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"2022-11-29T09:20:47"

"2022-11-30T12:00:09"

"2022-11-30T11:57:16"

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"2022-11-28T16:59:39"

"2022-12-07T12:55:20"

"2022-12-07T12:52:15"

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"2022-11-28T14:42:41"

"2022-12-09T12:58:55"

"2022-12-09T12:55:59"

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"2022-11-28T13:59:10"

"2022-11-28T15:30:37"

"2022-11-28T15:27:26"

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[ "Forgot to actually convert the feature dict to a Feature object. Closing." ]

"2022-11-28T07:55:44"

"2022-11-28T08:11:45"

"2022-11-28T08:11:44"

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[ "Thanks for reporting @JoelNiklaus.\r\n\r\nPlease note that since we moved all dataset loading scripts to the Hub, the issues and pull requests relative to specific datasets are directly handled on the Hub, in their Community tab. I'm transferring this issue there: https://huggingface.co/datasets/joelito/mc4_legal/discussions\r\n\r\nI am also having a look at the bug in your script.", "Issue transferred to: https://huggingface.co/datasets/joelito/mc4_legal/discussions/1" ]

"2022-11-28T00:16:16"

"2022-11-28T07:22:42"

"2022-11-28T07:22:42"

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[ "The [timit_asr.py](https://huggingface.co/datasets/timit_asr/blob/main/timit_asr.py) script iterates over the WAV files per split directory using this:\r\n```python\r\nwav_paths = sorted(Path(data_dir).glob(f\"**/{split}/**/*.wav\"))\r\nwav_paths = wav_paths if wav_paths else sorted(Path(data_dir).glob(f\"**/{split.upper()}/**/*.WAV\"))\r\n```\r\n\r\nCan you check that there is a directory named \"test\" somewhere in your timit data directory ?" ]

"2022-11-26T10:18:22"

"2023-02-10T16:33:21"

"2023-02-10T16:33:21"

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"2022-11-25T18:39:09"

"2023-02-13T16:50:42"

"2023-02-13T16:43:47"

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"2022-11-25T17:09:21"

"2022-12-09T14:20:15"

"2022-12-09T14:17:20"

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"2022-11-25T16:35:54"

"2023-02-21T13:13:13"

"2023-02-21T13:13:13"

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"2022-11-25T15:37:42"

"2022-12-07T12:55:39"

"2022-12-07T12:52:51"

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"2022-11-25T15:35:28"

"2022-11-29T08:23:58"

"2022-11-29T08:21:24"

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"2022-11-25T15:21:32"

"2022-11-29T08:21:25"

"2022-11-29T08:21:25"

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"2022-11-25T13:30:17"

"2022-11-29T08:07:39"

"2022-11-29T08:05:12"

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"2022-11-25T13:29:33"

"2022-11-29T08:05:13"

"2022-11-29T08:05:13"

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"2022-11-24T18:04:38"

"2022-11-29T07:09:08"

"2022-11-29T07:06:32"

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"2022-11-24T17:52:08"

"2022-11-29T07:06:33"

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[ "- Build docs for 2.6.2:\r\n - Commit: a6a5a1cf4cdf1e0be65168aed5a327f543001fe8\r\n - Build docs GH Action: https://github.com/huggingface/datasets/actions/runs/3539470622/jobs/5941404044\r\n- Build docs for 2.7.1:\r\n - Commit: 5ef1ab1cc06c2b7a574bf2df454cd9fcb071ccb2\r\n - Build docs GH Action: https://github.com/huggingface/datasets/actions/runs/3539574442/jobs/5941636792" ]

"2022-11-24T09:42:10"

"2022-11-24T10:10:02"

"2022-11-24T10:10:02"

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"2022-11-24T08:54:47"

"2022-11-24T09:14:10"

"2022-11-24T09:11:15"

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"2022-11-23T15:33:25"

"2022-11-28T15:43:14"

"2022-11-28T12:53:22"

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[ "I found a solution \r\n\r\nIf you specifically install datasets==1.18 and then run\r\n\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.en')\r\nthen this should work (it worked for me.)" ]

"2022-11-23T14:54:15"

"2022-11-25T11:33:14"

"2022-11-25T11:33:14"

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"2022-11-23T10:55:54"

"2022-11-24T14:11:41"

"2022-11-24T14:08:37"

