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1,160,657,937
PR_kwDODunzps40ATVw
3,834
Fix dead dataset scripts creation link.
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Previous link gives 404 error. Updated with a new dataset scripts creation link.
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1,160,543,713
PR_kwDODunzps4z_99t
3,833
Small typos in How-to-train tutorial.
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1,160,503,446
I_kwDODunzps5FK-CW
3,832
Making Hugging Face the place to go for Graph NNs datasets
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Let's make Hugging Face Datasets the central hub for GNN datasets :) **Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the GNN field. What are some datasets worth integrating into the Hugging Face hub? Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Special thanks to @napoles-uach for his collaboration on identifying the first ones: - [ ] [SNAP-Stanford OGB Datasets](https://github.com/snap-stanford/ogb). - [ ] [SNAP-Stanford Pretrained GNNs Chemistry and Biology Datasets](https://github.com/snap-stanford/pretrain-gnns). cc @osanseviero
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when using to_tf_dataset with shuffle is true, not all completed batches are made
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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```" ]
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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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[📄 Docs] Create a `datasets` performance guide.
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## Brief Overview Downloading, saving, and preprocessing large datasets from the `datasets` library can often result in [performance bottlenecks](https://github.com/huggingface/datasets/issues/3735). These performance snags can be challenging to identify and to debug, especially for users who are less experienced with building deep learning experiments. ## Feature Request Could we create a performance guide for using `datasets`, similar to: * [Better performance with the `tf.data` API](https://github.com/huggingface/datasets/issues/3735) * [Analyze `tf.data` performance with the TF Profiler](https://www.tensorflow.org/guide/data_performance_analysis) This performance guide should detail practical options for improving performance with `datasets`, and enumerate any common best practices. It should also show how to use tools like the PyTorch Profiler or the TF Profiler to identify any performance bottlenecks (example below). ![image](https://user-images.githubusercontent.com/3712347/156859152-a3cb9565-3ec6-4d39-8e77-56d0a75a4954.png) ## Related Issues * [wiki_dpr pre-processing performance #1670](https://github.com/huggingface/datasets/issues/1670) * [Adjusting chunk size for streaming datasets #3499](https://github.com/huggingface/datasets/issues/3499) * [how large datasets are handled under the hood #1004](https://github.com/huggingface/datasets/issues/1004) * [using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? #1830](https://github.com/huggingface/datasets/issues/1830) * [Best way to batch a large dataset? #315](https://github.com/huggingface/datasets/issues/315) * [Saving processed dataset running infinitely #1911](https://github.com/huggingface/datasets/issues/1911)
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The Pile's _FEATURE spec seems to be incorrect
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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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Remove deprecated `remove_columns` param in `filter`
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3827). All of your documentation changes will be reflected on that endpoint." ]
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A leftover from #3803.
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Add IterableDataset.filter
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3826). All of your documentation changes will be reflected on that endpoint." ]
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_Needs https://github.com/huggingface/datasets/pull/3801 to be merged first_ I added `IterableDataset.filter` with an API that is a subset of `Dataset.filter`: ```python def filter(self, function, batched=False, batch_size=1000, with_indices=false, input_columns=None): ``` TODO: - [ ] tests - [ ] docs related to https://github.com/huggingface/datasets/issues/3444
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Update version and date in Wikipedia dataset
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3825). All of your documentation changes will be reflected on that endpoint." ]
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CC: @geohci
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Allow not specifying feature cols other than `predictions`/`references` in `Metric.compute`
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3824). All of your documentation changes will be reflected on that endpoint." ]
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Fix #3818
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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." ]
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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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## 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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Update Wikipedia dataset
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3821). All of your documentation changes will be reflected on that endpoint.", "I'm starting to generate the pre-processed data for some of the languages (for backward compatibility).\r\n\r\nOnce this merged, we will create the pre-processed data on the Hub under the Wikimedia namespace." ]
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This PR combines all updates to Wikipedia dataset. Once approved, this will be used to generate the pre-processed Wikipedia datasets. Finally, this PR will be able to be merged into master: - NOT using squash - BUT a regular MERGE (or REBASE+MERGE), so that all commits are preserved TODO: - [x] #3435 - [x] #3789 - [ ] #3825 - [ ] Run to get the pre-processed data for big languages (backward compatibility) - [ ] Update dataset card CC: @geohci
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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```" ]
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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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Fix typo in doc build yml
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3819). All of your documentation changes will be reflected on that endpoint." ]
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cc: @lhoestq
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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." ]
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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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Simplify Common Voice code
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[ "I think the script looks pretty clean and readable now! cool!\r\n" ]
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In #3736 we introduced one method to generate examples when streaming, that is different from the one when not streaming. In this PR I propose a new implementation which is simpler: it only has one function, based on `iter_archive`. And you still have access to local audio files when loading the dataset in non-streaming mode. cc @patrickvonplaten @polinaeterna @anton-l @albertvillanova since this will become the template for many audio datasets to come. This change can also trivially be applied to the other audio datasets that already exist. Using this line, you can get access to local files in non-streaming mode: ```python local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else None ```
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Fix iter_archive getting reset
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The `DownloadManager.iter_archive` method currently returns an iterator - which is **empty** once you iter over it once. This means you can't pass the same archive iterator to several splits. To fix that, I changed the ouput of `DownloadManager.iter_archive` to be an iterable that you can iterate over several times, instead of a one-time-use iterator. The `StreamingDownloadManager.iter_archive` already returns an appropriate iterable, and the code added in this PR is inspired from the one in `streaming_download_manager.py`
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This PR fixes an issue introduced by #3575 where `None` values stored in PyArrow arrays/structs would get ignored by `cast_storage` or by the `pa.array(cast_to_python_objects(..))` pattern. To fix the former, it also bumps the minimal PyArrow version to v5.0.0 to use the `mask` param in `pa.SturctArray`.
