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https://api.github.com/repos/huggingface/datasets/issues/3831
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I_kwDODunzps5FK9cI
3,831
when using to_tf_dataset with shuffle is true, not all completed batches are made
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[ "Maybe @Rocketknight1 can help here", "Hi @greenned, this is expected behaviour for `to_tf_dataset`. By default, we drop the smaller 'remainder' batch during training (i.e. when `shuffle=True`). If you really want to keep that batch, you can set `drop_remainder=False` when calling `to_tf_dataset()`.", "@Rocketknight1 Oh, thank you. I didn't get **drop_remainder** Have a nice day!", "No problem!\r\n" ]
2022-03-06T02:43:50Z
2022-03-08T15:18:56Z
2022-03-08T15:18:56Z
NONE
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## Describe the bug when converting a dataset to tf_dataset by using to_tf_dataset with shuffle true, the remainder is not converted to one batch ## Steps to reproduce the bug this is the sample code below https://colab.research.google.com/drive/1_oRXWsR38ElO1EYF9ayFoCU7Ou1AAej4?usp=sharing ## Expected results regardless of shuffle is true or not, 67 rows dataset should be 5 batches when batch size is 16. ## Actual results 4 batches ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 6.0.1
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Got error when load cnn_dailymail dataset
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[ "Was able to reproduce the issue on Colab; full logs below. \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNotADirectoryError Traceback (most recent call last)\r\n[<ipython-input-2-39967739ba7f>](https://localhost:8080/#) in <module>()\r\n 1 import datasets\r\n 2 \r\n----> 3 train_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"train\")\r\n\r\n5 frames\r\n[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) 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, script_version, **config_kwargs)\r\n 1705 ignore_verifications=ignore_verifications,\r\n 1706 try_from_hf_gcs=try_from_hf_gcs,\r\n-> 1707 use_auth_token=use_auth_token,\r\n 1708 )\r\n 1709 \r\n\r\n[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n 593 if not downloaded_from_gcs:\r\n 594 self._download_and_prepare(\r\n--> 595 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 596 )\r\n 597 # Sync info\r\n\r\n[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 659 split_dict = SplitDict(dataset_name=self.name)\r\n 660 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 661 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n 662 \r\n 663 # Checksums verification\r\n\r\n[/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234/cnn_dailymail.py](https://localhost:8080/#) in _split_generators(self, dl_manager)\r\n 253 def _split_generators(self, dl_manager):\r\n 254 dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n--> 255 train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)\r\n 256 # Generate shared vocabulary\r\n 257 \r\n\r\n[/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234/cnn_dailymail.py](https://localhost:8080/#) in _subset_filenames(dl_paths, split)\r\n 154 else:\r\n 155 logger.fatal(\"Unsupported split: %s\", split)\r\n--> 156 cnn = _find_files(dl_paths, \"cnn\", urls)\r\n 157 dm = _find_files(dl_paths, \"dm\", urls)\r\n 158 return cnn + dm\r\n\r\n[/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234/cnn_dailymail.py](https://localhost:8080/#) in _find_files(dl_paths, publisher, url_dict)\r\n 133 else:\r\n 134 logger.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 135 files = sorted(os.listdir(top_dir))\r\n 136 \r\n 137 ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'\r\n```", "Hi @jon-tow, thanks for reporting. And hi @dynamicwebpaige, thanks for your investigation. \r\n\r\nThis issue was already reported \r\n- #3784\r\n\r\nand its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe have already fixed it. See:\r\n- #3787 \r\n\r\nWe are planning to make a patch release today (indeed, we were planning to do it last Friday).\r\n\r\nIn the meantime, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```\r\n\r\nCC: @lhoestq " ]
2022-03-05T01:43:12Z
2022-03-07T06:53:41Z
2022-03-07T06:53:41Z
NONE
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When using datasets.load_dataset method to load cnn_dailymail dataset, got error as below: - windows os: FileNotFoundError: [WinError 3] 系统找不到指定的路径。: 'D:\\SourceCode\\DataScience\\HuggingFace\\Data\\downloads\\1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\\cnn\\stories' - google colab: NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' The code is to load dataset: windows os: ``` from datasets import load_dataset dataset = load_dataset("cnn_dailymail", "3.0.0", cache_dir="D:\\SourceCode\\DataScience\\HuggingFace\\Data") ``` google colab: ``` import datasets train_data = datasets.load_dataset("cnn_dailymail", "3.0.0", split="train") ```
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The Pile's _FEATURE spec seems to be incorrect
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[ "Hi @dlwh, thanks for reporting.\r\n\r\nPlease note, that the source data files for \"all\" config are different from the other configurations.\r\n\r\nThe \"all\" config contains the official Pile data files, from https://mystic.the-eye.eu/public/AI/pile/\r\nAll data examples contain a \"meta\" dict with a single \"pile_set_name\" key:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ds = load_dataset(\"the_pile\", \"all\", split=\"train\", streaming=True)\r\n item = next(iter(ds))\r\nDownloading builder script: 9.09kB [00:00, 4.42MB/s]\r\n\r\nIn [3]: item[\"meta\"]\r\nOut[3]: {'pile_set_name': 'Pile-CC'}\r\n```\r\n\r\nOn the other hand, all the other subset configs data files come from the Pile preliminary components directory: https://mystic.the-eye.eu/public/AI/pile_preliminary_components/\r\nFor theses components, the \"meta\" field may have different keys depending on the subset: \"id\", \"language\", \"pmid\",... Because of that, if we had kept the `dict` data format for the \"meta\" field, we would have an error when trying to concatenate different subsets, whose \"meta\" keys are not identical. In order to avoid that, the \"meta\" field is cast to `str` in all these cases, so that there is no incompatibility in their \"meta\" data type when concatenating.\r\n\r\nYou can check, for example, that for \"pubmed_central\" the \"meta\" field is cast to `str`:\r\n```python\r\nIn [4]: from datasets import load_dataset\r\n ds = load_dataset(\"the_pile\", \"pubmed_central\", split=\"train\", streaming=True)\r\n item = next(iter(ds))\r\n\r\nIn [5]: item[\"meta\"]\r\nOut[5]: \"{'id': 'PMC6071596'}\"\r\n```\r\n\r\nFeel free to reopen this issue if you have further questions. " ]
2022-03-04T21:25:32Z
2022-03-08T09:30:49Z
2022-03-08T09:30:48Z
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## Describe the bug If you look at https://huggingface.co/datasets/the_pile/blob/main/the_pile.py: For "all" * the pile_set_name is never set for data * there's actually an id field inside of "meta" For subcorpora pubmed_central and hacker_news: * the meta is specified to be a string, but it's actually a dict with an id field inside. ## Steps to reproduce the bug ## Expected results Feature spec should match the data I'd think? ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: - Python version: - PyArrow version:
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500 internal server error when trying to open a dataset composed of Zarr stores
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[ "Hi @jacobbieker, thanks for reporting!\r\n\r\nI have transferred this issue to our Hub team and they are investigating it. I keep you informed. ", "Hi @jacobbieker, we are investigating this issue on our side and we'll see if we can fix it, but please note that your repo is considered problematic for git. Here are the results of running https://github.com/github/git-sizer on it:\r\n\r\n```\r\nProcessing blobs: 147448 \r\nProcessing trees: 27 \r\nProcessing commits: 4 \r\nMatching commits to trees: 4 \r\nProcessing annotated tags: 0 \r\nProcessing references: 3 \r\n| Name | Value | Level of concern |\r\n| ---------------------------- | --------- | ------------------------------ |\r\n| Biggest objects | | |\r\n| * Trees | | |\r\n| * Maximum entries [1] | 167 k | !!!!!!!!!!!!!!!!!!!!!!!!!!!!!! |\r\n| | | |\r\n| Biggest checkouts | | |\r\n| * Number of files [2] | 189 k | *** |\r\n\r\n[1] aa057d2667c34c70c6146efc631f5c9917ff326e (refs/heads/main:2016.zarr/unknown)\r\n[2] 6897b7bf6440fdd16b2c39d08085a669e7eaa59d (refs/heads/main^{tree})\r\n```\r\n\r\nYou can check https://github.com/github/git-sizer for more information on how to avoid such pathological structures.", "Hi, thanks for getting back to me so quick! And yeah, I figured that was probably the problem. I was going to try to delete the repo, but couldn't through the website, so if that's the easiest way to solve it, I can regenerate the dataset in a different format with less tiny files, and you guys can delete the repo as it is. Zarr just saves everything as lots of small files to make chunks easy to load, which is why I was preferring that format, but maybne that just doesn't work well for HF datasets.", "Hi @jacobbieker,\r\n\r\nFor future use cases, our Hub team is still pondering whether to limit the maximum number of files per repo to avoid technical issues...\r\n\r\nOn the meantime, they have made a fix and your dataset is working: https://huggingface.co/datasets/openclimatefix/mrms" ]
2022-03-04T10:37:14Z
2022-03-08T09:47:39Z
2022-03-08T09:47:39Z
NONE
null
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## Describe the bug The dataset [openclimatefix/mrms](https://huggingface.co/datasets/openclimatefix/mrms) gives a 500 server error when trying to open it on the website, or through code. The dataset doesn't have a loading script yet, and I did push two [xarray](https://docs.xarray.dev/en/stable/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. In general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? For context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https://github.com/openclimatefix/metnet) [DGMR](https://github.com/openclimatefix/skillful_nowcasting), and [others](https://github.com/openclimatefix/graph_weather), which all have large, complex datasets. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("openclimatefix/mrms") ``` ## Expected results The dataset should be downloaded or open up ## Actual results A 500 internal server error ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35 - Python version: 3.9.10 - PyArrow version: 7.0.0
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Add Biwi Kinect Head Pose Database
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[ "Official dataset location : https://icu.ee.ethz.ch/research/datsets.html\r\nIn the \"Biwi Kinect Head Pose Database\" section, I do not find any information regarding \"Downloading the dataset.\" . Do we mail the authors regarding this ?\r\n\r\nI found the dataset on Kaggle : [Link](https://www.kaggle.com/kmader/biwi-kinect-head-pose-database) , but since 🤗 does not host any of the datasets, this would require the user to provide their Kaggle username and API key to download. \r\n\r\nAny inputs on how we could proceed ? Thank you.\r\n[ Need your inputs here, @lhoestq or @mariosasko ]", "Hi @dnaveenr! Thanks for tackling this issue. This link should work: https://data.vision.ee.ethz.ch/cvl/gfanelli/kinect_head_pose_db.tgz", "#self-assign", "Added in https://github.com/huggingface/datasets/pull/3903, thanks @dnaveenr !", "@mariosasko Hi,\nthis link [https://data.vision.ee.ethz.ch/cvl/gfanelli/kinect_head_pose_db.tgz]\nseems not working now. Do you have alternatives?", "It would be cool to find a copy of the data and host it directly in https://huggingface.co/datasets/ETHZurich/biwi_kinect_head_pose", "> It would be cool to find a copy of the data and host it directly in https://huggingface.co/datasets/ETHZurich/biwi_kinect_head_pose\n\nHi, you mean I can download it by running python script in your link?", "This dataset repository only contains a script that points to the URL of the file that doesn't exist anymore, so it's not usable anymore either :/", "> This dataset repository only contains a script that points to the URL of the file that doesn't exist anymore, so it's not usable anymore either :/\n\nThen what did you mean \n\"It would be cool to find a copy of the data and host it directly in https://huggingface.co/datasets/ETHZurich/biwi_kinect_head_pose\"?\nI don't understand the meaning of 'host'", "If we find a copy of the data we can upload the file in the dataset repository on HF, since the original data is not available anymore" ]
2022-03-04T08:48:39Z
2025-04-07T13:04:25Z
2022-06-01T13:00:47Z
CONTRIBUTOR
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## Adding a Dataset - **Name:** Biwi Kinect Head Pose Database - **Description:** Over 15K images of 20 people recorded with a Kinect while turning their heads around freely. For each frame, depth and rgb images are provided, together with ground in the form of the 3D location of the head and its rotation angles. - **Data:** [*link to the Github repository or current dataset location*](https://icu.ee.ethz.ch/research/datsets.html) - **Motivation:** Useful pose estimation dataset Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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`pubmed_qa` checksum mismatch
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[ "Hi @jon-tow, thanks for reporting.\r\n\r\nThis issue was already reported and its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe have already fixed it. See:\r\n- #3787 \r\n\r\nWe are planning to make a patch release today.\r\n\r\nIn the meantime, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```" ]
2022-03-04T00:28:08Z
2022-03-04T09:42:32Z
2022-03-04T09:42:32Z
CONTRIBUTOR
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## Describe the bug Loading [`pubmed_qa`](https://huggingface.co/datasets/pubmed_qa) results in a mismatched checksum error. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug import datasets try: datasets.load_dataset("pubmed_qa", "pqa_labeled") except Exception as e: print(e) try: datasets.load_dataset("pubmed_qa", "pqa_unlabeled") except Exception as e: print(e) try: datasets.load_dataset("pubmed_qa", "pqa_artificial") except Exception as e: print(e) ``` ## Expected results Successful download. ## Actual results Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.9/site-packages/datasets/load.py", line 1702, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 594, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 665, in _download_and_prepare verify_checksums( File "/usr/local/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=1RsGLINVce-0GsDkCLDuLZmoLuzfmoCuQ', 'https://drive.google.com/uc?export=download&id=15v1x6aQDlZymaHGP7cZJZZYFfeJt2NdS'] ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: macOS - Python version: 3.8.1 - PyArrow version: 3.0.0
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Support for "sources" parameter in the add() and add_batch() methods in datasets.metric - SARI
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[ "Hi, thanks for reporting! We can add a `sources: datasets.Value(\"string\")` feature to the `Features` dict in the `SARI` script to fix this. Would you be interested in submitting a PR?", "Hi Mario,\r\n\r\nThanks for your message. I did try to add `sources` into the `Features` dict using a script for the metric:\r\n```\r\n features=datasets.Features(\r\n {\r\n \"sources\": datasets.Value(\"string\", id=\"sequence\"),\r\n \"predictions\": datasets.Value(\"string\", id=\"sequence\"),\r\n \"references\": datasets.Sequence(datasets.Value(\"string\", id=\"sequence\"), id=\"references\"),\r\n }\r\n ),\r\n```\r\n\r\nBut that only avoids a failure in `encode_batch` in the `add_batch` method:\r\n```\r\n batch = {\"predictions\": predictions, \"references\": references}\r\n batch = self.info.features.encode_batch(batch)\r\n```\r\n\r\nThe real problem is that `add_batch()`, `add()` and `compute()` does not receive a `sources` param:\r\n```\r\ndef add_batch(self, *, predictions=None, references=None):\r\ndef add(self, *, prediction=None, reference=None):\r\ndef compute(self, *, predictions=None, references=None, **kwargs)\r\n```\r\n\r\nAnd then, it fails:\r\n`TypeError: add_batch() got an unexpected keyword argument sources`\r\n\r\nI need this for adding any metric based on SARI or alike, not only for sari.py :)\r\n\r\nLet me know if I understood correctly the proposed solution.\r\n", "The `Metric` class has been modified recently to support this use-case, but the `add_batch` + `compute` pattern still doesn't work correctly. I'll open a PR." ]
2022-03-03T18:57:54Z
2022-03-04T18:04:21Z
2022-03-04T18:04:21Z
NONE
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**Is your feature request related to a problem? Please describe.** The methods `add_batch` and `add` from the `Metric` [class](https://github.com/huggingface/datasets/blob/1675ad6a958435b675a849eafa8a7f10fe0f43bc/src/datasets/metric.py) does not work with [SARI](https://github.com/huggingface/datasets/blob/master/metrics/sari/sari.py) metric. This metric not only relies on the predictions and references, but also in the input. For example, when the `add_batch` method is used, then the `compute()` method fails: ``` metric = load_metric("sari") metric.add_batch( predictions=["About 95 you now get in ."], references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]) metric.compute() > TypeError: _compute() missing 1 required positional argument: 'sources' ``` Therefore, the `compute() `method can only be used standalone: ``` metric = load_metric("sari") result = metric.compute( sources=["About 95 species are currently accepted ."], predictions=["About 95 you now get in ."], references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]) > {'sari': 26.953601953601954} ``` **Describe the solution you'd like** Support for an additional parameter `sources` in the `add_batch` and `add` of the `Metric` class. ``` add_batch(*, sources=None, predictions=None, references=None, **kwargs) add(*, sources=None, predictions=None, references=None, **kwargs) compute() ``` **Describe alternatives you've considered** I've tried to override the `add_batch` and `add`, however, these are highly dependent to the `Metric` class. We could also write a simple function that compute the scores of a sentences list, but then we lose the functionality from the original [add](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add) and [add_batch method](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add_batch). **Additional context** These methods are used in the transformers [pytorch examples](https://github.com/huggingface/transformers/blob/master/examples/pytorch/summarization/run_summarization_no_trainer.py).
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I_kwDODunzps5FDOxr
3,813
Add MetaShift dataset
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[ "I would like to take this up and give it a shot. Any image specific - dataset guidelines to keep in mind ? Thank you.", "#self-assign", "I've started working on adding this dataset. I require some inputs on the following : \r\n\r\nRef for the initial draft [here](https://github.com/dnaveenr/datasets/blob/add_metashift_dataset/datasets/metashift/metashift.py)\r\n1. The dataset does not have a typical - train/test/val split. What do we do for the _split_generators() function ? How do we go about this ?\r\n2. This dataset builds on the Visual Genome dataset, using a metadata file. The dataset is generated using generate_full_MetaShift.py script. By default, the authors choose to generate the dataset only for a SELECTED_CLASSES. The following script is used : \r\nCode : https://github.com/Weixin-Liang/MetaShift/blob/main/dataset/generate_full_MetaShift.py \r\nInfo : https://metashift.readthedocs.io/en/latest/sub_pages/download_MetaShift.html#generate-the-full-metashift-dataset\r\nCan I just copy over the required functions into the metashift.py to generate the dataset ?\r\n3. How do we complete the _generate_examples for this dataset ?\r\n\r\nThe user has the ability to use default selected classes, get the complete dataset or add more specific additional classes. I think config would be a good option here.\r\n\r\nInputs, suggestions would be helpful. Thank you.", "I think @mariosasko and @lhoestq should be able to help here 😄 ", "Hi ! Thanks for adding this dataset :) Let me answer your questions:\r\n\r\n1. in this case you can put everything in the \"train\" split\r\n2. Yes you can copy the script (provided you also include the MIT license of the code in the file header for example). Though we ideally try to not create new directories nor files when generating dataset, so if possible this script should be adapted to not create the file structure they mentioned, but instead yield the images one by one in `_generate_examples`. Let me know if you think this is feasible\r\n3. see point 2 haha\r\n\r\n> The user has the ability to use default selected classes, get the complete dataset or add more specific additional classes. I think config would be a good option here.\r\n\r\nYup ! We can also define a `selected_classes` parameter such that users can do\r\n```python\r\nload_dataset(\"metashift\", selected_classes=[\"cat\", \"dog\", ...])\r\n```", "Great. This is helpful. Thanks @lhoestq .\r\nRegarding Point 2, I'll try using yield instead of creating the directories and see if its feasible. selected_classes config sounds good.", "Closed via #3900 " ]
2022-03-03T14:26:45Z
2022-04-10T13:39:59Z
2022-04-10T13:39:59Z
CONTRIBUTOR
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## Adding a Dataset - **Name:** MetaShift - **Description:** collection of 12,868 sets of natural images across 410 classes- - **Paper:** https://arxiv.org/abs/2202.06523v1 - **Data:** https://github.com/weixin-liang/metashift Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Checksums didn't match for datasets on Google Drive
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[ "Hi @muelletm, thanks for reporting.\r\n\r\nThis issue was already reported and its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe have already fixed it. See:\r\n- #3787 \r\n\r\nUntil our next `datasets` library release, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```" ]
2022-03-03T09:01:10Z
2022-03-03T09:24:58Z
2022-03-03T09:24:05Z
NONE
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## Describe the bug Datasets hosted on Google Drive do not seem to work right now. Loading them fails with a checksum error. ## Steps to reproduce the bug ```python from datasets import load_dataset for dataset in ["head_qa", "yelp_review_full"]: try: load_dataset(dataset) except Exception as exception: print("Error", dataset, exception) ``` Here is a [colab](https://colab.research.google.com/drive/1wOtHBmL8I65NmUYakzPV5zhVCtHhi7uQ#scrollTo=cDzdCLlk-Bo4). ## Expected results The datasets should be loaded. ## Actual results ``` Downloading and preparing dataset head_qa/es (download: 75.69 MiB, generated: 2.86 MiB, post-processed: Unknown size, total: 78.55 MiB) to /root/.cache/huggingface/datasets/head_qa/es/1.1.0/583ab408e8baf54aab378c93715fadc4d8aa51b393e27c3484a877e2ac0278e9... Error head_qa Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t'] Downloading and preparing dataset yelp_review_full/yelp_review_full (download: 187.06 MiB, generated: 496.94 MiB, post-processed: Unknown size, total: 684.00 MiB) to /root/.cache/huggingface/datasets/yelp_review_full/yelp_review_full/1.0.0/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43... Error yelp_review_full Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0'] ``` ## Environment info - `datasets` version: 1.18.3 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 6.0.1
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I_kwDODunzps5FAFZ7
3,808
Pre-Processing Cache Fails when using a Factory pattern
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[ "Ok - this is still an issue but I believe the root cause is different than I originally thought. I'm now able to get caching to work consistently with the above example as long as I fix the python hash seed `export PYTHONHASHSEED=1234`", "Hi! \r\n\r\nYes, our hasher should work with decorators. For instance, this dummy example:\r\n```python\r\ndef f(arg):\r\n def f1(ex):\r\n return {\"a\": ex[\"col1\"] + arg}\r\n return f1\r\n```\r\ngives the same hash across different Python sessions (`datasets.fingerprint.Hasher.hash(f(\"string1\")` returns `\"408c9059f89dbd6c\"` on my machine).\r\n\r\nCould you please make the example self-contained? This way, we can reproduce the bug. Additionally, you can try to find the problematic object yourself by testing their hash with `datasets.fingerprint.Hasher.hash(obj)`\r\n\r\nThis could be related to https://github.com/huggingface/datasets/issues/3638.", "#3638 was indeed my issue. Thanks!" ]
2022-03-02T20:18:43Z
2022-03-10T23:01:47Z
2022-03-10T23:01:47Z
NONE
null
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## Describe the bug If you utilize a pre-processing function which is created using a factory pattern, the function hash changes on each run (even if the function is identical) and therefore the data will be reproduced each time. ## Steps to reproduce the bug ```python def preprocess_function_factory(augmentation=None): def preprocess_function(examples): # Tokenize the texts if augmentation: conversions1 = [ augmentation(example) for example in examples[sentence1_key] ] if sentence2_key is None: args = (conversions1,) else: conversions2 = [ augmentation(example) for example in examples[sentence2_key] ] args = (conversions1, conversions2) else: args = ( (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key]) ) result = tokenizer( *args, padding=padding, max_length=max_seq_length, truncation=True ) # Map labels to IDs (not necessary for GLUE tasks) if label_to_id is not None and "label" in examples: result["label"] = [ (label_to_id[l] if l != -1 else -1) for l in examples["label"] ] return result return preprocess_function capitalize = lambda x: x.capitalize() preprocess_function = preprocess_function_factory(augmentation=capitalize) print(hash(preprocess_function)) # This will change on each run raw_datasets = raw_datasets.map( preprocess_function, batched=True, load_from_cache_file=True, desc="Running transformation and tokenizer on dataset", ) ``` ## Expected results Running the code twice will cause the cache to be re-used. ## Actual results Running the code twice causes the whole dataset to be re-processed
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NonMatchingChecksumError in xcopa dataset
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[ "@albertvillanova here's a separate issue for a bug similar to #3792", "Hi @afcruzs-ms, thanks for opening this separate issue for your problem.\r\n\r\nThe root problem in the other issue (#3792) was a change in the service of Google Drive.\r\n\r\nBut in your case, the `xcopa` dataset is not hosted on Google Drive. Therefore, the root cause should be a different one.\r\n\r\nLet me look at it... ", "@afcruzs-ms, I'm not able to reproduce the issue you reported:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ...: dataset = load_dataset(\"xcopa\", \"it\")\r\nDownloading builder script: 5.21kB [00:00, 2.75MB/s] \r\nDownloading metadata: 28.6kB [00:00, 14.5MB/s] \r\nDownloading and preparing dataset xcopa/it (download: 627.09 KiB, generated: 76.43 KiB, post-processed: Unknown size, total: 703.52 KiB) to .../.cache/huggingface/datasets/xcopa/it/1.0.0/e1fab65f984b24c8b66bcf7ac27a26a1182f84adfb2e74035861be65e214b9e6...\r\nDownloading data: 642kB [00:00, 5.42MB/s]\r\nDataset xcopa downloaded and prepared to .../.cache/huggingface/datasets/xcopa/it/1.0.0/e1fab65f984b24c8b66bcf7ac27a26a1182f84adfb2e74035861be65e214b9e6. Subsequent calls will reuse this data. \r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 733.27it/s]\r\n\r\nIn [2]: dataset\r\nOut[2]: \r\nDatasetDict({\r\n test: Dataset({\r\n features: ['premise', 'choice1', 'choice2', 'question', 'label', 'idx', 'changed'],\r\n num_rows: 500\r\n })\r\n validation: Dataset({\r\n features: ['premise', 'choice1', 'choice2', 'question', 'label', 'idx', 'changed'],\r\n num_rows: 100\r\n })\r\n})\r\n```\r\n\r\nMaybe you have some issue with your cached data... Could you please try to force the redownload of the data?\r\n```python\r\ndataset = load_dataset(\"xcopa\", \"it\", download_mode=\"force_redownload\")\r\n```", "It works indeed, thanks! ", "unfortunately, i am having a similar problem with the irc_disentaglement dataset :/\r\nmy code:\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"irc_disentangle\", download_mode=\"force_redownload\")\r\n```\r\n\r\nhowever, it produces the same error as @afcruzs-ms \r\n```\r\n[38](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=37) if len(bad_urls) > 0:\r\n [39](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=38) error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> [40](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=39) raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n [41](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=40) logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://github.com/jkkummerfeld/irc-disentanglement/tarball/master']\r\n```\r\n\r\nI attempted to use the `ignore_verifications' as such:\r\n```\r\nds = datasets.load_dataset('irc_disentangle', download_mode=\"force_redownload\", ignore_verifications=True)\r\n\r\n```\r\n```\r\nDownloading builder script: 12.0kB [00:00, 5.92MB/s] \r\nDownloading metadata: 7.58kB [00:00, 3.48MB/s] \r\nNo config specified, defaulting to: irc_disentangle/ubuntu\r\nDownloading and preparing dataset irc_disentangle/ubuntu (download: 112.98 MiB, generated: 60.05 MiB, post-processed: Unknown size, total: 173.03 MiB) to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5...\r\nDownloading data: 118MB [00:09, 12.1MB/s] \r\n \r\nDataset irc_disentangle downloaded and prepared to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5. Subsequent calls will reuse this data.\r\n100%|██████████| 3/3 [00:00<00:00, 675.38it/s]\r\n```\r\nbut, this returns an empty set?\r\n\r\n```\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n test: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n validation: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n})\r\n```\r\n\r\nnot sure what else to try at this point?\r\nThanks in advanced🤗", "Thanks @labouz for reporting: yes, better opening a new GitHub issue as you did. I'm addressing it:\r\n- #4376" ]
2022-03-02T18:10:19Z
2022-05-20T06:00:42Z
2022-03-03T17:40:31Z
NONE
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## Describe the bug Loading the xcopa dataset doesn't work, it fails due to a mismatch in the checksum. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("xcopa", "it") ``` ## Expected results The dataset should be loaded correctly. ## Actual results Fails with: ```python in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/cambridgeltl/xcopa/archive/master.zip'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3, and 1.18.4.dev0 - Platform: - Python version: 3.8 - PyArrow version:
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can not flatten natural_questions dataset
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[ "same issue. downgrade it to a lower version.", "Thanks for reporting, I'll take a look tomorrow :)" ]
2022-02-27T13:57:40Z
2022-03-21T14:36:12Z
2022-03-21T14:36:12Z
NONE
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## Describe the bug after downloading the natural_questions dataset, can not flatten the dataset considering there are `long answer` and `short answer` in `annotations`. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('natural_questions',cache_dir = 'data/dataset_cache_dir') dataset['train'].flatten() ``` ## Expected results a dataset with `long_answer` as features ## Actual results Traceback (most recent call last): File "temp.py", line 5, in <module> dataset['train'].flatten() File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/fingerprint.py", line 413, in wrapper out = func(self, *args, **kwargs) File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1296, in flatten dataset._data = update_metadata_with_features(dataset._data, dataset.features) File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 536, in update_metadata_with_features features = Features({col_name: features[col_name] for col_name in table.column_names}) File "/Users/hannibal046/anaconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 536, in <dictcomp> features = Features({col_name: features[col_name] for col_name in table.column_names}) KeyError: 'annotations.long_answer' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.8.13 - Platform: MBP - Python version: 3.8 - PyArrow version: 6.0.1
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Checksums didn't match for dataset source
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[ "Same issue with `dataset = load_dataset(\"dbpedia_14\")`\r\n```\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k']", "I think this is a side-effect of #3787. The checksums won't match because the URLs have changed. @rafikg @Y0mingZhang, while this is fixed, maybe you can load the datasets as such:\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\", ignore_verifications=True)`\r\n`dataset = load_dataset(\"dbpedia_14\", ignore_verifications=True)`\r\n\r\nThis will, most probably, skip the verifications and integrity checks listed [here](https://huggingface.co/docs/datasets/loading_datasets.html#integrity-verifications)", "Hi! Installing the `datasets` package from master (`pip install git+https://github.com/huggingface/datasets.git`) and then redownloading the datasets with `download_mode` set to `force_redownload` (e.g. `dataset = load_dataset(\"dbpedia_14\", download_mode=\"force_redownload\")`) should fix the issue.", "Hi @rafikg and @Y0mingZhang, thanks for reporting.\r\n\r\nIndeed it seems that Google Drive changed their way to access their data files. We have recently handled that change:\r\n- #3787\r\n\r\nbut it will be accessible to users only in our next release of the `datasets` version.\r\n- Note that our latest release (version 1.18.3) was made before this fix: https://github.com/huggingface/datasets/releases/tag/1.18.3\r\n\r\nIn the meantime, as @mariosasko explained, you can incorporate this \"fix\" by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\nThen, you should force the redownload of the data (before the fix, you are just downloading/caching the virus scan warning page, instead of the data file):\r\n```shell\r\ndata = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\", download_mode=\"force_redownload\")", "@albertvillanova by running:\r\n```\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\ndata = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\", download_mode=\"force_redownload\", ignore_verifications=True)\r\n```\r\n\r\nI had a pickle error **UnpicklingError: invalid load key, '<'** in this part of code both `locally and on google colab`:\r\n\r\n```\r\n\"\"\"Yields examples.\"\"\"\r\nwith open(filepath, \"rb\") as f:\r\n data = pickle.load(f)\r\nfor id_, row in enumerate(data.items()):\r\n yield id_, {\"url\": row[0], \"article\": self._process_article(row[1])}\r\n```\r\n", "This issue impacts many more datasets than the ones mention in this thread. Can we post # of downloads for each dataset by day (by successes and failures)? If so, it should be obvious which ones are failing.", "I can see this problem too in xcopa, unfortunately installing the latest master (1.18.4.dev0) doesn't work, @albertvillanova .\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"xcopa\", \"it\")\r\n```\r\n\r\nThrows\r\n\r\n```\r\nin verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n 38 if len(bad_urls) > 0:\r\n 39 error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n 41 logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n 42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://github.com/cambridgeltl/xcopa/archive/master.zip']\r\n```", "Hi @rafikg, I think that is another different issue. Let me check it... \r\n\r\nI guess maybe you are using a different Python version that the one the dataset owner used to create the pickle file...", "@kwchurch the datasets impacted for this specific issue are the ones which are hosted at Google Drive.", "@afcruzs-ms I think your issue is a different one, because that dataset is not hosted at Google Drive. Would you mind open another issue for that other problem, please? Thanks! :)", "@albertvillanova just to let you know that I tried it locally and on colab and it is the same error", "There are many many datasets on HugggingFace that are receiving this checksum error. Some of these datasets are very popular. There must be a way to track these errors, or to do regression testing. We don't want to catch each of these errors on each dataset, one at a time.", "@rafikg I am sorry, but I can't reproduce your issue. For me it works OK for all languages. See: https://colab.research.google.com/drive/1yIcLw1it118-TYE3ZlFmV7gJcsF6UCsH?usp=sharing", "@kwchurch the PR #3787 fixes this issue (generated by a change in Google Drive service) for ALL datasets with this issue. Once we make our next library release (in a couple of days), the fix will be accessible to all users that update our library from PyPI.", "By the way, @rafikg, I discovered the URL for Spanish was wrong. I've created a PR to fix it:\r\n- #3806 ", "I have the same problem with \"wider_face\" dataset. It seems that \"load_dataset\" function can not download the dataset from google drive.\r\n", "still getting this issue with datasets==2.2.2 for \r\ndataset_fever_original_dev = load_dataset('fever', \"v1.0\", split=\"labelled_dev\")\r\n(this one seems to be hosted by aws though)\r\n\r\nupdate: also tried to install from source to get the latest 2.2.3.dev0, but still get the error below (and also force-redownloaded)\r\n\r\nupdate2: Seems like this issues is linked to a change in the links in the specific fever datasets: https://fever.ai/\r\n\"28/04/2022\r\nDataset download URLs have changed\r\nDownload URLs for shared task data for FEVER, FEVER2.0 and FEVEROUS have been updated. New URLS begin with https://fever.ai/download/[task name]/[filename]. All resource pages have been updated with the new URLs. Previous dataset URLs may not work and should be updated if you require these in your scripts. \"\r\n\r\n=> I don't know how to update the links for HF datasets - would be great if someone could update them :) \r\n\r\n```\r\n\r\nDownloading and preparing dataset fever/v1.0 (download: 42.78 MiB, generated: 38.39 MiB, post-processed: Unknown size, total: 81.17 MiB) to /root/.cache/huggingface/datasets/fever/v1.0/1.0.0/956b0a9c4b05e126fd956be73e09da5710992b5c85c30f0e5e1c500bc6051d0a...\r\n\r\nDownloading data files: 100%\r\n6/6 [00:07<00:00, 1.21s/it]\r\nDownloading data:\r\n278/? [00:00<00:00, 2.34kB/s]\r\nDownloading data:\r\n278/? [00:00<00:00, 1.53kB/s]\r\nDownloading data:\r\n278/? [00:00<00:00, 7.43kB/s]\r\nDownloading data:\r\n278/? [00:00<00:00, 5.54kB/s]\r\nDownloading data:\r\n278/? [00:00<00:00, 6.19kB/s]\r\nDownloading data:\r\n278/? [00:00<00:00, 7.51kB/s]\r\nExtracting data files: 100%\r\n6/6 [00:00<00:00, 108.05it/s]\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNonMatchingChecksumError Traceback (most recent call last)\r\n\r\n[<ipython-input-20-92ec5c728ecf>](https://localhost:8080/#) in <module>()\r\n 27 # get labels for fever-nli-dev from original fever - only works for dev\r\n 28 # \"(The labels for both dev and test are hidden but you can retrieve the label for dev using the cid and the original FEVER data.)