e.g. `camelcase_to_snakecase(\"__DummyDataset__\")` would return `___dummy_dataset__`; notice one extra underscore at the beginning). The implementation is based on the [inflection](https://pypi.org/project/inflection/) library.\r\n", "Thank you for adding this feature, @mariosasko - this is really awesome!\r\n\r\nTried with:\r\n```\r\npython -c \"from datasets import load_dataset_builder; b = load_dataset_builder('openwebtext-10k'); print(b.cache_dir)\"\r\nUsing the latest cached version of the module from /home/stas/.cache/huggingface/modules/datasets_modules/datasets\r\n/openwebtext-10k/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b (last modified on Wed May 12 \r\n20:22:53 2021) \r\n\r\nsince it couldn't be found locally at openwebtext-10k/openwebtext-10k.py \r\n\r\nor remotely (FileNotFoundError).\r\n\r\n/home/stas/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\r\n```\r\n\r\nThe logger message (edited by me to add new lines to point the issues out) is a bit confusing to the user - that is what does `FileNotFoundError` refer to? \r\n\r\n1. May be replace `FileNotFoundError` with where it was looking for a file online. But then the remote file is there - it's found \r\n2. I'm not sure why it says \"since it couldn't be found locally\" - as it is locally found at the cache folder and again what does \" locally at openwebtext-10k/openwebtext-10k.py\" mean - i.e. where does it look for it? Is it `./openwebtext-10k/openwebtext-10k.py` it's looking for? or in some specific dir?\r\n\r\nIf the cached version always supersedes any other versions perhaps this is what it should say?\r\n```\r\nfound cached version at xxx, not looking for a local at yyy, not downloading remote at zzz\r\n```", "Hi ! Thanks for the comments\r\n\r\nRegarding your last message:\r\nYou must pass `stas/openwebtext-10k` as in `load_dataset` instead of `openwebtext-10k`. Otherwise it doesn't know how to retrieve the builder from the HF Hub.\r\n\r\nWhen you specify a dataset name without a slash, it tries to load a canonical dataset or it looks locally at ./openwebtext-10k/openwebtext-10k.py\r\nHere since `openwebtext-10k` is not a canonical dataset and doesn't exist locally at ./openwebtext-10k/openwebtext-10k.py: it raised a FileNotFoundError.\r\nAs a fallback it managed to find the dataset script in your cache and it used this one.", "Oh, I see, so I actually used an incorrect input. so it was a user error. Correcting it:\r\n\r\n```\r\npython -c \"from datasets import load_dataset_builder; b = load_dataset_builder('stas/openwebtext-10k'); print(b.cache_dir)\"\r\n/home/stas/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\r\n```\r\n\r\nNow there is no logger message. Got it!\r\n\r\nOK, I'm not sure the magical recovery it did in first place is most beneficial in the long run. I'd have rather it failed and said: \"incorrect input there is no such dataset as 'openwebtext-10k' at <this path> or <this url>\" - because if it doesn't fail I may leave it in the code and it'll fail later when another user tries to use my code and won't have the cache. Does it make sense? Giving me `this url` allows me to go to the datasets hub and realize that the dataset is missing the username qualifier.\r\n\r\n> Here since openwebtext-10k is not a canonical dataset and doesn't exist locally at ./openwebtext-10k/openwebtext-10k.py: it raised a FileNotFoundError.\r\n\r\nExcept it slapped the exception name to ` remotely (FileNotFoundError).` which makes no sense.\r\n\r\nPlus for the local it's not clear where is it looking relatively too when it gets `FileNotFoundError` - perhaps it'd help to use absolute path and use it in the message?\r\n\r\n---------------\r\n\r\nFinally, the logger format is not set up so the user gets a warning w/o knowing it's a warning. As you can see it's missing the WARNING pre-amble in https://github.com/huggingface/datasets/pull/2500#issuecomment-874250500\r\n\r\ni.e. I had no idea it was warning me of something, I was just trying to make sense of the message that's why I started the discussion and otherwise I'd have completely missed the point of me making an error." ]

