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https://api.github.com/repos/huggingface/datasets/issues/3375
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1,070,454,913
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3,375
Support streaming zipped dataset repo by passing only repo name
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[ "I just tested and I think this only opens one file ? If there are several files in the ZIP, only the first one is opened. To open several files from a ZIP, one has to call `open` several times.\r\n\r\nWhat about updating the CSV loader to make it `download_and_extract` zip files, and open each extracted file ?", "I have implemented the glob of ZIP files in the packaged modules:\r\n- csv\r\n- json\r\n- text", "Also for streaming and non-streaming.", "In https://github.com/huggingface/datasets/pull/3375/commits/c10275fe36085601cb7bdb9daee9a8f1fc734f48, there were 3 failing tests, only on Linux:\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_streaming_download_manager.py::test_streaming_dl_manager_get_extraction_protocol[https://drive.google.com/uc?export=download&id=1k92sUfpHxKq8PXWRr7Y5aNHXwOCNUmqh-zip]\r\nFAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive - Fi...\r\nFAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped\r\n= 3 failed, 3553 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 192.79s (0:03:12) =\r\n```\r\n\r\nAfter re-running the CI in https://github.com/huggingface/datasets/pull/3375/commits/57bfe1f342cd3c59d2510b992d5f06a0761eb147, there was only 1 failing test:\r\n- On Linux:\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped\r\n= 1 failed, 3555 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 199.76s (0:03:19) =\r\n```\r\n- On Windows:\r\n```\r\n=========================== short test summary info ===========================\r\nFAILED tests/test_load.py::test_load_dataset_builder_for_community_dataset_without_script\r\n= 1 failed, 3551 passed, 2954 skipped, 2 xfailed, 1 xpassed, 121 warnings in 478.58s (0:07:58) =\r\n```\r\n\r\nThe test `tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped` passes locally.\r\n\r\nI guess the issue is caused by those tests and has nothing to do with this PR.", "@lhoestq my final proposed solution:\r\n- I have added the method `iter_files` to DownloadManager and StreamingDownloadManager\r\n- I use this in modules: \"csv\", \"json\", \"text\"\r\n- I test for CSV/JSONL/TXT zipped (and non-zipped) files, both in streaming and non-streaming modes", "> Note that at one point we might consider switching to using `iter_archive` for ZIP files in the json/text/csv loaders since it should be faster.\r\n\r\nAs far as the functionality is kept... ;)" ]
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Proposed solution: - I have added the method `iter_files` to DownloadManager and StreamingDownloadManager - I use this in modules: "csv", "json", "text" - I test for CSV/JSONL/TXT zipped (and non-zipped) files, both in streaming and non-streaming modes Fix #3373.
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NonMatchingChecksumError for the CLUE:cluewsc2020, chid, c3 and tnews
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[ "Seems like the issue still exists,:\r\n`Downloading and preparing dataset clue/chid (download: 127.15 MiB, generated: 259.71 MiB, post-processed: Unknown size, total: 386.86 MiB) to /mnt/cache/tanhaochen/.cache/huggingface/datasets/clue/chid/1.0.0/e55b490cb7809dcd8db31b9a87119f2e2ec87cdc060da8a9ac070b070ca3e379...\r\nTraceback (most recent call last):\r\n File \"/mnt/cache/tanhaochen/PromptCLUE/test_datasets.py\", line 3, in <module>\r\n cluewsc2020 = datasets.load_dataset(\"clue\",\"chid\")\r\n File \"/mnt/cache/tanhaochen/dependencies/datasets/src/datasets/load.py\", line 1667, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/mnt/cache/tanhaochen/dependencies/datasets/src/datasets/builder.py\", line 593, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/mnt/cache/tanhaochen/dependencies/datasets/src/datasets/builder.py\", line 663, in _download_and_prepare\r\n verify_checksums(\r\n File \"/mnt/cache/tanhaochen/dependencies/datasets/src/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://storage.googleapis.com/cluebenchmark/tasks/chid_public.zip']\r\n`", "Hi,\r\n\r\nthe fix hasn't been merged yet (it should be merged early next week)." ]
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Hi, it seems like there are updates in cluewsc2020, chid, c3 and tnews, since i could not load them due to the checksum error.