\"\" https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md\r\n---> 29 dataset_fever_original_dev = load_dataset('fever', \"v1.0\", split=\"labelled_dev\")\r\n 30 df_fever_original_dev = pd.DataFrame(data={\"id\": dataset_fever_original_dev[\"id\"], \"label\": dataset_fever_original_dev[\"label\"], \"claim\": dataset_fever_original_dev[\"claim\"], \"evidence_id\": dataset_fever_original_dev[\"evidence_id\"]})\r\n 31 df_fever_dev = pd.merge(df_fever_dev, df_fever_original_dev, how=\"left\", left_on=\"cid\", right_on=\"id\")\r\n\r\n4 frames\r\n\r\n[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n 38 if len(bad_urls) > 0:\r\n 39 error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n 41 logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n 42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://s3-eu-west-1.amazonaws.com/fever.public/train.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_dev.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_dev_public.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/shared_task_test.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/paper_dev.jsonl', 'https://s3-eu-west-1.amazonaws.com/fever.public/paper_test.jsonl']\r\n```\r\n", "I think this has to be fixed on the google drive side, but you also have to delete the bad stuff from your local cache. This is not a great design, but it is what it is.", "We have fixed the issues with the datasets:\r\n- wider_face: by hosting their data files on the HuggingFace Hub (CC: @HosseynGT)\r\n- fever: by updating to their new data URLs (CC: @MoritzLaurer)", "The yelp_review_full datasets has this problem as well and can't be fixed with the suggestion.", "This is a super-common failure mode. We really need to find a better workaround. My solution was to wait until the owner of the dataset in question did the right thing, and then I had to delete my cached versions of the datasets with the bad checksums. I don't understand why this happens. Would it be possible to maintain a copy of the most recent version that was known to work, and roll back to that automatically if the checksums fail? And if the checksums fail, couldn't the system automatically flush the cached versions with the bad checksums? It feels like we are blaming the provider of the dataset, when in fact, there are things that the system could do to ease the pain. Let's take these error messages seriously. There are too many of them involving too many different datasets.", "the [exams](https://huggingface.co/datasets/exams) dataset also has this issue and the provided fix above doesn't work", "Same for [DART dataset](https://huggingface.co/datasets/dart):\r\n```\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-train.json', 'https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-dev.json', 'https://raw.githubusercontent.com/Yale-LILY/dart/master/data/v1.1.1/dart-v1.1.1-full-test.json']\r\n```", "same for multi_news dataset", "- @thesofakillers the issue with `exams` was fixed on 16 Aug by this PR:\r\n - #4853\r\n- @Aktsvigun the issue with `dart` has been transferred to the Hub: https://huggingface.co/datasets/dart/discussions/1\r\n - and fixed by PR: https://huggingface.co/datasets/dart/discussions/2\r\n- @Carol-gutianle the issue with `multi_news` have been transferred to the Hub as well: https://huggingface.co/datasets/multi_news/discussions/1\r\n - not reproducible: maybe you should try to update `datasets`\r\n\r\nFor information to everybody, we are removing the checksum verifications (that were creating a bad user experience). This will be in place in the following weeks.", "auto_gptq is required for real quantization\r\n['/home/sam/Doctorproject/OmniQuant-main/main.py', '--model', '/home/sam/Doctorproject/OmniQuant-main/PATH/TO/LLaMA/llama-7b/', '--epochs', '20', '--output_dir', '/home/sam/Doctorproject/OmniQuant-main/outdir/llama-7b-w3a16/', '--eval_ppl', '--wbits', '3', '--abits', '16', '--lwc', '--net', 'llama-7b', '--aug_loss']\r\n[2024-03-13 17:58:48 root](main.py 262): INFO Namespace(model='/home/sam/Doctorproject/OmniQuant-main/PATH/TO/LLaMA/llama-7b/', cache_dir='./cache', output_dir='/home/sam/Doctorproject/OmniQuant-main/outdir/llama-7b-w3a16/', save_dir=None, resume=None, real_quant=False, calib_dataset='wikitext2', nsamples=128, batch_size=1, seed=2, tasks='', eval_ppl=True, num_fewshot=0, wbits=3, abits=16, group_size=None, alpha=0.5, let_lr=0.005, lwc_lr=0.01, wd=0, epochs=20, let=False, lwc=True, aug_loss=True, symmetric=False, disable_zero_point=False, a_dynamic_method='per_token', w_dynamic_method='per_channel', limit=-1, multigpu=False, deactive_amp=False, attn_implementation='eager', net='llama-7b', act_scales=None, act_shifts=None)\r\nLoading checkpoint shards: 0%| | 0/33 [00:00<?, ?it/s]/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/torch/_utils.py:776: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\r\n return self.fget.__get__(instance, owner)()\r\nLoading checkpoint shards: 100%|██████████| 33/33 [00:11<00:00, 2.98it/s]\r\nvocab size: 32000\r\n[2024-03-13 17:58:59 root](main.py 331): INFO === start quantization ===\r\nget_wikitext2\r\n[2024-03-13 18:02:20 datasets.load](load.py 1586): WARNING Using the latest cached version of the module from /home/sam/.cache/huggingface/modules/datasets_modules/datasets/wikitext/a241db52902eaf2c6aa732210bead40c090019a499ceb13bcbfa3f8ab646a126 (last modified on Wed Mar 13 16:54:26 2024) since it couldn't be found locally at wikitext, or remotely on the Hugging Face Hub.\r\nUsing the latest cached version of the module from /home/sam/.cache/huggingface/modules/datasets_modules/datasets/wikitext/a241db52902eaf2c6aa732210bead40c090019a499ceb13bcbfa3f8ab646a126 (last modified on Wed Mar 13 16:54:26 2024) since it couldn't be found locally at wikitext, or remotely on the Hugging Face Hub.\r\nDownloading data: 243B [00:00, 877kB/s]\r\nGenerating test split: 0%| | 0/4358 [00:00<?, ? examples/s]\r\nTraceback (most recent call last):\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/builder.py\", line 1742, in _prepare_split_single\r\n for key, record in generator:\r\n File \"/home/sam/.cache/huggingface/modules/datasets_modules/datasets/wikitext/a241db52902eaf2c6aa732210bead40c090019a499ceb13bcbfa3f8ab646a126/wikitext.py\", line 187, in _generate_examples\r\n with open(data_file, encoding=\"utf-8\") as f:\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/streaming.py\", line 75, in wrapper\r\n return function(*args, download_config=download_config, **kwargs)\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 507, in xopen\r\n return open(main_hop, mode, *args, **kwargs)\r\nNotADirectoryError: [Errno 20] Not a directory: '/home/sam/.cache/huggingface/datasets/downloads/94be2a7b3fff32ae7379658c8d3821035b666baddad3a06d29b55ab3a4ab3115/wikitext-2-raw/wiki.test.raw'\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/home/sam/Doctorproject/OmniQuant-main/main.py\", line 382, in <module>\r\n main()\r\n File \"/home/sam/Doctorproject/OmniQuant-main/main.py\", line 339, in main\r\n dataloader, _ = get_loaders(\r\n File \"/home/sam/Doctorproject/OmniQuant-main/datautils.py\", line 178, in get_loaders\r\n return get_wikitext2(nsamples, seed, seqlen, model)\r\n File \"/home/sam/Doctorproject/OmniQuant-main/datautils.py\", line 37, in get_wikitext2\r\n traindata = load_dataset(path='wikitext', name='wikitext-2-raw-v1', split='train', download_mode=\"force_redownload\")\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/load.py\", line 2598, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/builder.py\", line 1021, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/builder.py\", line 1783, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/builder.py\", line 1116, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/builder.py\", line 1621, in _prepare_split\r\n for job_id, done, content in self._prepare_split_single(\r\n File \"/home/sam/anaconda3/envs/omniquant/lib/python3.10/site-packages/datasets/builder.py\", line 1778, in _prepare_split_single\r\n raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\ndatasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset\r\n\r\n\r\n@albertvillanova @Y0mingZhang @kwchurch @HosseynGT @rafikg I tried the solutions you provided above, but none of them worked. Could you please give me some guidance\r\n" ]
2022-02-25T19:55:09Z
2024-03-13T12:25:08Z
2022-02-28T08:44:18Z
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## Dataset viewer issue for 'wiki_lingua*' **Link:** *link to the dataset viewer page* `data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]") ` *short description of the issue* ``` [NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]() ``` Am I the one who added this dataset ? No
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Bug downloading Virus scan warning page from Google Drive URLs
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[ "Once the PR merged into master and until our next `datasets` library release, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```" ]
2022-02-25T09:32:23Z
2022-03-03T09:25:59Z
2022-02-25T11:56:35Z
MEMBER
null
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## Describe the bug Recently, some issues were reported with URLs from Google Drive, where we were downloading the Virus scan warning page instead of the data file itself. See: - #3758 - #3773 - #3784
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I_kwDODunzps5EjH3j
3,784
Unable to Download CNN-Dailymail Dataset
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[ "#self-assign", "@AngadSethi thanks for reporting and thanks for your PR!", "Glad to help @albertvillanova! Just fine-tuning the PR, will comment once I am able to get it up and running 😀", "Fixed by:\r\n- #3787" ]
2022-02-25T05:24:47Z
2022-03-03T14:05:17Z
2022-03-03T14:05:17Z
NONE
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## Describe the bug I am unable to download the CNN-Dailymail dataset. Upon closer investigation, I realised why this was happening: - The dataset sits in Google Drive, and both the CNN and DM datasets are large. - Google is unable to scan the folder for viruses, **so the link which would originally download the dataset, now downloads the source code of this web page:** ![image](https://user-images.githubusercontent.com/58678541/155658435-c2f497d7-7601-4332-94b1-18a62dd96422.png) - **This leads to the following error**: ```python NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' ``` ## Steps to reproduce the bug ```python import datasets dataset = datasets.load_dataset("cnn_dailymail", "3.0.0", split="train") ``` ## Expected results That the dataset is downloaded and processed just like other datasets. ## Actual results Hit with this error: ```python NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 6.0.1
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3,778
Not be able to download dataset - "Newsroom"
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[ "Hi @Darshan2104, thanks for reporting.\r\n\r\nPlease note that at Hugging Face we do not host the data of this dataset, but just a loading script pointing to the host of the data owners.\r\n\r\nApparently the data owners changed their data host server. After googling it, I found their new website at: https://lil.nlp.cornell.edu/newsroom/index.html\r\n- Download page: https://lil.nlp.cornell.edu/newsroom/download/index.html\r\n\r\nI'm fixing the link in our Datasets library.", "@albertvillanova Thanks for the solution and link you made my day!" ]
2022-02-23T10:15:50Z
2022-02-23T17:05:04Z
2022-02-23T13:26:40Z
NONE
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Hello, I tried to download the **newsroom** dataset but it didn't work out for me. it said me to **download it manually**! For manually, Link is also didn't work! It is sawing some ad or something! If anybody has solved this issue please help me out or if somebody has this dataset please share your google drive link, it would be a great help! Thanks Darshan Tank
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Checksum mismatch for the reddit_tifu dataset
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[ "Thanks for reporting, @anna-kay. We are fixing it.", "@albertvillanova Thank you for the fast response! However I am still getting the same error:\r\n\r\nDownloading: 2.23kB [00:00, ?B/s]\r\nTraceback (most recent call last):\r\n File \"C:\\Users\\Anna\\PycharmProjects\\summarization\\main.py\", line 17, in <module>\r\n dataset = load_dataset('reddit_tifu', 'long')\r\n File \"C:\\Users\\Anna\\Desktop\\summarization\\summarization_env\\lib\\site-packages\\datasets\\load.py\", line 1702, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"C:\\Users\\Anna\\Desktop\\summarization\\summarization_env\\lib\\site-packages\\datasets\\builder.py\", line 594, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"C:\\Users\\Anna\\Desktop\\summarization\\summarization_env\\lib\\site-packages\\datasets\\builder.py\", line 665, in _download_and_prepare\r\n verify_checksums(\r\n File \"C:\\Users\\Anna\\Desktop\\summarization\\summarization_env\\lib\\site-packages\\datasets\\utils\\info_utils.py\", line 40, in verify_checksums\r\n raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://drive.google.com/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']\r\n\r\nI have cleaned the cache/huggingface/datasets & cache/huggingface/modules files and also tried on another machine with a fresh installation of trasnformers & datasets. \r\nThe reddit_tifu.py that gets downloaded still has the previous url on line 51, _URL = \"https://drive.google.com/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF\" ", "Hi @anna-kay, I'm sorry I didn't clearly explain the details to you:\r\n- the error has been fixed in our `master` branch on GitHub: https://github.com/huggingface/datasets/commit/8ae21bf6a77175dc803ce2f1b93d18b8fbf45586\r\n- the fix will not be accessible to users in PyPI until our next release of the `datasets` library\r\n - our latest release (version 1.18.3) was made 23 days ago: https://github.com/huggingface/datasets/releases/tag/1.18.3\r\n- in the meantime, you can get the fix if you install datasets from our GitHub `master` branch:\r\n ```\r\n pip install git+https://github.com/huggingface/datasets#egg=datasets\r\n ```", "@albertvillanova Ok great, makes sence. Thank you very much for the explanation!" ]
2022-02-22T10:57:07Z
2022-02-25T19:27:49Z
2022-02-22T12:38:44Z
CONTRIBUTOR
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## Describe the bug A checksum occurs when downloading the reddit_tifu data (both long & short). ## Steps to reproduce the bug reddit_tifu_dataset = load_dataset('reddit_tifu', 'long') ## Expected results The expected result is for the dataset to be downloaded and cached locally. ## Actual results File "/.../lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF'] ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.13.0-30-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 7.0.0
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DuplicatedKeysError on msr_sqa dataset
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[ "Thanks for reporting, @kolk.\r\n\r\nWe are fixing it. " ]
2022-02-22T00:43:33Z
2022-02-22T08:12:39Z
2022-02-22T08:12:39Z
NONE
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### Describe the bug Failure to generate dataset msr_sqa because of duplicate keys. ### Steps to reproduce the bug ``` from datasets import load_dataset load_dataset("msr_sqa") ``` ### Expected results The examples keys should be unique. **Actual results** ``` >>> load_dataset("msr_sqa") Downloading: 6.72k/? [00:00<00:00, 148kB/s] Downloading: 2.93k/? [00:00<00:00, 53.8kB/s] Using custom data configuration default Downloading and preparing dataset msr_sqa/default (download: 4.57 MiB, generated: 26.25 MiB, post-processed: Unknown size, total: 30.83 MiB) to /root/.cache/huggingface/datasets/msr_sqa/default/0.0.0/70b2a497bd3cc8fc960a3557d2bad1eac5edde824505e15c9c8ebe4c260fd4d1... Downloading: 100% 4.80M/4.80M [00:00<00:00, 7.49MB/s] --------------------------------------------------------------------------- DuplicatedKeysError Traceback (most recent call last) [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator) 1080 example = self.info.features.encode_example(record) -> 1081 writer.write(example, key) 1082 finally: 8 frames DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: nt-639 Keys should be unique and deterministic in nature During handling of the above exception, another exception occurred: DuplicatedKeysError Traceback (most recent call last) [/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in check_duplicate_keys(self) 449 for hash, key in self.hkey_record: 450 if hash in tmp_record: --> 451 raise DuplicatedKeysError(key) 452 else: 453 tmp_record.add(hash) DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: nt-639 Keys should be unique and deterministic in nature ``` ### Environment info datasets version: 1.18.3 Platform: Google colab notebook Python version: 3.7 PyArrow version: 6.0.1
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!
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2022-02-20T19:05:43Z
2022-02-21T08:55:58Z
2022-02-21T08:55:58Z
NONE
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## Dataset viewer issue for '*name of the dataset*' **Link:** *link to the dataset viewer page* *short description of the issue* Am I the one who added this dataset ? Yes-No
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It's not possible download `20200501.pt` dataset
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[ "Hi @jvanz, thanks for reporting.\r\n\r\nPlease note that Wikimedia website does not longer host Wikipedia dumps for so old dates.\r\n\r\nFor a list of accessible dump dates of `pt` Wikipedia, please see: https://dumps.wikimedia.org/ptwiki/\r\n\r\nYou can load for example `20220220` `pt` Wikipedia:\r\n```python\r\ndataset = load_dataset(\"wikipedia\", language=\"pt\", date=\"20220220\", beam_runner=\"DirectRunner\")\r\n```", "> ```python\r\n> dataset = load_dataset(\"wikipedia\", language=\"pt\", date=\"20220220\", beam_runner=\"DirectRunner\")\r\n> ```\r\n\r\nThank you! I did not know that I can do this. I was following the example in the error message when I do not define which language dataset I'm trying to download.\r\n\r\nI've tried something similar changing the date in the `load_dataset` call that I've shared in the bug description. Obviously, it did not work. I need to read the docs more carefully next time. My bad!\r\n\r\nThanks again and sorry for the noise.\r\n\r\n" ]
2022-02-20T18:34:58Z
2022-02-21T12:06:12Z
2022-02-21T09:25:06Z
NONE
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## Describe the bug The dataset `20200501.pt` is broken. The available datasets: https://dumps.wikimedia.org/ptwiki/ ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("wikipedia", "20200501.pt", beam_runner='DirectRunner') ``` ## Expected results I expect to download the dataset locally. ## Actual results ``` >>> from datasets import load_dataset >>> dataset = load_dataset("wikipedia", "20200501.pt", beam_runner='DirectRunner') Downloading and preparing dataset wikipedia/20200501.pt to /home/jvanz/.cache/huggingface/datasets/wikipedia/20200501.pt/1.0.0/009f923d9b6dd00c00c8cdc7f408f2b47f45dd4f5fb7982a21f9448f4afbe475... /home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/apache_beam/__init__.py:79: UserWarning: This version of Apache Beam has not been sufficiently tested on Python 3.9. You may encounter bugs or missing features. warnings.warn( 0%| | 0/1 [00:00<?, ?it/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/load.py", line 1702, in load_dataset builder_instance.download_and_prepare( File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/builder.py", line 594, in download_and_prepare self._download_and_prepare( File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/builder.py", line 1245, in _download_and_prepare super()._download_and_prepare( File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/builder.py", line 661, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/jvanz/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/009f923d9b6dd00c00c8cdc7f408f2b47f45dd4f5fb7982a21f9448f4afbe475/wikipedia.py", line 420, in _split_generators downloaded_files = dl_manager.download_and_extract({"info": info_url}) File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 307, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 195, in download downloaded_path_or_paths = map_nested( File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 260, in map_nested mapped = [ File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 261, in <listcomp> _single_map_nested((function, obj, types, None, True)) File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 196, in _single_map_nested return function(data_struct) File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 216, in _download return cached_path(url_or_filename, download_config=download_config) File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 298, in cached_path output_path = get_from_cache( File "/home/jvanz/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 612, in get_from_cache raise FileNotFoundError(f"Couldn't find file at {url}") FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/ptwiki/20200501/dumpstatus.json ``` ## Environment info ``` - `datasets` version: 1.18.3 - Platform: Linux-5.3.18-150300.59.49-default-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 6.0.1 ```
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1,144,849,557
I_kwDODunzps5EPQSV
3,762
`Dataset.class_encode` should support custom class names
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[ "Hi @Dref360, thanks a lot for your proposal.\r\n\r\nIt totally makes sense to have more flexibility when class encoding, I agree.\r\n\r\nYou could even further customize the class encoding by passing an instance of `ClassLabel` itself (instead of replicating `ClassLabel` instantiation arguments as `Dataset.class_encode_column` arguments).\r\n\r\nAnd the latter made me think of `Dataset.cast_column`...\r\n\r\nMaybe better to have some others' opinions @lhoestq @mariosasko ", "Hi @Dref360! You can use [`Dataset.align_labels_with_mapping`](https://huggingface.co/docs/datasets/master/package_reference/main_classes.html#datasets.Dataset.align_labels_with_mapping) after `Dataset.class_encode_column` to assign a different mapping of labels to ids.\r\n\r\n@albertvillanova I'd like to avoid adding more complexity to the API where it's not (absolutely) needed, so I don't think introducing a new param in `Dataset.class_encode_column` is a good idea.\r\n\r\n", "I wasn't aware that it existed thank you for the link.\n\nClosing then! " ]
2022-02-19T21:21:45Z
2022-02-21T12:16:35Z
2022-02-21T12:16:35Z
CONTRIBUTOR
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I can make a PR, just wanted approval before starting. **Is your feature request related to a problem? Please describe.** It is often the case that classes are not ordered in alphabetical order. Current `class_encode_column` sort the classes before indexing. https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L1235 **Describe the solution you'd like** I would like to add a **optional** parameter `class_names` to `class_encode_column` that would be used for the mapping instead of sorting the unique values. **Describe alternatives you've considered** One can use map instead. I find it harder to read. ```python CLASS_NAMES = ['apple', 'orange', 'potato'] ds = ds.map(lambda item: CLASS_NAMES.index(item[label_column])) # Proposition ds = ds.class_encode_column(label_column, CLASS_NAMES) ``` **Additional context** I can make the PR if this feature is accepted.
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I_kwDODunzps5EPLru
3,761
Know your data for HF hub
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[ "Hi @Muhtasham you should take a look at https://huggingface.co/blog/data-measurements-tool and accompanying demo app at https://huggingface.co/spaces/huggingface/data-measurements-tool\r\n\r\nWe would be interested in your feedback. cc @meg-huggingface @sashavor @yjernite " ]
2022-02-19T19:48:47Z
2022-02-21T14:15:23Z
2022-02-21T14:15:23Z
NONE
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**Is your feature request related to a problem? Please describe.** Would be great to see be able to understand datasets with the goal of improving data quality, and helping mitigate fairness and bias issues. **Describe the solution you'd like** Something like https://knowyourdata.withgoogle.com/ for HF hub
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1,144,804,558
I_kwDODunzps5EPFTO
3,760
Unable to view the Gradio flagged call back dataset
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[ "Hi @kingabzpro.\r\n\r\nI think you need to create a loading script that creates the dataset from the CSV file and the image paths.\r\n\r\nAs example, you could have a look at the Food-101 dataset: https://huggingface.co/datasets/food101\r\n- Loading script: https://huggingface.co/datasets/food101/blob/main/food101.py\r\n\r\nOnce the loading script is created, the viewer will show a previsualization of your dataset. ", "@albertvillanova I don't think this is the issue. I have created another dataset with similar files and format and it works. https://huggingface.co/datasets/kingabzpro/savtadepth-flags-V2", "Yes, you are right, that was not the issue.\r\n\r\nJust take into account that sometimes the viewer can take some time until it shows the preview of the dataset.\r\nAfter some time, yours is finally properly shown: https://huggingface.co/datasets/kingabzpro/savtadepth-flags", "The problem was resolved by deleted the dataset and creating new one with similar name and then clicking on flag button.", "I think if you make manual changes to dataset the whole system breaks. " ]
2022-02-19T17:45:08Z
2022-03-22T07:12:11Z
2022-03-22T07:12:11Z
NONE
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## Dataset viewer issue for '*savtadepth-flags*' **Link:** *[savtadepth-flags](https://huggingface.co/datasets/kingabzpro/savtadepth-flags)* *with the Gradio 2.8.1 the dataset viers stopped working. I tried to add values manually but its not working. The dataset is also not showing the link with the app https://huggingface.co/spaces/kingabzpro/savtadepth.* Am I the one who added this dataset ? Yes
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head_qa file missing
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[ "We usually find issues with files hosted at Google Drive...\r\n\r\nIn this case we download the Google Drive Virus scan warning instead of the data file.", "Fixed: https://huggingface.co/datasets/head_qa/viewer/en/train. Thanks\r\n\r\n<img width=\"1551\" alt=\"Capture d’écran 2022-02-28 à 15 29 04\" src=\"https://user-images.githubusercontent.com/1676121/156000224-fd3f62c6-8b54-4df1-8911-bdcb0bac3f1a.png\">\r\n" ]
2022-02-18T16:32:43Z
2022-02-28T14:29:18Z
2022-02-21T14:39:19Z
COLLABORATOR
null
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## Describe the bug A file for the `head_qa` dataset is missing (https://drive.google.com/u/0/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t/HEAD_EN/train_HEAD_EN.json) ## Steps to reproduce the bug ```python >>> from datasets import load_dataset >>> load_dataset("head_qa", name="en") ``` ## Expected results The dataset should be loaded ## Actual results ``` Downloading and preparing dataset head_qa/en (download: 75.69 MiB, generated: 2.69 MiB, post-processed: Unknown size, total: 78.38 MiB) to /home/slesage/.cache/huggingface/datasets/head_qa/en/1.1.0/583ab408e8baf54aab378c93715fadc4d8aa51b393e27c3484a877e2ac0278e9... Downloading data: 2.21kB [00:00, 2.05MB/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/load.py", line 1729, in load_dataset builder_instance.download_and_prepare( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 594, in download_and_prepare self._download_and_prepare( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 665, in _download_and_prepare verify_checksums( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t'] ``` ## Environment info - `datasets` version: 1.18.4.dev0 - Platform: Linux-5.11.0-1028-aws-x86_64-with-glibc2.31 - Python version: 3.9.6 - PyArrow version: 6.0.1
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Images get decoded when using `map()` with `input_columns` argument on a dataset
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null
[ "Hi! If I'm not mistaken, this behavior is intentional, but I agree it could be more intuitive.\r\n\r\n@albertvillanova Do you remember why you decided not to decode columns in the `Audio` feature PR when `input_columns` is not `None`? IMO we should decode those columns, and we don't even have to use lazy structures here because the user explicitly requires them in the map transform. \r\n\r\ncc @lhoestq for visibility", "I think I excluded to decorate the function when `input_columns` were passed as a quick fix for some non-passing tests: \r\n- https://github.com/huggingface/datasets/pull/2324/commits/9d7c3e8fa53e23ec636859b4407eeec904b1b3f9\r\n\r\nThat PR was quite complex and I decided to focus on the main feature requests, leaving refinements for subsequent PRs.\r\n\r\nNote that when `input_columns` are passed, the signature of the function is effectively changed, while the decorated function expects an item (whether an example or a batch) as first arg (which is not the case when passing `input_columns`.\r\n\r\nI agree we should consider supporting the case when `input_columns` are passed." ]
2022-02-18T15:35:38Z
2022-12-13T16:59:06Z
2022-12-13T16:59:06Z
NONE
null
null
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## Describe the bug The `datasets.features.Image` feature class decodes image data by default. Expectedly, when indexing a dataset or using the `map()` method, images are returned as PIL Image instances. However, when calling `map()` and setting a specific data column with the `input_columns` argument, the image data is passed as raw byte representation to the mapping function. ## Steps to reproduce the bug ```python from datasets import load_dataset from torchvision import transforms from PIL.Image import Image dataset = load_dataset('mnist', split='train') def transform_all_columns(example): # example['image'] is encoded as PIL Image assert isinstance(example['image'], Image) return example def transform_image_column(image): # image is decoded here and represented as raw bytes assert isinstance(image, Image) return image # single-sample dataset for debugging purposes dev = dataset.select([0]) dev.map(transform_all_columns) dev.map(transform_image_column, input_columns='image') ``` ## Expected results Image data should be passed in decoded form, i.e. as PIL Image objects to the mapping function unless the `decode` attribute on the image feature is set to `False`. ## Actual results The mapping function receives images as raw byte data. ## Environment info - `datasets` version: 1.18.3 - Platform: Linux-5.11.0-49-generic-x86_64-with-glibc2.32 - Python version: 3.8.0b4 - PyArrow version: 7.0.0
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Cannot preview dataset
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null
[ "Thanks for reporting. The dataset viewer depends on some backend treatments, and for now, they might take some hours to get processed. We're working on improving it.", "It has finally been processed. Thanks for the patience.", "Thanks for the info @severo !" ]
2022-02-18T13:06:45Z
2022-02-19T14:30:28Z
2022-02-18T15:41:33Z
NONE
null
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## Dataset viewer issue for '*rubrix/news*' **Link:https://huggingface.co/datasets/rubrix/news** *link to the dataset viewer page* Cannot see the dataset preview: ``` Status code: 400 Exception: Status400Error Message: Not found. Cache is waiting to be refreshed. ``` Am I the one who added this dataset ? No
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Overflowing indices in `select`
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[ "Fixed on master (see https://github.com/huggingface/datasets/pull/3719).", "Awesome, I did not find that one! Thanks." ]
2022-02-18T11:30:52Z
2022-02-18T11:38:23Z
2022-02-18T11:38:23Z
MEMBER
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## Describe the bug The `Dataset.select` function seems to accept indices that are larger than the dataset size and seems to effectively use `index %len(ds)`. ## Steps to reproduce the bug ```python from datasets import Dataset ds = Dataset.from_dict({"test": [1,2,3]}) ds = ds.select(range(5)) print(ds) print() print(ds["test"]) ``` Result: ```python Dataset({ features: ['test'], num_rows: 5 }) [1, 2, 3, 1, 2] ``` This behaviour is not documented and can lead to unexpected behaviour when for example taking a sample larger than the dataset and thus creating a lot of duplicates. ## Expected results It think this should throw an error or at least a very big warning: ```python IndexError: Invalid key: 5 is out of bounds for size 3 ``` ## Environment info - `datasets` version: 1.18.3 - Platform: macOS-12.0.1-x86_64-i386-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0
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I_kwDODunzps5EF8SL
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`NonMatchingSplitsSizesError` for cats_vs_dogs dataset
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null
[ "Thnaks for reporting @jaketae. We are fixing it. " ]
2022-02-18T05:46:39Z
2022-02-18T14:56:11Z
2022-02-18T14:56:11Z
CONTRIBUTOR
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## Describe the bug Cannot download cats_vs_dogs dataset due to `NonMatchingSplitsSizesError`. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("cats_vs_dogs") ``` ## Expected results Loading is successful. ## Actual results ``` NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=7503250, num_examples=23422, dataset_name='cats_vs_dogs'), 'recorded': SplitInfo(name='train', num_bytes=7262410, num_examples=23410, dataset_name='cats_vs_dogs')}] ``` ## Environment info Reproduced on a fresh [Colab notebook](https://colab.research.google.com/drive/13GTvrSJbBGvL2ybDdXCBZwATd6FOkMub?usp=sharing). ## Additional Context Originally reported in https://github.com/huggingface/transformers/issues/15698. cc @mariosasko
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Better shards shuffling in streaming mode
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2022-02-17T15:07:21Z
2022-02-23T15:00:58Z
2022-02-23T15:00:58Z
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Sometimes a dataset script has a `_split_generators` that returns several files as well as the corresponding metadata of each file. It often happens that they end up in two separate lists in the `gen_kwargs`: ```python gen_kwargs = { "files": [os.path.join(data_dir, filename) for filename in all_files], "metadata_files": [all_metadata[filename] for filename in all_files], } ``` It happened for Multilingual Spoken Words for example in #3666 However currently **the two lists are shuffled independently** when shuffling the shards in streaming mode. This leads to `_generate_examples` not having the right metadata for each file. To prevent this issue I suggest that we always shuffle lists of the same length the exact same way to avoid such a big but silent issue. cc @polinaeterna
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Pubmed dataset does not work in streaming mode
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[ "Thanks for reporting, @abhi-mosaic (related to #3655).\r\n\r\nPlease note that `xml.etree.ElementTree.parse` already supports streaming:\r\n- #3476\r\n\r\nNo need to refactor to use `open`/`xopen`. Is is enough with importing the package `as ET` (instead of `as etree`)." ]
2022-02-16T17:13:37Z
2022-02-18T14:42:13Z
2022-02-18T14:42:13Z
CONTRIBUTOR
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## Describe the bug Trying to use the `pubmed` dataset with `streaming=True` fails. ## Steps to reproduce the bug ```python import datasets pubmed_train = datasets.load_dataset('pubmed', split='train', streaming=True) print (next(iter(pubmed_train))) ``` ## Expected results I would expect to see the first training sample from the pubmed dataset. ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/abhinav/Documents/mosaicml/mosaicml_venv/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 367, in __iter__ for key, example in self._iter(): File "/Users/abhinav/Documents/mosaicml/mosaicml_venv/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 364, in _iter yield from ex_iterable File "/Users/abhinav/Documents/mosaicml/mosaicml_venv/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 79, in __iter__ for key, example in self.generate_examples_fn(**self.kwargs): File "/Users/abhinav/.cache/huggingface/modules/datasets_modules/datasets/pubmed/9715addf10c42a7877a2149ae0c5f2fddabefc775cd1bd9b03ac3f012b86ce46/pubmed.py", line 373, in _generate_examples tree = etree.parse(filename) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/xml/etree/ElementTree.py", line 1202, in parse tree.parse(source, parser) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/xml/etree/ElementTree.py", line 584, in parse source = open(source, "rb") FileNotFoundError: [Errno 2] No such file or directory: 'gzip://pubmed21n0001.xml::ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed21n0001.xml.gz' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.2 - Platform: macOS-11.4-x86_64-i386-64bit - Python version: 3.8.2 - PyArrow version: 6.0.0 ## Comments The error looks like an issue with `open` vs. `xopen` inside the `xml` package. It looks like it's trying to open the remote source URL, which has been edited with prefix `gzip://...`. Maybe there can be an explicit `xopen` before passing the raw data to `etree`, something like: ```python # Before tree = etree.parse(filename) root = tree.getroot() # After with xopen(filename) as f: data_str = f.read() root = etree.fromstring(data_str) ```
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Bugs in NewsQA dataset
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2022-02-16T13:17:37Z
2022-02-17T07:54:25Z
2022-02-17T07:54:25Z
MEMBER
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## Describe the bug NewsQA dataset has the following bugs: - the field `validated_answers` is an exact copy of the field `answers` but with the addition of `'count': [0]` to each dict - the field `badQuestion` does not appear in `answers` nor `validated_answers` ## Steps to reproduce the bug By inspecting the dataset script we can see that: - the parsing of `validated_answers` is a copy-paste of the one for `answers` - the `badQuestion` field is ignored in the parsing of both `answers` and `validated_answers`
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I_kwDODunzps5D7BYN
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Checksum Error when loading multi-news dataset
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[ "Thanks for reporting @byw2.\r\nWe are fixing it.\r\nIn the meantime, you can load the dataset by passing `ignore_verifications=True`:\r\n ```python\r\ndataset = load_dataset(\"multi_news\", ignore_verifications=True)" ]
2022-02-16T05:11:08Z
2022-02-16T20:05:06Z
2022-02-16T08:48:46Z
NONE
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## Describe the bug When using the load_dataset function from datasets module to load the Multi-News dataset, does not load the dataset but throws Checksum Error instead. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("multi_news") ``` ## Expected results Should download and load Multi-News dataset. ## Actual results Throws the following error and cannot load data successfully: ``` NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C'] ``` Could this issue please be looked at? Thanks!