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Add MetaShift dataset
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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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benchmark streaming speed with tar vs zip archives
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# do not merge ## Hypothesis packing data into a single zip archive could allow us not to care about splitting data into several tar archives for efficient streaming which is annoying (since data creators usually host the data in a single tar) ## Data I host it [here](https://huggingface.co/datasets/polinaeterna/benchmark_dataset/) ## I checked three configurations: 1. All data in one zip archive, streaming only those files that exist in split metadata file (we can access them directrly with no need to iterate over full archive), see [this func](https://github.com/huggingface/datasets/compare/master...polinaeterna:benchmark-tar-zip?expand=1#diff-4f5200d4586aec5b2a89fcf34441c5f92156f9e9d408acc7e50666f9a1921ddcR196) 2. All data in three splits, the standart way to make streaming efficient, see [this func](https://github.com/huggingface/datasets/compare/master...polinaeterna:benchmark-tar-zip?expand=1#diff-4f5200d4586aec5b2a89fcf34441c5f92156f9e9d408acc7e50666f9a1921ddcR174) 3. All data in single tar, iterate over the full archive and take only files existing in split metadata file, see [this func](https://github.com/huggingface/datasets/compare/master...polinaeterna:benchmark-tar-zip?expand=1#diff-4f5200d4586aec5b2a89fcf34441c5f92156f9e9d408acc7e50666f9a1921ddcR150) ## Results 1. one zip ![image](https://user-images.githubusercontent.com/16348744/156567611-e3652087-7147-4cf0-9047-9cbc00ec71f5.png) 2. three tars ![image](https://user-images.githubusercontent.com/16348744/156567688-2a462107-f83e-4722-8ea3-71a13b56c998.png) 3. one tar ![image](https://user-images.githubusercontent.com/16348744/156567772-1bceb5f7-e7d9-4fa3-b31b-17fec5f9a5a7.png) didn't check on the full data as it's time consuming but anyway it's pretty obvious that one-zip-way is not a good idea. here it's even worse than full iteration over tar containing all three splits (but that would depend on the case).
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Update dev doc gh workflows
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Reflect changes from https://github.com/huggingface/transformers/pull/15891
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Update version of xcopa dataset
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Note that there was a version update of the `xcopa` dataset: https://github.com/cambridgeltl/xcopa/releases We updated our loading script, but we did not bump a new version number: - #3254 This PR updates our loading script version from `1.0.0` to `1.1.0`.
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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```" ]
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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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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." ]
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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! " ]
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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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Fix Spanish data file URL in wiki_lingua dataset
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This PR fixes the URL for Spanish data file. Previously, Spanish had the same URL as Vietnamese data file.
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This was erroneously added in https://github.com/huggingface/datasets/commit/701f128de2594e8dc06c0b0427c0ba1e08be3054. This PR removes it.