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[ "👀 @TalSchuster", "Thanks @VictorSanh!\r\nThere's also a [notebook](https://aka.ms/python_puzzles) and [demo](https://aka.ms/python_puzzles_study) available now to try out some of the puzzles" ]

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[ "That’s interesting thanks, let’s see what we can do. Can you detail your last sentence? I’m not sure I understand it well.", "Hi ! I just re-ran a quick benchmark and using `to_numpy()` seems to be faster now:\r\n\r\n```python\r\nimport pyarrow as pa # I used pyarrow 3.0.0\r\nimport numpy as np\r\n\r\nn, max_length = 1_000, 512\r\nlow, high, size = 0, 2 << 16, (n, max_length)\r\n\r\ntable = pa.Table.from_pydict({\r\n \"input_ids\": np.random.default_rng(42).integers(low=low, high=high, size=size).tolist()\r\n})\r\n\r\n\r\n%%timeit\r\n_ = table.to_pandas()[\"input_ids\"].to_numpy()\r\n# 1.44 ms ± 80.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\r\n\r\n%%timeit\r\n_ = table[\"input_ids\"].to_pandas().to_numpy()\r\n# 461 µs ± 14.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\r\n\r\n%%timeit\r\n_ = table[\"input_ids\"].to_numpy()\r\n# 317 µs ± 5.06 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\r\n```\r\n\r\nCurrently the conversion from arrow to numpy is done in the NumpyArrowExtractor here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/d6d0ede9486ffad7944642ca9a326e058b676788/src/datasets/formatting/formatting.py#L143-L166\r\n\r\nLet's update the NumpyArrowExtractor to call `to_numpy` directly and see how our github benchmarks evolve ?__", "Sounds like a plan @lhoestq If you create a PR I'll pick it up and try it out right away! ", "@lhoestq I can also prepare the PR, just lmk. ", "I’m not exactly sure how to read the graph but it seems that to_categorical take a lot of time here. Could you share more informations on the features/stats of your datasets so we could maybe design a synthetic datasets that looks more similar for debugging testing?", "I created https://github.com/huggingface/datasets/pull/2505 if you want to play with it @vblagoje ", "> I’m not exactly sure how to read the graph but it seems that to_categorical take a lot of time here. Could you share more informations on the features/stats of your datasets so we could maybe design a synthetic datasets that looks more similar for debugging testing?\r\n\r\n@thomwolf starting from the top, each rectangle represents the cumulative amount of it takes to execute the method call. Therefore, format_batch in torch_formatter.py takes ~20 sec, and the largest portion of that call is taken by to_pandas call and the smaller portion (grey rectangle) by the other method invocation(s) in format_batch (series_to_numpy etc). \r\n\r\nFeatures of the dataset are BERT pre-training model input columns i.e:\r\n```\r\nf = Features({ \r\n \"input_ids\": Sequence(feature=Value(dtype=\"int32\")), \r\n \"attention_mask\": Sequence(feature=Value(dtype=\"int8\")), \r\n \"token_type_ids\": Sequence(feature=Value(dtype=\"int8\")), \r\n \"labels\": Sequence(feature=Value(dtype=\"int32\")), \r\n \"next_sentence_label\": Value(dtype=\"int8\")\r\n})\r\n```\r\n\r\nI'll work with @lhoestq till we get to the bottom of this one. \r\n ", "@lhoestq the proposed branch is faster, but overall training speedup is a few percentage points. I couldn't figure out how to include the GitHub branch into setup.py, so I couldn't start NVidia optimized Docker-based pre-training run. But on bare metal, there is a slight improvement. I'll do some more performance traces. ", "Hi @vblagoje, to install Datasets from @lhoestq PR reference #2505, you can use:\r\n```shell\r\npip install git+ssh://git@github.com/huggingface/datasets.git@refs/pull/2505/head#egg=datasets\r\n```", "Hey @albertvillanova yes thank you, I am aware, I can easily pull it from a terminal command line but then I can't automate docker image builds as dependencies are picked up from setup.py and for some reason setup.py doesn't accept this string format.", "@vblagoje in that case, you can add this to your `setup.py`:\r\n```python\r\n install_requires=[\r\n \"datasets @ git+ssh://git@github.com/huggingface/datasets.git@refs/pull/2505/head\",\r\n```", "@lhoestq @thomwolf @albertvillanova The new approach is definitely faster, dataloader now takes less than 3% cumulative time (pink rectangle two rectangles to the right of tensor.py backward invocation)\r\n\r\n\r\n\r\nWhen we drill down into dataloader next invocation we get:\r\n\r\n\r\n\r\nAnd finally format_batch:\r\n\r\n\r\n\r\n\r\nNot sure this could be further improved but this is definitely a decent step forward.\r\n\r\n", "> ```python\r\n> datasets @ git+ssh://git@github.com/huggingface/datasets.git@refs/pull/2505/head\r\n> ```\r\n\r\n@albertvillanova how would I replace datasets dependency in https://github.com/huggingface/transformers/blob/master/setup.py as the above approach is not working. ", "@vblagoje I tested my proposed approach before posting it here and it worked for me. \r\n\r\nIs it not working in your case because of the SSH protocol? In that case you could try the same approach but using HTTPS:\r\n```\r\n\"datasets @ git+https://github.com/huggingface/datasets.git@refs/pull/2505/head\",\r\n``` ", "Also note the blanks before and after the `@`.", "@albertvillanova of course it works. Apologies. I needed to change datasets in all deps references , like [here](https://github.com/huggingface/transformers/blob/master/setup.py#L235) for example. ", "Is time spent casting an issue here? See https://github.com/huggingface/datasets/issues/4676 that Datasets can spend huge amounts of time repeatedly casting to Python objects." ]