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I_kwDODunzps5D6dcq
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Wrong number of examples when loading a text dataset
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[ "Hi @kg-nlp, thanks for reporting.\r\n\r\nThat is weird... I guess we would need some sample data file where this behavior appears to reproduce the bug for further investigation... ", "ok, I found the reason why that two results are not same.\r\nthere is /u2029 in the text, the datasets will split sentence according to the /u2029,but when I use open function will not do that .\r\nso I want to know which function shell do that\r\nthanks" ]
2022-02-16T01:13:31Z
2022-03-15T16:16:09Z
2022-03-15T16:16:09Z
NONE
null
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## Describe the bug when I use load_dataset to read a txt file I find that the number of the samples is incorrect ## Steps to reproduce the bug ``` fr = open('train.txt','r',encoding='utf-8').readlines() print(len(fr)) # 1199637 datasets = load_dataset('text', data_files={'train': ['train.txt']}, streaming=False) print(len(datasets['train'])) # 1199649 ``` I also use command line operation to verify it ``` $ wc -l train.txt 1199637 train.txt ``` ## Expected results please fix that issue ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.8.3 - Platform:windows&linux - Python version:3.7 - PyArrow version:6.0.1
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VoxPopuli
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[ "duplicate of https://github.com/huggingface/datasets/issues/2300" ]
2022-02-15T23:04:55Z
2022-02-16T18:49:12Z
2022-02-16T18:49:12Z
CONTRIBUTOR
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## Adding a Dataset - **Name:** VoxPopuli - **Description:** A Large-Scale Multilingual Speech Corpus - **Paper:** https://arxiv.org/pdf/2101.00390.pdf - **Data:** https://github.com/facebookresearch/voxpopuli - **Motivation:** one of the largest (if not the largest) multilingual speech corpus: 400K hours of multilingual unlabeled speech + 17k hours of labeled speech Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). 👀 @kahne @Molugan
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Bug while streaming CSV dataset with pandas 1.4
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2022-02-15T15:16:19Z
2022-02-15T16:55:44Z
2022-02-15T16:55:44Z
MEMBER
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## Describe the bug If we upgrade to pandas `1.4`, the patching of the pandas module is no longer working ``` AttributeError: '_PatchedModuleObj' object has no attribute '__version__' ``` ## Steps to reproduce the bug ``` pip install pandas==1.4 ``` ```python from datasets import load_dataset ds = load_dataset("lvwerra/red-wine", split="train", streaming=True) item = next(iter(ds)) item ```
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Builder Configuration Update Required on Common Voice Dataset
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[ "Hi @aasem, thanks for reporting.\r\n\r\nPlease note that currently Commom Voice is hosted on our Hub as a community dataset by the Mozilla Foundation. See all Common Voice versions here: https://huggingface.co/mozilla-foundation\r\n\r\nMaybe we should add an explaining note in our \"legacy\" Common Voice canonical script? What do you think @lhoestq @mariosasko ?", "Thank you, @albertvillanova, for the quick response. I am not sure about the exact flow but I guess adding the following lines under the `_Languages` dictionary definition in [common_voice.py](https://github.com/huggingface/datasets/blob/master/datasets/common_voice/common_voice.py) might resolve the issue. I guess the dataset is recently made available so the file needs updating.\r\n\r\n```\r\n\"ur\": {\r\n \"Language\": \"Urdu\",\r\n \"Date\": \"2022-01-19\",\r\n \"Size\": \"68 MB\",\r\n \"Version\": \"ur_3h_2022-01-19\",\r\n \"Validated_Hr_Total\": 1,\r\n \"Overall_Hr_Total\": 3,\r\n \"Number_Of_Voice\": 48,\r\n },\r\n```\r\n", "@aasem for compliance reasons, we are no longer updating the `common_voice.py` script.\r\n\r\nWe agreed with Mozilla Foundation to use their community datasets instead, which will ask you to accept their terms of use:\r\n```\r\nYou need to share your contact information to access this dataset.\r\n\r\nThis repository is publicly accessible, but you have to register to access its content — don't worry, it's just one click!\r\n\r\nBy clicking on “Access repository” below, you accept that your contact information (email address and username) can be shared with the repository authors. This will let the authors get in touch for instance if some parts of the repository's contents need to be taken down for licensing reasons.\r\n\r\nBy clicking on “Access repository” below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset.\r\n\r\nYou will immediately be granted access to the contents of the dataset. \r\n```\r\n\r\nIn order to use e.g. their Common Voice dataset version 8.0, please:\r\n- First visit their dataset page: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0\r\n- Accept their term of use by clicking \"Access repository\"\r\n- You can then load their dataset with:\r\n ```python\r\n load_dataset(\"mozilla-foundation/common_voice_8_0\", \"ur\", split=\"train+validation\")\r\n ```", "@albertvillanova \r\n>Maybe we should add an explaining note in our \"legacy\" Common Voice canonical script?\r\n\r\nYes, I agree we should have a deprecation notice in the canonical script to redirect users to the new script.", "@albertvillanova, \r\nI now get the following error after downloading my access token from the huggingface and passing it to `load_dataset` call:\r\n\r\n`AttributeError: 'DownloadManager' object has no attribute 'download_config'`\r\n\r\nAny quick pointer on how it might be resolved?", "@aasem What version of `datasets` are you using? We renamed that attribute from `_download_config` to `download_conig` fairly recently, so updating to the newest version should resolve the issue:\r\n```\r\npip install -U datasets\r\n```", "Thanks a lot, @mariosasko. That completely resolved the issue. " ]
2022-02-14T16:21:41Z
2024-04-28T18:03:08Z
2024-04-28T18:03:08Z
NONE
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Missing language in Common Voice dataset **Link:** https://huggingface.co/datasets/common_voice I tried to call the Urdu dataset using `load_dataset("common_voice", "ur", split="train+validation")` but couldn't due to builder configuration not found. I checked the source file here for the languages support: https://github.com/huggingface/datasets/blob/master/datasets/common_voice/common_voice.py and Urdu isn't included there. I assume a quick update will fix the issue as Urdu speech is now available at the Common Voice dataset. Am I the one who added this dataset? No
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wrong condition in `Features ClassLabel encode_example`
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[ "Hi @Tudyx, \r\n\r\nPlease note that in Python, the boolean NOT operator (`not`) has lower precedence than comparison operators (`<=`, `<`), thus the expression you mention is equivalent to:\r\n```python\r\n not (-1 <= example_data < self.num_classes)\r\n```\r\n\r\nAlso note that as expected, the exception is raised if:\r\n- `example_data < -1`\r\n- or `example_data >= self.num_classes`\r\n\r\nThe raise of the exception is expected when `example_data` equals 4 and `self.num_classes` equals 4 too." ]
2022-02-14T11:44:35Z
2022-02-14T15:09:36Z
2022-02-14T15:07:43Z
NONE
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## Describe the bug The `encode_example` function in *features.py* seems to have a wrong condition. ```python if not -1 <= example_data < self.num_classes: raise ValueError(f"Class label {example_data:d} greater than configured num_classes {self.num_classes}") ``` ## Expected results The `not - 1` condition change the result of the condition. For instance, if `example_data` equals 4 and ` self.num_classes` equals 4 too, `example_data < self.num_classes` will give `False` as expected . But if i add the `not - 1` condition, `not -1 <= example_data < self.num_classes` will give `True` and raise an exception. ## Environment info - `datasets` version: 1.18.3 - Python version: 3.8.10 - PyArrow version: 7.00
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`FaissIndex` to support multiple GPU and `custom_index`
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[ "Hi @rentruewang, thansk for reporting and for your PR!!! We should definitely support this. ", "@albertvillanova Great! :)" ]
2022-02-14T06:21:43Z
2022-03-07T16:28:56Z
2022-03-07T16:28:56Z
CONTRIBUTOR
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**Is your feature request related to a problem? Please describe.** Currently, because `device` is of the type `int | None`, to leverage `faiss-gpu`'s multi-gpu support, you need to create a `custom_index`. However, if using a `custom_index` created by e.g. `faiss.index_cpu_to_all_gpus`, then `FaissIndex.save` does not work properly because it checks the device id (which is an int, so no multiple GPUs). **Describe the solution you'd like** I would like `FaissIndex` to support multiple GPUs, by passing in a list to `add_faiss_index`. **Describe alternatives you've considered** Alternatively, I would like it to at least provide a warning cause it wasn't the behavior that I expected. **Additional context** Relavent source code here: https://github.com/huggingface/datasets/blob/6ed6ac9448311930557810383d2cfd4fe6aae269/src/datasets/search.py#L340-L349 Device management needs changing to support multiple GPUs, probably by `isinstance` calls. I can provide a PR if you like :) Thanks for reading!
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tatoeba_mt: File not found error and key error
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[ "Looks like I solved my problems ..." ]
2022-02-13T16:35:45Z
2022-02-13T20:44:04Z
2022-02-13T20:44:04Z
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## Dataset viewer issue for 'tatoeba_mt' **Link:** https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt My data loader script does not seem to work. The files are part of the local repository but cannot be found. An example where it should work is the subset for "afr-eng". Another problem is that I do not have validation data for all subsets and I don't know how to properly check whether validation exists in the configuration before I try to download it. An example is the subset for "afr-deu". Am I the one who added this dataset ? Yes
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`.select`: unexpected behavior with `indices`
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[ "Hi! Currently, we compute the final index as `index % len(dset)`. I agree this behavior is somewhat unexpected and that it would be more appropriate to raise an error instead (this is what `df.iloc` in Pandas does, for instance).\r\n\r\n@albertvillanova @lhoestq wdyt?", "I agree. I think `index % len(dset)` was used to support negative indices.\r\n\r\nI think this needs to be fixed in `datasets.formatting.formatting._check_valid_index_key` if I'm not mistaken" ]
2022-02-11T15:20:01Z
2022-02-14T19:19:21Z
2022-02-14T19:19:21Z
NONE
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## Describe the bug The `.select` method will not throw when sending `indices` bigger than the dataset length; `indices` will be wrapped instead. This behavior is not documented anywhere, and is not intuitive. ## Steps to reproduce the bug ```python from datasets import Dataset ds = Dataset.from_dict({"text": ["d", "e", "f"], "label": [4, 5, 6]}) res1 = ds.select([1, 2, 3])['text'] res2 = ds.select([1000])['text'] ``` ## Expected results Both results should throw an `Error`. ## Actual results `res1` will give `['e', 'f', 'd']` `res2` will give `['e']` ## Environment info Bug found from this environment: - `datasets` version: 1.16.1 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.7 - PyArrow version: 6.0.1 It was also replicated on `master`.
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I_kwDODunzps5DfEn6
3,706
Unable to load dataset 'big_patent'
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[ "Hi @ankitk2109,\r\n\r\nHave you tried passing the split name with the keyword `split=`? See e.g. an example in our Quick Start docs: https://huggingface.co/docs/datasets/quickstart.html#load-the-dataset-and-model\r\n```python\r\n ds = load_dataset(\"big_patent\", \"d\", split=\"validation\")", "Hi @albertvillanova,\r\n\r\nThanks for your response.\r\n\r\nYes, I tried the `split='validation'` as well. But getting the same issue. ", "I'm sorry, but I can't reproduce your problem:\r\n```python\r\nIn [5]: ds = load_dataset(\"big_patent\", \"d\", split=\"validation\")\r\nDownloading and preparing dataset big_patent/d (download: 6.01 GiB, generated: 169.61 MiB, post-processed: Unknown size, total: 6.17 GiB) to .../.cache/big_patent/d/1.0.0/bdefa7c0b39fba8bba1c6331b70b738e30d63c8ad4567f983ce315a5fef6131c...\r\nDownloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6.45G/6.45G [27:36<00:00, 3.89MB/s]\r\nExtracting data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:18<00:00, 66.08s/it]\r\nDataset big_patent downloaded and prepared to .../.cache/big_patent/d/1.0.0/bdefa7c0b39fba8bba1c6331b70b738e30d63c8ad4567f983ce315a5fef6131c. Subsequent calls will reuse this data. \r\n\r\nIn [6]: ds\r\nOut[6]: \r\nDataset({\r\n features: ['description', 'abstract'],\r\n num_rows: 565\r\n})\r\n", "Maybe you had a connection issue while downloading the file and this was corrupted?\r\nOur cache system uses the file you downloaded first time.\r\nIf so, you could try forcing redownload of the file with:\r\n```python\r\nds = load_dataset(\"big_patent\", \"d\", split=\"validation\", download_mode=\"force_redownload\")", "I am able to download the dataset with ``` download_mode=\"force_redownload\"```. As you mentioned it was an issue with the cached version which was failed earlier due to a network issue. I am closing the issue now, once again thank you." ]
2022-02-11T09:48:34Z
2022-02-14T15:26:03Z
2022-02-14T15:26:03Z
NONE
null
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## Describe the bug Unable to load the "big_patent" dataset ## Steps to reproduce the bug ```python load_dataset('big_patent', 'd', 'validation') ``` ## Expected results Download big_patents' validation split from the 'd' subset ## Getting an error saying: {FileNotFoundError}Local file ..\huggingface\datasets\downloads\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\bigPatentData\train.tar.gz doesn't exist ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version:1.18.3 - Platform: Windows - Python version:3.8 - PyArrow version:7.0.0
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OSCAR-2109 datasets are misaligned and truncated
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[ "Hi @adrianeboyd, thanks for reporting.\r\n\r\nThere is indeed a bug in that community dataset:\r\nLine:\r\n```python\r\nmetadata_and_text_files = list(zip(metadata_files, text_files))\r\n``` \r\nshould be replaced with\r\n```python\r\nmetadata_and_text_files = list(zip(sorted(metadata_files), sorted(text_files)))\r\n```\r\n\r\nI am going to contact their owners (https://huggingface.co/oscar-corpus) in order to inform them about the bug.\r\n\r\nI keep you informed.", "That fix is part of it, but it's clearly not the only issue.\r\n\r\nI also already contacted the OSCAR creators, but I reported it here because it looked like huggingface members were the main authors in the git history. Is there a better place to have reported this?", "Hello,\r\n\r\nWe've had an issue that could be linked to this one here: https://github.com/oscar-corpus/corpus/issues/15.\r\n\r\nI have been spot checking the source (`.txt`/`.jsonl`) files for a while, and have not found issues, especially in the start/end of corpora (but I conceed that more integration testing would be necessary on our side).\r\n\r\nThe text and metadata files are designed to be used in sync (with `lang_part_n.txt` and `lang_meta_part_n.jsonl` working together), while staying independent from part to part, so that anyone could randomly choose a part and work with it.\r\n\r\nThe fix @albertvillanova proposed should fix the problem, as the parts will be in sync again.\r\n\r\nLet me know if you need help or more details, I'd be glad to help!", "I'm happy to move the discussion to the other repo!\r\n\r\nMerely sorting the files only **maybe** fixes the processing of the first part. If the first part contains non-unix newlines, it will still be misaligned/truncated, and all the following parts will be truncated with incorrect text offsets and metadata due the offset and newline bugs.", "Fixed:\r\n- https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/commit/3cd7e95aa1799b73c5ea8afc3989635f3e19b86b", "Hi @Uinelj, This is a total noobs question but how can I integrate that bugfix into my code? I reinstalled the datasets library this time from source. Should that have fixed the issue? I am still facing the misalignment issue. Do I need to download the dataset from scratch?", "Hi, I re-downloaded the dataset and still have the problem. See: https://github.com/oscar-corpus/corpus/issues/18", "Sorry @norakassner for the late reply.\r\n\r\nThere are indeed several issues creating the misalignment, as @adrianeboyd cleverly pointed out:\r\n- https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/commit/3cd7e95aa1799b73c5ea8afc3989635f3e19b86b fixed one of them\r\n- but there are still others to be fixed", "Normally, the issues should be fixed now:\r\n- Fix offset initialization for each file: https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/commit/1ad9b7bfe00798a9258a923b887bb1c8d732b833\r\n- Disable default universal newline support: https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/commit/0c2f307d3167f03632f502af361ac6c3c393f510\r\n\r\nFeel free to reopen if you find additional misalignments/truncations.\r\n\r\nCC: @adrianeboyd @norakassner @Uinelj ", "Thanks for the updates!\r\n\r\nThe purist in me would still like to have the rstrip not strip additional characters from the original text (unicode whitespace mainly in practice, I think), but the differences are extremely small in practice and it doesn't actually matter for my current task:\r\n\r\n```python\r\ntext = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip(\"\\n\")\r\n```" ]
2022-02-11T08:14:59Z
2022-03-17T18:01:04Z
2022-03-16T16:21:28Z
NONE
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## Describe the bug The `oscar-corpus/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines. ## Steps to reproduce the bug A few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations: ```python from datasets import load_dataset dataset = load_dataset("oscar-corpus/OSCAR-2109", "deduplicated_fi", split="train", use_auth_token=True) entry = dataset[0] # entry["text"] is from fi_part_3.txt.gz # entry["meta"] is from fi_meta_part_2.jsonl.gz dataset = load_dataset("oscar-corpus/OSCAR-2109", "deduplicated_no", split="train", use_auth_token=True) entry = dataset[900000] # entry["text"] is from no_part_3.txt.gz and contains a blank line # entry["meta"] is from no_meta_part_1.jsonl.gz dataset = load_dataset("oscar-corpus/OSCAR-2109", "deduplicated_mk", split="train", streaming=True, use_auth_token=True) # 9088 texts in the dataset are empty ``` For `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table. For `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens. For `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. I'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424. ## Issues * The meta / text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts). * The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped. * Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation. ## Expected results All texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata. ## Fixes Not necessarily the exact fixes/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues: ```diff diff --git a/OSCAR-2109.py b/OSCAR-2109.py index bbac1076..5eee8de7 100644 --- a/OSCAR-2109.py +++ b/OSCAR-2109.py @@ -20,6 +20,7 @@ import collections import gzip import json +import os import datasets @@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder): with open(checksum_file, encoding="utf-8") as f: data_filenames = [line.split()[1] for line in f if line] data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames] - text_files = dl_manager.download([url for url in data_urls if url.endswith(".txt.gz")]) - metadata_files = dl_manager.download([url for url in data_urls if url.endswith(".jsonl.gz")]) + # sort filenames so corresponding parts are aligned + text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(".txt.gz")])) + metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(".jsonl.gz")])) + assert len(text_files) == len(metadata_files) metadata_and_text_files = list(zip(metadata_files, text_files)) + for meta_path, text_path in metadata_and_text_files: + # check that meta/text part numbers are the same + if "part" in os.path.basename(text_path): + assert ( + os.path.basename(text_path).replace(".txt.gz", "").split("_")[-1] + == os.path.basename(meta_path).replace(".jsonl.gz", "").split("_")[-1] + ) + else: + assert len(metadata_and_text_files) == 1 return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"metadata_and_text_files": metadata_and_text_files}), ] @@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder): def _generate_examples(self, metadata_and_text_files): """This function returns the examples in the raw (text) form by iterating on all the files.""" id_ = 0 - offset = 0 for meta_path, text_path in metadata_and_text_files: + # line offsets are per text file + offset = 0 logger.info("generating examples from = %s", text_path) - with gzip.open(open(text_path, "rb"), "rt", encoding="utf-8") as text_f: + # some texts contain non-Unix newlines that should not be + # interpreted as line breaks for the line counts in the metadata + # with readline() + with gzip.open(open(text_path, "rb"), "rt", encoding="utf-8", newline="\n") as text_f: with gzip.open(open(meta_path, "rb"), "rt", encoding="utf-8") as meta_f: for line in meta_f: # read meta @@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder): offset += 1 text_f.readline() # read text - text = "".join([text_f.readline() for _ in range(meta["nb_sentences"])]).rstrip() + text_lines = [text_f.readline() for _ in range(meta["nb_sentences"])] + # all lines contain text (no blank lines or EOF) + assert all(text_lines) + assert "\n" not in text_lines offset += meta["nb_sentences"] + # only strip the trailing newline + text = "".join(text_lines).rstrip("\n") yield id_, {"id": id_, "text": text, "meta": meta} id_ += 1 ``` I've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data.
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ImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance'
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[ "![图片](https://user-images.githubusercontent.com/28425091/153547502-6bb0938d-788b-4857-b946-c3cf08fefce4.png)\r\nMy datasets version", "![图片](https://user-images.githubusercontent.com/28425091/153547587-f4677166-af9b-44a0-95ad-b6dba873978a.png)\r\n", "Hi! Some of our metrics require additional dependencies to work. In your case, simply installing the `seqeval` package with `pip install seqeval` should resolve the issue.", "> Hi! Some of our metrics require additional dependencies to work. In your case, simply installing the `seqeval` package with `pip install seqeval` should resolve the issue.\r\nI installed seqeval, but still reported the same error. That's too bad.\r\n", "> > Hi! Some of our metrics require additional dependencies to work. In your case, simply installing the `seqeval` package with `pip install seqeval` should resolve the issue.\r\n> > I installed seqeval, but still reported the same error. That's too bad.\r\n\r\nSame issue here. What should I do to fix this error? Please help! Thank you.", "I tried to install **seqeval** package through anaconda instead of pip:\r\n`conda install -c conda-forge seqeval`\r\nIt worked for me!", "I can run it through the following steps:\r\n![image](https://user-images.githubusercontent.com/69563759/159264511-1e252a4e-c8c8-44ab-b7bc-b4aac609bd9e.png)\r\nThank you for answering for me!", "just change the file name seqeval.py to myseqeval.py", "Metrics are deprecated in `datasets` and `evaluate` should be used instead: https://github.com/huggingface/evaluate" ]
2022-02-11T06:38:42Z
2023-07-11T09:31:59Z
2023-07-11T09:31:59Z
NONE
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hi : I want to use the seqeval indicator because of direct load_ When metric ('seqeval '), it will prompt that the network connection fails. So I downloaded the seqeval Py to load locally. Loading code: metric = load_ metric(path='mymetric/seqeval/seqeval.py') But tips: Traceback (most recent call last): File "/home/ubuntu/Python3.6_project/zyf_project/transformers/examples/pytorch/token-classification/run_ner.py", line 604, in <module> main() File "/home/ubuntu/Python3.6_project/zyf_project/transformers/examples/pytorch/token-classification/run_ner.py", line 481, in main metric = load_metric(path='mymetric/seqeval/seqeval.py') File "/home/ubuntu/Python3.6_project/zyf_project/transformers_venv_0209/lib/python3.7/site-packages/datasets/load.py", line 610, in load_metric dataset=False, File "/home/ubuntu/Python3.6_project/zyf_project/transformers_venv_0209/lib/python3.7/site-packages/datasets/load.py", line 450, in prepare_module f"To be able to use this {module_type}, you need to install the following dependencies" ImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance' **What should I do? Please help me, thank you**
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Unable to load a dataset
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[ "Hi! `load_dataset` is intended to be used to load a canonical dataset (`wikipedia`), a packaged dataset (`csv`, `json`, ...) or a dataset hosted on the Hub. For local datasets saved with `save_to_disk(\"path/to/dataset\")`, use `load_from_disk(\"path/to/dataset\")`.", "Maybe we should raise an informative error message in this case...", "How should we load a locally self-gathered dataset then?" ]
2022-02-10T15:05:53Z
2024-07-04T08:39:23Z
2022-02-11T22:56:39Z
NONE
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## Describe the bug Unable to load a dataset from Huggingface that I have just saved. ## Steps to reproduce the bug On Google colab `! pip install datasets ` `from datasets import load_dataset` `my_path = "wiki_dataset"` `dataset = load_dataset('wikipedia', "20200501.fr")` `dataset.save_to_disk(my_path)` `dataset = load_dataset(my_path)` ## Expected results Loading the dataset ## Actual results ValueError: Couldn't cast _data_files: list<item: struct<filename: string>> child 0, item: struct<filename: string> child 0, filename: string _fingerprint: string _format_columns: null _format_kwargs: struct<> _format_type: null _indexes: struct<> _output_all_columns: bool _split: string to {'builder_name': Value(dtype='string', id=None), 'citation': Value(dtype='string', id=None), 'config_name': Value(dtype='string', id=None), 'dataset_size': Value(dtype='int64', id=None), 'description': Value(dtype='string', id=None), 'download_checksums': {}, 'download_size': Value(dtype='int64', id=None), 'features': {'title': {'dtype': Value(dtype='string', id=None), 'id': Value(dtype='null', id=None), '_type': Value(dtype='string', id=None)}, 'text': {'dtype': Value(dtype='string', id=None), 'id': Value(dtype='null', id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'post_processed': Value(dtype='null', id=None), 'post_processing_size': Value(dtype='null', id=None), 'size_in_bytes': Value(dtype='int64', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='string', id=None)}}, 'supervised_keys': Value(dtype='null', id=None), 'task_templates': Value(dtype='null', id=None), 'version': {'version_str': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'major': Value(dtype='int64', id=None), 'minor': Value(dtype='int64', id=None), 'patch': Value(dtype='int64', id=None)}} because column names don't match ## Environment info - `datasets` version: 1.18.3 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 6.0.1
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Pyarrow version error
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[ "Hi @Zaker237, thanks for reporting.\r\n\r\nThis is weird: the error you get is only thrown if the installed pyarrow version is less than 3.0.0.\r\n\r\nCould you please check that you install pyarrow in the same Python virtual environment where you installed datasets?\r\n\r\nFrom the Python command line (or terminal) where you get the error, please type:\r\n```\r\nimport pyarrow\r\nprint(pyarrow.__version__)\r\nimport datasets\r\nprint(datasets.__version__)\r\n``` ", "hi @albertvillanova i try yesterday to create a new python environement with python 7 and try it on the environement and it worked. so i think that the error was not the package but may be jupyter notebook on conda. still yet i'm not yet sure but it worked in an environment created with venv", "OK, thanks @Zaker237 for your feedback.\r\n\r\nI close this issue then. Please, feel free to reopen it if the problem arises again." ]
2022-02-08T12:53:59Z
2022-02-09T06:35:33Z
2022-02-09T06:35:32Z
NONE
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## Describe the bug I installed datasets(version 1.17.0, 1.18.0, 1.18.3) but i'm right now nor able to import it because of pyarrow. when i try to import it, i get the following error: `To use datasets, the module pyarrow>=3.0.0 is required, and the current version of pyarrow doesn't match this condition`. i tryed with all version of pyarrow execpt `4.0.0` but still get the same error. ## Steps to reproduce the bug ```python import datasets ``` ## Expected results A clear and concise description of the expected results. ## Actual results AttributeError Traceback (most recent call last) <ipython-input-19-652e886d387f> in <module> ----> 1 import datasets ~\AppData\Local\Continuum\anaconda3\lib\site-packages\datasets\__init__.py in <module> 26 27 ---> 28 if _version.parse(pyarrow.__version__).major < 3: 29 raise ImportWarning( 30 "To use `datasets`, the module `pyarrow>=3.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n" AttributeError: 'Version' object has no attribute 'major' ## Environment info Traceback (most recent call last): File "c:\users\alex\appdata\local\continuum\anaconda3\lib\runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) File "c:\users\alex\appdata\local\continuum\anaconda3\lib\runpy.py", line 85, in _run_code exec(code, run_globals) File "C:\Users\Alex\AppData\Local\Continuum\anaconda3\Scripts\datasets-cli.exe\__main__.py", line 5, in <module> File "c:\users\alex\appdata\local\continuum\anaconda3\lib\site-packages\datasets\__init__.py", line 28, in <module> if _version.parse(pyarrow.__version__).major < 3: AttributeError: 'Version' object has no attribute 'major' - `datasets` version: - Platform: Linux(Ubuntu) and Windows: conda on the both - Python version: 3.7 - PyArrow version: 7.0.0
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Can't get the text data when calling to_tf_dataset
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[ "cc @Rocketknight1 ", "You are correct that `to_tf_dataset` only handles numerical columns right now, yes, though this is a limitation we might remove in future! The main reason we do this is that our models mostly do not include the tokenizer as a model layer, because it's very difficult to compile some of them in TF. So the \"normal\" Huggingface workflow is to first tokenize your dataset, and then pass tokenized tensors to the model.\r\n\r\nFor your use case, would you prefer to pass strings to the model, and use some text processing layers instead of the built-in tokenizers?", "Also tagging @gante just so he's aware, but I can handle this one!", "Thanks for the quick follow-up to my issue.\r\n\r\nFor my use-case, instead of the built-in tokenizers I wanted to use the `TextVectorization` layer to map from strings to integers. To achieve this, I came up with the following solution:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import DefaultDataCollator\r\nimport tensorflow as tf\r\nimport string\r\nimport re\r\nfrom tensorflow.keras.layers.experimental.preprocessing import TextVectorization\r\n\r\n#some hyper-parameters for the text-to-integer mapping\r\nmax_features = 20000\r\nembedding_dim = 128\r\nsequence_length = 210\r\n\r\ndata_collator = DefaultDataCollator(return_tensors=\"tf\")\r\ndataset = load_dataset(\"sst\", \"default\")\r\n\r\n#adapt the vectorization layer on train data only\r\nvectorize_layer.adapt(dataset[\"train\"].to_dict(batched=False)[\"sentence\"])\r\n\r\ndef prepare_features(text, label):\r\n text = tf.expand_dims(text, -1)\r\n return {\"vectorized_text\": vectorize_layer(text)[0], \"label\": tf.expand_dims(label, axis=-1)}\r\n\r\nencoded_dataset = dataset.map(lambda example: prepare_features(example[\"sentence\"], example[\"label\"]), batched=False)\r\n\r\n\r\ndef custom_standardization(input_data):\r\n lowercase = tf.strings.lower(input_data)\r\n return tf.strings.regex_replace(\r\n lowercase, f\"[{re.escape(string.punctuation)}]\", \"\"\r\n )\r\n\r\nvectorize_layer = TextVectorization(\r\n standardize=custom_standardization,\r\n max_tokens=max_features,\r\n output_mode=\"int\",\r\n output_sequence_length=sequence_length,\r\n)\r\n\r\ntrain_dataset = encoded_dataset[\"train\"].to_tf_dataset(columns=['vectorized_text'], label_cols=[\"label\"],\r\n shuffle=True, batch_size=1, collate_fn=data_collator).unbatch()\r\n#similar for the other sub-sets\r\n\r\n```\r\n\r\nSince the strings would have been mapped to integers or floats at some point, it's no drawback that this mapping is done early in the process. \r\n\r\nFor the future, however, it'd be more convenient to get the string data, since I am also inspecting the dataset (longest sentence, shortest sentence), which is more challenging when working with integer or float. For now, this can be done by calling `to_dict`.", "> For the future, however, it'd be more convenient to get the string data, since I am also inspecting the dataset (longest sentence, shortest sentence), which is more challenging when working with integer or float.\r\n\r\nYes, I agree, so let's keep this issue open.", "Going to close this now - methods like `to_tf_dataset` and `prepare_tf_dataset` now support string data, and have done for a while! If anyone sees this and is encountering issues with string data in those methods, please file a new issue!" ]
2022-02-08T11:52:10Z
2023-01-19T14:55:18Z
2023-01-19T14:55:18Z
NONE
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I am working with the SST2 dataset, and am using TensorFlow 2.5 I'd like to convert it to a `tf.data.Dataset` by calling the `to_tf_dataset` method. The following snippet is what I am using to achieve this: ``` from datasets import load_dataset from transformers import DefaultDataCollator data_collator = DefaultDataCollator(return_tensors="tf") dataset = load_dataset("sst") train_dataset = dataset["train"].to_tf_dataset(columns=['sentence'], label_cols="label", shuffle=True, batch_size=8,collate_fn=data_collator) ``` However, this only gets me the labels; the text--the most important part--is missing: ``` for s in train_dataset.take(1): print(s) #prints something like: ({}, <tf.Tensor: shape=(8,), ...>) ``` As you can see, it only returns the label part, not the data, as indicated by the empty dictionary, `{}`. So far, I've played with various settings of the method arguments, but to no avail; I do not want to perform any text processing at this time. On my quest to achieve what I want ( a `tf.data.Dataset`), I've consulted these resources: [https://www.philschmid.de/huggingface-transformers-keras-tf](https://www.philschmid.de/huggingface-transformers-keras-tf) [https://huggingface.co/docs/datasets/use_dataset.html?highlight=tensorflow](https://huggingface.co/docs/datasets/use_dataset.html?highlight=tensorflow) I was surprised to not find more extensive examples on how to transform a Hugginface dataset to one compatible with TensorFlow. If you could point me to where I am going wrong, please do so. Thanks in advance for your support. --- Edit: In the [docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset), I found the following description: _In general, only columns that the model can use as input should be included here (numeric data only)._ Does this imply that no textual, i.e., `string` data can be loaded?