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[ "Related to https://github.com/huggingface/datasets/issues/5245", "#self-assign", "Thanks @lhoestq and @alvarobartt!\r\n\r\nThis would be extremely helpful to have working for the Whisper fine-tuning event - we're **only** training using streaming mode, so it'll be quite important to have this feature working to make training as easy as possible!\r\n\r\n_c.f._ https://twitter.com/sanchitgandhi99/status/1592188332171493377", "> Thanks @lhoestq and @alvarobartt!\n> \n> \n> \n> This would be extremely helpful to have working for the Whisper fine-tuning event - we're **only** training using streaming mode, so it'll be quite important to have this feature working to make training as easy as possible!\n> \n> \n> \n> _c.f._ https://twitter.com/sanchitgandhi99/status/1592188332171493377\n\nI'm almost done with at least a temporary fix to `rename_column`, `rename_columns`, and `remove_columns`, just trying to figure out how to extend it to the `map` function itself!\n\nI'll probably open the PR for review either tomorrow or Sunday hopefully! Glad I can help you and HuggingFace 🤗 ", "Awesome - thank you so much for this PR @alvarobartt! Is much appreciated!", "@sanchit-gandhi PR is ready and open for review at #5287, but there's still one issue I may need @lhoestq's input :hugs:", "Let us know @sanchit-gandhi if you need a new release of `datasets` soon with this fix included :)", "Thanks for the fix guys! We can direct people to install `datasets` from main if that's easier!", "Hey guys, any update around this? I'm facing the same issue with a streamable dataset. ", "Hi @asennoussi so this was already fixed and released as part of https://github.com/huggingface/datasets/releases/tag/2.8.0, so you should be able to install it as `pip install datasets==2.8.0` or just to use `pip install datasets --upgrade` to get the latest version, as of now, the https://github.com/huggingface/datasets/releases/tag/2.9.0 released last week! 🤗", "Still facing the same issue though: \r\n```\r\nfrom datasets import IterableDatasetDict, load_dataset\r\n\r\nraw_datasets = vectorized_datasets = IterableDatasetDict()\r\n\r\n\r\nraw_datasets[\"train\"] = load_dataset(\"asennoussi/private\", split=\"train\", use_auth_token=True, streaming=True)\r\nraw_datasets[\"test\"] = load_dataset(\"asennoussi/private\", split=\"test\", use_auth_token=True, streaming=True)\r\n\r\nprint(\"Original features: \", raw_datasets['train'].features.keys())\r\n\r\n...\r\n\r\ndef prepare_dataset(batch):\r\n\r\n # load and (possibly) resample audio datato 16kHz\r\n audio = batch[\"audio\"]\r\n\r\n # compute log-Mel input features from input audio array \r\n batch[\"input_features\"] = processor.feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\r\n # compute input length of audio sample in seconds\r\n batch[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\r\n \r\n # optional pre-processing steps\r\n transcription = batch[\"sentence\"]\r\n \r\n # encode target text to label ids\r\n batch[\"labels\"] = processor.tokenizer(transcription).input_ids\r\n batch[\"labels_length\"] = len(batch[\"labels\"])\r\n return batch\r\n...\r\nvectorized_datasets = vectorized_datasets.remove_columns(['input_length', 'labels_length']+list(next(iter(raw_datasets.values())).features))\r\nprint(\"Processed features: \", vectorized_datasets['train'].features)\r\nprint(\"First sample:\", next(iter(vectorized_datasets['train'])))\r\n\r\n```\r\n\r\nOutput: \r\n```\r\nOriginal features: dict_keys(['path', 'audio', 'sentence'])\r\nProcessed features: None\r\n```", "Hmm weird, could you try to print\r\n\r\n```python\r\nprint(\"Processed features: \", vectorized_datasets['train'].features)\r\n```\r\n\r\nagain after iterating over the `vectorized_datasets`? In the code above, should be last line :)", "Didn't seem to fix it: \r\n```\r\nOriginal features: dict_keys(['path', 'audio', 'sentence'])\r\nProcessed features: None\r\nProcessed features: None\r\n```", "Actually the culprit looks to be this one: \r\n`vectorized_datasets = raw_datasets.map(prepare_dataset).with_format(\"torch\")`\r\nWhen I remove this line: `vectorized_datasets = vectorized_datasets.remove_columns(['input_length', 'labels_length']+list(next(iter(raw_datasets.values())).features))`\r\n\r\nI still get \r\n```\r\nProcessed features: None\r\n```", "The culprit is definitely `.map` \r\nJust validated it. \r\nAny idea please? ", "> The culprit is definitely `.map` Just validated it. Any idea please?\r\n\r\nYes, indeed `.map` losses the features, because AFAIK pre-fetching the data to infer the features is expensive and not ideal, that's part of this issue https://github.com/huggingface/datasets/issues/3888\r\n\r\nAnyway, now you can pass the `features` as a param to `.map` as follows:\r\n\r\n```python\r\nfrom datasets import Features\r\nvectorized_datasets = raw_datasets.map(\r\n prepare_dataset,\r\n features=Features(\r\n {\"path\": raw_datasets[\"train\"].info.features[\"path\"], \"audio\": raw_datasets[\"train\"].info.features[\"audio\"], \"sentence\": raw_datasets[\"train\"].info.features[\"sentence\"]}\r\n ),\r\n).with_format(\"torch\")\r\n```\r\n\r\nAlso, to let you know, when calling `.remove_columns` over an `IterableDataset`, the `features` are not lost, as well as `.rename_column` and `rename_columns` :)\r\n\r\nMore information about the latter at https://github.com/huggingface/datasets/pull/5287", "@asennoussi alternatively you can just call `._resolve_features()` from your `IterableDataset` and it will pre-fetch the data to resolve the features, but note that feature-inference is not as accurate as if you manually specify which features and feature-types the `IterableDataset` has, as mentioned in the comment above, the alternative is to provide `features` param to `.map` :hugs:", "Got it thanks a lot! " ]

"2022-11-23T10:54:59"

"2023-02-02T09:05:51"

"2022-11-28T12:53:24"

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"2022-11-22T17:36:24"

"2022-11-22T17:50:12"

"2022-11-22T17:47:02"

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"2022-11-22T16:58:54"

"2022-11-22T17:21:28"

"2022-11-22T17:21:27"

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[ "Hi ! Can you \r\n```python\r\nimport platform\r\nprint(platform.python_version())\r\n```\r\nto see that it returns ?", "Hi,\n\n3.8.13\n\nGet Outlook for Android<https://aka.ms/AAb9ysg>\n________________________________\nFrom: Quentin Lhoest ***@***.***>\nSent: Tuesday, November 22, 2022 2:37:02 PM\nTo: huggingface/datasets ***@***.***>\nCc: feketedavid1012 ***@***.***>; Author ***@***.***>\nSubject: Re: [huggingface/datasets] Import error (Issue #5280)\n\n\nHi ! Can you\n\nimport platform\nprint(platform.python_version())\n\nto see that it returns ?\n\n—\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/5280#issuecomment-1323691385>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AJW7F5YGG32W6WABYC25NJTWJTD75ANCNFSM6AAAAAASHZJ2AU>.\nYou are receiving this because you authored the thread.Message ID: ***@***.***>\n", "Then it should work as expected if you use the same python when using `datasets`\r\n\r\nPlease make sure you're running your code in the right environment", "It's the right environment. But in if statement I have\n\"3.8.13\" < 3.7\nAnd in the error message is Python>=3.7 which is true in my case (3.8.13 is greater then 3.7), so I don't understand my python should be below the 3.7 which case the if statement is right, but the message is wrong, or above 3.7 which case if statement is wrong, error message is right.\n\nGet Outlook for Android<https://aka.ms/AAb9ysg>\n________________________________\nFrom: Quentin Lhoest ***@***.***>\nSent: Tuesday, November 22, 2022 2:41:43 PM\nTo: huggingface/datasets ***@***.***>\nCc: feketedavid1012 ***@***.***>; Author ***@***.***>\nSubject: Re: [huggingface/datasets] Import error (Issue #5280)\n\n\nThen it should work as expected if you use the same python when using datasets\n\nPlease make sure you're running your code in the right environment\n\n—\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/5280#issuecomment-1323697094>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AJW7F54JURTAJJWWDO2QGI3WJTERPANCNFSM6AAAAAASHZJ2AU>.\nYou are receiving this because you authored the thread.Message ID: ***@***.***>\n", "If you're having an error then you're not running your code in the right environment." ]