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[ "I believe we don't use PyArrow >= 4.0.0 because of some segfault issues:\r\nhttps://github.com/huggingface/datasets/blob/1206ffbcd42dda415f6bfb3d5040708f50413c93/setup.py#L78\r\nCan you confirm @lhoestq ?", "@SBrandeis pyarrow version 4.0.1 has fixed that issue: #2489 😉 " ]

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[ "Hi @albertvillanova, I hope you are doing well.\r\n\r\nI am interested in this issue, is this still unresolved and open ?\r\n\r\nThe link you have provided in the above message directs to a webpage that does not exist.\r\n\r\nThanks and Regards" ]

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[ "@albertvillanova done!" ]

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[ "Closing this PR as I'll submit a new one - bug free" ]

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[ "i need some help with this" ]

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[ "Let me push a small fix... 😉 ", "Thanks !" ]

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[ "Hi ! Thanks for adding this dataset :)\r\n\r\nRegarding your questions:\r\n1. We can have them as different configuration of the `rico` dataset\r\n2. Yes please use the path to the image and not open the image directly, so that we can let users open the image one at at time during training if they want to for example. In the future we'll have an Image feature type that will decode the encoded image data on the fly when accessing the examples.\r\n3. Feel free to keep the hierarchies as strings if they don't follow a fixed format\r\n4. You can just return the path\r\n\r\n", "Thanks for your contribution, @ncoop57. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest you create this dataset there. Please, feel free to tell us if you need some help." ]

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[ "#self-assign" ]

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[ "I continue thinking that the origin of the issue has to do with tqdm (and not with Arrow): this issue only arises for version 4.50.0 (and later) of tqdm, not for previous versions of tqdm.\r\n\r\nMy guess is that tqdm made a change from version 4.50.0 that does not properly release the iterable. ", "FR" ]

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[ "My suggestion for this would be to have this enabled by default.\r\n\r\nPlus I don't know if there should be a dedicated issue to that is another functionality. But I propose layered building rather than all at once. That is:\r\n\r\n1. uncompress a handful of files via a generator enough to generate one arrow file\r\n2. process arrow file 1\r\n3. delete all the files that went in and aren't needed anymore.\r\n\r\nrinse and repeat.\r\n\r\n1. This way much less disc space will be required - e.g. on JZ we won't be running into inode limitation, also it'd help with the collaborative hub training project\r\n2. The user doesn't need to go and manually clean up all the huge files that were left after pre-processing\r\n3. It would already include deleting temp files this issue is talking about\r\n\r\nI wonder if the new streaming API would be of help, except here the streaming would be into arrow files as the destination, rather than dataloaders." ]