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`Translation` features cannot be `flatten`ed
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null
[ "Thanks for reporting, @SBrandeis! Some additional feature types that don't behave as expected when flattened: `Audio`, `Image` and `TranslationVariableLanguages`" ]
2022-02-08T11:33:48Z
2022-03-18T17:28:13Z
2022-03-18T17:28:13Z
CONTRIBUTOR
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## Describe the bug (`Dataset.flatten`)[https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L1265] fails for columns with feature (`Translation`)[https://github.com/huggingface/datasets/blob/3edbeb0ec6519b79f1119adc251a1a6b379a2c12/src/datasets/features/translation.py#L8] ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("europa_ecdc_tm", "en2fr", split="train[:10]") print(dataset.features) # {'translation': Translation(languages=['en', 'fr'], id=None)} print(dataset[0]) # {'translation': {'en': 'Vaccination against hepatitis C is not yet available.', 'fr': 'Aucune vaccination contre l’hépatite C n’est encore disponible.'}} dataset.flatten() ``` ## Expected results `dataset.flatten` should flatten the `Translation` column as if it were a dict of `Value("string")` ```python dataset[0] # {'translation.en': 'Vaccination against hepatitis C is not yet available.', 'translation.fr': 'Aucune vaccination contre l’hépatite C n’est encore disponible.' } dataset.features # {'translation.en': Value("string"), 'translation.fr': Value("string")} ``` ## Actual results ```python In [31]: dset.flatten() --------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-31-bb88eb5276ee> in <module> ----> 1 dset.flatten() [...]\site-packages\datasets\fingerprint.py in wrapper(*args, **kwargs) 411 # Call actual function 412 --> 413 out = func(self, *args, **kwargs) 414 415 # Update fingerprint of in-place transforms + update in-place history of transforms [...]\site-packages\datasets\arrow_dataset.py in flatten(self, new_fingerprint, max_depth) 1294 break 1295 dataset.info.features = self.features.flatten(max_depth=max_depth) -> 1296 dataset._data = update_metadata_with_features(dataset._data, dataset.features) 1297 logger.info(f'Flattened dataset from depth {depth} to depth {1 if depth + 1 < max_depth else "unknown"}.') 1298 dataset._fingerprint = new_fingerprint [...]\site-packages\datasets\arrow_dataset.py in update_metadata_with_features(table, features) 534 def update_metadata_with_features(table: Table, features: Features): 535 """To be used in dataset transforms that modify the features of the dataset, in order to update the features stored in the metadata of its schema.""" --> 536 features = Features({col_name: features[col_name] for col_name in table.column_names}) 537 if table.schema.metadata is None or b"huggingface" not in table.schema.metadata: 538 pa_metadata = ArrowWriter._build_metadata(DatasetInfo(features=features)) [...]\site-packages\datasets\arrow_dataset.py in <dictcomp>(.0) 534 def update_metadata_with_features(table: Table, features: Features): 535 """To be used in dataset transforms that modify the features of the dataset, in order to update the features stored in the metadata of its schema.""" --> 536 features = Features({col_name: features[col_name] for col_name in table.column_names}) 537 if table.schema.metadata is None or b"huggingface" not in table.schema.metadata: 538 pa_metadata = ArrowWriter._build_metadata(DatasetInfo(features=features)) KeyError: 'translation.en' ``` ## Environment info - `datasets` version: 1.18.3 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.7.10 - PyArrow version: 3.0.0
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Download datasets from a private hub
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[ "For reference:\r\nhttps://github.com/huggingface/transformers/issues/15514\r\nhttps://github.com/huggingface/huggingface_hub/issues/650", "Hi ! For information one can set the environment variable `HF_ENDPOINT` (default is `https://huggingface.co`) if they want to use a private hub.\r\n\r\nWe may need to coordinate with the other libraries to have a consistent way of changing the hub endpoint", "Yes, I tested it successfully this morning. Thanks." ]
2022-02-04T10:49:06Z
2022-02-22T11:08:07Z
2022-02-22T11:08:07Z
NONE
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In the context of a private hub deployment, customers would like to use load_dataset() to load datasets from their hub, not from the public hub. This doesn't seem to be configurable at the moment and it would be nice to add this feature. The obvious workaround is to clone the repo first and then load it from local storage, but this adds an extra step. It'd be great to have the same experience regardless of where the hub is hosted. The same issue exists with the transformers library and the CLI. I'm going to create issues there as well, and I'll reference them below.
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Discovery cannot be streamed anymore
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null
[ "Seems like a regression from https://github.com/huggingface/datasets/pull/2843\r\n\r\nOr maybe it's an issue with the hosting. I don't think so, though, because https://www.dropbox.com/s/aox84z90nyyuikz/discovery.zip seems to work as expected\r\n\r\n", "Hi @severo, thanks for reporting.\r\n\r\nSome servers do not support HTTP range requests, and those are required to stream some file formats (like ZIP in this case).\r\n\r\nLet me try to propose a workaround. " ]
2022-02-03T15:02:03Z
2022-02-10T16:51:24Z
2022-02-10T16:51:24Z
COLLABORATOR
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## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python from datasets import load_dataset iterable_dataset = load_dataset("discovery", name="discovery", split="train", streaming=True) list(iterable_dataset.take(1)) ``` ## Expected results The first row of the train split. ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 365, in __iter__ for key, example in self._iter(): File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 362, in _iter yield from ex_iterable File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 272, in __iter__ yield from islice(self.ex_iterable, self.n) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 79, in __iter__ yield from self.generate_examples_fn(**self.kwargs) File "/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/discovery/542fab7a9ddc1d9726160355f7baa06a1ccc44c40bc8e12c09e9bc743aca43a2/discovery.py", line 333, in _generate_examples with open(data_file, encoding="utf8") as f: File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 64, in wrapper return function(*args, use_auth_token=use_auth_token, **kwargs) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/utils/streaming_download_manager.py", line 369, in xopen file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open() File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/core.py", line 456, in open return open_files( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/core.py", line 288, in open_files fs, fs_token, paths = get_fs_token_paths( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/core.py", line 611, in get_fs_token_paths fs = filesystem(protocol, **inkwargs) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 253, in filesystem return cls(**storage_options) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 68, in __call__ obj = super().__call__(*args, **kwargs) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 57, in __init__ self.zip = zipfile.ZipFile(self.fo) File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/zipfile.py", line 1257, in __init__ self._RealGetContents() File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/zipfile.py", line 1320, in _RealGetContents endrec = _EndRecData(fp) File "/home/slesage/.pyenv/versions/3.9.6/lib/python3.9/zipfile.py", line 263, in _EndRecData fpin.seek(0, 2) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 676, in seek raise ValueError("Cannot seek streaming HTTP file") ValueError: Cannot seek streaming HTTP file ``` ## Environment info - `datasets` version: 1.18.3 - Platform: Linux-5.11.0-1027-aws-x86_64-with-glibc2.31 - Python version: 3.9.6 - PyArrow version: 6.0.1
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`None` replaced by `[]` after first batch in map
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null
[ "It looks like this is because of this behavior in pyarrow:\r\n```python\r\nimport pyarrow as pa\r\n\r\narr = pa.array([None, [0]])\r\nreconstructed_arr = pa.ListArray.from_arrays(arr.offsets, arr.values)\r\nprint(reconstructed_arr.to_pylist())\r\n# [[], [0]]\r\n```\r\n\r\nIt seems that `arr.offsets` can reconstruct the array properly, but an offsets array with null values can:\r\n```python\r\nfixed_offsets = pa.array([None, 0, 1])\r\nfixed_arr = pa.ListArray.from_arrays(fixed_offsets, arr.values)\r\nprint(fixed_arr.to_pylist())\r\n# [None, [0]]\r\n\r\nprint(arr.offsets.to_pylist())\r\n# [0, 0, 1]\r\nprint(fixed_offsets.to_pylist())\r\n# [None, 0, 1]\r\n```\r\nEDIT: this is because `arr.offsets` is not enough to reconstruct the array, we also need the validity bitmap", "The offsets don't have nulls because they don't include the validity bitmap from `arr.buffers()[0]`, which is used to say which values are null and which values are non-null.\r\n\r\nThough the validity bitmap also seems to be wrong:\r\n```python\r\nbin(int(arr.buffers()[0].hex(), 16))\r\n# '0b10'\r\n# it should be 0b110 - 1 corresponds to non-null and 0 corresponds to null, if you take the bits in reverse order\r\n```\r\n\r\nSo apparently I can't even create the fixed offsets array using this.\r\n\r\nIf I understand correctly it's always missing the 1 on the left, so I can add it manually as a hack to fix the issue until this is fixed in pyarrow EDIT: actually it may be more complicated than that\r\n\r\nEDIT2: actuall it's right, it corresponds to the validity bitmap of the array of logical length 2. So if we use the offsets array, the values array, and this validity bitmap it should be possible to reconstruct the array properly", "I created an issue on Apache Arrow's JIRA: https://issues.apache.org/jira/browse/ARROW-15837", "And another one: https://issues.apache.org/jira/browse/ARROW-15839", "FYI the behavior is the same with:\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.8.0-50-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.11\r\n- PyArrow version: 6.0.1\r\n\r\n\r\nbut not with:\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-4.18.0-305.40.2.el8_4.x86_64-x86_64-with-redhat-8.4-Ootpa\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n\r\ni.e. it outputs:\r\n```py\r\n0 [None, [0]]\r\n1 [None, [0]]\r\n2 [None, [0]]\r\n3 [None, [0]]\r\n```\r\n", "Thanks for the insights @PaulLerner !\r\n\r\nI found a way to workaround this issue for the code example presented in this issue.\r\n\r\nNote that empty lists will still appear when you explicitly `cast` a list of lists that contain None values like [None, [0]] to a new feature type (e.g. to change the integer precision). In this case it will show a warning that it happened. If you don't cast anything, then the None values will be kept as expected.\r\n\r\nLet me know what you think !", "Hi! I feel like I’m missing something in your answer, *what* is the workaround? Is it fixed in some `datasets` version?", "`pa.ListArray.from_arrays` returns empty lists instead of None values. The workaround I added inside `datasets` simply consists in not using `pa.ListArray.from_arrays` :)\r\n\r\nOnce this PR [here ](https://github.com/huggingface/datasets/pull/4282)is merged, we'll release a new version of `datasets` that currectly returns the None values in the case described in this issue\r\n\r\nEDIT: released :) but let's keep this issue open because it might happen again if users change the integer precision for example" ]
2022-02-03T13:36:48Z
2022-10-28T13:13:20Z
2022-10-28T13:13:20Z
MEMBER
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Sometimes `None` can be replaced by `[]` when running map: ```python from datasets import Dataset ds = Dataset.from_dict({"a": range(4)}) ds = ds.map(lambda x: {"b": [[None, [0]]]}, batched=True, batch_size=1, remove_columns=["a"]) print(ds.to_pandas()) # b # 0 [None, [0]] # 1 [[], [0]] # 2 [[], [0]] # 3 [[], [0]] ``` This issue has been experienced when running the `run_qa.py` example from `transformers` (see issue https://github.com/huggingface/transformers/issues/15401) This can be due to a bug in when casting `None` in nested lists. Casting only happens after the first batch, since the first batch is used to infer the feature types. cc @sgugger
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Add CodeContests dataset
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[ "@mariosasko Can I take this up?", "This dataset is now available here: https://huggingface.co/datasets/deepmind/code_contests." ]
2022-02-03T13:20:00Z
2022-07-20T11:07:05Z
2022-07-20T11:07:05Z
COLLABORATOR
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## Adding a Dataset - **Name:** CodeContests - **Description:** CodeContests is a competitive programming dataset for machine-learning. - **Paper:** - **Data:** https://github.com/deepmind/code_contests - **Motivation:** This dataset was used when training [AlphaCode](https://deepmind.com/blog/article/Competitive-programming-with-AlphaCode). Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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`load_dataset("snli")` is different from dataset viewer
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[ "Yes, we decided to replace the encoded label with the corresponding label when possible in the dataset viewer. But\r\n1. maybe it's the wrong default\r\n2. we could find a way to show both (with a switch, or showing both ie. `0 (neutral)`).\r\n", "Hi @severo,\r\n\r\nThanks for clarifying. \r\n\r\nI think this default is a bit counterintuitive for the user. However, this is a personal opinion that might not be general. I think it is nice to have the actual (non-encoded) labels in the viewer. On the other hand, it would be nice to match what the user sees with what they get when they download a dataset. I don't know - I can see the difficulty of choosing a default :)\r\nMaybe having non-encoded labels as a default can be useful?\r\n\r\nAnyway, I think the issue has been addressed. Thanks a lot for your super-quick answer!\r\n\r\n ", "Thanks for the 👍 in https://github.com/huggingface/datasets/issues/3673#issuecomment-1029008349 @mariosasko @gary149 @pietrolesci, but as I proposed various solutions, it's not clear to me which you prefer. Could you write your preferences as a comment?\r\n\r\n_(note for myself: one idea per comment in the future)_", "As I am working with seq2seq, I prefer having the label in string form rather than numeric. So the viewer is fine and the underlying dataset should be \"decoded\" (from int to str). In this way, the user does not have to search for a mapping `int -> original name` (even though is trivial to find, I reckon). Also, encoding labels is rather easy.\r\n\r\nI hope this is useful", "I like the idea of \"0 (neutral)\". The label name can even be greyed to make it clear that it's not part of the actual item in the dataset, it's just the meaning.", "I like @lhoestq's idea of having grayed-out labels.", "Proposals by @gary149. Which one do you prefer? Please vote with the thumbs\r\n\r\n- 👍 \r\n\r\n ![image](https://user-images.githubusercontent.com/1676121/152387949-883c7d7e-a9f3-48aa-bff9-11a691555e6e.png)\r\n\r\n- 👎 \r\n\r\n ![image (1)](https://user-images.githubusercontent.com/1676121/152388061-32d95e42-cade-4ae4-9a77-7365e7b72b8f.png)\r\n\r\n", "I like Option 1 better as it shows clearly what the user is downloading", "Thanks! ", "It's [live](https://huggingface.co/datasets/glue/viewer/cola/train):\r\n\r\n<img width=\"1126\" alt=\"Capture d’écran 2022-02-14 à 10 26 03\" src=\"https://user-images.githubusercontent.com/1676121/153836716-25f6205b-96af-42d8-880a-7c09cb24c420.png\">\r\n\r\nThanks all for the help to improve the UI!", "Love it ! thanks :)" ]
2022-02-03T12:10:43Z
2022-02-16T11:22:31Z
2022-02-11T17:01:21Z
NONE
null
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## Describe the bug The dataset that is downloaded from the Hub via `load_dataset("snli")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., "neutral", "entailment", "contradiction"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2). Is this expected? ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: Ubuntu 20.4 - Python version: 3.7
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I_kwDODunzps5C5Fro
3,668
Couldn't cast array of type string error with cast_column
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[ "Hi ! I wasn't able to reproduce the error, are you still experiencing this ? I tried calling `cast_column` on a string column containing paths.\r\n\r\nIf you manage to share a reproducible code example that would be perfect", "Hi,\r\n\r\nI think my team mate got this solved. Clolsing it for now and will reopen if I experience this again.\r\nThanks :) ", "Hi @R4ZZ3,\r\n\r\nIf it is not too much of a bother, can you please help me how to resolve this error? I am exactly getting the same error where I am going as per the documentation guideline:\r\n\r\n`my_audio_dataset = my_audio_dataset.cast_column(\"audio_paths\", Audio())`\r\n\r\nwhere `\"audio_paths\"` is a dataset column (feature) having strings of absolute paths to mp3 files of the dataset.\r\n\r\n", "I was having the same issue with this code:\r\n\r\n```\r\ndataset = dataset.map(\r\n lambda batch: {\"full_path\" : os.path.join(self.data_path, batch[\"path\"])},\r\n num_procs = 4\r\n)\r\nmy_audio_dataset = dataset.cast_column(\"full_path\", Audio(sampling_rate=16_000))\r\n```\r\n\r\nRemoving the \"num_procs\" argument fixed it somehow.\r\nUsing a mac with m1 chip", "Hi @Hubert-Bonisseur, I think this will be fixed by https://github.com/huggingface/datasets/pull/4614" ]
2022-02-02T18:33:29Z
2022-07-19T13:36:24Z
2022-07-19T13:36:24Z
NONE
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## Describe the bug In OVH cloud during Huggingface Robust-speech-recognition event on a AI training notebook instance using jupyter lab and running jupyter notebook When using the dataset.cast_column("audio",Audio(sampling_rate=16_000)) method I get error ![image](https://user-images.githubusercontent.com/25264037/152214027-9c42a71a-dd24-463c-a346-57e0287e5a8f.png) This was working with datasets version 1.17.1.dev0 but now with version 1.18.3 produces the error above. ## Steps to reproduce the bug load dataset: ![image](https://user-images.githubusercontent.com/25264037/152216145-159553b6-cddc-4f0b-8607-7e76b600e22a.png) remove columns: ![image](https://user-images.githubusercontent.com/25264037/152214707-7c7e89d1-87d8-4b4f-8cfc-5d7223d35644.png) run my fix_path function. This also creates the audio column that is referring to the absolute file path of the audio ![image](https://user-images.githubusercontent.com/25264037/152214773-51f71ccf-d31b-4449-b63a-1af56436e49f.png) Then I concatenate few other datasets and finally try the cast_column method ![image](https://user-images.githubusercontent.com/25264037/152215032-f341ec86-9d6d-48c9-943b-e2efe37a4d98.png) but get error: ![image](https://user-images.githubusercontent.com/25264037/152215073-b85bd057-98e8-413c-9b05-51e9805f2c24.png) ## Expected results A clear and concise description of the expected results. ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: OVH Cloud, AI Training section, container for Huggingface Robust Speech Recognition event image(baaastijn/ovh_huggingface) ![image](https://user-images.githubusercontent.com/25264037/152215161-b4ff7bfb-2736-4afb-9223-761a3338d23c.png) - Python version: 3.8.8 - PyArrow version: ![image](https://user-images.githubusercontent.com/25264037/152215936-4d365760-557e-456b-b5eb-ad1d15cf5073.png)
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[Audio] Path of Common Voice cannot be used for audio loading anymore
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[ "Having talked to @lhoestq, I see that this feature is no longer supported. \r\n\r\nI really don't think this was a good idea. It is a major breaking change and one for which we don't even have a working solution at the moment, which is bad for PyTorch as we don't want to force people to have `datasets` decode audio files automatically, but **really** bad for Tensorflow and Flax where we **currently cannot** even use `datasets` to load `.mp3` files - e.g. `common_voice` doesn't work anymore in a TF training script. Note this worked perfectly fine before making the change (think it was done [here](https://github.com/huggingface/datasets/pull/3290) no?)\r\n\r\nIMO, it's really important to think about a solution here and I strongly favor to make a difference here between loading a dataset in streaming mode and in non-streaming mode, so that in non-streaming mode the actual downloaded file is displayed. It's really crucial for people to be able to analyse the original files IMO when the dataset is not downloaded in streaming mode. \r\n\r\nThere are the following reasons why it is paramount to have access to the **original** audio file in my opinion (in non-streaming mode):\r\n- There are a wide variety of different libraries to load audio data with varying support on different platforms. For me it was quite clear that there is simply to single good library to load audio files for all platforms - so we have to leave the option to the user to decide which loading to use.\r\n- We had support for audio datasets a long time before streaming audio was possible. There were quite some versions where we advertised **everywhere** to load the audio from the path name (and there are many places where we still do even though it's not possible anymore). To give some examples:\r\n - Official example of TF Wav2Vec2: https://github.com/huggingface/transformers/blob/f427e750490b486944cc9be3c99834ad5cf78b57/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py#L1423 Wav2Vec2 is as important for speech as BERT is for NLP - so it's **very** important. The official example currently doesn't work and we don't even have a workaround for it for MP3 files at the moment. Same goes for Flax.\r\n - The most downloaded non-nlp checkpoint: https://huggingface.co/facebook/wav2vec2-base-960h#usage has a usage example which doesn't work anymore with the current datasets implementation. I'll update this now, but we have >1000 wav2vec2 checkpoints on the Hub and we can't update all the model cards.\r\n => This is a big breaking change with no current solution. For `transformers` breaking changes are one of the biggest complaints.\r\n- Similar to this we also shouldn't assume that there is only one resampling method for Audio. I think it's good to have one offered automatically by `datasets`, but we have to leave the user the freedom to choose her/his own resampling as well. Resampling can take very different filtering windows and other parameters which are currently somewhat hardcoded in `datasets`, which users might very well want to change.\r\n\r\n\r\n=> IMO, it's a **very** big priority to again have the correct absolute path in non-streaming mode. The other solution of providing a path-like object derived from the bytes stocked in the `.array` file is not nearly as user-friendly, but better than nothing. ", "Agree that we need to have access to the original sound files. Few days ago I was looking for these original files because I suspected there is bug in the audio resampling (confirmed in https://github.com/huggingface/datasets/issues/3662) and I want to do my own resampling to workaround the bug, which is now not possible anymore due to the unavailability of the original files.", "@patrickvonplaten \r\n> The other solution of providing a path-like object derived from the bytes stocked in the .array file is not nearly as user-friendly, but better than nothing\r\n\r\nJust to clarify, here you describe the approach that uses the `Audio.decode` attribute to access the underlying bytes?\r\n\r\n> The official example currently doesn't work and we don't even have a workaround for it for MP3 files at the moment\r\n\r\nI'd assume this is because we use `sox_io` as a backend for decoding. However, soon we should be able to use `soundfile`, which supports path-like objects, for MP3 (https://github.com/huggingface/datasets/pull/3667#issuecomment-1030090627).\r\n\r\nYour concern is reasonable, but there are situations where we can only serve bytes (see https://github.com/huggingface/datasets/pull/3685 for instance). IMO it makes sense to fix the affected datasets for now, but I don't think we should care too much whether we rely on local paths or bytes after soundfile adds support for MP3 as long as our examples work (shouldn't be too hard to update the `map_to_array` functions) and we properly document how to access the underlying path/bytes for custom decoding (via `ds.cast_column(\"audio\", Audio(decode=False))`).\r\n", "Related to this discussion: in https://github.com/huggingface/datasets/pull/3664#issuecomment-1031866858 I propose how we could change `iter_archive` to work for streaming and also return local paths (as it used too !). I'd love your opinions on this", "> @patrickvonplaten\r\n> \r\n> > The other solution of providing a path-like object derived from the bytes stocked in the .array file is not nearly as user-friendly, but better than nothing\r\n> \r\n> Just to clarify, here you describe the approach that uses the `Audio.decode` attribute to access the underlying bytes?\r\n\r\nYes! \r\n\r\n> \r\n> > The official example currently doesn't work and we don't even have a workaround for it for MP3 files at the moment\r\n> \r\n> I'd assume this is because we use `sox_io` as a backend for decoding. However, soon we should be able to use `soundfile`, which supports path-like objects, for MP3 ([#3667 (comment)](https://github.com/huggingface/datasets/pull/3667#issuecomment-1030090627)). \r\n> Your concern is reasonable, but there are situations where we can only serve bytes (see #3685 for instance). IMO it makes sense to fix the affected datasets for now, but I don't think we should care too much whether we rely on local paths or bytes after soundfile adds support for MP3 as long as our examples work (shouldn't be too hard to update the `map_to_array` functions) and we properly document how to access the underlying path/bytes for custom decoding (via `ds.cast_column(\"audio\", Audio(decode=False))`).\r\n\r\nYes this might be, but I highly doubt that `soundfile` is the go-to library for audio then. @anton-l and I have tried out a bunch of different audio loading libraries (`soundfile`, `librosa`, `torchaudio`, pure `ffmpeg`, `audioread`, ...). One thing that was pretty clear to me is that there is just no \"de-facto standard\" library and they all have pros and cons. None of the libraries really supports \"batch\"-ed audio loading. Some depend on PyTorch. `torchaudio` is 100x faster (really!) than `librosa's` fallback on MP3. `torchaudio` often has problems with multi-proessing, ... Also we should keep in mind that resampling is similarly not as simple as reading a text file. It's a pretty complex signal processing transform and people very well might want to use special filters, etc...at the moment we just hard-code `torchaudio's` or `librosa's` default filter when doing resampling.\r\n\r\n=> All this to say that we **should definitely** care about whether we rely on local paths or bytes IMO. We don't want to loose all users that are forced to use `datasets` decoding or resampling or have to built a very much not intuitive way of loading bytes into a numpy array. It's much more intuitive to be able to inspect a local file. I feel pretty strongly about this and am happy to also jump on a call. Keeping libraries flexible and lean as well as exposing internals is very important IMO (this philosophy has worked quite well so far with Transformers).\r\n\r\n", "Thanks a lot for the very detailed explanation. Now everything makes much more sense.", "From https://github.com/huggingface/datasets/pull/3736 the Common Voice dataset now gives access to the local audio files as before", "I understand the argument that it is bad to have a breaking change. How to deal with the introduction of breaking changes is a topic of its own and not sure how you want to deal with that (or is the policy this is never allowed, and there must be a `load_dataset_v2` or so if you really want to introduce a breaking change?).\r\n\r\nRegardless of whether it is a breaking change, however, I don't see the other arguments.\r\n\r\n> but **really** bad for Tensorflow and Flax where we **currently cannot** even use `datasets` to load `.mp3` files\r\n\r\nI don't exactly understand this. Why not?\r\n\r\nWhy does the HF dataset on-the-fly decoding mechanism not work? Why is it anyway specific to PyTorch or TensorFlow? Isn't this independent?\r\n\r\nBut even if you just provide the raw bytes to TF, on TF you could just use sth like `tfio.audio.decode_mp3` or `tf.audio.decode_ogg` or `tfio.audio.decode_flac`?\r\n\r\n> There are the following reasons why it is paramount to have access to the original audio file in my opinion ...\r\n\r\nI don't really understand the arguments (despite that it maybe breaks existing code). You anyway have the original audio files but it is just embedded in the dataset? I don't really know about any library which cannot also load the audio from memory (i.e. from the dataset).\r\n\r\nBtw, on librosa being slow for decoding audio files, I saw that as well, so we have this comment RETURNN:\r\n\r\n> Don't use librosa.load which internally uses audioread which would use Gstreamer as a backend which has multiple issues:\r\n> https://github.com/beetbox/audioread/issues/62\r\n> https://github.com/beetbox/audioread/issues/63\r\n> Instead, use PySoundFile (soundfile), which is also faster. See here for discussions:\r\n> https://github.com/beetbox/audioread/issues/64\r\n> https://github.com/librosa/librosa/issues/681\r\n\r\nResampling is also a separate aspect, which is also less straightforward and with different compromises between speed and quality. So there the different tradeoffs and different implementations can make a difference.\r\n\r\nHowever, I don't see how this is related to the question whether there should be the raw bytes inside the dataset or as separate local files.\r\n", "Thanks for your comments here @albertz - cool to get your input! \r\n\r\nAnswering a bit here between the lines:\r\n\r\n> I understand the argument that it is bad to have a breaking change. How to deal with the introduction of breaking changes is a topic of its own and not sure how you want to deal with that (or is the policy this is never allowed, and there must be a `load_dataset_v2` or so if you really want to introduce a breaking change?).\r\n> \r\n> Regardless of whether it is a breaking change, however, I don't see the other arguments.\r\n> \r\n> > but **really** bad for Tensorflow and Flax where we **currently cannot** even use `datasets` to load `.mp3` files\r\n> \r\n> I don't exactly understand this. Why not?\r\n\r\n> Why does the HF dataset on-the-fly decoding mechanism not work? Why is it anyway specific to PyTorch or TensorFlow? Isn't this independent?\r\n\r\nThe problem with decoding on the fly is that we currently rely on `torchaudio` for this now which relies on `torch` which is not necessarily something people would like to install when using `tensorflow` or `flax`. Therefore we cannot just rely on people using the decoding on the fly method. We just didn't find a library that is ML framework independent and fast enough for all formats. `torchaudio` is currently in our opinion by far the best here.\r\n\r\nSo for TF and Flax it's important that users can load audio files or bytes they way the want to - this might become less important if we find (or make) a good library with few dependencies that is fast for all kinds of platforms / use cases.\r\n\r\n\r\nNow the question is whether it's better to store audio data as a path to a file or as raw bytes I guess.\\\r\nMy main arguments for storing the audio data as a path to a file is pretty much all about users experience - I don't really expect our users to understand the inner workings of datasets:\r\n\r\n- 1. It's not straightforward to know which function to use to decode it - not all `load_audio(...)` or `read_audio(...)` work on raw bytes. E.g. Looking at https://pytorch.org/audio/stable/torchaudio.html?highlight=load#torchaudio.load one would not see directly how to load raw bytes . There are also some functions of other libraries which only work on files which would require the user to save the bytes as a file first before being able to load it.\r\n- 2. It's difficult to see which format the bytes are coming from (mp3, ogg, ...) - guess this could be remedied by adding the format to each sample though\r\n- 3. It is a bit scary IMO to see raw bytes for users. Overall, I think it's better to leave the data in it's raw form as this way it's much easier for people to play around with the audio files, less need to read docs because people don't worry about what happened to the audio files (are the bytes already resampled?)\r\n\r\nBut the argument that the audio should be loadable directly from memory is good - haven't thought about this too much. \r\nI guess it's still very much possible for the user to do this:\r\n\r\n```python\r\ndef save_as_bytes:\r\n batch[\"bytes\"] = read_in_bytes_from_file(batch[\"file\"])\\\r\n os.remove(batch[\"file\"])\r\n\r\nds = ds.map(save_as_bytes)\r\n\r\nds.save_to_disk(...)\r\n```\r\n\r\nGuess the question is more a bit about what should be the default case?", "> The problem with decoding on the fly is that we currently rely on `torchaudio` for this now which relies on `torch` which is not necessarily something people would like to install when using `tensorflow` or `flax`. Therefore we cannot just rely on people using the decoding on the fly method. We just didn't find a library that is ML framework independent and fast enough for all formats. `torchaudio` is currently in our opinion by far the best here.\r\n\r\nBut how is this relevant for this issue here? I thought this issue here is about having the (correct) path in the dataset or having raw bytes in the dataset.\r\n\r\nHow did TF users use it at all then? Or they just do not use on-the-fly decoding? I did not even notice this problem (maybe because I had `torchaudio` installed). But what do they use instead?\r\n\r\nBut as I outlined before, they could just use `tfio.audio.decode_flac` and co, where it would be more natural if you already provide the raw bytes.\r\n\r\n> Looking at https://pytorch.org/audio/stable/torchaudio.html?highlight=load#torchaudio.load one would not see directly how to load raw bytes\r\n\r\nI was not really familiar with `torchaudio`. It seems that they really don't provide an easy/direct API to operate on raw bytes. Which is very strange and unfortunate because as far as I can see, all the underlying backend libraries (e.g. soundfile) easily allow that. So I would say that this is the fault of `torchaudio` then. But despite, if you anyway use `torchaudio` with `soundfile` backend, why not just use `soundfile` directly. It's very simple to use and crossplatform.\r\n\r\nBut ok, now we are just discussing how to handle the on-the-fly decoding. I still think this is a separate issue and having raw bytes in the dataset instead of local files should just be fine as well.\r\n\r\n\r\n> It is a bit scary IMO to see raw bytes for users.\r\n\r\nI think nobody who writes code is scared by seeing the raw bytes content of a binary file. :)\r\n\r\n\r\n> I guess it's still very much possible for the user to do this:\r\n> \r\n> ```python\r\n> def save_as_bytes:\r\n> batch[\"bytes\"] = read_in_bytes_from_file(batch[\"file\"])\\\r\n> os.remove(batch[\"file\"])\r\n> \r\n> ds = ds.map(save_as_bytes)\r\n> \r\n> ds.save_to_disk(...)\r\n> ```\r\n\r\nIn https://github.com/huggingface/datasets/pull/4184#issuecomment-1105191639, you said/proposed that this `map` is not needed anymore and `save_to_disk` could do it automatically (maybe via some option)?\r\n\r\n> Guess the question is more a bit about what should be the default case?\r\n\r\nYea this is up to you. I'm happy as long as we can get it the way we want easily and this is a well supported use case. :)\r\n", "> In https://github.com/huggingface/datasets/pull/4184#issuecomment-1105191639, you said/proposed that this map is not needed anymore and save_to_disk could do it automatically (maybe via some option)?\r\n\r\nYes! Should be super easy now see discussion here: https://github.com/rwth-i6/i6_core/issues/257#issuecomment-1105494468\r\n\r\nThanks for the super useful input :-)", "Despite the comments that this has been fixed, I am finding the exact same problem is occurring again (with datasets version 2.3.2)", "> Despite the comments that this has been fixed, I am finding the exact same problem is occurring again (with datasets version 2.3.2)\r\n\r\nIt appears downgrading to torchaudio 0.11.0 fixed this problem.", "@DCNemesis, sorry which problem exactly is occuring again? Also cc @lhoestq @polinaeterna here", "@patrickvonplaten @lhoestq @polinaeterna I was unable to load audio from Common Voice using 🤗 with the current version of torchaudio, but downgrading to torchaudio 0.11.0 fixed it. This is probably more of a torch problem than a Hugging Face problem.", "@DCNemesis that's interesting, could you please share the error message if you still can access it? ", "@polinaeterna I believe it is the same exact error as above. It occurs on other .mp3 sources as well, but the problem is with torchaudio > 0.11.0. I've created a short colab notebook that reproduces the error, and the fix here: https://colab.research.google.com/drive/18wsuwdHwBPN3JkcnhEtk8MUYqF9swuWZ?usp=sharing", "Hi @DCNemesis,\r\n\r\nYour issue was slightly different from the original one in this issue page. Yours seems related to a change in the backend used by `torchaudio` (`ffmpeg` instead of `sox`). Refer to the issue page here:\r\n- #4776\r\n\r\nNormally, it should be circumvented with the patch made by @polinaeterna in:\r\n- #4923", "I think the original issue reported here was already fixed by:\r\n- #3736\r\n\r\nOtherwise, feel free to reopen." ]