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Text builder with custom separator line boundaries
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[ "Gently pinging @lhoestq", "Hi ! Interresting :)\r\n\r\nCould you give more details on what kind of separators you would like to use instead ?", "In my case, I just want to use `\\n` but not `U+2028`.", "Ok I see, maybe there can be a `sep` parameter to allow users to specify what line/paragraph separator they'd like to use", "Related to:\r\n- #3729 " ]
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**Is your feature request related to a problem? Please describe.** The current [Text](https://github.com/huggingface/datasets/blob/207be676bffe9d164740a41a883af6125edef135/src/datasets/packaged_modules/text/text.py#L23) builder implementation splits texts with `splitlines()` which splits the text on several line boundaries. Not all of them are always wanted. **Describe the solution you'd like** ```python if self.config.sample_by == "line": batch_idx = 0 while True: batch = f.read(self.config.chunksize) if not batch: break batch += f.readline() # finish current line if self.config.custom_newline is None: batch = batch.splitlines(keepends=self.config.keep_linebreaks) else: batch = batch.split(self.config.custom_newline)[:-1] pa_table = pa.Table.from_arrays([pa.array(batch)], schema=schema) # Uncomment for debugging (will print the Arrow table size and elements) # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}") # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows))) yield (file_idx, batch_idx), pa_table batch_idx += 1 ``` **A clear and concise description of what you want to happen.** Creating the dataset rows with a subset of the `splitlines()` line boundaries.
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Remove deprecated methods/params (preparation for v2.0)
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This PR removes the following deprecated methos/params: * `Dataset.cast_`/`DatasetDict.cast_` * `Dataset.dictionary_encode_column_`/`DatasetDict.dictionary_encode_column_` * `Dataset.remove_columns_`/`DatasetDict.remove_columns_` * `Dataset.rename_columns_`/`DatasetDict.rename_columns_` * `prepare_module` * param `script_version` in `load_dataset`/`load_metric` * param `version` in `hf_github_url`
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[ "This is awesome ! The dataset card and the dataset script look amazing :)\r\n\r\nI wanted to ask you if you'd be interested to have this dataset under the namespace of you research group at https://huggingface.co/coastalcph ? If yes, then you can actually create a dataset repository under your research group name and upload the files from this PR there", "Hi @lhoestq,\r\n\r\nYeah, I could do that. I see that people do that a lot of models, but not for datasets. \r\n\r\nIs there any good reason to have it under the organization domain instead of the general domain?\r\n\r\n Thanks!", "It's nice to have it under your namespace:\r\n- it will appear on your research group page, along with your models\r\n- you can edit or create datasets at any time - you don't need to open PRs on GitHub\r\n\r\nAll the datasets that are not under a namespace are this way because we started adding datasets from GitHub. Now we encourage users to upload them directly to make things simpler, and aligned with the workflow for models\r\n\r\n(the documentation will be updated in the following days)\r\n\r\nNote that we will keep accepting PRs here though when there is no clear namespace under which a dataset should be, or for users that want a review from us", "Ok, I'll do that. So, I'll just have to upload all the files under the `/fairlex` directory in my PR, right?", "Yes exactly !", "Ok, I uploaded most of them from the UI environment (https://huggingface.co/datasets/coastalcph/fairlex). Can I possibly upload the dummy data as well from the UI environment. I really want to avoid the CLI right now 😄 ", "Yea sure, feel free to use the UI of the website, even for the dummy data ^^", "Did you upload them yourself? Because I see the data preview, and I'm pretty sure, I didn't do that 😄 ", "The preview is computed from the real data ;)\r\n\r\nThe dummy data are used for testing only", "Haha, ok I was shocked! Cool, I close this PR, then. Thanks, again! ", "Thank you 🤗" ]
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**FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing** We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian, and Chinese), and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP. *Ilias Chalkidis, Tommaso Pasini, Sheng Zhang, Letizia Tomada, Letizia, Sebastian Felix Schwemer, Anders Søgaard. FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing. 2022. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.* Note: Please review this initial commit, and I'll update the publication link, once I'll have the ArXived version. Thanks!
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[Breaking] Align `map` when streaming: update instead of overwrite + add missing parameters
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Currently the datasets in streaming mode and in non-streaming mode have two distinct API for `map` processing. In this PR I'm aligning the two by changing `map` in streamign mode. This includes a **major breaking change** and will require a major release of the library: **Datasets 2.0** In particular, `Dataset.map` adds new columns (with dict.update) BUT `IterableDataset.map` used to discard previous columns (it overwrites the dict). In this PR I'm chaning the `IterableDataset.map` to behave the same way as `Dataset.map`: it will update the examples instead of overwriting them. I'm also adding those missing parameters to streaming `map`: with_indices, input_columns, remove_columns ### TODO - [x] tests - [x] docs Related to https://github.com/huggingface/datasets/issues/3444
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Previous PR was in my fork so thought it'd be easier if I do it from a branch. Added computer vision task datasets according to HF tasks.