"2022-11-22T12:56:43"

"2022-12-15T19:57:40"

"2022-12-15T19:57:40"

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"2022-11-22T10:58:48"

"2022-11-23T11:43:50"

"2022-11-23T09:47:02"

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[ "Can you try to remove \"drop_labels=false\" ? It may force the loader to infer the labels instead of reading the metadata", "Hi, thanks for responding. I tried that, but it does not change anything.", "Can you try updating `datasets` ? Metadata support was added in `datasets` 2.4", "Probably the issue, will report back asap!", "Okay, now it seems to actually load the metadata and create the train_split, but it still says only returns \"image\" and \"label\", which is always 0 since all images are from same folder", "> Can you try updating `datasets` ? Metadata support was added in `datasets` 2.4\r\n\r\nUpdate: This was the issue." ]

"2022-11-22T10:24:46"

"2023-02-14T14:48:16"

"2022-11-23T11:38:35"

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"2022-11-22T08:34:07"

"2022-11-22T13:58:26"

"2022-11-22T13:55:49"

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[ "@huggingface/datasets if you agree, I can make the bulk edit on the Hub to fix integer keys into strings.", "Ok for me, and we can merge (internal) https://github.com/huggingface/moon-landing/pull/4609", "FYI there are still 2k+ weekly users on `datasets` 2.6.1 which doesn't support the string label format for class labels. And among those, some are using datasets with class labels like imdb (60 users), conllpp (40), msra_ner (40), peoples_daily_enr (40), weibo_ner (30), conll2003 (20), etc. And renaming to string would break these users code.", "but isn't `datasets 2.6.1` downloading files from the Hub with the corresponding tag? I thought we had something like this before", "We're using `main` as models do. Some datasets need to be updated from time to time, e.g. when a link to download the data is dead.\r\n\r\nBut yea a year ago we had those tags, we just ended up not using them", "I opened https://github.com/huggingface/datasets/issues/5406 to communicate on this. Let me know what you think, and if it sounds good to you I can pin this issue", "So, is it OK to make the bulk edit on the Hub now or should we wait longer? If the latter, how long?", "I think we can do it. If you want to be extra cautious you can do it for all datasets except imdb and conllpp for now which are actively used by 2.6.1 users. For those two we can keep the YAML like this for some more time, or alternatively use the old dataset_infos.json file", "The bulk edit of canonical datasets (except imdb and conllpp) is running. \r\n\r\nSee e.g.: https://huggingface.co/datasets/acronym_identification/discussions/3\r\n\r\nEDITED: \r\nDone, except for \"universal_morphologies\", where I get\r\n```\r\nHTTPError: 413 Client Error: Payload Too Large for url: https://huggingface.co/api/validate-yaml\r\n```\r\n\r\nAlso not done for the datasets missing matadata \"dataset_info\":\r\n- mc4: https://huggingface.co/datasets/mc4/discussions/3\r\n- the_pile: https://huggingface.co/datasets/the_pile/discussions/6\r\n- timit_asr: https://huggingface.co/datasets/timit_asr/discussions/1", "Thank you !", "@lhoestq, there are 6 community datasets with YAML integer keys in their `dataset_info` `class_label`:\r\n- indonlp/indonlu\r\n- rcds/swiss_judgment_prediction\r\n- Jean-Baptiste/wikiner_fr\r\n- Bingsu/Cat_and_Dog\r\n- taskydata/tasky_or_not\r\n- RCC-MSU/collection3\r\n\r\nMaybe we could open a PR on them as well?", "Let's do this then:\r\n\r\n- [x] [indonlp/indonlu](https://huggingface.co/datasets/indonlp/indonlu/discussions/3)\r\n- [x] rcds/swiss_judgment_prediction\r\n- [x] Jean-Baptiste/wikiner_fr\r\n- [x] Bingsu/Cat_and_Dog -> merged\r\n- [x] taskydata/tasky_or_not (was already using quotes)\r\n- [x] RCC-MSU/collection3\r\n\r\nEDIT: all done :)", "@lhoestq I was not asking you to do it, but asking if you agree me to do it... :man_facepalming: \r\nAs I self-assigned this issue... :sweat_smile: " ]

"2022-11-22T08:14:47"

"2023-01-26T10:52:35"

"2023-01-26T10:40:21"

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[ "@BradleyHsu", "Btw, thanks very much for finding the hub rollback temporary fix and bringing the issue to our attention @KhoomeiK!", "I see the same issue when calling `load_dataset('poloclub/diffusiondb', 'large_random_1k')` with `datasets==2.7.1` and `huggingface-hub=0.11.0`. No issue with `datasets=2.6.1` and `huggingface_hub==0.10.1`.\r\n\r\nhttps://github.com/poloclub/diffusiondb/issues/7", "I fixed my issue by specifying `repo_type` in `hf_hub_url()`. https://github.com/poloclub/diffusiondb/commit/9eb91c79aaca98b0515a0ce45778b8af65b84652\r\n\r\nI opened a PR on the Winoground's repo: https://huggingface.co/datasets/facebook/winoground/discussions/2", "This is a bug in the script, indeed. The most robust fix is to use a relative path instead of `hf_hub_url`, which does not depend on `huggingface_hub`'s version 🙂. I've opened a PR here: https://huggingface.co/datasets/facebook/winoground/discussions/3.", "Awesome, big thanks to both @xiaohk and @mariosasko!" ]

"2022-11-21T21:59:53"

"2022-11-28T02:50:42"

"2022-11-28T02:50:42"

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"2022-11-20T07:50:49"

"2022-11-21T15:07:19"

"2022-11-21T15:06:38"

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[ "I don't think we need completion on the datasets-cli, since we're mainly developing huggingface-cli", "I see." ]

"2022-11-19T13:48:59"

"2022-11-21T15:06:15"

"2022-11-21T15:06:14"

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"2022-11-18T18:50:01"

"2022-12-14T18:25:52"

"2022-12-14T18:22:58"

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"2022-11-18T16:55:22"

"2022-11-18T17:28:58"

"2022-11-18T17:25:27"

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"2022-11-18T14:58:47"

"2022-11-21T15:45:02"

"2022-11-21T15:41:57"

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[ "I think `stream` could be misleading since the data is not being streamed from remote endpoints (one could think that's the case when they see `load_dataset` followed by `stream`). Hence, I prefer the second option.\r\n\r\nPS: When we resolve https://github.com/huggingface/datasets/issues/4542, we could add `as_tf_dataset` to the API for consistency and deprecate `to_tf_dataset`." ]