2022-02-01T18:40:10Z
2022-09-21T15:03:09Z
2022-09-21T14:56:22Z
CONTRIBUTOR
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null
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## Describe the bug ## Steps to reproduce the bug ```python from datasets import load_dataset from torchaudio import load ds = load_dataset("common_voice", "ab", split="train") # both of the following commands fail at the moment load(ds[0]["audio"]["path"]) load(ds[0]["path"]) ``` ## Expected results The path should be the complete absolute path to the downloaded audio file not some relative path. ## Actual results ```bash ~/hugging_face/venv_3.9/lib/python3.9/site-packages/torchaudio/backend/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format) 150 filepath, frame_offset, num_frames, normalize, channels_first, format) 151 filepath = os.fspath(filepath) --> 152 return torch.ops.torchaudio.sox_io_load_audio_file( 153 filepath, frame_offset, num_frames, normalize, channels_first, format) 154 RuntimeError: Error loading audio file: failed to open file cv-corpus-6.1-2020-12-11/ab/clips/common_voice_ab_19904194.mp3 ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3.dev0 - Platform: Linux-5.4.0-96-generic-x86_64-with-glibc2.27 - Python version: 3.9.1 - PyArrow version: 3.0.0
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[Audio] MP3 resampling is incorrect when dataset's audio files have different sampling rates
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[ "Thanks @lhoestq for finding the reason of incorrect resampling. This issue affects all languages which have sound files with different sampling rates such as Turkish and Luganda.", "@cahya-wirawan - do you know how many languages have different sampling rates in Common Voice? I'm quite surprised to see this for multiple languages actually", "@cahya-wirawan, I can reproduce the problem for Common Voice 7 for Turkish. Here a script you can use:\r\n\r\n\r\n```python\r\n#!/usr/bin/env python3\r\nfrom datasets import load_dataset\r\nimport torchaudio\r\nfrom io import BytesIO\r\nfrom datasets import Audio\r\nfrom collections import Counter\r\nimport sys\r\n\r\nds_name = str(sys.argv[1])\r\nlang = str(sys.argv[2])\r\n\r\nds = load_dataset(ds_name, lang, split=\"train\", use_auth_token=True)\r\nds = ds.cast_column(\"audio\", Audio(decode=False))\r\n\r\nall_sampling_rates = []\r\n\r\n\r\ndef print_sampling_rate(x):\r\n x, sr = torchaudio.load(BytesIO(x[\"audio\"][\"bytes\"]), format=\"mp3\")\r\n all_sampling_rates.append(sr)\r\n\r\nds.map(print_sampling_rate)\r\n\r\n\r\nprint(Counter(all_sampling_rates))\r\n```\r\n\r\ncan be run with:\r\n\r\n```bash\r\npython run.py mozilla-foundation/common_voice_7_0 tr\r\n```\r\n\r\nFor CV 6.1 all samples seem to have the same audio", "It actually shows that many more samples are in 32kHz format than it 48kHz which is unexpected. Thanks a lot for flagging! Will contact Common Voice about this as well", "I only checked the CV 7.0 for Turkish, Luganda and Indonesian, they have audio files with difference sampling rates, and all of them are affected by this issue. Percentage of incorrect resampling as follow, Turkish: 9.1%, Luganda: 88.2% and Indonesian: 64.1%.\r\nI checked it using the original CV files. I check the original sampling rates and the length of audio array of each files and compare it with the length of audio array (and the sampling rate which is always 48kHz) from mozilla-foundation/common_voice_7_0 datasets. if the length of audio array from dataset is not equal to 48kHz/original sampling rate * length of audio array of the original audio file then it is affected,", "Ok wow, thanks a lot for checking this - you've found a pretty big bug :sweat_smile: It seems like **a lot** more datasets are actually affected than I original thought. We'll try to solve this as soon as possible and make an announcement tomorrow." ]
2022-02-01T17:55:04Z
2022-02-02T10:52:25Z
2022-02-02T10:52:25Z
MEMBER
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The Audio feature resampler for MP3 gets stuck with the first original frequencies it meets, which leads to subsequent decoding to be incorrect. Here is a code to reproduce the issue: Let's first consider two audio files with different sampling rates 32000 and 16000: ```python # first download a mp3 file with sampling_rate=32000 !wget https://file-examples-com.github.io/uploads/2017/11/file_example_MP3_700KB.mp3 import torchaudio audio_path = "file_example_MP3_700KB.mp3" audio_path2 = audio_path.replace(".mp3", "_resampled.mp3") resample = torchaudio.transforms.Resample(32000, 16000) # create a new file with sampling_rate=16000 torchaudio.save(audio_path2, resample(torchaudio.load(audio_path)[0]), 16000) ``` Then we can see an issue here when decoding: ```python from datasets import Dataset, Audio dataset = Dataset.from_dict({"audio": [audio_path, audio_path2]}).cast_column("audio", Audio(48000)) dataset[0] # decode the first audio file sets the resampler orig_freq to 32000 print(dataset .features["audio"]._resampler.orig_freq) # 32000 print(dataset[0]["audio"]["array"].shape) # here decoding is fine # (1308096,) dataset = Dataset.from_dict({"audio": [audio_path, audio_path2]}).cast_column("audio", Audio(48000)) dataset[1] # decode the second audio file sets the resampler orig_freq to 16000 print(dataset .features["audio"]._resampler.orig_freq) # 16000 print(dataset[0]["audio"]["array"].shape) # here decoding uses orig_freq=16000 instead of 32000 # (2616192,) ``` The value of `orig_freq` doesn't change no matter what file needs to be decoded cc @patrickvonplaten @anton-l @cahya-wirawan @albertvillanova The issue seems to be here in `Audio.decode_mp3`: https://github.com/huggingface/datasets/blob/4c417d52def6e20359ca16c6723e0a2855e5c3fd/src/datasets/features/audio.py#L176-L180
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push_to_hub but preview not working
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closed
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[ "Hi @thomas-happify, please note that the preview may take some time before rendering the data.\r\n\r\nI've seen it is already working.\r\n\r\nI close this issue. Please feel free to reopen it if the problem arises again." ]
2022-02-01T16:23:57Z
2022-02-09T08:00:37Z
2022-02-09T08:00:37Z
NONE
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## Dataset viewer issue for '*happifyhealth/twitter_pnn*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/happifyhealth/twitter_pnn)* I used ``` dataset.push_to_hub("happifyhealth/twitter_pnn") ``` but the preview is not working. Am I the one who added this dataset ? Yes
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Dataset viewer issue for *P3*
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[ "The error is now:\r\n\r\n```\r\nStatus code: 400\r\nException: Status400Error\r\nMessage: this dataset is not supported for now.\r\n```\r\n\r\nWe've disabled the dataset viewer for several big datasets like this one. We hope being able to reenable it soon.", "The list of splits cannot be obtained. cc @huggingface/datasets ", "```\r\nError code: SplitsNamesError\r\nException: SplitsNotFoundError\r\nMessage: The split names could not be parsed from the dataset config.\r\nTraceback: Traceback (most recent call last):\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 354, in get_dataset_config_info\r\n for split_generator in builder._split_generators(\r\n File \"/tmp/modules-cache/datasets_modules/datasets/bigscience--P3/12c0badfecad4564ecb8a6f81b5d0559656f269f08b13c59c93283f3a84134ba/P3.py\", line 154, in _split_generators\r\n data_dir = dl_manager.download_and_extract(_URLs)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 944, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 907, in extract\r\n urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 393, in map_nested\r\n mapped = [\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 394, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True, None))\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 346, in _single_map_nested\r\n return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 346, in <dictcomp>\r\n return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 346, in _single_map_nested\r\n return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 346, in <dictcomp>\r\n return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 330, in _single_map_nested\r\n return function(data_struct)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 912, in _extract\r\n protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 402, in _get_extraction_protocol\r\n return _get_extraction_protocol_with_magic_number(f)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 367, in _get_extraction_protocol_with_magic_number\r\n magic_number = f.read(MAGIC_NUMBER_MAX_LENGTH)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py\", line 574, in read\r\n return super().read(length)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py\", line 1575, in read\r\n out = self.cache._fetch(self.loc, self.loc + length)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/caching.py\", line 377, in _fetch\r\n self.cache = self.fetcher(start, bend)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py\", line 111, in wrapper\r\n return sync(self.loop, func, *args, **kwargs)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py\", line 96, in sync\r\n raise return_result\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py\", line 53, in _runner\r\n result[0] = await coro\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py\", line 616, in async_fetch_range\r\n out = await r.read()\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/client_reqrep.py\", line 1036, in read\r\n self._body = await self.content.read()\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/streams.py\", line 375, in read\r\n block = await self.readany()\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/streams.py\", line 397, in readany\r\n await self._wait(\"readany\")\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/streams.py\", line 304, in _wait\r\n await waiter\r\n aiohttp.client_exceptions.ClientPayloadError: Response payload is not completed\r\n \r\n The above exception was the direct cause of the following exception:\r\n \r\n Traceback (most recent call last):\r\n File \"/src/services/worker/src/worker/responses/splits.py\", line 75, in get_splits_response\r\n split_full_names = get_dataset_split_full_names(dataset, hf_token)\r\n File \"/src/services/worker/src/worker/responses/splits.py\", line 35, in get_dataset_split_full_names\r\n return [\r\n File \"/src/services/worker/src/worker/responses/splits.py\", line 38, in <listcomp>\r\n for split in get_dataset_split_names(dataset, config, use_auth_token=hf_token)\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 404, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 359, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\n datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```", "Closing in favor of https://huggingface.co/datasets/bigscience/P3/discussions/6 and https://github.com/huggingface/datasets-server/issues/1689" ]
2022-02-01T15:57:56Z
2023-09-25T12:16:21Z
2023-09-25T12:16:21Z
NONE
null
null
{ "completed": 0, "percent_completed": 0, "total": 0 }
## Dataset viewer issue for '*P3*' **Link: https://huggingface.co/datasets/bigscience/P3** ``` Status code: 400 Exception: SplitsNotFoundError Message: The split names could not be parsed from the dataset config. ``` Am I the one who added this dataset ? No
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checksum error subjqa dataset
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[ "Hi @RensDimmendaal, \r\n\r\nI'm sorry but I can't reproduce your bug:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ...: ds = load_dataset(\"subjqa\", \"electronics\")\r\nDownloading builder script: 9.15kB [00:00, 4.10MB/s] \r\nDownloading metadata: 17.7kB [00:00, 8.51MB/s] \r\nDownloading and preparing dataset subjqa/electronics (download: 10.86 MiB, generated: 3.01 MiB, post-processed: Unknown size, total: 13.86 MiB) to .../.cache/huggingface/datasets/subjqa/electronics/1.1.0/e5588f9298ff2d70686a00cc377e4bdccf4e32287459e3c6baf2dc5ab57fe7fd...\r\nDownloading data: 11.4MB [00:03, 3.50MB/s]\r\nDataset subjqa downloaded and prepared to .../.cache/huggingface/datasets/subjqa/electronics/1.1.0/e5588f9298ff2d70686a00cc377e4bdccf4e32287459e3c6baf2dc5ab57fe7fd. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 605.09it/s]\r\n\r\nIn [2]: ds\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['domain', 'nn_mod', 'nn_asp', 'query_mod', 'query_asp', 'q_reviews_id', 'question_subj_level', 'ques_subj_score', 'is_ques_subjective', 'review_id', 'id', 'title', 'context', 'question', 'answers'],\r\n num_rows: 1295\r\n })\r\n test: Dataset({\r\n features: ['domain', 'nn_mod', 'nn_asp', 'query_mod', 'query_asp', 'q_reviews_id', 'question_subj_level', 'ques_subj_score', 'is_ques_subjective', 'review_id', 'id', 'title', 'context', 'question', 'answers'],\r\n num_rows: 358\r\n })\r\n validation: Dataset({\r\n features: ['domain', 'nn_mod', 'nn_asp', 'query_mod', 'query_asp', 'q_reviews_id', 'question_subj_level', 'ques_subj_score', 'is_ques_subjective', 'review_id', 'id', 'title', 'context', 'question', 'answers'],\r\n num_rows: 255\r\n })\r\n})\r\n```\r\n\r\nCould you please try again and see if the problem persists?\r\n\r\nIf that is the case, you can circumvent the issue by passing `ignore_verifications`:\r\n```python\r\nds = load_dataset(\"subjqa\", \"electronics\", ignore_verifications=True)", "Thanks checking!\r\n\r\nYou're totally right. I don't know what's changed, but I'm glad it's working now!\r\n\r\n" ]
2022-02-01T10:53:33Z
2022-02-10T10:56:59Z
2022-02-10T10:56:38Z
NONE
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## Describe the bug I get a checksum error when loading the `subjqa` dataset (used in the transformers book). ## Steps to reproduce the bug ```python from datasets import load_dataset subjqa = load_dataset("subjqa","electronics") ``` ## Expected results Loading the dataset ## Actual results ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-2-d2857d460155> in <module>() 2 from datasets import load_dataset 3 ----> 4 subjqa = load_dataset("subjqa","electronics") 3 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/lewtun/SubjQA/archive/refs/heads/master.zip'] ``` ## Environment info Google colab - `datasets` version: 1.18.2 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 3.0.0
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Pubmed dataset not reachable
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[ "Hi @abhi-mosaic, thanks for reporting.\r\n\r\nI'm looking at it... ", "also hitting this issue", "Hey @albertvillanova, sorry to reopen this... I can confirm that on `master` branch the dataset is downloadable now but it is still broken in streaming mode:\r\n\r\n```python\r\n >>> import datasets\r\n >>> pubmed_train = datasets.load_dataset('pubmed', split='train', streaming=True)\r\n >>> next(iter(pubmed_train))\r\n```\r\n```\r\n No such file or directory: 'gzip://pubmed22n0001.xml::ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed22n0001.xml.gz'\r\n```\r\n", "Hi @abhi-mosaic, would you mind opening another issue for this new problem?\r\n\r\nFirst issue (already solved) was a ConnectionError due to the yearly update release of PubMed: we fixed it by updating the URLs from year 2021 to year 2022.\r\n\r\nHowever this is another problem: to make pubmed streamable. Please note that NOT all our datastes are streamable: we are making streamable more and more of them... but this is an on-going process...\r\n\r\nThanks.", "@albertvillanova \r\nWhen I tried below codes, I got the similar error\r\n\r\n```\r\n\r\ndataset=load_dataset(\"pubmed\",split=\"train\")\r\n\r\nCouldn't reach ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed21n0601.xml.gz\r\n```", "@y-rok you need to update `datasets`:\r\n```shell\r\npip install -U datasets\r\n```" ]
2022-01-31T18:45:47Z
2022-12-19T19:18:10Z
2022-02-14T14:15:41Z
CONTRIBUTOR
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## Describe the bug Trying to use the `pubmed` dataset fails to reach / download the source files. ## Steps to reproduce the bug ```python pubmed_train = datasets.load_dataset('pubmed', split='train') ``` ## Expected results Should begin downloading the pubmed dataset. ## Actual results ``` ConnectionError: Couldn't reach ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed21n0865.xml.gz (InvalidSchema("No connection adapters were found for 'ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed21n0865.xml.gz'")) ``` ## Environment info - `datasets` version: 1.18.2 - Platform: macOS-11.4-x86_64-i386-64bit - Python version: 3.8.2 - PyArrow version: 6.0.0
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Streaming dataset based on dl_manager.iter_archive/iter_files are not reset correctly
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2022-01-27T17:17:41Z
2022-01-28T16:34:28Z
2022-01-28T16:34:28Z
MEMBER
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Hi ! When iterating over a streaming dataset once, it's not reset correctly because of some issues with `dl_manager.iter_archive` and `dl_manager.iter_files`. Indeed they are generator functions (so the iterator that is returned can be exhausted). They should be iterables instead, and be reset if we do a for loop again: ```python from datasets import load_dataset d = load_dataset("common_voice", "ab", split="test", streaming=True) i = 0 for i, _ in enumerate(d): pass print(i) # 8 # let's do it again i = 0 for i, _ in enumerate(d): pass print(i) # 0 ```
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Issues with custom dataset in Wav2Vec2
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[ "Closed and moved to transformers." ]
2022-01-27T12:09:05Z
2022-01-27T12:29:48Z
2022-01-27T12:29:48Z
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We are training Vav2Vec using the run_speech_recognition_ctc_bnb.py-script. This is working fine with Common Voice, however using our custom dataset and data loader at [NbAiLab/NPSC]( https://huggingface.co/datasets/NbAiLab/NPSC) it crashes after roughly 1 epoch with the following stack trace: ![image](https://user-images.githubusercontent.com/9079808/151355893-6d5887cc-ca19-4b12-948a-124eb6dac372.png) We are able to work around the issue, for instance by adding this check in line#222 in transformers/models/wav2vec2/modeling_wav2vec2.py: ```python if input_length - (mask_length - 1) < num_masked_span: num_masked_span = input_length - (mask_length - 1) ``` Interestingly, these are the variable values before the adjustment: ``` input_length=10 mask_length=10 num_masked_span=2 ```` After adjusting num_masked_spin to 1, the training script runs. The issue is also fixed by setting “replace=True” in the same function. Do you have any idea what is causing this, and how to fix this error permanently? If you do not think this is an Datasets issue, feel free to move the issue.
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same value of precision, recall, f1 score at each epoch for classification task.
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[ "Hi @Dhanachandra, \r\n\r\nWe have tests for all our metrics and they work as expected: under the hood, we use scikit-learn implementations.\r\n\r\nMaybe the cause is somewhere else. For example:\r\n- Is it a binary or a multiclass or a multilabel classification? Default computation of these metrics is for binary classification; if you would like multiclass or multilabel, you should pass the corresponding parameters; see their documentation (e.g.: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) or code below:\r\n\r\nhttps://huggingface.co/docs/datasets/using_metrics.html#computing-the-metric-scores\r\n\r\n```python\r\nIn [1]: from datasets import load_metric\r\n\r\nIn [2]: precision = load_metric(\"precision\")\r\n\r\nIn [3]: print(precision.inputs_description)\r\n\r\nArgs:\r\n predictions: Predicted labels, as returned by a model.\r\n references: Ground truth labels.\r\n labels: The set of labels to include when average != 'binary', and\r\n their order if average is None. Labels present in the data can\r\n be excluded, for example to calculate a multiclass average ignoring\r\n a majority negative class, while labels not present in the data will\r\n result in 0 components in a macro average. For multilabel targets,\r\n labels are column indices. By default, all labels in y_true and\r\n y_pred are used in sorted order.\r\n average: This parameter is required for multiclass/multilabel targets.\r\n If None, the scores for each class are returned. Otherwise, this\r\n determines the type of averaging performed on the data:\r\n binary: Only report results for the class specified by pos_label.\r\n This is applicable only if targets (y_{true,pred}) are binary.\r\n micro: Calculate metrics globally by counting the total true positives,\r\n false negatives and false positives.\r\n macro: Calculate metrics for each label, and find their unweighted mean.\r\n This does not take label imbalance into account.\r\n weighted: Calculate metrics for each label, and find their average\r\n weighted by support (the number of true instances for each label).\r\n This alters ‘macro’ to account for label imbalance; it can result\r\n in an F-score that is not between precision and recall.\r\n samples: Calculate metrics for each instance, and find their average\r\n (only meaningful for multilabel classification).\r\n sample_weight: Sample weights.\r\n\r\nReturns:\r\n precision: Precision score.\r\n\r\nExamples:\r\n\r\n >>> precision_metric = datasets.load_metric(\"precision\")\r\n >>> results = precision_metric.compute(references=[0, 1], predictions=[0, 1])\r\n >>> print(results)\r\n {'precision': 1.0}\r\n\r\n >>> predictions = [0, 2, 1, 0, 0, 1]\r\n >>> references = [0, 1, 2, 0, 1, 2]\r\n >>> results = precision_metric.compute(predictions=predictions, references=references, average='macro')\r\n >>> print(results)\r\n {'precision': 0.2222222222222222}\r\n >>> results = precision_metric.compute(predictions=predictions, references=references, average='micro')\r\n >>> print(results)\r\n {'precision': 0.3333333333333333}\r\n >>> results = precision_metric.compute(predictions=predictions, references=references, average='weighted')\r\n >>> print(results)\r\n {'precision': 0.2222222222222222}\r\n >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)\r\n >>> print(results)\r\n {'precision': array([0.66666667, 0. , 0. ])}\r\n```\r\n" ]
2022-01-27T10:14:16Z
2022-02-24T09:02:18Z
2022-02-24T09:02:17Z
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**1st Epoch:** 1/27/2022 09:30:48 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/f1/default/default_experiment-1-0.arrow.59it/s] 01/27/2022 09:30:48 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/precision/default/default_experiment-1-0.arrow 01/27/2022 09:30:49 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/recall/default/default_experiment-1-0.arrow PRECISION: {'precision': 0.7612903225806451} RECALL: {'recall': 0.7612903225806451} F1: {'f1': 0.7612903225806451} {'eval_loss': 1.4658324718475342, 'eval_accuracy': 0.7612903118133545, 'eval_runtime': 30.0054, 'eval_samples_per_second': 46.492, 'eval_steps_per_second': 46.492, 'epoch': 3.0} **4th Epoch:** 1/27/2022 09:56:55 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/f1/default/default_experiment-1-0.arrow.92it/s] 01/27/2022 09:56:56 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/precision/default/default_experiment-1-0.arrow 01/27/2022 09:56:56 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/recall/default/default_experiment-1-0.arrow PRECISION: {'precision': 0.7698924731182796} RECALL: {'recall': 0.7698924731182796} F1: {'f1': 0.7698924731182796} ## Environment info !git clone https://github.com/huggingface/transformers %cd transformers !pip install . !pip install -r /content/transformers/examples/pytorch/token-classification/requirements.txt !pip install datasets
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[TypeError: Couldn't cast array of type] Cannot load dataset in v1.18
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[ "Hi @lewtun!\r\n \r\nThis one was tricky to debug. Initially, I tought there is a bug in the recently-added (by @lhoestq ) `cast_array_to_feature` function because `git bisect` points to the https://github.com/huggingface/datasets/commit/6ca96c707502e0689f9b58d94f46d871fa5a3c9c commit. Then, I noticed that the feature tpye of the `dialogue` field is `list`, which explains why you didn't get an error in earlier versions. Is there a specific reason why you use `list` instead of `Sequence` in the script? Maybe to avoid turning list of dicts to dicts of lists as it's done by `Sequence` for compatibility with TFDS or for performance reasons? If the field was `Sequence`, you would get an error in `encode_nested_example` because **the scripts yields some additional (nested) columns which are not specified in the `features` dictionary**. Previously, these additional columns would've been ignored by PyArrow (1), but now we have a check for them (2).\r\n(1) See PyArrow behavior:\r\n```\r\n>>> pa.array([{\"a\": 2, \"b\": 3}], type=pa.struct({\"a\": pa.int32()})) # pyarrow ignores the extra column\r\n-- is_valid: all not null\r\n-- child 0 type: int32\r\n [\r\n 2\r\n ]\r\n ```\r\n\r\n(2) Check:\r\nhttps://github.com/huggingface/datasets/blob/4c417d52def6e20359ca16c6723e0a2855e5c3fd/src/datasets/table.py#L1059\r\n\r\nThe fix is very simple: just add the missing columns to the _EMPTY_BELIEF_STATE list:\r\n```python\r\n_EMPTY_BELIEF_STATE.extend(['通用-产品类别', '火车-舱位档次', '通用-系列', '通用-价格区间', '通用-品牌'])\r\n```", "Hey @mariosasko, thank you so much for figuring this one out - it certainly looks like a tricky bug 😱 ! I don't think there's a specific reason to use `list` instead of `Sequence` with the script, but I'll let the dataset creators know to see if your suggestion is acceptable.\r\n\r\nThank you again!", "Thanks, this was indeed the fix! Would it make sense to produce a more informative error message in such cases? \r\n\r\nThe issue can be closed. \r\n\r\n" ]
2022-01-26T21:38:02Z
2022-02-09T16:15:53Z
2022-02-09T16:15:53Z