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**Added datasets (TODO)**: - [x] MLS - [x] Covost2 - [x] Minds-14 (need splits) - [x] Voxpopuli - [ ] FLoRes (need data) - [x] Babel **Metrics**: Done
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Fix error message in CSV loader for newer Pandas versions
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Fix the error message in the CSV loader for `Pandas >= 1.4`. To fix this, I directly print the current file name in the for-loop. An alternative would be to use a check similar to this: ```python csv_file_reader.handle.handle if datasets.config.PANDAS_VERSION >= version.parse("1.4") else csv_file_reader.f ``` CC: @SBrandeis
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Reddit dataset card contribution
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Description tags for webis-tldr-17 added.
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Skip checksum computation if `ignore_verifications` is `True`
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This will speed up the loading of the datasets where the number of data files is large (can easily happen with `imagefoler`, for instance)
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can not flatten natural_questions dataset
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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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Add Mahalanobis distance metric
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Mahalanobis distance is a very useful metric to measure the distance from one datapoint X to a distribution P. In this PR I implement the metric in a simple way with the help of numpy only. Similar to the [MAUVE implementation](https://github.com/huggingface/datasets/blob/master/metrics/mauve/mauve.py), we can make this metric accept texts as input and encode them with a featurize model, if that is desirable.
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Docs new UI actions no self hosted
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[ "It seems like the doc can't be compiled right now because of the following:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/usr/local/bin/doc-builder\", line 33, in <module>\r\n sys.exit(load_entry_point('doc-builder', 'console_scripts', 'doc-builder')())\r\n File \"/__w/datasets/datasets/doc-builder/src/doc_builder/commands/doc_builder_cli.py\", line 39, in main\r\n args.func(args)\r\n File \"/__w/datasets/datasets/doc-builder/src/doc_builder/commands/build.py\", line 95, in build_command\r\n build_doc(\r\n File \"/__w/datasets/datasets/doc-builder/src/doc_builder/build_doc.py\", line 361, in build_doc\r\n anchors_mapping = build_mdx_files(package, doc_folder, output_dir, page_info)\r\n File \"/__w/datasets/datasets/doc-builder/src/doc_builder/build_doc.py\", line 200, in build_mdx_files\r\n raise type(e)(f\"There was an error when converting {file} to the MDX format.\\n\" + e.args[0]) from e\r\nTypeError: There was an error when converting datasets/docs/source/package_reference/table_classes.mdx to the MDX format.\r\nexpected string or bytes-like object\r\n```", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3793). All of your documentation changes will be reflected on that endpoint.", "This is due to the injection of docstrings from PyArrow. I think I can fix that by moving all the docstrings and fix them manually.", "> It seems like the doc can't be compiled right now because of the following:\r\n\r\nit is expected since there is something I need to change on doc-builder side.\r\n\r\n> This is due to the injection of docstrings from PyArrow. I think I can fix that by moving all the docstrings and fix them manually.\r\n\r\n@lhoestq I will let you know if we need to change it manually.\r\n\r\n@LysandreJik thanks a lot for this PR! I only had one question [here](https://github.com/huggingface/datasets/pull/3793#discussion_r816100194)", "> @lhoestq I will let you know if we need to change it manually.\r\n\r\nIt would be simpler to change it manually anyway - I don't want our documentation to break if PyArrow has documentation issues", "For some reason it fails when `Installing node dependencies` when running `npm ci` from the `kit` directory, any idea why @mishig25 ?", "Checking it rn", "It's very likely linked to an OOM error: https://github.com/huggingface/transformers/pull/15710#issuecomment-1051737337" ]
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Removes the need to have a self-hosted runner for the dev documentation
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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 " ]
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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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Add `data_dir` to `data_files` resolution and misc improvements to HfFileSystem
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As discussed in https://github.com/huggingface/datasets/pull/2830#issuecomment-1048989764, this PR adds a QOL improvement to easily reference the files inside a directory in `load_dataset` using the `data_dir` param (very handy for ImageFolder because it avoids globbing, but also useful for the other loaders). Additionally, it fixes the issue with `HfFileSystem.isdir`, which would previously always return `False`, and aligns the path-handling logic in `HfFileSystem` with `fsspec.GitHubFileSystem`.