"2022-11-18T14:54:40"

"2023-02-01T16:36:03"

"2023-02-01T16:36:03"

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[]

[ "Could you share the full stack trace please ?\r\n\r\n\r\nCan you also try running this code ? It can be useful to determine if the issue comes from `datasets` or `fsspec` (streaming) or `pyarrow` (parquet reading):\r\n```python\r\nds = load_dataset(\"parquet\", data_files=a_parquet_file_url, use_auth_token=True)\r\n```", "Here's the full trace\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/loubna_huggingface_co/load.py\", line 15, in <module>\r\n ds_all = load_dataset(\"bigcode/the-stack-dedup-pjj\", data_dir=\"data/java\",use_auth_token=True, split=\"train\", revision=\"v1.1.a1\")\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 1742, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 814, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 905, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 1502, in _prepare_split\r\n for key, table in logging.tqdm(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/std.py\", line 1195, in __iter__\r\n for obj in iterable:\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py\", line 67, in _generate_tables\r\n parquet_file = pq.ParquetFile(f)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/parquet/__init__.py\", line 286, in __init__\r\n self.reader.open(\r\n File \"pyarrow/_parquet.pyx\", line 1227, in pyarrow._parquet.ParquetReader.open\r\n File \"pyarrow/error.pxi\", line 100, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.\r\n```\r\n\r\nwhen running\r\n```python\r\nds = load_dataset(\"parquet\", data_files=\"https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj/blob/v1.1.a1/data/java/data_0000.parquet\", use_auth_token=True)\r\n```\r\nI get 401 error, but that's the case for the python subset too which I can load properly\r\n```\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 1719, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 1497, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 1134, in dataset_module_factory\r\n return PackagedDatasetModuleFactory(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 707, in get_module\r\n data_files = DataFilesDict.from_local_or_remote(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/data_files.py\", line 795, in from_local_or_remote\r\n DataFilesList.from_local_or_remote(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/data_files.py\", line 764, in from_local_or_remote\r\n origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/data_files.py\", line 710, in _get_origin_metadata_locally_or_by_urls\r\n return thread_map(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/contrib/concurrent.py\", line 94, in thread_map\r\n return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/contrib/concurrent.py\", line 76, in _executor_map\r\n return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs))\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/std.py\", line 1183, in __iter__\r\n for obj in iterable:\r\n File \"/opt/conda/envs/venv/lib/python3.9/concurrent/futures/_base.py\", line 609, in result_iterator\r\n yield fs.pop().result()\r\n File \"/opt/conda/envs/venv/lib/python3.9/concurrent/futures/_base.py\", line 446, in result\r\n return self.__get_result()\r\n File \"/opt/conda/envs/venv/lib/python3.9/concurrent/futures/_base.py\", line 391, in __get_result\r\n raise self._exception\r\n File \"/opt/conda/envs/venv/lib/python3.9/concurrent/futures/thread.py\", line 58, in run\r\n result = self.fn(*self.args, **self.kwargs)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/data_files.py\", line 701, in _get_single_origin_metadata_locally_or_by_urls\r\n return (request_etag(data_file, use_auth_token=use_auth_token),)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 411, in request_etag\r\n response.raise_for_status()\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/requests/models.py\", line 960, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 401 Client Error: Unauthorized for url: https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj/blob/v1.1.a1/data/python/data_0000.parquet```", "Can you check you used the right token ? You shouldn't get a 401 using your token", "I checked it’s the right token, when loading the full dataset I get the error after data extraction so I can access the files. \r\n```\r\nDownloading and preparing dataset parquet/bigcode--the-stack-dedup-pjj to /home/loubna_huggingface_co/.cache/huggingface/datasets/bigcode___parquet/bigcode--the-stack-dedup-pjj-872ffac7f4bb46ca/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\r\nDownloading data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 22.38it/s]\r\nExtracting data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 49.91it/s]\r\nTraceback (most recent call last):\r\n File \"/home/loubna_huggingface_co/load_ds.py\", line 5, in <module>\r\n ds = load_dataset(\"bigcode/the-stack-dedup-pjj\", data_dir=\"data/java\", use_auth_token=True,split=\"train\", revision=\"v1.1.a1\")\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py\", line 1742, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 814, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 905, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py\", line 1502, in _prepare_split\r\n for key, table in logging.tqdm(\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/std.py\", line 1195, in __iter__\r\n for obj in iterable:\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py\", line 67, in _generate_tables\r\n parquet_file = pq.ParquetFile(f)\r\n File \"/opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/parquet/__init__.py\", line 286, in __init__\r\n self.reader.open(\r\n File \"pyarrow/_parquet.pyx\", line 1227, in pyarrow._parquet.ParquetReader.open\r\n File \"pyarrow/error.pxi\", line 100, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.