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## Describe the bug I am trying to load the [`GEM/RiSAWOZ` dataset](https://huggingface.co/datasets/GEM/RiSAWOZ) in `datasets` v1.18.1 and am running into a type error when casting the features. The strange thing is that I can load the dataset with v1.17.0. Note that the error is also present if I install from `master` too. As far as I can tell, the dataset loading script is correct and the problematic features [here](https://huggingface.co/datasets/GEM/RiSAWOZ/blob/main/RiSAWOZ.py#L237) also look fine to me. ## Steps to reproduce the bug ```python from datasets import load_dataset dset = load_dataset("GEM/RiSAWOZ") ``` ## Expected results I can load the dataset without error. ## Actual results <details><summary>Traceback</summary> ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _prepare_split(self, split_generator) 1083 example = self.info.features.encode_example(record) -> 1084 writer.write(example, key) 1085 finally: ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write(self, example, key, writer_batch_size) 445 --> 446 self.write_examples_on_file() 447 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_examples_on_file(self) 403 batch_examples[col] = [row[0][col] for row in self.current_examples] --> 404 self.write_batch(batch_examples=batch_examples) 405 self.current_examples = [] ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 496 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col) --> 497 arrays.append(pa.array(typed_sequence)) 498 inferred_features[col] = typed_sequence.get_inferred_type() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib.array() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type) 204 # We only do it if trying_type is False - since this is what the user asks for. --> 205 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) 206 return out ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 919 else: --> 920 return func(array, *args, **kwargs) 921 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1064 if isinstance(feature, list): -> 1065 return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0])) 1066 elif isinstance(feature, Sequence): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 919 else: --> 920 return func(array, *args, **kwargs) 921 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 919 else: --> 920 return func(array, *args, **kwargs) 921 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 919 else: --> 920 return func(array, *args, **kwargs) 921 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1086 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) -> 1087 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") 1088 TypeError: Couldn't cast array of type struct<医院-3.0T MRI: string, 医院-CT: string, 医院-DSA: string, 医院-公交线路: string, 医院-区域: string, 医院-名称: string, 医院-地址: string, 医院-地铁可达: string, 医院-地铁线路: string, 医院-性质: string, 医院-挂号时间: string, 医院-电话: string, 医院-等级: string, 医院-类别: string, 医院-重点科室: string, 医院-门诊时间: string, 天气-城市: string, 天气-天气: string, 天气-日期: string, 天气-温度: string, 天气-紫外线强度: string, 天气-风力风向: string, 旅游景点-区域: string, 旅游景点-名称: string, 旅游景点-地址: string, 旅游景点-开放时间: string, 旅游景点-是否地铁直达: string, 旅游景点-景点类型: string, 旅游景点-最适合人群: string, 旅游景点-消费: string, 旅游景点-特点: string, 旅游景点-电话号码: string, 旅游景点-评分: string, 旅游景点-门票价格: string, 汽车-价格(万元): string, 汽车-倒车影像: string, 汽车-动力水平: string, 汽车-厂商: string, 汽车-发动机排量(L): string, 汽车-发动机马力(Ps): string, 汽车-名称: string, 汽车-定速巡航: string, 汽车-巡航系统: string, 汽车-座位数: string, 汽车-座椅加热: string, 汽车-座椅通风: string, 汽车-所属价格区间: string, 汽车-油耗水平: string, 汽车-环保标准: string, 汽车-级别: string, 汽车-综合油耗(L/100km): string, 汽车-能源类型: string, 汽车-车型: string, 汽车-车系: string, 汽车-车身尺寸(mm): string, 汽车-驱动方式: string, 汽车-驾驶辅助影像: string, 火车-出发地: string, 火车-出发时间: string, 火车-到达时间: string, 火车-坐席: string, 火车-日期: string, 火车-时长: string, 火车-目的地: string, 火车-票价: string, 火车-舱位档次: string, 火车-车型: string, 火车-车次信息: string, 电影-主演: string, 电影-主演名单: string, 电影-具体上映时间: string, 电影-制片国家/地区: string, 电影-导演: string, 电影-年代: string, 电影-片名: string, 电影-片长: string, 电影-类型: string, 电影-豆瓣评分: string, 电脑-CPU: string, 电脑-CPU型号: string, 电脑-产品类别: string, 电脑-价格: string, 电脑-价格区间: string, 电脑-内存容量: string, 电脑-分类: string, 电脑-品牌: string, 电脑-商品名称: string, 电脑-屏幕尺寸: string, 电脑-待机时长: string, 电脑-显卡型号: string, 电脑-显卡类别: string, 电脑-游戏性能: string, 电脑-特性: string, 电脑-硬盘容量: string, 电脑-系列: string, 电脑-系统: string, 电脑-色系: string, 电脑-裸机重量: string, 电视剧-主演: string, 电视剧-主演名单: string, 电视剧-制片国家/地区: string, 电视剧-单集片长: string, 电视剧-导演: string, 电视剧-年代: string, 电视剧-片名: string, 电视剧-类型: string, 电视剧-豆瓣评分: string, 电视剧-集数: string, 电视剧-首播时间: string, 辅导班-上课方式: string, 辅导班-上课时间: string, 辅导班-下课时间: string, 辅导班-价格: string, 辅导班-区域: string, 辅导班-年级: string, 辅导班-开始日期: string, 辅导班-教室地点: string, 辅导班-教师: string, 辅导班-教师网址: string, 辅导班-时段: string, 辅导班-校区: string, 辅导班-每周: string, 辅导班-班号: string, 辅导班-科目: string, 辅导班-结束日期: string, 辅导班-课时: string, 辅导班-课次: string, 辅导班-课程网址: string, 辅导班-难度: string, 通用-产品类别: string, 通用-价格区间: string, 通用-品牌: string, 通用-系列: string, 酒店-价位: string, 酒店-停车场: string, 酒店-区域: string, 酒店-名称: string, 酒店-地址: string, 酒店-房型: string, 酒店-房费: string, 酒店-星级: string, 酒店-电话号码: string, 酒店-评分: string, 酒店-酒店类型: string, 飞机-准点率: string, 飞机-出发地: string, 飞机-到达时间: string, 飞机-日期: string, 飞机-目的地: string, 飞机-票价: string, 飞机-航班信息: string, 飞机-舱位档次: string, 飞机-起飞时间: string, 餐厅-人均消费: string, 餐厅-价位: string, 餐厅-区域: string, 餐厅-名称: string, 餐厅-地址: string, 餐厅-推荐菜: string, 餐厅-是否地铁直达: string, 餐厅-电话号码: string, 餐厅-菜系: string, 餐厅-营业时间: string, 餐厅-评分: string> to {'旅游景点-名称': Value(dtype='string', id=None), '旅游景点-区域': Value(dtype='string', id=None), '旅游景点-景点类型': Value(dtype='string', id=None), '旅游景点-最适合人群': Value(dtype='string', id=None), '旅游景点-消费': Value(dtype='string', id=None), '旅游景点-是否地铁直达': Value(dtype='string', id=None), '旅游景点-门票价格': Value(dtype='string', id=None), '旅游景点-电话号码': Value(dtype='string', id=None), '旅游景点-地址': Value(dtype='string', id=None), '旅游景点-评分': Value(dtype='string', id=None), '旅游景点-开放时间': Value(dtype='string', id=None), '旅游景点-特点': Value(dtype='string', id=None), '餐厅-名称': Value(dtype='string', id=None), '餐厅-区域': Value(dtype='string', id=None), '餐厅-菜系': Value(dtype='string', id=None), '餐厅-价位': Value(dtype='string', id=None), '餐厅-是否地铁直达': Value(dtype='string', id=None), '餐厅-人均消费': Value(dtype='string', id=None), '餐厅-地址': Value(dtype='string', id=None), '餐厅-电话号码': Value(dtype='string', id=None), '餐厅-评分': Value(dtype='string', id=None), '餐厅-营业时间': Value(dtype='string', id=None), '餐厅-推荐菜': Value(dtype='string', id=None), '酒店-名称': Value(dtype='string', id=None), '酒店-区域': Value(dtype='string', id=None), '酒店-星级': Value(dtype='string', id=None), '酒店-价位': Value(dtype='string', id=None), '酒店-酒店类型': Value(dtype='string', id=None), '酒店-房型': Value(dtype='string', id=None), '酒店-停车场': Value(dtype='string', id=None), '酒店-房费': Value(dtype='string', id=None), '酒店-地址': Value(dtype='string', id=None), '酒店-电话号码': Value(dtype='string', id=None), '酒店-评分': Value(dtype='string', id=None), '电脑-品牌': Value(dtype='string', id=None), '电脑-产品类别': Value(dtype='string', id=None), '电脑-分类': Value(dtype='string', id=None), '电脑-内存容量': Value(dtype='string', id=None), '电脑-屏幕尺寸': Value(dtype='string', id=None), '电脑-CPU': Value(dtype='string', id=None), '电脑-价格区间': Value(dtype='string', id=None), '电脑-系列': Value(dtype='string', id=None), '电脑-商品名称': Value(dtype='string', id=None), '电脑-系统': Value(dtype='string', id=None), '电脑-游戏性能': Value(dtype='string', id=None), '电脑-CPU型号': Value(dtype='string', id=None), '电脑-裸机重量': Value(dtype='string', id=None), '电脑-显卡类别': Value(dtype='string', id=None), '电脑-显卡型号': Value(dtype='string', id=None), '电脑-特性': Value(dtype='string', id=None), '电脑-色系': Value(dtype='string', id=None), '电脑-待机时长': Value(dtype='string', id=None), '电脑-硬盘容量': Value(dtype='string', id=None), '电脑-价格': Value(dtype='string', id=None), '火车-出发地': Value(dtype='string', id=None), '火车-目的地': Value(dtype='string', id=None), '火车-日期': Value(dtype='string', id=None), '火车-车型': Value(dtype='string', id=None), '火车-坐席': Value(dtype='string', id=None), '火车-车次信息': Value(dtype='string', id=None), '火车-时长': Value(dtype='string', id=None), '火车-出发时间': Value(dtype='string', id=None), '火车-到达时间': Value(dtype='string', id=None), '火车-票价': Value(dtype='string', id=None), '飞机-出发地': Value(dtype='string', id=None), '飞机-目的地': Value(dtype='string', id=None), '飞机-日期': Value(dtype='string', id=None), '飞机-舱位档次': Value(dtype='string', id=None), '飞机-航班信息': Value(dtype='string', id=None), '飞机-起飞时间': Value(dtype='string', id=None), '飞机-到达时间': Value(dtype='string', id=None), '飞机-票价': Value(dtype='string', id=None), '飞机-准点率': Value(dtype='string', id=None), '天气-城市': Value(dtype='string', id=None), '天气-日期': Value(dtype='string', id=None), '天气-天气': Value(dtype='string', id=None), '天气-温度': Value(dtype='string', id=None), '天气-风力风向': Value(dtype='string', id=None), '天气-紫外线强度': Value(dtype='string', id=None), '电影-制片国家/地区': Value(dtype='string', id=None), '电影-类型': Value(dtype='string', id=None), '电影-年代': Value(dtype='string', id=None), '电影-主演': Value(dtype='string', id=None), '电影-导演': Value(dtype='string', id=None), '电影-片名': Value(dtype='string', id=None), '电影-主演名单': Value(dtype='string', id=None), '电影-具体上映时间': Value(dtype='string', id=None), '电影-片长': Value(dtype='string', id=None), '电影-豆瓣评分': Value(dtype='string', id=None), '电视剧-制片国家/地区': Value(dtype='string', id=None), '电视剧-类型': Value(dtype='string', id=None), '电视剧-年代': Value(dtype='string', id=None), '电视剧-主演': Value(dtype='string', id=None), '电视剧-导演': Value(dtype='string', id=None), '电视剧-片名': Value(dtype='string', id=None), '电视剧-主演名单': Value(dtype='string', id=None), '电视剧-首播时间': Value(dtype='string', id=None), '电视剧-集数': Value(dtype='string', id=None), '电视剧-单集片长': Value(dtype='string', id=None), '电视剧-豆瓣评分': Value(dtype='string', id=None), '辅导班-班号': Value(dtype='string', id=None), '辅导班-难度': Value(dtype='string', id=None), '辅导班-科目': Value(dtype='string', id=None), '辅导班-年级': Value(dtype='string', id=None), '辅导班-区域': Value(dtype='string', id=None), '辅导班-校区': Value(dtype='string', id=None), '辅导班-上课方式': Value(dtype='string', id=None), '辅导班-开始日期': Value(dtype='string', id=None), '辅导班-结束日期': Value(dtype='string', id=None), '辅导班-每周': Value(dtype='string', id=None), '辅导班-上课时间': Value(dtype='string', id=None), '辅导班-下课时间': Value(dtype='string', id=None), '辅导班-时段': Value(dtype='string', id=None), '辅导班-课次': Value(dtype='string', id=None), '辅导班-课时': Value(dtype='string', id=None), '辅导班-教室地点': Value(dtype='string', id=None), '辅导班-教师': Value(dtype='string', id=None), '辅导班-价格': Value(dtype='string', id=None), '辅导班-课程网址': Value(dtype='string', id=None), '辅导班-教师网址': Value(dtype='string', id=None), '汽车-名称': Value(dtype='string', id=None), '汽车-车型': Value(dtype='string', id=None), '汽车-级别': Value(dtype='string', id=None), '汽车-座位数': Value(dtype='string', id=None), '汽车-车身尺寸(mm)': Value(dtype='string', id=None), '汽车-厂商': Value(dtype='string', id=None), '汽车-能源类型': Value(dtype='string', id=None), '汽车-发动机排量(L)': Value(dtype='string', id=None), '汽车-发动机马力(Ps)': Value(dtype='string', id=None), '汽车-驱动方式': Value(dtype='string', id=None), '汽车-综合油耗(L/100km)': Value(dtype='string', id=None), '汽车-环保标准': Value(dtype='string', id=None), '汽车-驾驶辅助影像': Value(dtype='string', id=None), '汽车-巡航系统': Value(dtype='string', id=None), '汽车-价格(万元)': Value(dtype='string', id=None), '汽车-车系': Value(dtype='string', id=None), '汽车-动力水平': Value(dtype='string', id=None), '汽车-油耗水平': Value(dtype='string', id=None), '汽车-倒车影像': Value(dtype='string', id=None), '汽车-定速巡航': Value(dtype='string', id=None), '汽车-座椅加热': Value(dtype='string', id=None), '汽车-座椅通风': Value(dtype='string', id=None), '汽车-所属价格区间': Value(dtype='string', id=None), '医院-名称': Value(dtype='string', id=None), '医院-等级': Value(dtype='string', id=None), '医院-类别': Value(dtype='string', id=None), '医院-性质': Value(dtype='string', id=None), '医院-区域': Value(dtype='string', id=None), '医院-地址': Value(dtype='string', id=None), '医院-电话': Value(dtype='string', id=None), '医院-挂号时间': Value(dtype='string', id=None), '医院-门诊时间': Value(dtype='string', id=None), '医院-公交线路': Value(dtype='string', id=None), '医院-地铁可达': Value(dtype='string', id=None), '医院-地铁线路': Value(dtype='string', id=None), '医院-重点科室': Value(dtype='string', id=None), '医院-CT': Value(dtype='string', id=None), '医院-3.0T MRI': Value(dtype='string', id=None), '医院-DSA': Value(dtype='string', id=None)} During handling of the above exception, another exception occurred: TypeError Traceback (most recent call last) /var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_44306/2896005239.py in <module> ----> 1 dset = load_dataset("GEM/RiSAWOZ") 2 dset ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/load.py 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, script_version, **config_kwargs) 1692 1693 # Download and prepare data -> 1694 builder_instance.download_and_prepare( 1695 download_config=download_config, 1696 download_mode=download_mode, ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 593 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 594 if not downloaded_from_gcs: --> 595 self._download_and_prepare( 596 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 597 ) ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 682 try: 683 # Prepare split will record examples associated to the split --> 684 self._prepare_split(split_generator, **prepare_split_kwargs) 685 except OSError as e: 686 raise OSError( ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _prepare_split(self, split_generator) 1084 writer.write(example, key) 1085 finally: -> 1086 num_examples, num_bytes = writer.finalize() 1087 1088 split_generator.split_info.num_examples = num_examples ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in finalize(self, close_stream) 525 # Re-intializing to empty list for next batch 526 self.hkey_record = [] --> 527 self.write_examples_on_file() 528 if self.pa_writer is None: 529 if self.schema: ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_examples_on_file(self) 402 # Since current_examples contains (example, key) tuples 403 batch_examples[col] = [row[0][col] for row in self.current_examples] --> 404 self.write_batch(batch_examples=batch_examples) 405 self.current_examples = [] 406 ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 495 col_try_type = try_features[col] if try_features is not None and col in try_features else None 496 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col) --> 497 arrays.append(pa.array(typed_sequence)) 498 inferred_features[col] = typed_sequence.get_inferred_type() 499 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib.array() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol() ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type) 203 # Also, when trying type "string", we don't want to convert integers or floats to "string". 204 # We only do it if trying_type is False - since this is what the user asks for. --> 205 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) 206 return out 207 except (TypeError, pa.lib.ArrowInvalid) as e: # handle type errors and overflows ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"): 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 946 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 919 else: --> 920 return func(array, *args, **kwargs) 921 922 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1063 # feature must be either [subfeature] or Sequence(subfeature) 1064 if isinstance(feature, list): -> 1065 return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0])) 1066 elif isinstance(feature, Sequence): 1067 if feature.length > -1: ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"): 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 946 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 919 else: --> 920 return func(array, *args, **kwargs) 921 922 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1058 } 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) 1062 elif pa.types.is_list(array.type): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 1058 } 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) 1062 elif pa.types.is_list(array.type): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"): 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 946 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 919 else: --> 920 return func(array, *args, **kwargs) 921 922 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1058 } 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) 1062 elif pa.types.is_list(array.type): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 1058 } 1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature): -> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()] 1061 return pa.StructArray.from_arrays(arrays, names=list(feature)) 1062 elif pa.types.is_list(array.type): ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"): 943 array = _sanitize(array) --> 944 return func(array, *args, **kwargs) 945 946 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs) 918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) 919 else: --> 920 return func(array, *args, **kwargs) 921 922 return wrapper ~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str) 1085 elif not isinstance(feature, (Sequence, dict, list, tuple)): 1086 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) -> 1087 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") 1088 1089 TypeError: Couldn't cast array of type struct<医院-3.0T MRI: string, 医院-CT: string, 医院-DSA: string, 医院-公交线路: string, 医院-区域: string, 医院-名称: string, 医院-地址: string, 医院-地铁可达: string, 医院-地铁线路: string, 医院-性质: string, 医院-挂号时间: string, 医院-电话: string, 医院-等级: string, 医院-类别: string, 医院-重点科室: string, 医院-门诊时间: string, 天气-城市: string, 天气-天气: string, 天气-日期: string, 天气-温度: string, 天气-紫外线强度: string, 天气-风力风向: string, 旅游景点-区域: string, 旅游景点-名称: string, 旅游景点-地址: string, 旅游景点-开放时间: string, 旅游景点-是否地铁直达: string, 旅游景点-景点类型: string, 旅游景点-最适合人群: string, 旅游景点-消费: string, 旅游景点-特点: string, 旅游景点-电话号码: string, 旅游景点-评分: string, 旅游景点-门票价格: string, 汽车-价格(万元): string, 汽车-倒车影像: string, 汽车-动力水平: string, 汽车-厂商: string, 汽车-发动机排量(L): string, 汽车-发动机马力(Ps): string, 汽车-名称: string, 汽车-定速巡航: string, 汽车-巡航系统: string, 汽车-座位数: string, 汽车-座椅加热: string, 汽车-座椅通风: string, 汽车-所属价格区间: string, 汽车-油耗水平: string, 汽车-环保标准: string, 汽车-级别: string, 汽车-综合油耗(L/100km): string, 汽车-能源类型: string, 汽车-车型: string, 汽车-车系: string, 汽车-车身尺寸(mm): string, 汽车-驱动方式: string, 汽车-驾驶辅助影像: string, 火车-出发地: string, 火车-出发时间: string, 火车-到达时间: string, 火车-坐席: string, 火车-日期: string, 火车-时长: string, 火车-目的地: string, 火车-票价: string, 火车-舱位档次: string, 火车-车型: string, 火车-车次信息: string, 电影-主演: string, 电影-主演名单: string, 电影-具体上映时间: string, 电影-制片国家/地区: string, 电影-导演: string, 电影-年代: string, 电影-片名: string, 电影-片长: string, 电影-类型: string, 电影-豆瓣评分: string, 电脑-CPU: string, 电脑-CPU型号: string, 电脑-产品类别: string, 电脑-价格: string, 电脑-价格区间: string, 电脑-内存容量: string, 电脑-分类: string, 电脑-品牌: string, 电脑-商品名称: string, 电脑-屏幕尺寸: string, 电脑-待机时长: string, 电脑-显卡型号: string, 电脑-显卡类别: string, 电脑-游戏性能: string, 电脑-特性: string, 电脑-硬盘容量: string, 电脑-系列: string, 电脑-系统: string, 电脑-色系: string, 电脑-裸机重量: string, 电视剧-主演: string, 电视剧-主演名单: string, 电视剧-制片国家/地区: string, 电视剧-单集片长: string, 电视剧-导演: string, 电视剧-年代: string, 电视剧-片名: string, 电视剧-类型: string, 电视剧-豆瓣评分: string, 电视剧-集数: string, 电视剧-首播时间: string, 辅导班-上课方式: string, 辅导班-上课时间: string, 辅导班-下课时间: string, 辅导班-价格: string, 辅导班-区域: string, 辅导班-年级: string, 辅导班-开始日期: string, 辅导班-教室地点: string, 辅导班-教师: string, 辅导班-教师网址: string, 辅导班-时段: string, 辅导班-校区: string, 辅导班-每周: string, 辅导班-班号: string, 辅导班-科目: string, 辅导班-结束日期: string, 辅导班-课时: string, 辅导班-课次: string, 辅导班-课程网址: string, 辅导班-难度: string, 通用-产品类别: string, 通用-价格区间: string, 通用-品牌: string, 通用-系列: string, 酒店-价位: string, 酒店-停车场: string, 酒店-区域: string, 酒店-名称: string, 酒店-地址: string, 酒店-房型: string, 酒店-房费: string, 酒店-星级: string, 酒店-电话号码: string, 酒店-评分: string, 酒店-酒店类型: string, 飞机-准点率: string, 飞机-出发地: string, 飞机-到达时间: string, 飞机-日期: string, 飞机-目的地: string, 飞机-票价: string, 飞机-航班信息: string, 飞机-舱位档次: string, 飞机-起飞时间: string, 餐厅-人均消费: string, 餐厅-价位: string, 餐厅-区域: string, 餐厅-名称: string, 餐厅-地址: string, 餐厅-推荐菜: string, 餐厅-是否地铁直达: string, 餐厅-电话号码: string, 餐厅-菜系: string, 餐厅-营业时间: string, 餐厅-评分: string> to {'旅游景点-名称': Value(dtype='string', id=None), '旅游景点-区域': Value(dtype='string', id=None), '旅游景点-景点类型': Value(dtype='string', id=None), '旅游景点-最适合人群': Value(dtype='string', id=None), '旅游景点-消费': Value(dtype='string', id=None), '旅游景点-是否地铁直达': Value(dtype='string', id=None), '旅游景点-门票价格': Value(dtype='string', id=None), '旅游景点-电话号码': Value(dtype='string', id=None), '旅游景点-地址': Value(dtype='string', id=None), '旅游景点-评分': Value(dtype='string', id=None), '旅游景点-开放时间': Value(dtype='string', id=None), '旅游景点-特点': Value(dtype='string', id=None), '餐厅-名称': Value(dtype='string', id=None), '餐厅-区域': Value(dtype='string', id=None), '餐厅-菜系': Value(dtype='string', id=None), '餐厅-价位': Value(dtype='string', id=None), '餐厅-是否地铁直达': Value(dtype='string', id=None), '餐厅-人均消费': Value(dtype='string', id=None), '餐厅-地址': Value(dtype='string', id=None), '餐厅-电话号码': Value(dtype='string', id=None), '餐厅-评分': Value(dtype='string', id=None), '餐厅-营业时间': Value(dtype='string', id=None), '餐厅-推荐菜': Value(dtype='string', id=None), '酒店-名称': Value(dtype='string', id=None), '酒店-区域': Value(dtype='string', id=None), '酒店-星级': Value(dtype='string', id=None), '酒店-价位': Value(dtype='string', id=None), '酒店-酒店类型': Value(dtype='string', id=None), '酒店-房型': Value(dtype='string', id=None), '酒店-停车场': Value(dtype='string', id=None), '酒店-房费': Value(dtype='string', id=None), '酒店-地址': Value(dtype='string', id=None), '酒店-电话号码': Value(dtype='string', id=None), '酒店-评分': Value(dtype='string', id=None), '电脑-品牌': Value(dtype='string', id=None), '电脑-产品类别': Value(dtype='string', id=None), '电脑-分类': Value(dtype='string', id=None), '电脑-内存容量': Value(dtype='string', id=None), '电脑-屏幕尺寸': Value(dtype='string', id=None), '电脑-CPU': Value(dtype='string', id=None), '电脑-价格区间': Value(dtype='string', id=None), '电脑-系列': Value(dtype='string', id=None), '电脑-商品名称': Value(dtype='string', id=None), '电脑-系统': Value(dtype='string', id=None), '电脑-游戏性能': Value(dtype='string', id=None), '电脑-CPU型号': Value(dtype='string', id=None), '电脑-裸机重量': Value(dtype='string', id=None), '电脑-显卡类别': Value(dtype='string', id=None), '电脑-显卡型号': Value(dtype='string', id=None), '电脑-特性': Value(dtype='string', id=None), '电脑-色系': Value(dtype='string', id=None), '电脑-待机时长': Value(dtype='string', id=None), '电脑-硬盘容量': Value(dtype='string', id=None), '电脑-价格': Value(dtype='string', id=None), '火车-出发地': Value(dtype='string', id=None), '火车-目的地': Value(dtype='string', id=None), '火车-日期': Value(dtype='string', id=None), '火车-车型': Value(dtype='string', id=None), '火车-坐席': Value(dtype='string', id=None), '火车-车次信息': Value(dtype='string', id=None), '火车-时长': Value(dtype='string', id=None), '火车-出发时间': Value(dtype='string', id=None), '火车-到达时间': Value(dtype='string', id=None), '火车-票价': Value(dtype='string', id=None), '飞机-出发地': Value(dtype='string', id=None), '飞机-目的地': Value(dtype='string', id=None), '飞机-日期': Value(dtype='string', id=None), '飞机-舱位档次': Value(dtype='string', id=None), '飞机-航班信息': Value(dtype='string', id=None), '飞机-起飞时间': Value(dtype='string', id=None), '飞机-到达时间': Value(dtype='string', id=None), '飞机-票价': Value(dtype='string', id=None), '飞机-准点率': Value(dtype='string', id=None), '天气-城市': Value(dtype='string', id=None), '天气-日期': Value(dtype='string', id=None), '天气-天气': Value(dtype='string', id=None), '天气-温度': Value(dtype='string', id=None), '天气-风力风向': Value(dtype='string', id=None), '天气-紫外线强度': Value(dtype='string', id=None), '电影-制片国家/地区': Value(dtype='string', id=None), '电影-类型': Value(dtype='string', id=None), '电影-年代': Value(dtype='string', id=None), '电影-主演': Value(dtype='string', id=None), '电影-导演': Value(dtype='string', id=None), '电影-片名': Value(dtype='string', id=None), '电影-主演名单': Value(dtype='string', id=None), '电影-具体上映时间': Value(dtype='string', id=None), '电影-片长': Value(dtype='string', id=None), '电影-豆瓣评分': Value(dtype='string', id=None), '电视剧-制片国家/地区': Value(dtype='string', id=None), '电视剧-类型': Value(dtype='string', id=None), '电视剧-年代': Value(dtype='string', id=None), '电视剧-主演': Value(dtype='string', id=None), '电视剧-导演': Value(dtype='string', id=None), '电视剧-片名': Value(dtype='string', id=None), '电视剧-主演名单': Value(dtype='string', id=None), '电视剧-首播时间': Value(dtype='string', id=None), '电视剧-集数': Value(dtype='string', id=None), '电视剧-单集片长': Value(dtype='string', id=None), '电视剧-豆瓣评分': Value(dtype='string', id=None), '辅导班-班号': Value(dtype='string', id=None), '辅导班-难度': Value(dtype='string', id=None), '辅导班-科目': Value(dtype='string', id=None), '辅导班-年级': Value(dtype='string', id=None), '辅导班-区域': Value(dtype='string', id=None), '辅导班-校区': Value(dtype='string', id=None), '辅导班-上课方式': Value(dtype='string', id=None), '辅导班-开始日期': Value(dtype='string', id=None), '辅导班-结束日期': Value(dtype='string', id=None), '辅导班-每周': Value(dtype='string', id=None), '辅导班-上课时间': Value(dtype='string', id=None), '辅导班-下课时间': Value(dtype='string', id=None), '辅导班-时段': Value(dtype='string', id=None), '辅导班-课次': Value(dtype='string', id=None), '辅导班-课时': Value(dtype='string', id=None), '辅导班-教室地点': Value(dtype='string', id=None), '辅导班-教师': Value(dtype='string', id=None), '辅导班-价格': Value(dtype='string', id=None), '辅导班-课程网址': Value(dtype='string', id=None), '辅导班-教师网址': Value(dtype='string', id=None), '汽车-名称': Value(dtype='string', id=None), '汽车-车型': Value(dtype='string', id=None), '汽车-级别': Value(dtype='string', id=None), '汽车-座位数': Value(dtype='string', id=None), '汽车-车身尺寸(mm)': Value(dtype='string', id=None), '汽车-厂商': Value(dtype='string', id=None), '汽车-能源类型': Value(dtype='string', id=None), '汽车-发动机排量(L)': Value(dtype='string', id=None), '汽车-发动机马力(Ps)': Value(dtype='string', id=None), '汽车-驱动方式': Value(dtype='string', id=None), '汽车-综合油耗(L/100km)': Value(dtype='string', id=None), '汽车-环保标准': Value(dtype='string', id=None), '汽车-驾驶辅助影像': Value(dtype='string', id=None), 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Value(dtype='string', id=None), '医院-3.0T MRI': Value(dtype='string', id=None), '医院-DSA': Value(dtype='string', id=None)} ``` </details> ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.1 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.10 - PyArrow version: 3.0.0
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I_kwDODunzps5Cd5Vf
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Dataset.shuffle(seed=None) gives fixed row permutation
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[ "I'm not sure if this is expected behavior.\r\n\r\nAm I supposed to work with a copy of the dataset, i.e. `shuffled_dataset = data.shuffle(seed=None)`?\r\n\r\n```diff\r\nimport datasets\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"c\", \"d\", \"e\"]}\r\n)\r\n\r\n+shuffled_data = data.shuffle(seed=None)\r\n\r\n# Doesn't work as expected\r\nprint(\"Shuffle dataset\")\r\nfor _ in range(3):\r\n+ shuffled_data = shuffled_data.shuffle(seed=None)\r\n+ print(shuffled_data[:])\r\n- print(data.shuffle(seed=None)[:])\r\n\r\n# This seems to work with pandas\r\nprint(\"\\nShuffle via pandas\")\r\nfor _ in range(3):\r\n df = data.to_pandas().sample(frac=1.0)\r\n print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])\r\n\r\n```\r\n\r\nor provide a `generator` instead?\r\n\r\n```diff\r\nimport datasets\r\n+from numpy.random import default_rng\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"c\", \"d\", \"e\"]}\r\n)\r\n\r\n+rng = default_rng()\r\n\r\n# Doesn't work as expected\r\nprint(\"Shuffle dataset\")\r\nfor _ in range(3):\r\n+ print(data.shuffle(generator=rng)[:])\r\n- print(data.shuffle(seed=None)[:])\r\n\r\n# This seems to work with pandas\r\nprint(\"\\nShuffle via pandas\")\r\nfor _ in range(3):\r\n df = data.to_pandas().sample(frac=1.0)\r\n print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])\r\n\r\n```", "Hi! Thanks for reporting! Yes, this is not expected behavior. I've opened a PR with the fix." ]
2022-01-26T15:13:08Z
2022-01-27T18:16:07Z
2022-01-27T18:16:07Z
NONE
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## Describe the bug Repeated attempts to `shuffle` a dataset without specifying a seed give the same results. ## Steps to reproduce the bug ```python import datasets # Some toy example data = datasets.Dataset.from_dict( {"feature": [1, 2, 3, 4, 5], "label": ["a", "b", "c", "d", "e"]} ) # Doesn't work as expected print("Shuffle dataset") for _ in range(3): print(data.shuffle(seed=None)[:]) # This seems to work with pandas print("\nShuffle via pandas") for _ in range(3): df = data.to_pandas().sample(frac=1.0) print(datasets.Dataset.from_pandas(df, preserve_index=False)[:]) ``` ## Expected results I assumed that the default setting would initialize a new/random state of a `np.random.BitGenerator` (see [docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=shuffle#datasets.Dataset.shuffle)). Wouldn't that reshuffle the rows each time I call `data.shuffle()`? ## Actual results ```bash Shuffle dataset {'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']} {'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']} {'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']} Shuffle via pandas {'feature': [4, 2, 3, 1, 5], 'label': ['d', 'b', 'c', 'a', 'e']} {'feature': [2, 5, 3, 4, 1], 'label': ['b', 'e', 'c', 'd', 'a']} {'feature': [5, 2, 3, 1, 4], 'label': ['e', 'b', 'c', 'a', 'd']} ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.0 - Platform: Linux-5.13.0-27-generic-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 6.0.1
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1,115,027,185
I_kwDODunzps5Cdfbx
3,632
Adding CC-100: Monolingual Datasets from Web Crawl Data (Datasets links are invalid)
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[ "Hi @AnzorGozalishvili,\r\n\r\nMaybe their site was temporarily down, but it seems to work fine now.\r\n\r\nCould you please try again and confirm if the problem persists? ", "Hi @albertvillanova \r\nI checked and it works. \r\nIt seems that it was really temporarily down.\r\nThanks!" ]
2022-01-26T13:35:37Z
2022-02-10T06:58:11Z
2022-02-10T06:58:11Z
CONTRIBUTOR
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## Describe the bug The dataset links are no longer valid for CC-100. It seems that the website which was keeping these files are no longer accessible and therefore this dataset became unusable. Check out the dataset [homepage](http://data.statmt.org/cc-100/) which isn't accessible. Also the URLs for dataset file per language isn't accessible: http://data.statmt.org/cc-100/<language code here>.txt.xz (language codes: am, sr, ka, etc.) ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("cc100", "ka") ``` It throws 503 error. ## Expected results It should successfully download and load dataset but it throws an exception because the dataset files are no longer accessible. ## Environment info Run from google colab. Just installed the library using pip: ```!pip install -U datasets```
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Labels conflict when loading a local CSV file.
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null
[ "Hi @pichljan, thanks for reporting.\r\n\r\nThis should be fixed. I'm looking at it. " ]
2022-01-26T10:00:33Z
2022-02-11T23:02:31Z
2022-02-11T23:02:31Z
NONE
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## Describe the bug I am trying to load a local CSV file with a separate file containing label names. It is successfully loaded for the first time, but when I try to load it again, there is a conflict between provided labels and the cached dataset info. Disabling caching globally and/or using `download_mode="force_redownload"` did not help. ## Steps to reproduce the bug ```python load_dataset('csv', data_files='data/my_data.csv', features=Features(text=Value(dtype='string'), label=ClassLabel(names_file='data/my_data_labels.txt'))) ``` `my_data.csv` file has the following structure: ``` text,label "example1",0 "example2",1 ... ``` and the `my_data_labels.txt` looks like this: ``` label1 label2 ... ``` ## Expected results Successfully loaded dataset. ## Actual results ```python File "/usr/local/lib/python3.8/site-packages/datasets/load.py", line 1706, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 766, in as_dataset datasets = utils.map_nested( File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 261, in map_nested mapped = [ File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 262, in <listcomp> _single_map_nested((function, obj, types, None, True)) File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 797, in _build_single_dataset ds = self._as_dataset( File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 872, in _as_dataset return Dataset(fingerprint=fingerprint, **dataset_kwargs) File "/usr/local/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 638, in __init__ inferred_features = Features.from_arrow_schema(arrow_table.schema) File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1242, in from_arrow_schema return Features.from_dict(metadata["info"]["features"]) File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1271, in from_dict obj = generate_from_dict(dic) File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1076, in generate_from_dict return {key: generate_from_dict(value) for key, value in obj.items()} File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1076, in <dictcomp> return {key: generate_from_dict(value) for key, value in obj.items()} File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1083, in generate_from_dict return class_type(**{k: v for k, v in obj.items() if k in field_names}) File "<string>", line 7, in __init__ File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 776, in __post_init__ raise ValueError("Please provide either names or names_file but not both.") ValueError: Please provide either names or names_file but not both. ``` ## Environment info - `datasets` version: 1.18.0 - Python version: 3.8.2
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DuplicatedKeysError of NewsQA dataset
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null
[ "Thanks for reporting, @StevenTang1998.\r\n\r\nI'm fixing it. " ]
2022-01-26T03:05:49Z
2022-02-14T08:37:19Z
2022-02-14T08:37:19Z
NONE
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After processing the dataset following official [NewsQA](https://github.com/Maluuba/newsqa), I used datasets to load it: ``` a = load_dataset('newsqa', data_dir='news') ``` and the following error occurred: ``` Using custom data configuration default-data_dir=news Downloading and preparing dataset newsqa/default to /root/.cache/huggingface/datasets/newsqa/default-data_dir=news/1.0.0/b0b23e22d94a3d352ad9d75aff2b71375264a122fae301463079ee8595e05ab9... Traceback (most recent call last): File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1084, in _prepare_split writer.write(example, key) File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 442, in write self.check_duplicate_keys() File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 453, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: ./cnn/stories/6a0f9c8a5d0c6e8949b37924163c92923fe5770d.story Keys should be unique and deterministic in nature During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 1694, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 595, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 684, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1086, in _prepare_split num_examples, num_bytes = writer.finalize() File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 524, in finalize self.check_duplicate_keys() File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 453, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: ./cnn/stories/6a0f9c8a5d0c6e8949b37924163c92923fe5770d.story Keys should be unique and deterministic in nature ```
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2022-01-25T07:43:33Z
2022-02-14T08:40:58Z
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## Describe the bug The Pile had issues with their previous host server and have mirrored its content to another server. The new URL server should be updated.
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Extend support for streaming datasets that use os.path.relpath
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2022-01-24T15:58:23Z
2022-02-04T14:03:54Z
2022-02-04T14:03:54Z
MEMBER
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Extend support for streaming datasets that use `os.path.relpath`. This feature will also be useful to yield the relative path of audio or image files.
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Consider adding `ipywidgets` as a dependency.
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[ "Hi! We use `tqdm` to display progress bars, so I suggest you open this issue in their repo.", "It depends on how you use `tqdm`, no? \r\n\r\nDoesn't this library import via; \r\n\r\n```\r\nfrom tqdm.notebook import tqdm\r\n```", "Hi! Sorry for the late reply. We import `tqdm` as `from tqdm.auto import tqdm`, which should be equal to `from tqdm.notebook import tqdm` in Jupyter.", "Any objection if I make a PR that checks if the widgets library is installed beforehand? " ]
2022-01-24T14:27:11Z
2022-02-24T09:04:36Z
2022-02-24T09:04:36Z
NONE
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When I install `datasets` in a fresh virtualenv with jupyterlab I always see this error. ``` ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html ``` It's a bit of a nuisance, because I need to run shut down the jupyterlab server in order to install the required dependency. Might it be an option to just include it as a dependency here?