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[ "For example to be able to send uncompressed and temp build files to another volume/partition, so that the user gets the minimal disk usage on their primary setup - and ends up with just the downloaded compressed data + arrow files, but outsourcing the huge files and building to another partition. e.g. on JZ there is a special partition for fast data, but it's also volatile, so only temp files should go there.\r\n\r\nThink of it as `TMPDIR` so we need the equivalent for `datasets`." ]

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[ "I've aligned the release script with Transformers in #6004, so I think this issue can be closed." ]

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[ "I see that @lhoestq overlooked this PR with his commit 07e2b05. 😢 \r\n\r\nI'm adding a script so that this issue does not happen again.\r\n", "For the moment, the script only includes `update_custom_js`, but in a follow-up PR I will include all the required steps to make a package release.", "I think we just need to clarify the release process in setup.py instead of adding a script that does the replacement", "@lhoestq I really think we should implement a script that performs the release (instead of doing it manually as it is done now), as it is already the case in `transformers`. I will do it in a next PR.\r\n\r\nFor the moment, this PR includes one of the steps of the release script." ]

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[ "Hi @lhoestq,\r\nI've pushed the updated README and tags. Let me know if anything is missing/needs some improvement!\r\n\r\n~PS. I don't know why it's not triggering the build~" ]

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[ "This bug was fixed in #1995. Upgrading datasets to version 1.6 fixes the issue!", "Indeed was a fixed bug.\r\nWorks on version 1.8\r\nThanks " ]

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[ "Hi ! `load_from_disk` doesn't move the data. If you specify a local path to your mounted drive, then the dataset is going to be loaded directly from the arrow file in this directory. The cache files that result from `map` operations are also stored in the same directory by default.\r\n\r\nHowever note than writing data to your google drive actually fills the VM's disk (see https://github.com/huggingface/datasets/issues/643)\r\n\r\nGiven that, I don't think that changing the cache directory changes anything.\r\n\r\nLet me know what you think", "Thanks for your answer! I am a little surprised since I just want to read the dataset.\r\n\r\nAfter debugging a bit, I noticed that the VM’s disk fills up when the tables (generator) are converted to a list:\r\n\r\nhttps://github.com/huggingface/datasets/blob/5ba149773d23369617563d752aca922081277ec2/src/datasets/table.py#L850\r\n\r\nIf I try to iterate through the table’s generator e.g.: \r\n\r\n`length = sum(1 for x in tables)`\r\n\r\nthe VM’s disk fills up as well.\r\n\r\nI’m running out of Ideas 😄 ", "Indeed reading the data shouldn't increase the VM's disk. Not sure what google colab does under the hood for that to happen", "Apparently, Colab uses a local cache of the data files read/written from Google Drive. See:\r\n- https://github.com/googlecolab/colabtools/issues/2087#issuecomment-860818457\r\n- https://github.com/googlecolab/colabtools/issues/1915#issuecomment-804234540\r\n- https://github.com/googlecolab/colabtools/issues/2147#issuecomment-885052636" ]

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[ "Sounds great! It'll make things easier for the user while accessing the dataset. I'll make some changes to the current file then.", "I've updated with the suggested changes. Updated the README, YAML tags as well (not sure of Size category tag as I couldn't pass the path of `dataset_infos.json` for this dataset)\r\n" ]

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[ "I have received a reply from Zenodo support:\r\n> We are currently investigating and fixing this issue related to GitHub releases. As soon as we have solved it we will reach back to you.", "Other repo maintainers had the same problem with Zenodo. \r\n\r\nThere is an open issue on their GitHub repo: zenodo/zenodo#2181", "I have received the following request from Zenodo support:\r\n> Could you send us the link to the repository as well as the release tag?\r\n\r\nMy reply:\r\n> Sure, here it is:\r\n> - Link to the repository: https://github.com/huggingface/datasets\r\n> - Link to the repository at the release tag: https://github.com/huggingface/datasets/releases/tag/1.8.0\r\n> - Release tag: 1.8.0", "Zenodo issue has been fixed. The 1.8.0 release DOI can be found here: https://zenodo.org/record/4946100#.YMd6vKj7RPY" ]

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