\r\n```\r\nCould it be that I'm using a wrong url, I just copied it from the address bar", "The URL is wrong indeed, the right one is the one with \"resolve\" (the one you get when clicking on \"download\")- otherwise you try to download an html page ;)\r\n```\r\nhttps://huggingface.co/datasets/bigcode/the-stack-dedup-pjj/resolve/v1.1.a1/data/java/data_0000.parquet\r\n```", "Ah thanks! So I tried it with the first parquet file and it works, is there a way to know which parquet file was causing the issue since there are a lot of shards?", "I think you have to try them all :/\r\n\r\nAlternatively you can add a try/catch in `parquet.py` in `datasets` to raise the name of the file that fails at doing `parquet_file = pq.ParquetFile(f)` when you run your initial code\r\n```python\r\nload_dataset(\"bigcode/the-stack-dedup-pjj\", data_dir=\"data/java\", split=\"train\", revision=\"v1.1.a1\", use_auth_token=True)\r\n```\r\nbut it will still iterate on all the files until it fails", "Ok I will do that", "I did find the file, and I get the same error as before \r\n```\r\nDownloading data files: 100%|███████████████████| 1/1 [00:00<00:00, 8160.12it/s]\r\nExtracting data files: 100%|████████████████████| 1/1 [00:00<00:00, 1447.81it/s]\r\n \r\n---------------------------------------------------------------------------\r\nArrowInvalid Traceback (most recent call last)\r\nInput In [22], in <cell line: 7>()\r\n 4 data_features = (data[\"train\"].features)\r\n 6 url = \"/home/loubna_huggingface_co/.cache/huggingface/datasets/downloads/93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7\"\r\n----> 7 data = load_dataset(\"parquet\", \r\n 8 data_files=url,\r\n 9 split=\"train\",\r\n 10 features=data_features,\r\n 11 use_auth_token=True)\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/load.py:1742, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)\r\n 1739 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES\r\n 1741 # Download and prepare data\r\n-> 1742 builder_instance.download_and_prepare(\r\n 1743 download_config=download_config,\r\n 1744 download_mode=download_mode,\r\n 1745 ignore_verifications=ignore_verifications,\r\n 1746 try_from_hf_gcs=try_from_hf_gcs,\r\n 1747 use_auth_token=use_auth_token,\r\n 1748 )\r\n 1750 # Build dataset for splits\r\n 1751 keep_in_memory = (\r\n 1752 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1753 )\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py:814, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, storage_options, **download_and_prepare_kwargs)\r\n 808 if not downloaded_from_gcs:\r\n 809 prepare_split_kwargs = {\r\n 810 \"file_format\": file_format,\r\n 811 \"max_shard_size\": max_shard_size,\r\n 812 **download_and_prepare_kwargs,\r\n 813 }\r\n--> 814 self._download_and_prepare(\r\n 815 dl_manager=dl_manager,\r\n 816 verify_infos=verify_infos,\r\n 817 **prepare_split_kwargs,\r\n 818 **download_and_prepare_kwargs,\r\n 819 )\r\n 820 # Sync info\r\n 821 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py:905, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 901 split_dict.add(split_generator.split_info)\r\n 903 try:\r\n 904 # Prepare split will record examples associated to the split\r\n--> 905 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n 906 except OSError as e:\r\n 907 raise OSError(\r\n 908 \"Cannot find data file. \"\r\n 909 + (self.manual_download_instructions or \"\")\r\n 910 + \"\\nOriginal error:\\n\"\r\n 911 + str(e)\r\n 912 ) from None\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/builder.py:1502, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, max_shard_size)\r\n 1500 total_num_examples, total_num_bytes = 0, 0\r\n 1501 try:\r\n-> 1502 for key, table in logging.tqdm(\r\n 1503 generator,\r\n 1504 unit=\" tables\",\r\n 1505 leave=False,\r\n 1506 disable=not logging.is_progress_bar_enabled(),\r\n 1507 ):\r\n 1508 if max_shard_size is not None and writer._num_bytes > max_shard_size:\r\n 1509 num_examples, num_bytes = writer.finalize()\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/tqdm/std.py:1195, in tqdm.__iter__(self)\r\n 1192 time = self._time\r\n 1194 try:\r\n-> 1195 for obj in iterable:\r\n 1196 yield obj\r\n 1197 # Update and possibly print the progressbar.\r\n 1198 # Note: does not call self.update(1) for speed optimisation.\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py:67, in Parquet._generate_tables(self, files)\r\n 65 for file_idx, file in enumerate(itertools.chain.from_iterable(files)):\r\n 66 with open(file, \"rb\") as f:\r\n---> 67 parquet_file = pq.ParquetFile(f)\r\n 68 try:\r\n 69 for batch_idx, record_batch in enumerate(\r\n 70 parquet_file.iter_batches(batch_size=self.config.batch_size, columns=self.config.columns)\r\n 71 ):\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/parquet/__init__.py:286, in ParquetFile.__init__(self, source, metadata, common_metadata, read_dictionary, memory_map, buffer_size, pre_buffer, coerce_int96_timestamp_unit, decryption_properties, thrift_string_size_limit, thrift_container_size_limit)\r\n 280 def __init__(self, source, *, metadata=None, common_metadata=None,\r\n 281 read_dictionary=None, memory_map=False, buffer_size=0,\r\n 282 pre_buffer=False, coerce_int96_timestamp_unit=None,\r\n 283 decryption_properties=None, thrift_string_size_limit=None,\r\n 284 thrift_container_size_limit=None):\r\n 285 self.reader = ParquetReader()\r\n--> 286 self.reader.open(\r\n 287 source, use_memory_map=memory_map,\r\n 288 buffer_size=buffer_size, pre_buffer=pre_buffer,\r\n 289 read_dictionary=read_dictionary, metadata=metadata,\r\n 290 coerce_int96_timestamp_unit=coerce_int96_timestamp_unit,\r\n 291 decryption_properties=decryption_properties,\r\n 292 thrift_string_size_limit=thrift_string_size_limit,\r\n 293 thrift_container_size_limit=thrift_container_size_limit,\r\n 294 )\r\n 295 self.common_metadata = common_metadata\r\n 296 self._nested_paths_by_prefix = self._build_nested_paths()\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/_parquet.pyx:1227, in pyarrow._parquet.ParquetReader.open()\r\n\r\nFile /opt/conda/envs/venv/lib/python3.9/site-packages/pyarrow/error.pxi:100, in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.\r\n```", "Can you check the JSON file associated to `/home/loubna_huggingface_co/.cache/huggingface/datasets/downloads/93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7` ? In the JSON file we can know from where it was downloaded\r\n\r\nYou can find it at `/home/loubna_huggingface_co/.cache/huggingface/datasets/downloads/93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7.json`", "It's this file `https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj/resolve/f48656daa9f3a3607dacf8b57a65810a6a7a7f73/data/java/data_0022.parquet` loading it gives the same error", "I'm able to load it properly using\r\n```python\r\nds = load_dataset(\"parquet\", data_files=a_parquet_file_url, use_auth_token=token)\r\n```\r\n\r\nMy guess is that your download was corrupted. Please delete `93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7` and `93431bc4380de07de8b0ab533666cb5a6120cbe266779e0a63c86bf7717475d7.json` locally and try again", "That worked, thanks! But I thought if something went wrong with a download `datasets` creates new cache for all the files, that's not the case? (at some point I even changed dataset versions so it was still using that cache?)", "Cool !\r\n\r\n> But I thought if something went wrong with a download datasets creates new cache for all the files\r\n\r\nWe don't perform integrity verifications if we don't know in advance the hash of the file to download.\r\n\r\n> at some point I even changed dataset versions so it was still using that cache?\r\n\r\n`datasets` caches the files by URL and ETag. If the content of a file changes, then the ETag changes and so it redownloads the file", "I see, thank you!\r\n" ]