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TIMIT Dataset not working with GPU
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[ "Hi ! I think you should avoid calling `timit_train['audio']`. Indeed by doing so you're **loading all the audio column in memory**. This is problematic in your case because the TIMIT dataset is huge.\r\n\r\nIf you want to access the audio data of some samples, you should do this instead `timit_train[:10][\"train\"]` for example.\r\n\r\nOther than that, I'm not sure why you get a `TypeError: string indices must be integers`, do you have a code snippet that reproduces the issue that you can share here ?", "I get the same error when I try to do `timit_train[0]` or really any indexing into the whole thing. \r\n\r\nReally, that IS the code snippet that reproduces the issue. If you index into other fields like 'file' or whatever, it works. As soon as one of the fields you're looking into is 'audio', you get that issue. It's a weird issue and I suspect it's Sagemaker/environment related, maybe the mix of libraries and dependencies are not good. \r\n\r\n\r\nExample code snippet with issue. \r\n```python\r\nfrom datasets import load_dataset\r\n\r\ntimit_train = load_dataset('timit_asr', split='train')\r\nprint(timit_train[0])\r\n```", "Ok I see ! From the error you got, it looks like the `value` encoded in the arrow file of the TIMIT dataset you loaded is a string instead of a dictionary with keys \"path\" and \"bytes\" but we don't support this since 1.18\r\n\r\nCan you try regenerating the dataset with `load_dataset('timit_asr', download_mode=\"force_redownload\")` please ? I think it should fix the issue." ]
2022-01-24T03:26:03Z
2023-07-25T15:20:20Z
2023-07-25T15:20:20Z
NONE
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## Describe the bug I am working trying to use the TIMIT dataset in order to fine-tune Wav2Vec2 model and I am unable to load the "audio" column from the dataset when working with a GPU. I am working on Amazon Sagemaker Studio, on the Python 3 (PyTorch 1.8 Python 3.6 GPU Optimized) environment, with a single ml.g4dn.xlarge instance (corresponds to a Tesla T4 GPU). I don't know if the issue is GPU related or Python environment related because everything works when I work off of the CPU Optimized environment with a non-GPU instance. My code also works on Google Colab with a GPU instance. This issue is blocking because I cannot get the 'audio' column in any way due to this error, which means that I can't pass it to any functions. I later use the dataset.map function and that is where I originally noticed this error. ## Steps to reproduce the bug ```python from datasets import load_dataset timit_train = load_dataset('timit_asr', split='train') print(timit_train['audio']) ``` ## Expected results Expected to see inside the 'audio' column, which contains an 'array' nested field with the array data I actually need. ## Actual results Traceback ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-6-ceeac555e921> in <module> ----> 1 timit_train['audio'] /opt/conda/lib/python3.6/site-packages/datasets/arrow_dataset.py in __getitem__(self, key) 1917 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" 1918 return self._getitem( -> 1919 key, 1920 ) 1921 /opt/conda/lib/python3.6/site-packages/datasets/arrow_dataset.py in _getitem(self, key, decoded, **kwargs) 1902 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) 1903 formatted_output = format_table( -> 1904 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1905 ) 1906 return formatted_output /opt/conda/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns) 529 python_formatter = PythonFormatter(features=None) 530 if format_columns is None: --> 531 return formatter(pa_table, query_type=query_type) 532 elif query_type == "column": 533 if key in format_columns: /opt/conda/lib/python3.6/site-packages/datasets/formatting/formatting.py in __call__(self, pa_table, query_type) 280 return self.format_row(pa_table) 281 elif query_type == "column": --> 282 return self.format_column(pa_table) 283 elif query_type == "batch": 284 return self.format_batch(pa_table) /opt/conda/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_column(self, pa_table) 315 column = self.python_arrow_extractor().extract_column(pa_table) 316 if self.decoded: --> 317 column = self.python_features_decoder.decode_column(column, pa_table.column_names[0]) 318 return column 319 /opt/conda/lib/python3.6/site-packages/datasets/formatting/formatting.py in decode_column(self, column, column_name) 221 222 def decode_column(self, column: list, column_name: str) -> list: --> 223 return self.features.decode_column(column, column_name) if self.features else column 224 225 def decode_batch(self, batch: dict) -> dict: /opt/conda/lib/python3.6/site-packages/datasets/features/features.py in decode_column(self, column, column_name) 1337 return ( 1338 [self[column_name].decode_example(value) if value is not None else None for value in column] -> 1339 if self._column_requires_decoding[column_name] 1340 else column 1341 ) /opt/conda/lib/python3.6/site-packages/datasets/features/features.py in <listcomp>(.0) 1336 """ 1337 return ( -> 1338 [self[column_name].decode_example(value) if value is not None else None for value in column] 1339 if self._column_requires_decoding[column_name] 1340 else column /opt/conda/lib/python3.6/site-packages/datasets/features/audio.py in decode_example(self, value) 85 dict 86 """ ---> 87 path, file = (value["path"], BytesIO(value["bytes"])) if value["bytes"] is not None else (value["path"], None) 88 if path is None and file is None: 89 raise ValueError(f"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.") TypeError: string indices must be integers ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.0 - Platform: Linux-4.14.256-197.484.amzn2.x86_64-x86_64-with-debian-buster-sid - Python version: 3.6.13 - PyArrow version: 6.0.1
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Dataset BnL Historical Newspapers does not work in streaming mode
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[ "@albertvillanova let me know if there is anything I can do to help with this. I had a quick look at the code again and though I could try the following changes:\r\n- use `download` instead of `download_and_extract`\r\nhttps://github.com/huggingface/datasets/blob/d3d339fb86d378f4cb3c5d1de423315c07a466c6/datasets/bnl_newspapers/bnl_newspapers.py#L136\r\n- swith to using `iter_archive` to loop through downloaded data to replace\r\nhttps://github.com/huggingface/datasets/blob/d3d339fb86d378f4cb3c5d1de423315c07a466c6/datasets/bnl_newspapers/bnl_newspapers.py#L159\r\n\r\nLet me know if it's useful for me to try and make those changes. ", "Thanks @davanstrien.\r\n\r\nI have already been working on it so that it can be used in the BigScience workshop.\r\n\r\nI agree that the `rglob()` is not efficient in this case.\r\n\r\nI tried different solutions without success:\r\n- `iter_archive` cannot be used in this case because it does not support ZIP files yet\r\n\r\nFinally I have used `iter_files()`.", "I see this is fixed now 🙂. I also picked up a few other tips from your redactors so hopefully my next attempts will support streaming from the start. " ]
2022-01-22T14:12:59Z
2022-02-04T14:05:21Z
2022-02-04T14:05:21Z
MEMBER
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## Describe the bug When trying to load in streaming mode, it "hangs"... ## Steps to reproduce the bug ```python ds = load_dataset("bnl_newspapers", split="train", streaming=True) ``` ## Expected results The code should be optimized, so that it works fast in streaming mode. CC: @davanstrien
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Files not updating in dataset viewer
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[ "Yes. The jobs queue is full right now, following an upgrade... Back to normality in the next hours hopefully. I'll look at your datasets to be sure the dataset viewer works as expected on them.", "Should have been fixed now." ]
2022-01-21T16:47:20Z
2022-01-22T08:13:13Z
2022-01-22T08:13:13Z
MEMBER
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## Dataset viewer issue for '*name of the dataset*' **Link:** Some examples: * https://huggingface.co/datasets/abidlabs/crowdsourced-speech4 * https://huggingface.co/datasets/abidlabs/test-audio-13 *short description of the issue* It seems that the dataset viewer is reading a cached version of the dataset and it is not updating to reflect new files that are added to the dataset. I get this error: ![image](https://user-images.githubusercontent.com/1778297/150566660-30dc0dcd-18fd-4471-b70c-7c4bdc6a23c6.png) Am I the one who added this dataset? Yes
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1,110,399,096
I_kwDODunzps5CL1h4
3,611
Indexing bug after dataset.select()
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null
[ "Hi! Thanks for reporting! I've opened a PR with the fix." ]
2022-01-21T12:09:30Z
2022-01-27T18:16:22Z
2022-01-27T18:16:22Z
NONE
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## Describe the bug A clear and concise description of what the bug is. Dataset indexing is not working as expected after `dataset.select(range(100))` ## Steps to reproduce the bug ```python # Sample code to reproduce the bug import datasets task_to_keys = { "cola": ("sentence", None), "mnli": ("premise", "hypothesis"), "mrpc": ("sentence1", "sentence2"), "qnli": ("question", "sentence"), "qqp": ("question1", "question2"), "rte": ("sentence1", "sentence2"), "sst2": ("sentence", None), "stsb": ("sentence1", "sentence2"), "wnli": ("sentence1", "sentence2"), } task_name = "sst2" raw_datasets = datasets.load_dataset("glue", task_name) train_dataset = raw_datasets["train"] print("before select: ",train_dataset[-2:]) # before select: {'sentence': ['a patient viewer ', 'this new jangle of noise , mayhem and stupidity must be a serious contender for the title . '], 'label': [1, 0], 'idx': [67347, 67348]} train_dataset = train_dataset.select(range(100)) print("after select: ",train_dataset[-2:]) # after select: {'sentence': [], 'label': [], 'idx': []} ``` link to colab: https://colab.research.google.com/drive/1LngeRC9f0jE7eSQ4Kh1cIeb411lRXQD-?usp=sharing ## Expected results A clear and concise description of the expected results. showing 98, 99 index data ## Actual results Specify the actual results or traceback. empty ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.17.0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 3.0.0
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I_kwDODunzps5CJdui
3,610
Checksum error when trying to load amazon_review dataset
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closed
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null
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null
[ "It is solved now" ]
2022-01-20T21:20:32Z
2022-01-21T13:22:31Z
2022-01-21T13:22:31Z
NONE
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## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug I am getting the issue when trying to load dataset using ``` dataset = load_dataset("amazon_polarity") ``` ## Expected results dataset loaded ## Actual results ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-3-b4758ba980ae> in <module>() ----> 1 dataset = load_dataset("amazon_polarity") 2 dataset.set_format(type='pandas') 3 content_series = dataset['train']['content'] 4 label_series = dataset['train']['label'] 5 df = pd.concat([content_series, label_series], axis=1) 3 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.17.0 - Platform: Google colab - Python version: 3.7.12
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Add support for continuous metrics (RMSE, MAE)
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[ "Hey @ck37 \r\n\r\nYou can always use a custom metric as explained [in this guide from HF](https://huggingface.co/docs/datasets/master/loading_metrics.html#using-a-custom-metric-script).\r\n\r\nIf this issue needs to be contributed to (for enhancing the metric API) I think [this link](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html) would be helpful for the `MAE` metric.", "You can use a local metric script just by providing its path instead of the usual shortcut name ", "#self-assign I have starting working on this issue to enhance the metric API." ]
2022-01-20T13:35:36Z
2022-03-09T17:18:20Z
2022-03-09T17:18:20Z
NONE
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**Is your feature request related to a problem? Please describe.** I am uploading our dataset and models for the "Constructing interval measures" method we've developed, which uses item response theory to convert multiple discrete labels into a continuous spectrum for hate speech. Once we have this outcome our NLP models conduct regression rather than classification, so binary metrics are not relevant. The only continuous metrics available at https://huggingface.co/metrics are pearson & spearman correlation, which don't ensure that the prediction is on the same scale as the outcome. **Describe the solution you'd like** I would like to be able to tag our models on the Hub with the following metrics: - RMSE - MAE **Describe alternatives you've considered** I don't know if there are any alternatives. **Additional context** Our preprint is available here: https://arxiv.org/abs/2009.10277 . We are making it available for use in Jigsaw's Toxic Severity Rating Kaggle competition: https://www.kaggle.com/c/jigsaw-toxic-severity-rating/overview . I have our first model uploaded to the Hub at https://huggingface.co/ucberkeley-dlab/hate-measure-roberta-large Thanks, Chris
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audio column not saved correctly after resampling
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[ "Hi ! We just released a new version of `datasets` that should fix this.\r\n\r\nI tested resampling and using save/load_from_disk afterwards and it seems to be fixed now", "Hi @lhoestq, \r\n\r\nJust tested the latest datasets version, and confirming that this is fixed for me. \r\n\r\nThanks!", "Also, just an FYI, data that I had saved (with save_to_disk) previously from common voice using datasets==1.17.0 now give the error below when loading (with load_from disk) using datasets==1.18.0. \r\n\r\nHowever, when starting fresh using load_dataset, then doing the resampling, the save/load_from disk worked fine. \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nValueError Traceback (most recent call last)\r\n<timed exec> in <module>\r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_from_disk(dataset_path, fs, keep_in_memory)\r\n 1747 return Dataset.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)\r\n 1748 elif fs.isfile(Path(dest_dataset_path, config.DATASETDICT_JSON_FILENAME).as_posix()):\r\n-> 1749 return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)\r\n 1750 else:\r\n 1751 raise FileNotFoundError(\r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/dataset_dict.py in load_from_disk(dataset_dict_path, fs, keep_in_memory)\r\n 769 else Path(dest_dataset_dict_path, k).as_posix()\r\n 770 )\r\n--> 771 dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)\r\n 772 return dataset_dict\r\n 773 \r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in load_from_disk(dataset_path, fs, keep_in_memory)\r\n 1118 info=dataset_info,\r\n 1119 split=split,\r\n-> 1120 fingerprint=state[\"_fingerprint\"],\r\n 1121 )\r\n 1122 \r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n 655 if self.info.features.type != inferred_features.type:\r\n 656 raise ValueError(\r\n--> 657 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n 658 )\r\n 659 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'accent': Value(dtype='string', id=None), 'age': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=48000, mono=True, id=None), 'client_id': Value(dtype='string', id=None), 'down_votes': Value(dtype='int64', id=None), 'gender': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None)}\r\nwith type\r\nstruct<accent: string, age: string, audio: struct<bytes: binary, path: string>, client_id: string, down_votes: int64, gender: string, locale: string, path: string, segment: string, sentence: string, up_votes: int64>\r\n\r\nbut expected something like\r\n{'accent': Value(dtype='string', id=None), 'age': Value(dtype='string', id=None), 'audio': {'path': Value(dtype='string', id=None), 'bytes': Value(dtype='binary', id=None)}, 'client_id': Value(dtype='string', id=None), 'down_votes': Value(dtype='int64', id=None), 'gender': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None)}\r\nwith type\r\nstruct<accent: string, age: string, audio: struct<path: string, bytes: binary>, client_id: string, down_votes: int64, gender: string, locale: string, path: string, segment: string, sentence: string, up_votes: int64> \r\n```" ]
2022-01-20T06:37:10Z
2022-01-23T01:41:01Z
2022-01-23T01:24:14Z
NONE
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## Describe the bug After resampling the audio column, saving with save_to_disk doesn't seem to save with the correct type. ## Steps to reproduce the bug - load a subset of common voice dataset (48Khz) - resample audio column to 16Khz - save with save_to_disk() - load with load_from_disk() ## Expected results I expected that after saving the data, and then loading it back in, the audio column has the correct dataset.Audio type (i.e. same as before saving it) {'accent': Value(dtype='string', id=None), 'age': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None), 'client_id': Value(dtype='string', id=None), 'down_votes': Value(dtype='int64', id=None), 'gender': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None)} ## Actual results Audio column does not have the right type {'accent': Value(dtype='string', id=None), 'age': Value(dtype='string', id=None), 'audio': {'bytes': Value(dtype='binary', id=None), 'path': Value(dtype='string', id=None)}, 'client_id': Value(dtype='string', id=None), 'down_votes': Value(dtype='int64', id=None), 'gender': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None)} ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.17.0 - Platform: linux - Python version: - PyArrow version:
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Dataset Viewer not showing Previews for Private Datasets
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null
[ "Sure, it's on the roadmap.", "Closing in favor of https://github.com/huggingface/datasets-server/issues/39." ]
2022-01-19T19:29:26Z
2022-09-26T08:04:43Z
2022-09-26T08:04:43Z
MEMBER
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## Dataset viewer issue for 'abidlabs/test-audio-13' It seems that the dataset viewer does not show previews for `private` datasets, even for the user who's private dataset it is. See [1] for example. If I change the visibility to public, then it does show, but it would be useful to have the viewer even for private datasets. ![image](https://user-images.githubusercontent.com/1778297/150200515-93ff1545-11fd-4793-be64-6bed3cd895e2.png) **Link:** [1] https://huggingface.co/datasets/abidlabs/test-audio-13 **Am I the one who added this dataset?** Yes
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The `add_column()` method does not work if used on dataset sliced with `select()`
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null
[ "similar #3611 " ]
2022-01-19T13:36:50Z
2022-01-28T15:35:57Z
2022-01-28T15:35:57Z
NONE
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Hello, I posted this as a question on the forums ([here](https://discuss.huggingface.co/t/add-column-does-not-work-if-used-on-dataset-sliced-with-select/13893)): I have a dataset with 2000 entries > dataset = Dataset.from_dict({'colA': list(range(2000))}) and from which I want to extract the first one thousand rows, create a new dataset with these and also add a new column to it: > dataset2 = dataset.select(list(range(1000))) > final_dataset = dataset2.add_column('colB', list(range(1000))) This gives an error >ArrowInvalid: Added column's length must match table's length. Expected length 2000 but got length 1000 So it looks like even though it is a dataset with 1000 rows, it "remembers" the shape of the one it was sliced from. ## Actual results ``` ArrowInvalid Traceback (most recent call last) <ipython-input-138-e806860f3ce3> in <module> ----> 1 final_dataset = dataset2.add_column('colB', list(range(1000))) ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 468 } 469 # apply actual function --> 470 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 471 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 472 # re-apply format to the output ~/.local/lib/python3.8/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 404 # Call actual function 405 --> 406 out = func(self, *args, **kwargs) 407 408 # Update fingerprint of in-place transforms + update in-place history of transforms ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py in add_column(self, name, column, new_fingerprint) 3343 column_table = InMemoryTable.from_pydict({name: column}) 3344 # Concatenate tables horizontally -> 3345 table = ConcatenationTable.from_tables([self._data, column_table], axis=1) 3346 # Update features 3347 info = self.info.copy() ~/.local/lib/python3.8/site-packages/datasets/table.py in from_tables(cls, tables, axis) 729 table_blocks = to_blocks(table) 730 blocks = _extend_blocks(blocks, table_blocks, axis=axis) --> 731 return cls.from_blocks(blocks) 732 733 @property ~/.local/lib/python3.8/site-packages/datasets/table.py in from_blocks(cls, blocks) 668 @classmethod 669 def from_blocks(cls, blocks: TableBlockContainer) -> "ConcatenationTable": --> 670 blocks = cls._consolidate_blocks(blocks) 671 if isinstance(blocks, TableBlock): 672 table = blocks ~/.local/lib/python3.8/site-packages/datasets/table.py in _consolidate_blocks(cls, blocks) 664 return cls._merge_blocks(blocks, axis=0) 665 else: --> 666 return cls._merge_blocks(blocks) 667 668 @classmethod ~/.local/lib/python3.8/site-packages/datasets/table.py in _merge_blocks(cls, blocks, axis) 650 merged_blocks += list(block_group) 651 else: # both --> 652 merged_blocks = [cls._merge_blocks(row_block, axis=1) for row_block in blocks] 653 if all(len(row_block) == 1 for row_block in merged_blocks): 654 merged_blocks = cls._merge_blocks( ~/.local/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0) 650 merged_blocks += list(block_group) 651 else: # both --> 652 merged_blocks = [cls._merge_blocks(row_block, axis=1) for row_block in blocks] 653 if all(len(row_block) == 1 for row_block in merged_blocks): 654 merged_blocks = cls._merge_blocks( ~/.local/lib/python3.8/site-packages/datasets/table.py in _merge_blocks(cls, blocks, axis) 647 for is_in_memory, block_group in groupby(blocks, key=lambda x: isinstance(x, InMemoryTable)): 648 if is_in_memory: --> 649 block_group = [InMemoryTable(cls._concat_blocks(list(block_group), axis=axis))] 650 merged_blocks += list(block_group) 651 else: # both ~/.local/lib/python3.8/site-packages/datasets/table.py in _concat_blocks(blocks, axis) 626 else: 627 for name, col in zip(table.column_names, table.columns): --> 628 pa_table = pa_table.append_column(name, col) 629 return pa_table 630 else: ~/.local/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.append_column() ~/.local/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.add_column() ~/.local/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() ~/.local/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: Added column's length must match table's length. Expected length 2000 but got length 1000 ``` A solution provided by @mariosasko is to use `dataset2.flatten_indices()` after the `select()` and before attempting to add the new column: > dataset = Dataset.from_dict({'colA': list(range(2000))}) > dataset2 = dataset.select(list(range(1000))) > dataset2 = dataset2.flatten_indices() > final_dataset = dataset2.add_column('colB', list(range(1000))) which works. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.13.2 (note: also checked with version 1.17.0, still the same error) - Platform: Ubuntu 20.04.3 - Python version: 3.8.10 - PyArrow version: 6.0.0
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Readme info not being parsed to show on Dataset card page
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[ "i suspect a markdown parsing error, @severo do you want to take a quick look at it when you have some time?", "# Problem\r\nThe issue seems to coming from the front matter of the README\r\n```---\r\nannotations_creators:\r\n- no-annotation\r\nlanguage_creators:\r\n- machine-generated\r\nlanguages:\r\n- 'ca'\r\n- 'de'\r\nlicenses:\r\n- cc-by-4.0\r\nmultilinguality:\r\n- translation\r\npretty_name: Catalan-German aligned corpora to train NMT systems.\r\nsize_categories:\r\n- \"1M<n<10M\" \r\nsource_datasets:\r\n- extended|tilde_model\r\ntask_categories:\r\n- machine-translation\r\ntask_ids:\r\n- machine-translation\r\n---\r\n``` \r\n# Solution\r\nThe fix is to correctly style the README as explained [here](https://huggingface.co/docs/datasets/v1.12.0/dataset_card.html). I have also correctly parsed the font matter as shown below:\r\n```\r\n---\r\nannotations_creators: []\r\nlanguage_creators: [machine-generated]\r\nlanguages: ['ca', 'de']\r\nlicenses: []\r\nmultilinguality:\r\n- multilingual\r\npretty_name: 'Catalan-German aligned corpora to train NMT systems.'\r\nsize_categories: \r\n- 1M<n<10M\r\nsource_datasets: ['extended|tilde_model']\r\ntask_categories: ['machine-translation']\r\ntask_ids: ['machine-translation']\r\n---\r\n```\r\nYou can find the README for a sample dataset [here](https://huggingface.co/datasets/ritwikraha/Test)", "Thank you. It finally worked implementing your changes and leaving a white line between title and text in the description.", "Thanks, if this solves your issue, can you please close it?" ]
2022-01-19T13:32:29Z
2022-01-21T10:20:01Z
2022-01-21T10:20:01Z
NONE
null
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## Describe the bug The info contained in the README.md file is not being shown in the dataset main page. Basic info and table of contents are properly formatted in the README. ## Steps to reproduce the bug # Sample code to reproduce the bug The README file is this one: https://huggingface.co/datasets/softcatala/Tilde-MODEL-Catalan/blob/main/README.md ## Expected results README info should appear in the Dataset card page. ## Actual results Nothing is shown. However, labels are parsed and shown successfully.
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ERROR: File "setup.py" or "setup.cfg" not found. Directory cannot be installed in editable mode: /content
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[ "Hi! The `cd` command in Jupyer/Colab needs to start with `%`, so this should work:\r\n```\r\n!git clone https://github.com/huggingface/datasets.git\r\n%cd datasets\r\n!pip install -e \".[streaming]\"\r\n```", "thanks @mariosasko i had the same mistake and your solution is what was needed" ]
2022-01-19T13:19:28Z
2022-08-05T12:35:51Z
2022-02-14T08:46:34Z
NONE
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## Bug The install of streaming dataset is giving following error. ## Steps to reproduce the bug ```python ! git clone https://github.com/huggingface/datasets.git ! cd datasets ! pip install -e ".[streaming]" ``` ## Actual results Cloning into 'datasets'... remote: Enumerating objects: 50816, done. remote: Counting objects: 100% (2356/2356), done. remote: Compressing objects: 100% (1606/1606), done. remote: Total 50816 (delta 834), reused 1741 (delta 525), pack-reused 48460 Receiving objects: 100% (50816/50816), 72.47 MiB | 27.68 MiB/s, done. Resolving deltas: 100% (22541/22541), done. Checking out files: 100% (6722/6722), done. ERROR: File "setup.py" or "setup.cfg" not found. Directory cannot be installed in editable mode: /content
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Loss of cast `Image` feature on certain dataset method
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[ "Hi! Thanks for reporting! The issue with `cast_column` should be fixed by #3575 and after we merge that PR I'll start working on the `push_to_hub` support for the `Image`/`Audio` feature.", "> Hi! Thanks for reporting! The issue with `cast_column` should be fixed by #3575 and after we merge that PR I'll start working on the `push_to_hub` support for the `Image`/`Audio` feature.\r\n\r\nThanks, I'll keep an eye out for #3575 getting merged. I managed to use `push_to_hub` sucesfully with images when they were loaded via `map` - something like `ds.map(lambda example: {\"img\": load_image_function(example['fname']})`, this only pushed the images to the hub if the `load_image_function` return a PIL Image without the filename attribute though. I guess this might often be the prefered behaviour though. \r\n", "Hi ! We merged the PR and did a release of `datasets` that includes the changes. Can you try updating `datasets` and try again ?", "> Hi ! We merged the PR and did a release of `datasets` that includes the changes. Can you try updating `datasets` and try again ?\r\n\r\nThanks for checking. There is no longer an error when calling `select` but it appears the cast value isn't preserved. Before `select`\r\n\r\n```python\r\ndataset.features\r\n{'url': Image(id=None)}\r\n```\r\n\r\nafter select:\r\n```\r\n{'url': Value(dtype='string', id=None)}\r\n```\r\n\r\nUpdated Colab example [here](https://colab.research.google.com/gist/davanstrien/4e88f55a3675c279b5c2f64299ae5c6f/potential_casting_bug.ipynb) ", "Hmmm, if I re-run your google colab I'm getting the right type at the end:\r\n```\r\nsample.features\r\n# {'url': Image(id=None)}\r\n```", "Appolgies - I've just run again and also got this output. I have also sucesfully used the `push_to_hub` method. I think this is fixed now so will close this issue. ", "Fixed in #3575 " ]
2022-01-18T20:44:01Z
2022-01-21T18:07:28Z
2022-01-21T18:07:28Z
MEMBER
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## Describe the bug When an a column is cast to an `Image` feature, the cast type appears to be lost during certain operations. I first noticed this when using the `push_to_hub` method on a dataset that contained urls pointing to images which had been cast to an `image`. This also happens when using select on a dataset which has had a column cast to an `Image`. I suspect this might be related to https://github.com/huggingface/datasets/pull/3556 but I don't believe that pull request fixes this issue. ## Steps to reproduce the bug An example of casting a url to an image followed by using the `select` method: ```python from datasets import Dataset from datasets import features url = "https://cf.ltkcdn.net/cats/images/std-lg/246866-1200x816-grey-white-kitten.webp" data_dict = {"url": [url]*2} dataset = Dataset.from_dict(data_dict) dataset = dataset.cast_column('url',features.Image()) sample = dataset.select([1]) ``` [example notebook](https://gist.github.com/davanstrien/06e53f4383c28ae77ce1b30d0eaf0d70#file-potential_casting_bug-ipynb) ## Expected results The cast value is maintained when further methods are applied to the dataset. ## Actual results ```python --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-12-47f393bc2d0d> in <module>() ----> 1 sample = dataset.select([1]) 4 frames /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 487 } 488 # apply actual function --> 489 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 490 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 491 # re-apply format to the output /usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 409 # Call actual function 410 --> 411 out = func(self, *args, **kwargs) 412 413 # Update fingerprint of in-place transforms + update in-place history of transforms /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint) 2772 ) 2773 else: -> 2774 return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint) 2775 2776 @transmit_format /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in _new_dataset_with_indices(self, indices_cache_file_name, indices_buffer, fingerprint) 2688 split=self.split, 2689 indices_table=indices_table, -> 2690 fingerprint=fingerprint, 2691 ) 2692 /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint) 664 if self.info.features.type != inferred_features.type: 665 raise ValueError( --> 666 f"External features info don't match the dataset:\nGot\n{self.info.features}\nwith type\n{self.info.features.type}\n\nbut expected something like\n{inferred_features}\nwith type\n{inferred_features.type}" 667 ) 668 ValueError: External features info don't match the dataset: Got {'url': Image(id=None)} with type struct<url: extension<arrow.py_extension_type<ImageExtensionType>>> but expected something like {'url': Value(dtype='string', id=None)} with type struct<url: string> ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.17.1.dev0 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 3.0.0
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No module named 'fsspec.archive'
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2022-01-18T10:17:01Z
2022-08-11T09:57:54Z
2022-01-18T10:33:10Z
NONE
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## Describe the bug Cannot import datasets after installation. ## Steps to reproduce the bug ```shell $ python Python 3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0] :: Anaconda, Inc. on linux Type "help", "copyright", "credits" or "license" for more information. >>> import datasets Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/__init__.py", line 34, in <module> from .arrow_dataset import Dataset, concatenate_datasets File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 61, in <module> from .arrow_writer import ArrowWriter, OptimizedTypedSequence File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/arrow_writer.py", line 28, in <module> from .features import ( File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/features/__init__.py", line 2, in <module> from .audio import Audio File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/features/audio.py", line 7, in <module> from ..utils.streaming_download_manager import xopen File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/utils/streaming_download_manager.py", line 18, in <module> from ..filesystems import COMPRESSION_FILESYSTEMS File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/filesystems/__init__.py", line 6, in <module> from . import compression File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/filesystems/compression.py", line 5, in <module> from fsspec.archive import AbstractArchiveFileSystem ModuleNotFoundError: No module named 'fsspec.archive' ```
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Revisit `enable/disable_` toggle function prefix
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2022-01-18T04:09:55Z
2022-03-14T15:01:08Z
2022-03-14T15:01:08Z
CONTRIBUTOR
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As discussed in https://github.com/huggingface/transformers/pull/15167, we should revisit the `enable/disable_` toggle function prefix, potentially in favor of `set_enabled_`. Concretely, this translates to - De-deprecating `disable_progress_bar()` - Adding `enable_progress_bar()` - On the caching side, adding `enable_caching` and `disable_caching` Additional decisions have to be made with regards to the existing `set_enabled_X` functions; that is, whether to keep them as is or deprecate them in favor of the aforementioned functions. cc @mariosasko @lhoestq
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Datasets streaming + map doesn't work for `Audio`
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2022-01-17T12:55:42Z
2022-01-20T13:28:00Z
2022-01-20T13:28:00Z
CONTRIBUTOR
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## Describe the bug When using audio datasets in streaming mode, applying a `map(...)` before iterating leads to an error as the key `array` does not exist anymore. ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("common_voice", "en", streaming=True, split="train") def map_fn(batch): print("audio keys", batch["audio"].keys()) batch["audio"] = batch["audio"]["array"][:100] return batch ds = ds.map(map_fn) sample = next(iter(ds)) ``` I think the audio is somehow decoded before `.map(...)` is actually called. ## Expected results IMO, the above code snippet should work. ## Actual results ```bash audio keys dict_keys(['path', 'bytes']) Traceback (most recent call last): File "./run_audio.py", line 15, in <module> sample = next(iter(ds)) File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 341, in __iter__ for key, example in self._iter(): File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 338, in _iter yield from ex_iterable File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 192, in __iter__ yield key, self.function(example) File "./run_audio.py", line 9, in map_fn batch["input"] = batch["audio"]["array"][:100] KeyError: 'array' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.17.1.dev0 - Platform: Linux-5.3.0-64-generic-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 6.0.1
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2022-01-17T00:18:14Z
2022-02-14T08:51:27Z
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## Dataset viewer issue for '*name of the dataset*' **Link:** *link to the dataset viewer page* *short description of the issue* Am I the one who added this dataset ? Yes-No
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conll 2003 dataset source url is no longer valid
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[ "I came to open the same issue.", "Thanks for reporting !\r\n\r\nI pushed a temporary fix on `master` that uses an URL from a previous commit to access the dataset for now, until we have a better solution", "I changed the URL again to use another host, the fix is available on `master` and we'll probably do a new release of `datasets` tomorrow.\r\n\r\nIn the meantime, feel free to do `load_dataset(..., revision=\"master\")` to use the fixed script", "We just released a new version of `datasets` with a working URL. Feel free to update `datasets` and try again :)", "Hello! Unfortunately, this URL does not work for me. \r\nCould you please tell me how I can solve the problem?\r\n\r\n`>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"conll2003\")\r\nDownloading and preparing dataset conll2003/conll2003 (download: 4.63 MiB, generated: 9.78 MiB, post-processed: Unknown size, total: 14.41 MiB) to /home/dafedo/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/40e7cb6bcc374f7c349c83acd1e9352a4f09474eb691f64f364ee62eb65d0ca6...\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/load.py\", line 745, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/builder.py\", line 574, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/builder.py\", line 630, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/home/dafedo/.cache/huggingface/modules/datasets_modules/datasets/conll2003/40e7cb6bcc374f7c349c83acd1e9352a4f09474eb691f64f364ee62eb65d0ca6/conll2003.py\", line 196, in _split_generators\r\n downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/download_manager.py\", line 287, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/download_manager.py\", line 195, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 203, in map_nested\r\n mapped = [\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 204, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 142, in _single_map_nested\r\n return function(data_struct)\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/download_manager.py\", line 218, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 281, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/dafedo/efficient-task-transfer/venv/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 621, in get_from_cache\r\n raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt\r\n`\r\n\r\nI receive the same error when I run \"itrain run_configs/conll2003.json\" from https://github.com/adapter-hub/efficient-task-transfer\r\n\r\nThank you very much in advance!\r\n\r\nRegards, \r\nDaria\r\n", "Can you try updating `datasets` and try again ?\r\n```\r\npip install -U datasets\r\n```", "@lhoestq Thank you very much for your answer! \r\n\r\nIt works this way, but for my research I need datasets==1.6.3 or closest to it because otherwise the other package would not work as it is built on this version.\r\nDo you have any other suggestion? I would really appreciate it. Maybe which version of the datasets is without hard-coded link but closest to 1.6.3\r\n", "No problem, I have solved it. \r\nThank you anyway.", "Out of curiosity, which package has the `datasets==1.6.3` requirement ?" ]
2022-01-15T23:04:17Z
2022-07-20T13:06:40Z
2022-01-21T16:57:32Z
NONE
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## Describe the bug Loading `conll2003` dataset fails because it was removed (just yesterday 1/14/2022) from the location it is looking for. ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset("conll2003") ``` ## Expected results The dataset should load. ## Actual results It is looking for the dataset at `https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt` but it was removed from there yesterday (see [commit](https://github.com/davidsbatista/NER-datasets/commit/9d8f45cc7331569af8eb3422bbe1c97cbebd5690) that removed the file and related [issue](https://github.com/davidsbatista/NER-datasets/issues/8)). - We should replace this with an alternate valid location. - this is being referenced in the huggingface course chapter 7 [colab notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/chapter7/section2_pt.ipynb), which is also broken. ```python FileNotFoundError Traceback (most recent call last) <ipython-input-4-27c956bec93c> in <module>() 1 from datasets import load_dataset 2 ----> 3 raw_datasets = load_dataset("conll2003") 11 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params) 610 ) 611 elif response is not None and response.status_code == 404: --> 612 raise FileNotFoundError(f"Couldn't find file at {url}") 613 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") 614 if head_error is not None: FileNotFoundError: Couldn't find file at https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: - Python version: - PyArrow version:
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Bug in wiki bio load
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[ "+1, here's the error I got: \r\n\r\n```\r\n>>> from datasets import load_dataset\r\n>>>\r\n>>> load_dataset(\"wiki_bio\")\r\nDownloading: 7.58kB [00:00, 4.42MB/s]\r\nDownloading: 2.71kB [00:00, 1.30MB/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to /home/jxm3/.cache/huggingface/datasets/wiki_bio/default/1.1.0/5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/load.py\", line 1694, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/builder.py\", line 595, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/builder.py\", line 662, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/home/jxm3/.cache/huggingface/modules/datasets_modules/datasets/wiki_bio/5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9/wiki_bio.py\", line 125, in _split_generators\r\n data_dir = dl_manager.download_and_extract(my_urls)\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 308, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 196, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 251, in map_nested\r\n return function(data_struct)\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 217, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 298, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/jxm3/.conda/envs/torch/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 612, in get_from_cache\r\n raise FileNotFoundError(f\"Couldn't find file at {url}\")\r\nFileNotFoundError: Couldn't find file at https://drive.google.com/uc?export=download&id=1L7aoUXzHPzyzQ0ns4ApBbYepsjFOtXil\r\n>>>\r\n```\r\n", "@alejandrocros and @lhoestq - you added the wiki_bio dataset in #1173. It doesn't work anymore. Can you take a look at this?", "And if something is wrong with Google Drive, you could try to download (and collate and unzip) from here: https://github.com/DavidGrangier/wikipedia-biography-dataset", "Hi ! Thanks for reporting. I've downloaded the data and concatenated them into a zip file available here: https://huggingface.co/datasets/wiki_bio/tree/main/data\r\n\r\nI guess we can update the dataset script to use this zip file now :)" ]
2022-01-15T10:04:33Z
2022-01-31T08:38:09Z
2022-01-31T08:38:09Z
NONE
null
null
{ "completed": 0, "percent_completed": 0, "total": 0 }