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Add doc builder scripts
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[ "I think we're only missing the hosted runner to be configured for this repository and we should be good", "Regarding the self-hosted runner, I actually encourage using the approach defined here: https://github.com/huggingface/transformers/pull/15710, which doesn't leverage a self-hosted runner. This prevents queuing jobs, which is important when we expect several concurrent jobs.", "Opened a PR for that on your branch here: https://github.com/huggingface/datasets/pull/3793" ]
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I added the three scripts: - build_dev_documentation.yml - build_documentation.yml - delete_dev_documentation.yml I got them from `transformers` and did a few changes: - I removed the `transformers`-specific dependencies - I changed all the paths to be "datasets" instead of "transformers" - I passed the `--library_name datasets` arg to the `doc-builder build` command (according to https://github.com/huggingface/doc-builder/pull/94/files#diff-bcc33cf7c223511e498776684a9a433810b527a0a38f483b1487e8a42b6575d3R26) cc @LysandreJik @mishig25
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[ "Do you think we have a dedicated branch for all the changes we want to do to wikipedia ? Then once everything looks good + we have preprocessed the main languages, we can merge it on the `master` branch", "Yes, @lhoestq, I agree with you.\r\n\r\nI have just created the dedicated branch [`update-wikipedia`](https://github.com/huggingface/datasets/tree/update-wikipedia). We can merge every PR (once validated) to that branch; once all changes are merged to that branch, we could create the preprocessed datasets and then merge the branch to master. ", "@lhoestq I guess you approve this PR?" ]
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This PR adds the URL field, so that we conform to proper attribution, required by their license: provide credit to the authors by including a hyperlink (where possible) or URL to the page or pages you are re-using. About the conversion from title to URL, I found that apart from replacing blanks with underscores, some other special character must also be percent-encoded (e.g. `"` to `%22`): https://meta.wikimedia.org/wiki/Help:URL Therefore, I have finally used `urllib.parse.quote` function. This additionally percent-encodes non-ASCII characters, but Wikimedia docs say these are equivalent: > For the other characters either the code or the character can be used in internal and external links, they are equivalent. The system does a conversion when needed. > [[%C3%80_propos_de_M%C3%A9ta]] > is rendered as [À_propos_de_Méta](https://meta.wikimedia.org/wiki/%C3%80_propos_de_M%C3%A9ta), almost like [À propos de Méta](https://meta.wikimedia.org/wiki/%C3%80_propos_de_M%C3%A9ta), which leads to this page on Meta with in the address bar the URL > [http://meta.wikipedia.org/wiki/%C3%80_propos_de_M%C3%A9ta](https://meta.wikipedia.org/wiki/%C3%80_propos_de_M%C3%A9ta) > while [http://meta.wikipedia.org/wiki/À_propos_de_Méta](https://meta.wikipedia.org/wiki/%C3%80_propos_de_M%C3%A9ta) leads to the same. Fix #3398. CC: @geohci
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[ "I see two options:\r\n1. drop the \"dev\" keyword since it can be considered too generic\r\n2. improve the pattern to something more reasonable, e.g. asking for a separator before and after \"dev\"\r\n```python\r\n[\"*[ ._-]dev[ ._-]*\", \"dev[ ._-]*\"]\r\n```\r\n\r\nI think 2. is nice. If we agree on this one we can even decide to require the separation for the other split keywords \"train\", \"test\" etc.", "Yes, I had something like that on mind: \"dev\" not being part of a word.\r\n```\r\n\"[^a-zA-Z]dev[^a-zA-Z]\"", "Is there a reason why we want that regex? It feels like something that'll still be an issue for some weird case. \"my_dataset_dev\" doesn't match your regex, \"my_dataset_validation\" doesn't either ... Why not always \"train\" unless specified?", "The regex is needed as part of our effort to make datasets configurable without code. In particular we define some generic dataset repository structures that users can follow\r\n\r\n> ```\r\n> \"[^a-zA-Z]*dev[^a-zA-Z]*\"\r\n> ```\r\n\r\nunfortunately our glob doesn't support \"^\": \r\n\r\nhttps://github.com/fsspec/filesystem_spec/blob/3e739db7e53f5b408319dcc9d11e92bc1f938902/fsspec/spec.py#L465-L479", "> \"my_dataset_dev\" doesn't match your regex, \"my_dataset_validation\" doesn't either ... Why not always \"train\" unless specified?\r\n\r\nAnd `my_dataset_dev.foo` would match the pattern, and we also have the same pattern but for the \"validation\" keyword so `my_dataset_validation.foo` would work too", "> The regex is needed as part of our effort to make datasets configurable without code\r\n\r\nThis feels like coding with the filename ^^'", "This is still much easier than having to write a full dataset script right ? :p" ]
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## Describe the bug As reported by @thomasw21 and @lhoestq, a dataset containing only a data file whose name matches the pattern `*dev*` will be returned as VALIDATION split, even if this is not the desired behavior, e.g. a file named `datosdevision.jsonl.gz`.
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