"2022-11-18T14:44:01"

"2022-11-22T11:18:08"

"2022-11-22T11:18:08"

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"2022-11-18T14:43:54"

"2022-12-14T18:22:59"

"2022-12-14T18:22:59"

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[ "Hi ! It looks like your \"isDif\" column is a Sequence of Value(\"string\"), not a Sequence of ClassLabel.\r\n\r\nYou can convert your Value(\"string\") feature type to a ClassLabel feature type this way:\r\n```python\r\nfrom datasets import ClassLabel, Sequence\r\n\r\n# provide the label_names yourself\r\nlabel_names = [...]\r\n# OR get them from the dataset\r\nlabel_names = sorted(set(label for labels in raw_datasets[\"train\"][\"isDif\"] for label in labels))\r\n\r\n# Cast to ClassLabel\r\nraw_datasets = raw_datasets.cast_column(\"isDif\", Sequence(ClassLabel(names=label_names)))\r\n```\r\n", "thank you \r\nit works 💯 " ]

"2022-11-18T13:58:42"

"2022-11-22T10:09:24"

"2022-11-22T10:09:23"

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[ "I notice a comment in the code says:\r\n`Having lists of different sizes makes sharding ambigious, raise an error in this case until we decide how to define sharding without ambiguity for users` \r\n \r\n ... which suggests this update was pushed knowing that it might break some things. But, it didn't seem to have a useful error message of an argument that could be passed to avoid the error.", "Sorry for the inconvenience, I opened a PR in your repo to fix this: https://huggingface.co/datasets/sil-ai/bloom-speech/discussions/2\r\n\r\nBasically we've always considered lists in `gen_kwargs` to be a shard list that we can split and pass into different workers to generate the dataset (e.g. if you pass `num_proc=` in `load_dataset()` to generate the dataset in parallel), but it was documented only recently", "@lhoestq Thanks for the help. It looks like that took care of it." ]

"2022-11-17T15:36:52"

"2022-12-24T01:44:02"

"2022-11-18T12:52:05"

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[ "The bulk edit is running...\r\n\r\nSee for example: \r\n- A single config: https://huggingface.co/datasets/acronym_identification/discussions/2\r\n- Multiple configs: https://huggingface.co/datasets/babi_qa/discussions/1", "TODO: Add \"dataset_info\" YAML metadata to:\r\n- [x] \"chr_en\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card\r\n - Fixing PR: https://huggingface.co/datasets/chr_en/discussions/1 \r\n- [x] \"conll2000\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [x] \"crime_and_punish\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [x] \"dart\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [x] \"iwslt2017\" has no metadata JSON file, but it has \"dataset_info\" YAML tag in its card\r\n- [ ] \"mc4\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card\r\n- [ ] \"the_pile\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card\r\n- [ ] \"timit_asr\" has no metadata JSON file, nor \"dataset_info\" YAML tag in its card", "The bulk edit is finished." ]

"2022-11-17T15:13:15"

"2023-02-16T09:49:05"

"2022-11-19T06:51:37"

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"2022-11-17T04:00:50"

"2022-11-18T11:04:08"

"2022-11-18T11:04:08"

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"2022-11-17T03:54:26"

"2022-11-18T11:05:32"

"2022-11-18T11:05:32"