wiki_bio is failing to load because of a failing drive link . Can someone fix this ? ![7E90023B-A3B1-4930-BA25-45CCCB4E1710](https://user-images.githubusercontent.com/3104771/149617870-5a32a2da-2c78-483b-bff6-d7534215a423.png) ![653C1C76-C725-4A04-A0D8-084373BA612F](https://user-images.githubusercontent.com/3104771/149617875-ef0e30b0-b76e-48cf-b3eb-93ba8e6e5465.png) a
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1,103,403,287
I_kwDODunzps5BxJkX
3,578
label information get lost after parquet serialization
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[ "Hi ! We did a release of `datasets` today that may fix this issue. Can you try updating `datasets` and trying again ?\r\n\r\nEDIT: the issue is still there actually\r\n\r\nI think we can fix that by storing the Features in the parquet schema metadata, and then reload them when loading the parquet file", "This info is stored in the Parquet schema metadata as of https://github.com/huggingface/datasets/pull/5516" ]
2022-01-14T10:10:38Z
2023-07-25T15:44:53Z
2023-07-25T15:44:53Z
NONE
null
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## Describe the bug In *dataset_info.json* file, information about the label get lost after the dataset serialization. ## Steps to reproduce the bug ```python from datasets import load_dataset # normal save dataset = load_dataset('glue', 'sst2', split='train') dataset.save_to_disk("normal_save") # save after parquet serialization dataset.to_parquet("glue-sst2-train.parquet") dataset = load_dataset("parquet", data_files='glue-sst2-train.parquet') dataset.save_to_disk("save_after_parquet") ``` ## Expected results I expected to keep label information in *dataset_info.json* file even after parquet serialization ## Actual results In the normal serialization i got ```json "label": { "num_classes": 2, "names": [ "negative", "positive" ], "names_file": null, "id": null, "_type": "ClassLabel" }, ``` And after parquet serialization i got ```json "label": { "dtype": "int64", "id": null, "_type": "Value" }, ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.0 - Platform: ubuntu 20.04 - Python version: 3.8.10 - PyArrow version: 6.0.1
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1,100,634,244
I_kwDODunzps5BmliE
3,572
ConnectionError in IndicGLUE dataset
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null
[ "@sahoodib, thanks for reporting.\r\n\r\nIndeed, none of the data links appearing in the IndicGLUE website are working, e.g.: https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/evaluations/soham-articles.tar.gz\r\n```\r\n<Error>\r\n<Code>UserProjectAccountProblem</Code>\r\n<Message>User project billing account not in good standing.</Message>\r\n<Details>\r\nThe billing account for the owning project is disabled in state delinquent\r\n</Details>\r\n</Error>\r\n```\r\n\r\nWe have contacted the data owners to inform them about their issue and ask them if they plan to fix it.", "Yesterday I resent a reminder email with more AI4Bharat-related people in the loop.\r\n\r\nI also opened an issue in their repos:\r\n- https://github.com/AI4Bharat/indicnlp_corpus/issues/14\r\n- https://github.com/AI4Bharat/ai4bharat.org/issues/71", "We have received a reply from the authors reporting they have updated the URLs of their data files and opened a PR. See:\r\n- #4978 " ]
2022-01-12T17:59:36Z
2022-09-15T21:57:34Z
2022-09-15T21:57:34Z
NONE
null
null
{ "completed": 0, "percent_completed": 0, "total": 0 }
While I am trying to load IndicGLUE dataset (https://huggingface.co/datasets/indic_glue) it is giving me with the error: ``` ConnectionError: Couldn't reach https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/evaluations/wikiann-ner.tar.gz (error 403)
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3,568
Downloading Hugging Face Medical Dialog Dataset NonMatchingSplitsSizesError
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null
[ "Hi @fabianslife, thanks for reporting.\r\n\r\nI think you were using an old version of `datasets` because this bug was already fixed in version `1.13.0` (13 Oct 2021):\r\n- Fix: 55fd140a63b8f03a0e72985647e498f1fc799d3f\r\n- PR: #3046\r\n- Issue: #2969 \r\n\r\nPlease, feel free to update the library: `pip install -U datasets`." ]
2022-01-12T14:03:44Z
2022-02-14T09:32:34Z
2022-02-14T09:32:34Z
NONE
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I wanted to download the Nedical Dialog Dataset from huggingface, using this github link: https://github.com/huggingface/datasets/tree/master/datasets/medical_dialog After downloading the raw datasets from google drive, i unpacked everything and put it in the same folder as the medical_dialog.py which is: ``` import copy import os import re import datasets _CITATION = """\ @article{chen2020meddiag, title={MedDialog: a large-scale medical dialogue dataset}, author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao}, journal={arXiv preprint arXiv:2004.03329}, year={2020} } """ _DESCRIPTION = """\ The MedDialog dataset (English) contains conversations (in English) between doctors and patients.\ It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. \ The raw dialogues are from healthcaremagic.com and icliniq.com.\ All copyrights of the data belong to healthcaremagic.com and icliniq.com. """ _HOMEPAGE = "https://github.com/UCSD-AI4H/Medical-Dialogue-System" _LICENSE = "" class MedicalDialog(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ datasets.BuilderConfig(name="en", description="The dataset of medical dialogs in English.", version=VERSION), datasets.BuilderConfig(name="zh", description="The dataset of medical dialogs in Chinese.", version=VERSION), ] @property def manual_download_instructions(self): return """\ \n For English:\nYou need to go to https://drive.google.com/drive/folders/1g29ssimdZ6JzTST6Y8g6h-ogUNReBtJD?usp=sharing,\ and manually download the dataset from Google Drive. Once it is completed, a file named Medical-Dialogue-Dataset-English-<timestamp-info>.zip will appear in your Downloads folder( or whichever folder your browser chooses to save files to). Unzip the folder to obtain a folder named "Medical-Dialogue-Dataset-English" several text files. Now, you can specify the path to this folder for the data_dir argument in the datasets.load_dataset(...) option. The <path/to/folder> can e.g. be "/Downloads/Medical-Dialogue-Dataset-English". The data can then be loaded using the below command:\ datasets.load_dataset("medical_dialog", name="en", data_dir="/Downloads/Medical-Dialogue-Dataset-English")`. \n For Chinese:\nFollow the above process. Change the 'name' to 'zh'.The download link is https://drive.google.com/drive/folders/1r09_i8nJ9c1nliXVGXwSqRYqklcHd9e2 **NOTE** - A caution while downloading from drive. It is better to download single files since creating a zip might not include files <500 MB. This has been observed mutiple times. - After downloading the files and adding them to the appropriate folder, the path of the folder can be given as input tu the data_dir path. """ datasets.load_dataset("medical_dialog", name="en", data_dir="Medical-Dialogue-Dataset-English") def _info(self): if self.config.name == "zh": features = datasets.Features( { "file_name": datasets.Value("string"), "dialogue_id": datasets.Value("int32"), "dialogue_url": datasets.Value("string"), "dialogue_turns": datasets.Sequence( { "speaker": datasets.ClassLabel(names=["病人", "医生"]), "utterance": datasets.Value("string"), } ), } ) if self.config.name == "en": features = datasets.Features( { "file_name": datasets.Value("string"), "dialogue_id": datasets.Value("int32"), "dialogue_url": datasets.Value("string"), "dialogue_turns": datasets.Sequence( { "speaker": datasets.ClassLabel(names=["Patient", "Doctor"]), "utterance": datasets.Value("string"), } ), } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, features=features, supervised_keys=None, # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" path_to_manual_file = os.path.abspath(os.path.expanduser(dl_manager.manual_dir)) if not os.path.exists(path_to_manual_file): raise FileNotFoundError( f"{path_to_manual_file} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('medical_dialog', data_dir=...)`. Manual download instructions: {self.manual_download_instructions})" ) filepaths = [ os.path.join(path_to_manual_file, txt_file_name) for txt_file_name in sorted(os.listdir(path_to_manual_file)) if txt_file_name.endswith("txt") ] return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": filepaths})] def _generate_examples(self, filepaths): """Yields examples. Iterates over each file and give the creates the corresponding features. NOTE: - The code makes some assumption on the structure of the raw .txt file. - There are some checks to separate different id's. Hopefully, should not cause further issues later when more txt files are added. """ data_lang = self.config.name id_ = -1 for filepath in filepaths: with open(filepath, encoding="utf-8") as f_in: # Parameters to just "sectionize" the raw data last_part = "" last_dialog = {} last_list = [] last_user = "" check_list = [] # These flags are present to have a single function address both chinese and english data # English data is a little hahazard (i.e. the sentences spans multiple different lines), # Chinese is compact with one line for doctor and patient. conv_flag = False des_flag = False while True: line = f_in.readline() if not line: break # Extracting the dialog id if line[:2] == "id": # Hardcode alert! # Handling ID references that may come in the description # These were observed in the Chinese dataset and were not # followed by numbers try: dialogue_id = int(re.findall(r"\d+", line)[0]) except IndexError: continue # Extracting the url if line[:4] == "http": # Hardcode alert! dialogue_url = line.rstrip() # Extracting the patient info from description. if line[:11] == "Description": # Hardcode alert! last_part = "description" last_dialog = {} last_list = [] last_user = "" last_conv = {"speaker": "", "utterance": ""} while True: line = f_in.readline() if (not line) or (line in ["\n", "\n\r"]): break else: if data_lang == "zh": # Condition in chinese if line[:5] == "病情描述:": # Hardcode alert! last_user = "病人" sen = f_in.readline().rstrip() des_flag = True if data_lang == "en": last_user = "Patient" sen = line.rstrip() des_flag = True if des_flag: if sen == "": continue if sen in check_list: last_conv["speaker"] = "" last_conv["utterance"] = "" else: last_conv["speaker"] = last_user last_conv["utterance"] = sen check_list.append(sen) des_flag = False break # Extracting the conversation info from dialogue. elif line[:8] == "Dialogue": # Hardcode alert! if last_part == "description" and len(last_conv["utterance"]) > 0: last_part = "dialogue" if data_lang == "zh": last_user = "病人" if data_lang == "en": last_user = "Patient" while True: line = f_in.readline() if (not line) or (line in ["\n", "\n\r"]): conv_flag = False last_user = "" last_list.append(copy.deepcopy(last_conv)) # To ensure close of conversation, only even number of sentences # are extracted last_turn = len(last_list) if int(last_turn / 2) > 0: temp = int(last_turn / 2) id_ += 1 last_dialog["file_name"] = filepath last_dialog["dialogue_id"] = dialogue_id last_dialog["dialogue_url"] = dialogue_url last_dialog["dialogue_turns"] = last_list[: temp * 2] yield id_, last_dialog break if data_lang == "zh": if line[:3] == "病人:" or line[:3] == "医生:": # Hardcode alert! user = line[:2] # Hardcode alert! line = f_in.readline() conv_flag = True # The elif block is to ensure that multi-line sentences are captured. # This has been observed only in english. if data_lang == "en": if line.strip() == "Patient:" or line.strip() == "Doctor:": # Hardcode alert! user = line.replace(":", "").rstrip() line = f_in.readline() conv_flag = True elif line[:2] != "id": # Hardcode alert! conv_flag = True # Continues till the next ID is parsed if conv_flag: sen = line.rstrip() if sen == "": continue if user == last_user: last_conv["utterance"] = last_conv["utterance"] + sen else: last_user = user last_list.append(copy.deepcopy(last_conv)) last_conv["utterance"] = sen last_conv["speaker"] = user ``` running this code gives me the error: ``` File "C:\Users\Fabia\AppData\Local\Programs\Python\Python39\lib\site-packages\datasets\utils\info_utils.py", line 74, in verify_splits raise NonMatchingSplitsSizesError(str(bad_splits)) datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='medical_dialog'), 'recorded': SplitInfo(name='train', num_bytes=292801173, num_examples=229674, dataset_name='medical_dialog')}] ```
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Dataset.from_pandas preserves useless index
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[ "Hi! That makes sense. Sure, feel free to open a PR! Just a small suggestion: let's make `preserve_index` a parameter of `Dataset.from_pandas` (which we then pass to `InMemoryTable.from_pandas`) with `None` as a default value to not have this as a breaking change. " ]
2022-01-11T12:07:07Z
2022-01-12T16:11:27Z
2022-01-12T16:11:27Z
CONTRIBUTOR
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## Describe the bug Let's say that you want to create a Dataset object from pandas dataframe. Most likely you will write something like this: ``` import pandas as pd from datasets import Dataset df = pd.read_csv('some_dataset.csv') # Some DataFrame preprocessing code... dataset = Dataset.from_pandas(df) ``` If your preprocessing code contain indexing operations like this: ``` df = df[df.col1 == some_value] ``` then your df.index can be changed from (default) ```RangeIndex(start=0, stop=16590, step=1)``` to something like this ```Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ... 83979, 83980, 83981, 83982, 83983, 83984, 83985, 83986, 83987, 83988], dtype='int64', length=16590)``` In this case, PyArrow (by default) will preserve this non-standard index. In the result, your dataset object will have the extra field that you likely don't want to have: '__index_level_0__'. You can easily fix this by just adding extra argument ```preserve_index=False``` to call of ```InMemoryTable.from_pandas``` in ```arrow_dataset.py```. If you approve that this isn't desirable behavior, I can make a PR fixing that. ## Environment info - `datasets` version: 1.16.1 - Platform: Linux-5.11.0-44-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 6.0.1
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Cannot load ‘bookcorpusopen’
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null
[ "The host of this copy of the dataset (https://the-eye.eu) is down and has been down for a good amount of time ([potentially months](https://www.reddit.com/r/Roms/comments/q82s15/theeye_downdied/))\r\n\r\nFinding this dataset is a little esoteric, as the original authors took down the official BookCorpus dataset some time ago.\r\n\r\nThere are community-created versions of BookCorpus, such as the files hosted in the link below.\r\nhttps://battle.shawwn.com/sdb/bookcorpus/\r\n\r\nAnd more discussion here:\r\nhttps://github.com/soskek/bookcorpus\r\n\r\nDo we want to remove this dataset entirely? There's a fair argument for this, given that the official BookCorpus dataset was taken down by the authors. If not, perhaps can open a PR with the link to the community-created tar above and updated dataset description.", "Hi! The `bookcorpusopen` dataset is not working for the same reason as explained in this comment: https://github.com/huggingface/datasets/issues/3504#issuecomment-1004564980", "Hi @HUIYINXUE, it should work now that the data owners created a mirror server with all data, and we updated the URL in our library." ]
2022-01-10T20:17:18Z
2022-02-14T09:19:27Z
2022-02-14T09:18:47Z
NONE
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## Describe the bug Cannot load 'bookcorpusopen' ## Steps to reproduce the bug ```python dataset = load_dataset('bookcorpusopen') ``` or ```python dataset = load_dataset('bookcorpusopen',script_version='master') ``` ## Actual results ConnectionError: Couldn't reach https://the-eye.eu/public/AI/pile_preliminary_components/books1.tar.gz ## Environment info - `datasets` version: 1.9.0 - Platform: Linux version 3.10.0-1160.45.1.el7.x86_64 - Python version: 3.6.13 - PyArrow version: 6.0.1
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DuplicatedKeysError when loading tweet_qa dataset
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[ "Hi, we've just merged the PR with the fix. The fixed version of the dataset can be downloaded as follows:\r\n```python\r\nimport datasets\r\ndset = datasets.load_dataset(\"tweet_qa\", revision=\"master\")\r\n```" ]
2022-01-10T10:53:11Z
2022-01-12T15:17:33Z
2022-01-12T15:13:56Z
NONE
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When loading the tweet_qa dataset with `load_dataset('tweet_qa')`, the following error occurs: `DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 2a167f9e016ba338e1813fed275a6a1e Keys should be unique and deterministic in nature ` Might be related to issues #2433 and #2333 - `datasets` version: 1.17.0 - Python version: 3.8.5
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ImportError: cannot import name 'is_valid_waiter_error'
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[ "Hi! I can't reproduce this error in Colab, but I'm assuming you are using Amazon SageMaker Studio Notebooks (you mention the `conda_pytorch_p36` kernel), so maybe @philschmid knows more about what might be causing this issue? ", "Hey @mariosasko. Yes, I am using **Amazon SageMaker Studio Jupyter Labs**. However, I no longer need this notebook; but it would be nice to have this problem solved for others. So don't stress too much if you two can't reproduce error.", "Hey @danielbellhv, \r\n\r\nThis issue might be related to Studio probably not having an up to date `botocore` and `boto3` version. I ran into this as well a while back. My workaround was \r\n```python\r\n# using older dataset due to incompatibility of sagemaker notebook & aws-cli with > s3fs and fsspec to >= 2021.10\r\n!pip install \"datasets==1.13\" --upgrade\r\n```\r\n\r\nIn `datasets` we use the latest `s3fs` and `fsspec` but aws-cli and notebook is not supporting this. You could also update the `aws-cli` and associated packages to get the latest `datasets` version\r\n" ]
2022-01-10T10:32:04Z
2022-02-14T09:35:57Z
2022-02-14T09:35:57Z
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Based on [SO post](https://stackoverflow.com/q/70606147/17840900). I'm following along to this [Notebook][1], cell "**Loading the dataset**". Kernel: `conda_pytorch_p36`. I run: ``` ! pip install datasets transformers optimum[intel] ``` Output: ``` Requirement already satisfied: datasets in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (1.17.0) Requirement already satisfied: transformers in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (4.15.0) Requirement already satisfied: optimum[intel] in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (0.1.3) Requirement already satisfied: numpy>=1.17 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (1.19.5) Requirement already satisfied: dill in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (0.3.4) Requirement already satisfied: tqdm>=4.62.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (4.62.3) Requirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (0.2.1) Requirement already satisfied: packaging in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (21.3) Requirement already satisfied: pyarrow!=4.0.0,>=3.0.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (6.0.1) Requirement already satisfied: pandas in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (1.1.5) Requirement already satisfied: xxhash in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (2.0.2) Requirement already satisfied: aiohttp in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (3.8.1) Requirement already satisfied: fsspec[http]>=2021.05.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (2021.11.1) Requirement already satisfied: dataclasses in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (0.8) Requirement already satisfied: multiprocess in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (0.70.12.2) Requirement already satisfied: importlib-metadata in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (4.5.0) Requirement already satisfied: requests>=2.19.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (2.25.1) Requirement already satisfied: pyyaml>=5.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (5.4.1) Requirement already satisfied: regex!=2019.12.17 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (2021.4.4) Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (0.10.3) Requirement already satisfied: filelock in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (3.0.12) Requirement already satisfied: sacremoses in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (0.0.46) Requirement already satisfied: torch>=1.9 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (1.10.1) Requirement already satisfied: sympy in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (1.8) Requirement already satisfied: coloredlogs in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (15.0.1) Requirement already satisfied: pycocotools in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (2.0.3) Requirement already satisfied: neural-compressor>=1.7 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (1.9) Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (3.10.0.0) Requirement already satisfied: sigopt in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.2.0) Requirement already satisfied: opencv-python in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (4.5.1.48) Requirement already satisfied: cryptography in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (3.4.7) Requirement already satisfied: py-cpuinfo in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.0.0) Requirement already satisfied: gevent in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (21.1.2) Requirement already satisfied: schema in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.7.5) Requirement already satisfied: psutil in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (5.8.0) Requirement already satisfied: gevent-websocket in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.10.1) Requirement already satisfied: hyperopt in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.2.7) Requirement already satisfied: Flask in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (2.0.1) Requirement already satisfied: prettytable in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (2.5.0) Requirement already satisfied: Flask-SocketIO in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (5.1.1) Requirement already satisfied: scikit-learn in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.24.2) Requirement already satisfied: Pillow in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.4.0) Requirement already satisfied: Flask-Cors in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (3.0.10) Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from packaging->datasets) (2.4.7) Requirement already satisfied: chardet<5,>=3.0.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests>=2.19.0->datasets) (4.0.0) Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests>=2.19.0->datasets) (2021.5.30) Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests>=2.19.0->datasets) (1.26.5) Requirement already satisfied: idna<3,>=2.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests>=2.19.0->datasets) (2.10) Requirement already satisfied: yarl<2.0,>=1.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (1.6.3) Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (2.0.9) Requirement already satisfied: attrs>=17.3.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (21.2.0) Requirement already satisfied: asynctest==0.13.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (0.13.0) Requirement already satisfied: idna-ssl>=1.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (1.1.0) Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (4.0.1) Requirement already satisfied: aiosignal>=1.1.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (1.2.0) Requirement already satisfied: frozenlist>=1.1.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (1.2.0) Requirement already satisfied: multidict<7.0,>=4.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (5.1.0) Requirement already satisfied: humanfriendly>=9.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from coloredlogs->optimum[intel]) (10.0) Requirement already satisfied: zipp>=0.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from importlib-metadata->datasets) (3.4.1) Requirement already satisfied: python-dateutil>=2.7.3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pandas->datasets) (2.8.1) Requirement already satisfied: pytz>=2017.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pandas->datasets) (2021.1) Requirement already satisfied: matplotlib>=2.1.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pycocotools->optimum[intel]) (3.3.4) Requirement already satisfied: cython>=0.27.3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pycocotools->optimum[intel]) (0.29.23) Requirement already satisfied: setuptools>=18.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pycocotools->optimum[intel]) (52.0.0.post20210125) Requirement already satisfied: joblib in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sacremoses->transformers) (1.0.1) Requirement already satisfied: click in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sacremoses->transformers) (8.0.1) Requirement already satisfied: six in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sacremoses->transformers) (1.16.0) Requirement already satisfied: mpmath>=0.19 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sympy->optimum[intel]) (1.2.1) Requirement already satisfied: kiwisolver>=1.0.1 in 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cachetools<5.0,>=2.0.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from google-auth>=1.0.1->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (4.2.2) Requirement already satisfied: parso<0.8.0,>=0.7.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from jedi>=0.10->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.1) Requirement already satisfied: ipython-genutils in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from traitlets>=4.2->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.2.0) Requirement already satisfied: ptyprocess>=0.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pexpect->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.0) Requirement already satisfied: oauthlib>=3.0.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests-oauthlib->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (3.1.1) ``` --- **Cell:** ```python from datasets import load_dataset, load_metric ``` OR ```python import datasets ``` **Traceback:** ``` --------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-7-34fb7ba3338d> in <module> ----> 1 from datasets import load_dataset, load_metric ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/__init__.py in <module> 32 ) 33 ---> 34 from .arrow_dataset import Dataset, concatenate_datasets 35 from .arrow_reader import ArrowReader, ReadInstruction 36 from .arrow_writer import ArrowWriter ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/arrow_dataset.py in <module> 59 from . import config, utils 60 from .arrow_reader import ArrowReader ---> 61 from .arrow_writer import ArrowWriter, OptimizedTypedSequence 62 from .features import ClassLabel, Features, FeatureType, Sequence, Value, _ArrayXD, pandas_types_mapper 63 from .filesystems import extract_path_from_uri, is_remote_filesystem ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/arrow_writer.py in <module> 26 27 from . import config, utils ---> 28 from .features import ( 29 Features, 30 ImageExtensionType, ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/features/__init__.py in <module> 1 # flake8: noqa ----> 2 from .audio import Audio 3 from .features import * 4 from .features import ( 5 _ArrayXD, ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/features/audio.py in <module> 5 import pyarrow as pa 6 ----> 7 from ..utils.streaming_download_manager import xopen 8 9 ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/utils/streaming_download_manager.py in <module> 16 17 from .. import config ---> 18 from ..filesystems import COMPRESSION_FILESYSTEMS 19 from .download_manager import DownloadConfig, map_nested 20 from .file_utils import ( ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/filesystems/__init__.py in <module> 11 12 if _has_s3fs: ---> 13 from .s3filesystem import S3FileSystem # noqa: F401 14 15 COMPRESSION_FILESYSTEMS: List[compression.BaseCompressedFileFileSystem] = [ ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/filesystems/s3filesystem.py in <module> ----> 1 import s3fs 2 3 4 class S3FileSystem(s3fs.S3FileSystem): 5 """ ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/s3fs/__init__.py in <module> ----> 1 from .core import S3FileSystem, S3File 2 from .mapping import S3Map 3 4 from ._version import get_versions 5 ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/s3fs/core.py in <module> 12 from fsspec.asyn import AsyncFileSystem, sync, sync_wrapper 13 ---> 14 import aiobotocore 15 import botocore 16 import aiobotocore.session ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/aiobotocore/__init__.py in <module> ----> 1 from .session import get_session, AioSession 2 3 __all__ = ['get_session', 'AioSession'] 4 __version__ = '1.3.0' ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/aiobotocore/session.py in <module> 4 from botocore import retryhandler, translate 5 from botocore.exceptions import PartialCredentialsError ----> 6 from .client import AioClientCreator, AioBaseClient 7 from .hooks import AioHierarchicalEmitter 8 from .parsers import AioResponseParserFactory ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/aiobotocore/client.py in <module> 11 from .args import AioClientArgsCreator 12 from .utils import AioS3RegionRedirector ---> 13 from . import waiter 14 15 history_recorder = get_global_history_recorder() ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/aiobotocore/waiter.py in <module> 4 from botocore.exceptions import ClientError 5 from botocore.waiter import WaiterModel # noqa: F401, lgtm[py/unused-import] ----> 6 from botocore.waiter import Waiter, xform_name, logger, WaiterError, \ 7 NormalizedOperationMethod as _NormalizedOperationMethod, is_valid_waiter_error 8 from botocore.docs.docstring import WaiterDocstring ImportError: cannot import name 'is_valid_waiter_error' ``` Please let me know if there's anything else I can add to post. [1]: https://github.com/huggingface/notebooks/blob/master/examples/text_classification_quantization_inc.ipynb
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set_format("np") no longer works for Image data
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[ "A quick fix for now is doing this:\r\n\r\n```python\r\nX_train = np.stack(dataset[\"train\"][\"image\"])[..., None]", "This error also propagates to jax and is even trickier to fix, since `.with_format(type='jax')` will use numpy conversion internally (and fail). For a three line failure:\r\n\r\n```python\r\ndataset = datasets.load_dataset(\"mnist\")\r\ndataset.set_format(\"jax\")\r\nX_train = dataset[\"train\"][\"image\"]\r\n```", "Hi! We've recently introduced a new Image feature that yields PIL Images (and caches transforms on them) instead of arrays.\r\n\r\nHowever, this feature requires a custom transform to yield np arrays directly:\r\n```python\r\nddict = datasets.load_dataset(\"mnist\")\r\n\r\ndef pil_image_to_array(batch):\r\n return {\"image\": [np.array(img) for img in batch[\"image\"]]} # or jnp.array(img) for Jax\r\n\r\nddict.set_transform(pil_image_to_array, columns=\"image\", output_all_columns=True)\r\n```\r\n\r\n[Docs](https://huggingface.co/docs/datasets/master/process.html#format-transform) on `set_transform`.\r\n\r\nAlso, the approach proposed by @cgarciae is not the best because it loads the entire column in memory.\r\n\r\n@albertvillanova @lhoestq WDYT? The Audio and the Image feature currently don't support the TF/Jax/PT Formatters, but for the Numpy Formatter maybe it makes more sense to return np arrays (and not a dict in the case of the Audio feature or a PIL Image object in the case of the Image feature).", "Yes I agree it should return arrays and not a PIL image (and possible an array instead of a dict for audio data).\r\nI'm currently finishing some code refactoring of the image and audio and opening a PR today. Maybe we can look into that after the refactoring", "This has been fixed in https://github.com/huggingface/datasets/pull/5072, which is included in the latest release of `datasets`." ]
2022-01-09T17:18:13Z
2022-10-14T12:03:55Z
2022-10-14T12:03:54Z
NONE
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## Describe the bug `dataset.set_format("np")` no longer works for image data, previously you could load the MNIST like this: ```python dataset = load_dataset("mnist") dataset.set_format("np") X_train = dataset["train"]["image"][..., None] # <== No longer a numpy array ``` but now it doesn't work, `set_format("np")` seems to have no effect and the dataset just returns a list/array of PIL images instead of numpy arrays as requested.
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I_kwDODunzps5BW5qJ
3,550
Bug in `openbookqa` dataset
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[ "Closed by:\r\n- #4259" ]
2022-01-07T17:32:57Z
2022-05-04T06:33:00Z
2022-05-04T06:32:19Z
CONTRIBUTOR
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## Describe the bug Dataset entries contains a typo. ## Steps to reproduce the bug ```python >>> from datasets import load_dataset >>> obqa = load_dataset('openbookqa', 'main') >>> obqa['train'][0] ``` ## Expected results ```python {'id': '7-980', 'question_stem': 'The sun is responsible for', 'choices': {'text': ['puppies learning new tricks', 'children growing up and getting old', 'flowers wilting in a vase', 'plants sprouting, blooming and wilting'], 'label': ['A', 'B', 'C', 'D']}, 'answerKey': 'D'} ``` ## Actual results ```python {'id': '7-980', 'question_stem': 'The sun is responsible for', 'choices': {'text': ['puppies learning new tricks', 'children growing up and getting old', 'flowers wilting in a vase', 'plants sprouting, blooming and wilting'], 'label': ['puppies learning new tricks', 'children growing up and getting old', 'flowers wilting in a vase', 'plants sprouting, blooming and wilting']}, 'answerKey': 'D'} ``` The bug is present in all configs and all splits. ## Environment info - `datasets` version: 1.17.0 - Platform: Linux-5.4.0-1057-aws-x86_64-with-glibc2.27 - Python version: 3.9.7 - PyArrow version: 4.0.1
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Specify the feature types of a dataset on the Hub without needing a dataset script
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[ "After looking into this, discovered that this is already supported if the `dataset_infos.json` file is configured correctly! Here is a working example: https://huggingface.co/datasets/abidlabs/test-audio-13\r\n\r\nThis should be probably be documented, though. " ]
2022-01-07T15:17:06Z
2022-01-20T14:48:38Z
2022-01-20T14:48:38Z
MEMBER
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**Is your feature request related to a problem? Please describe.** Currently if I upload a CSV with paths to audio files, the column type is string instead of Audio. **Describe the solution you'd like** I'd like to be able to specify the types of the column, so that when loading the dataset I directly get the features types I want. The feature types could read from the `dataset_infos.json` for example. **Describe alternatives you've considered** Create a dataset script to specify the features, but that seems complicated for a simple thing. cc @abidlabs
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Datasets created with `push_to_hub` can't be accessed in offline mode
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[ "Thanks for reporting. I think this can be fixed by improving the `CachedDatasetModuleFactory` and making it look into the `parquet` cache directory (datasets from push_to_hub are loaded with the parquet dataset builder). I'll look into it", "Hi, I'm having the same issue. Is there any update on this?", "We haven't had a chance to fix this yet. If someone would like to give it a try I'd be happy to give some guidance", "@lhoestq Do you have an idea of what changes need to be made to `CachedDatasetModuleFactory`? I would be willing to take a crack at it. Currently unable to train with datasets I have `push_to_hub` on a cluster whose compute nodes are not connected to the internet.\r\n\r\nIt looks like it might be this line:\r\n\r\nhttps://github.com/huggingface/datasets/blob/0c1d099f87a883e52c42d3fd1f1052ad3967e647/src/datasets/load.py#L994\r\n\r\nWhich wouldn't pick up the stuff saved under `\"datasets/allenai___parquet/*\"`. Additionally, the datasets saved under `\"datasets/allenai___parquet/*\"` appear to have hashes in their name, e.g. `\"datasets/allenai___parquet/my_dataset-def9ee5552a1043e\"`. This would not be detected by `CachedDatasetModuleFactory`, which currently looks for subdirectories\r\n\r\nhttps://github.com/huggingface/datasets/blob/0c1d099f87a883e52c42d3fd1f1052ad3967e647/src/datasets/load.py#L995-L999", "`importable_directory_path` is used to find a **dataset script** that was previously downloaded and cached from the Hub\r\n\r\nHowever in your case there's no dataset script on the Hub, only parquet files. So the logic must be extended for this case.\r\n\r\nIn particular I think you can add a new logic in the case where `hashes is None` (i.e. if there's no dataset script associated to the dataset in the cache).\r\n\r\nIn this case you can check directly in the in the datasets cache for a directory named `<namespace>__parquet` and a subdirectory named `<config_id>`. The config_id must match `{self.name.replace(\"/\", \"--\")}-*`. \r\n\r\nIn your case those two directories correspond to `allenai___parquet` and then `allenai--my_dataset-def9ee5552a1043e`\r\n\r\nThen you can find the most recent version of the dataset in subdirectories (e.g. sorting using the last modified time of the `dataset_info.json` file).\r\n\r\nFinally, we will need return the module that is used to load the dataset from the cache. It is the same module than the one that would have been normally used if you had an internet connection.\r\n\r\nAt that point you can ping me, because we will need to pass all this:\r\n- `module_path = _PACKAGED_DATASETS_MODULES[\"parquet\"][0]`\r\n- `hash` it corresponds the name of the directory that contains the .arrow file, inside `<namespace>__parquet/<config_id>`\r\n- ` builder_kwargs = {\"hash\": hash, \"repo_id\": self.name, \"config_id\": config_id}`\r\nand currently `config_id` is not a valid argument for a `DatasetBuilder`\r\n\r\nI think in the future we want to change this caching logic completely, since I don't find it super easy to play with.", "Hi! Is there a workaround for the time being?\r\nLike passing `data_dir` or something like that?\r\n\r\nI would like to use [this diffuser example](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation) on my cluster whose nodes are not connected to the internet. I have downloaded the dataset online form the login node.", "Hi ! Yes you can save your dataset locally with `my_dataset.save_to_disk(\"path/to/local\")` and reload it later with `load_from_disk(\"path/to/local\")`\r\n\r\n(removing myself from assignees since I'm currently not working on this right now)", "Still not fixed? ......", "Any idea @lhoestq who to tag to fix this ? This is a very annoying bug, which is becoming more and more present since the push_to_hub API is getting used more ?", "Perhaps @mariosasko ? Thanks a lot for the great work on the lib !", "It should be easier to implement now that we improved the caching of datasets from `push_to_hub`: each dataset has its own directory in the cache.\r\n\r\nThe cache structure has been improved in https://github.com/huggingface/datasets/pull/5331. Now the cache structure is `\"{namespace__}<dataset_name>/<config_name>/<version>/<hash>/\"` which contains the arrow files `\"<dataset_name>-<split>.arrow\"` and `\"dataset_info.json\"`. \r\n\r\nThe idea is to extend `CachedDatasetModuleFactory` to also check if this directory exists in the cache (in addition to the already existing cache check) and return the requested dataset module. The module name can be found in the JSON file in the `builder_name` field.", "Any progress?", "I started a PR to draft the logic to reload datasets from the cache fi they were created with push_to_hub: https://github.com/huggingface/datasets/pull/6459\r\n\r\nFeel free to try it out", "It seems that this does not support dataset with uppercase name ", "Which version of `datasets` are you using ? This issue has been fixed with `datasets` 2.16", "I can confirm that this problem is still happening with `datasets` 2.17.0, installed from pip", "Can you share a code or a dataset that reproduces the issue ? It seems to work fine on my side.", "Yeah, \r\n```python\r\ndataset = load_dataset(\"roneneldan/TinyStories\")\r\n```\r\nI tried it with:\r\n```python\r\ndataset = load_dataset(\"roneneldan/tinystories\")\r\n```\r\nand it worked.\r\n\r\n> It seems that this does not support dataset with uppercase name\r\n\r\n@fecet was right, but if you just put the name lowercase, it works. " ]
2022-01-07T15:12:25Z
2024-02-15T17:41:24Z
2023-12-21T15:13:12Z
CONTRIBUTOR
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## Describe the bug In offline mode, one can still access previously-cached datasets. This fails with datasets created with `push_to_hub`. ## Steps to reproduce the bug in Python: ``` import datasets mpwiki = datasets.load_dataset("teven/matched_passages_wikidata") ``` in bash: ``` export HF_DATASETS_OFFLINE=1 ``` in Python: ``` import datasets mpwiki = datasets.load_dataset("teven/matched_passages_wikidata") ``` ## Expected results `datasets` should find the previously-cached dataset. ## Actual results ConnectionError: Couln't reach the Hugging Face Hub for dataset 'teven/matched_passages_wikidata': Offline mode is enabled ## Environment info - `datasets` version: 1.16.2.dev0 - Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17 - Python version: 3.8.10 - PyArrow version: 3.0.0
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Allow loading community metrics from the hub, just like datasets
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[ "Hi ! Thanks for your message :) This is a great idea indeed. We haven't started working on this yet though. For now I guess you can host your metric on the Hub (either with your model or your dataset) and use `hf_hub_download` to download it (docs [here](https://github.com/huggingface/huggingface_hub/blob/main/docs/hub/how-to-downstream.md#cached_download))", "This is a great solution in the meantime, thanks!", "Here's the code I used, in case it can be of help to someone else:\r\n```python\r\nimport os, shutil\r\nfrom huggingface_hub import hf_hub_download\r\ndef download_metric(repo_id, file_path):\r\n # repo_id: for models \"username/model_name\", for datasets \"datasets/username/model_name\"\r\n local_metric_path = hf_hub_download(repo_id=repo_id, filename=file_path)\r\n updated_local_metric_path = (os.path.dirname(local_metric_path) + os.path.basename(local_metric_path).replace(\".\", \"_\") + \".py\")\r\n shutil.copy(local_metric_path, updated_local_metric_path)\r\n return updated_local_metric_path\r\n\r\nmetric = load_metric(download_metric(REPO_ID, FILE_PATH))\r\n```", "Solved with https://github.com/huggingface/evaluate 🤗 ", "Yay!! cc @lvwerra @sashavor @douwekiela \r\n\r\nPlease share your feedback @eladsegal =)" ]
2022-01-06T11:26:26Z
2022-05-31T20:59:14Z
2022-05-31T20:53:37Z
CONTRIBUTOR
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**Is your feature request related to a problem? Please describe.** Currently, I can load a metric implemented by me by providing the local path to the file in `load_metric`. However, there is no option to do it with the metric uploaded to the hub. This means that if I want to allow other users to use it, they must download it first which makes the usage less smooth. **Describe the solution you'd like** Load metrics from the hub just like datasets are loaded. In order to not break stuff, the convention can be to put the metric file in a "metrics" folder in the hub.
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