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[ "Also cc @mariosasko and @lhoestq ", "Cool ! Let us know if you have questions or if we can help :)\r\n\r\nI guess we'll also have to create the NYU CS Department on the Hub ?", "> I guess we'll also have to create the NYU CS Department on the Hub ?\r\n\r\nYes, you're right! Let me add it to my profile first, and then we can transfer. Meanwhile, if it's recommended to loop the dataset author in here, let me know. \r\n\r\nAlso, the NYU Depth dataset seems big. Any example scripts for creating image datasets that I could refer? ", "You can check the imagenet-1k one.\r\n\r\nPS: If the licenses allows it, it'b be nice to host the dataset as sharded TAR archives (like imagenet-1k) instead of the ZIP format they use:\r\n- it will make streaming much faster\r\n- ZIP compression is not well suited for images\r\n- it will allow parallel processing of the dataset (you can pass a subset of shards to each worker)\r\n\r\n> if it's recommended to loop the dataset author in here, let me know.\r\n\r\nIt's recommended indeed, you can send them an email once you have the dataset ready and invite them to the org on the Hub", "> You can check the imagenet-1k one.\r\n\r\nWhere can I find the script? Are you referring to https://huggingface.co/docs/datasets/image_process ? Or is there anything more specific? ", "You can find it here: https://huggingface.co/datasets/imagenet-1k/blob/main/imagenet-1k.py", "Update: started working on it here: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2. \r\n\r\nI am facing an issue and I have detailed it here: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2/discussions/1\r\n\r\nEdit: The issue is gone. \r\n\r\nHowever, since the dataset is distributed as a single TAR archive (following the [URL used in TensorFlow Datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py)) the loading is taking longer. How would suggest to shard the single TAR archive? \r\n\r\n@lhoestq \r\n\r\n", "A Colab Notebook demonstrating the dataset loading part: \r\n\r\nhttps://colab.research.google.com/gist/sayakpaul/aa0958c8d4ad8518d52a78f28044d871/scratchpad.ipynb\r\n\r\n@osanseviero @lhoestq \r\n\r\nI will work on a notebook to work with the dataset including data visualization.", "@osanseviero @lhoestq things seem to work fine with the current version of the dataset [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2). Here's a notebook I developed to help with visualization: https://colab.research.google.com/drive/1K3ZU8XUPRDOYD38MQS9nreQXJYitlKSW?usp=sharing. \r\n\r\n@lhoestq I need your help with the following:\r\n\r\n> However, since the dataset is distributed as a single TAR archive (following the [URL used in TensorFlow Datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py)) the loading is taking longer. How would suggest to shard the single TAR archive?\r\n\r\n@osanseviero @lhoestq question for you:\r\n\r\nWhere should we host the dataset? I think hosting it under hf.co/datasets (that is HF is the org) is fine as we have ImageNet-1k hosted similarly. We could then reach out to Diana Wofk (author of [Fast Depth](https://github.com/dwofk/fast-depth) and the owner of the repo on which TFDS NYU Depth V2 is based) for a review. WDYT? ", "> However, since the dataset is distributed as a single TAR archive (following the [URL used in TensorFlow Datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py)) the loading is taking longer. How would suggest to shard the single TAR archive?\r\n\r\nFirst you can separate the train data and the validation data.\r\n\r\nThen since the dataset is quite big, you can even shard the train split and the validation split in multiple TAR archives. Something around 16 archives for train and 4 for validation would be fine for example.\r\n\r\nAlso no need to gzip the TAR archives, the images are already compressed in png or jpeg.", "> Then since the dataset is quite big, you can even shard the train split and the validation split in multiple TAR archives. Something around 16 archives for train and 4 for validation would be fine for example.\r\n\r\nYes, I got you. But this process seems to be manual and should be tailored for the given dataset. Do you have any script that you used to create the ImageNet-1k shards? \r\n\r\n> Also no need to gzip the TAR archives, the images are already compressed in png or jpeg.\r\n\r\nI was not going to do that. Not sure what brought it up. ", "> Yes, I got you. But this process seems to be manual and should be tailored for the given dataset. Do you have any script that you used to create the ImageNet-1k shards?\r\n\r\nI don't, but I agree it'd be nice to have a script for that !\r\n\r\n> I was not going to do that. Not sure what brought it up.\r\n\r\nThe original dataset is gzipped for some reason", "Oh, I am using this URL for the download: https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/datasets/nyu_depth_v2/nyu_depth_v2_dataset_builder.py#L24. ", "> Where should we host the dataset? I think hosting it under hf.co/datasets (that is HF is the org) is fine as we have ImageNet-1k hosted similarly.\r\n\r\nMaybe you can create an org for NYU Courant (this is the institute of the lab of the main author of the dataset if I'm not mistaken), and invite the authors to join.\r\n\r\nWe don't add datasets without namespace anymore", "Updates: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2/discussions/5\r\n\r\nThe entire process (preparing multiple archives, preparing data loading script, etc.) was fun and engaging, thanks to the documentation. I believe we could work on a small blog post that would work as a reference for the future contributors following this path. What say? \r\n\r\nCc: @lhoestq @osanseviero ", "> I believe we could work on a small blog post that would work as a reference for the future contributors following this path. What say?\r\n\r\n@polinaeterna already mentioned it would be nice to present this process for audio (it's exactly the same), I believe it can be useful to many people", "Cool. Let's work on that after the NYU Depth Dataset is fully in on Hub (under the appropriate org). 🤗", "@lhoestq need to discuss something while I am adding the dataset card to https://huggingface.co/datasets/sayakpaul/nyu_depth_v2/. \r\n\r\nAs per [Papers With Code](https://paperswithcode.com/dataset/nyuv2), NYU Depth v2 is used for many different tasks:\r\n\r\n* Monocular depth estimation\r\n* Depth estimation \r\n* Semantic segmentation\r\n* Plane instance segmentation \r\n* ...\r\n\r\nSo, while writing the supported task part of the dataset card, should we focus on all these? IMO, we could focus on just depth estimation and semantic segmentation for now since we have supported models for these two. WDYT?\r\n\r\nAlso, I am getting: \r\n\r\n\r\n```\r\nremote: Your push was accepted, but with warnings:\r\nremote: - Warning: The task_ids \"depth-estimation\" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-generation, dialogue-modeling, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering\r\nremote: ----------------------------------------------------------\r\nremote: Please find the documentation at:\r\nremote: https://huggingface.co/docs/hub/model-cards#model-card-metadata\r\n```\r\n\r\nWhat should be the plan of action for this?\r\n\r\nCc: @osanseviero \r\n\r\n", "> What should be the plan of action for this?\r\n\r\nWhen you merged https://github.com/huggingface/hub-docs/pull/488, there is a JS Interfaces GitHub Actions workflow that runs https://github.com/huggingface/hub-docs/actions/workflows/js-interfaces-tests.yml. It has a step called [export-task scripts](https://github.com/huggingface/hub-docs/actions/runs/3622479064/jobs/6107238948) which exports an interface you can use in `dataset`. If you look at the logs, it prints out a map. This map can replace https://github.com/huggingface/datasets/blob/main/src/datasets/utils/resources/tasks.json (tasks.json was generated with this script), which should add depth estimation\r\n", "Thanks @osanseviero. \r\n\r\nhttps://github.com/huggingface/datasets/pull/5335", "Closing the issue as the dataset has been successfully added: https://huggingface.co/datasets/sayakpaul/nyu_depth_v2" ]

"2022-11-17T03:22:22"

"2022-12-17T12:20:38"

"2022-12-17T12:20:37"

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"2022-11-17T02:39:57"

"2022-11-18T10:53:45"

"2022-11-18T10:53:45"

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"2022-11-17T02:22:58"

"2022-11-18T10:53:11"

"2022-11-18T10:53:10"

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"2022-11-16T15:02:13"

"2022-12-13T17:01:54"

"2022-12-13T16:59:04"

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[ "After a discussion with @mishig25:\r\n- He said that this action should be triggered if we call our release branch according to the regex `v*-release`, as transformers does\r\n- I said that our procedure is different: our release branch is *temporary* and it is deleted just after the release PR is merged to main\r\n - Indeed the release tag is not yet created when we make the release PR (not event when this is merged to main), but when we make the Release itself.\r\n\r\nI was thinking that maybe we could change the triggering event: use `release` instead of `push`.\r\n\r\nWhat do you think, @huggingface/datasets?", "Why is it an issue if our branch is temporary ?", "He says not; but the branch has no tag yet; does the doc building require the tag? Or just the version number in `__init__.py` or setup.py?", "It uses `module.__version__` (i.e. the one defined in `__init__.py`) - no need to have a tag\r\n\r\nhttps://github.com/huggingface/doc-builder/blob/81575cf081964c30ea5fd39450f4820db963f18e/src/doc_builder/commands/build.py#L69", "Thanks, @lhoestq.\r\n\r\n@mishig25 has manually forced the generation of the docs, that are live for 2.7.0 version: https://huggingface.co/docs/datasets/v2.7.0/en/index ", "Cool ! this can be closed then ?", "I was waiting for #5250 to be merged to close this.", "just to confirm, is there anything I need to do from my side ? Or is everything good here ?" ]

"2022-11-16T14:59:31"

"2022-11-22T16:27:50"

"2022-11-22T16:27:50"

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"2022-11-16T14:35:32"

"2022-11-22T16:30:58"

"2022-11-22T16:27:48"

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"2022-11-16T10:41:04"

"2022-11-16T15:06:50"

"2022-11-16T10:41:10"

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"2022-11-16T10:17:31"

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[ "Hi @peregilk this bug is directly related to https://github.com/huggingface/datasets/issues/3888, and still not fixed... But I'll try to have a look!", "Thanks @alvarobartt. It is great if you are able to fix it, but when reading the explanation it seems like it is possible to work around it.\r\n\r\nWe also tried keeping the 'info.features' and then adding a modified version back after the remove/rename. Unforutunately that leads to a dataset that is not possible to iterate over.", "So if you iterate over the `IterableDataset` as `next(iter(ds))` and then run `rename_columns` when checking that data it will work, but in the end, it's just renaming the column one example/batch at a time, not renaming the column name for all the entries in the dataset, which is the ideal.", "@alvarobartt Thanks. My use case was that I wanted to do multiple things, ie removing all unnecessary columns, renaming some valid columns, and then using cast (in my case checking if the audio is not 16K and casting it). It is just convenient to look into the info.features between each of these operations. Alternatively, I will just plan ahead...;) To me it seems like all the operations are working.\r\n\r\nThanks for the advice. It was very useful.", "If we know the features before renaming, then we know the features after renaming, so we can pass the new features to the returned dataset in `rename_column` indeed ! If anyone is interested in contributing, feel free to open a PR and I'd be happy to help / give some pointers :)", "Sure @lhoestq thanks! I’ll try to work on that", "#self-assign" ]

"2022-11-15T21:04:41"

"2022-11-28T12:53:24"

"2022-11-28T12:53:24"

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"2022-11-14T18:05:47"

"2022-11-15T16:11:30"

"2022-11-15T16:09:31"

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"2022-11-14T12:47:40"

"2022-12-07T23:49:22"

"2022-12-07T23:46:34"

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"2022-11-14T11:42:09"

"2022-11-14T14:16:03"

"2022-11-14T14:13:22"

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[ "Thanks for reporting, @vinaykakade.\r\n\r\nWe are discussing about making a release early this week.\r\n\r\nPlease note that in the meantime, in your specific case (as we also pointed out here: https://github.com/huggingface/datasets/issues/5162#issuecomment-1291720293), you can circumvent the issue by pinning `multiprocess` to 0.70.13 version (instead of using latest 0.70.14).\r\n\r\nDuplicate of:\r\n- https://github.com/huggingface/datasets/issues/5162", "You can also make `pip-compile` work by using the backtracking resolver (instead of the legacy one): https://pip-tools.readthedocs.io/en/latest/#a-note-on-resolvers\r\n```\r\npip-compile --resolver=backtracking requirements.in\r\n```\r\nThis resolver will automatically use `multiprocess` 0.70.13 version.\r\n" ]

"2022-11-12T06:46:23"

"2022-11-14T08:24:43"

"2022-11-14T08:07:59"

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[ "In case others find this, the problem was not with set_format, but my usages of `to_pandas()` and `from_pandas()` which I was using during dataset splitting; somewhere in the chain of converting to and from pandas the `Array2D/Array3D` types get converted to series of `Sequence()` types" ]

"2022-11-11T18:54:36"

"2022-11-11T20:42:29"

"2022-11-11T18:59:50"

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[ "I opened [this issue](https://github.com/python/cpython/issues/99401).\r\nPython's maintainers say that the issue is caused by [this change](https://docs.python.org/3.11/whatsnew/3.11.html#dataclasses).\r\nI believe adding a `__hash__` method to `datasets.utils.version.Version` should solve (at least partially) this issue.", "Has this been fixed? I am running into this issue now. \r\n\r\nIf this has been fixed, could have a new release with this?\r\n" ]

"2022-11-11T13:53:49"

"2022-11-14T20:51:44"

"2022-11-14T15:27:37"

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[ "Hi! \r\n\r\nWe could address this by calling `.item()` on such tensors to extract the value, but this would lose us the type, which could lead to storing the generated dataset in a suboptimal format. Considering this, I think the only proper fix would be implementing support for 0-D tensors on Apache Arrow's side (Arrow is the underlying format we use to store datasets on disk/in memory). WDYT @lhoestq?", "I think we can just convert the item to a numpy typed scalar using `.numpy()` ?\r\n\r\nFor example this works:\r\n```python\r\nimport numpy as np\r\nimport pyarrow as pa\r\n\r\nassert pa.array([np.float64(1.0)]).type == pa.float64()\r\nassert pa.array([np.float32(1.0)]).type == pa.float32()\r\nassert pa.array([np.int32(1)]).type == pa.int32()\r\nassert pa.array([np.int64(1)]).type == pa.int64()\r\n```\r\n\r\nAnd therefore it would work the same as for PyTorch N-D Tensors: convert to Numpy Array to keep the type in `_cast_to_python_objects`, then convert to Arrow" ]

"2022-11-11T08:27:28"

"2023-01-13T16:00:53"

"2023-01-13T16:00:53"

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[ "Fixed. Please close." ]

"2022-11-10T21:57:06"

"2022-11-10T22:05:43"

"2022-11-10T22:05:43"

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[ "Hi ! Datasets are memory mapped from your disk, i.e. they're not loaded in RAM. This is possible thanks to the Arrow data format.\r\n\r\nTherefore the column you remove is not in RAM, so removing it doesn't cause the RAM to decrease.", "Thanks for the explanation! @lhoestq \r\nI wonder since it is memory mapped, can we reduce or remove this memory map?", "Yes you can `del common_voice` for example or wait for it to be garbage collected" ]

"2022-11-10T18:35:27"

"2022-11-29T15:10:10"

"2022-11-29T15:10:10"

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[ "I just tried to do the same but changing the `.wav` files to `.mp3` files and that doesn't fix it.", "I don't know if anyone will ever read this but I've tried to upload the same dataset with google colab and the output seems more clarifying. I didn't specify the train/test split so the dataset wasn't fully uploaded (or that is what I understood, might be wrong!!).\r\n\r\nNow, including the `drop_metadata` flag I can load the dataset normally (at least with colab notebook):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"audiofolder\", data_dir=\"../archive/Dataset\", , drop_metadata=True)\r\n```\r\n\r\nI'll close the issue." ]

"2022-11-10T10:29:31"

"2022-11-22T11:24:19"

"2022-11-22T11:24:19"

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"2022-11-09T19:10:27"

"2022-11-15T17:40:25"

"2022-11-15T17:40:21"

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