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Support streaming zipped CSV dataset repo by passing only repo name
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Given a community 🤗 dataset repository containing only a zipped CSV file (only raw data, no loading script), I would like to load it in streaming mode without passing `data_files`: ``` ds_name = "bigscience-catalogue-data/vietnamese_poetry_from_fsoft_ai_lab" ds = load_dataset(ds_name, split="train", streaming=True, use_auth_token=True) item = next(iter(ds)) ``` Currently, it gives a `FileNotFoundError` because there is no glob (no "\*" after "zip://": "zip://*") in the passed URL: ``` 'zip://::https://huggingface.co/datasets/bigscience-catalogue-data/vietnamese_poetry_from_fsoft_ai_lab/resolve/e5d45f1bd9a8a798cc14f0a45ebc1ce91907c792/poems_dataset.zip' ```
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[SEO improvement] Add Dataset Metadata to make datasets indexable
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Some people who host datasets on github seem to include a table of metadata at the end of their README.md to make the dataset indexable by [Google Dataset Search](https://datasetsearch.research.google.com/) (See [here](https://github.com/google-research/google-research/tree/master/goemotions#dataset-metadata) and [here](https://github.com/cvdfoundation/google-landmark#dataset-metadata)). This could be a useful addition to canonical datasets; perhaps even community datasets. I'll include a screenshot (as opposed to markdown) as an example so as not to have a github issue indexed as a dataset: > ![image](https://user-images.githubusercontent.com/3664563/144496173-953428cf-633a-4571-b75b-f099c6b2ed65.png) **_PS: It might very well be the case that this is already covered by some other markdown magic I'm not aware of._**
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New: Americas NLI dataset
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This PR adds the [Americas NLI](https://arxiv.org/abs/2104.08726) dataset, extension of XNLI to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. One odd thing (not sure) is that I had to set `datasets-cli dummy_data ./datasets/americas_nli/ --auto_generate --n_lines 7500` `n_lines` very large to successfully generate the dummy files for all the subsets. Happy to get some guidance here. Otherwise, I hope everything is in order :) e: missed a step, onto fixing the tests e2: there you go -- hope it's ok to have added more languages with their ISO codes to `languages.json`, need those tests to pass :laughing:
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Document a training loop for streaming dataset
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I added some docs about streaming dataset. In particular I added two subsections: - one on how to use `map` for preprocessing - one on how to use a streaming dataset in a pytorch training loop cc @patrickvonplaten @stevhliu if you have some comments cc @Rocketknight1 later we can add the one for TF and I might need your help ^^'
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[Audio] Allow resampling for audio datasets in streaming mode
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[ "This requires implementing `cast_column` for iterable datasets, it could be a very nice addition !\r\n\r\n<s>It can also be useful to be able to disable the audio/image decoding for the dataset viewer (see PR https://github.com/huggingface/datasets/pull/3430) cc @severo </s>\r\nEDIT: actually following https://github.com/huggingface/datasets/issues/3145 the dataset viewer might not need it anymore", "Just to clarify a bit. This feature is **always** needed when using the common voice dataset in streaming mode. So I think it's quite important" ]
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Many audio datasets like Common Voice always need to be resampled. This can very easily be done in non-streaming mode as follows: ```python from datasets import load_dataset ds = load_dataset("common_voice", "ab", split="test") ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) ``` However in streaming mode it fails currently: ```python from datasets import load_dataset ds = load_dataset("common_voice", "ab", split="test", streaming=True) ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) ``` with the following error: ``` AttributeError: 'IterableDataset' object has no attribute 'cast_column' ``` It would be great if we could add such a feature (I'm not 100% sure though how complex this would be)
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Fix dict source_datasets tagset validator
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Currently, the `source_datasets` tag validation does not support passing a dict with configuration keys. This PR: - Extends `tagset_validator` to support regex tags - Uses `tagset_validator` to validate dict `source_datasets`
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Fix typo in other-structured-to-text task tag
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Fix typo in task tag: - `other-stuctured-to-text` (before) - `other-structured-to-text` (now)
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Add multimodal datasets
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Epic issue to track the addition of multimodal datasets: - [ ] #2526 - [x] #1842 - [ ] #1810 Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). @VictorSanh feel free to add and sort by priority any interesting dataset. I have added the multimodal dataset requests which were already present as issues.
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Add task tags for multimodal datasets
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[ "The Hub pulls these tags from [here](https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts) (allows multimodal tasks) now, so I'm closing this issue." ]
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## **Is your feature request related to a problem? Please describe.** Currently, task tags are either exclusively related to text or speech processing: - https://github.com/huggingface/datasets/blob/master/src/datasets/utils/resources/tasks.json ## **Describe the solution you'd like** We should also add tasks related to: - multimodality - image - video CC: @VictorSanh @lewtun @lhoestq @merveenoyan @SBrandeis
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Use the Audio feature in the AutomaticSpeechRecognition template
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[ "Cool !\r\n\r\nI noticed that you removed the `audio_file_path_column` field of the template, note that you also have to update all the dataset_infos.json file that still contain this outdated field. For example in the common_voice you can find this:\r\n```\r\n\"task_templates\": [{\"task\": \"automatic-speech-recognition\", \"audio_file_path_column\": \"path\", \"transcription_column\": \"sentence\"}]\r\n```", "Yes, will do that. I'm just busy with the bigscience task.", "After we merge this, we should also update the following dataset scripts: https://huggingface.co/datasets?task_ids=task_ids:automatic-speech-recognition", "Closing in favor of https://github.com/huggingface/datasets/pull/4006" ]
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This updates the ASR template and all supported datasets to use the `Audio` feature
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Update URL of Jeopardy! dataset
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[ "Closing this PR in favor of #3266.", "I think you should also close this branch" ]
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Updates the URL of the Jeopardy! dataset. Fix #3361
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Adapt image datasets
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[ "This PR can be merged after #3163 is merged (this PR is pretty big because I was working on the forked branch).\r\n\r\n@lhoestq @albertvillanova Could you please take a look at the changes in `src/datasets/utils/streaming_download_manager.py`? These changes were required to support streaming of the `cats_vs_dogs` and the `beans` datasets.", "The CI failures are due to the missing fields in the README files.", "and thanks for adding support for Path.name and Path.parent for streaming :)" ]
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This PR: * adapts the ImageClassification template to use the new Image feature * adapts the following datasets to use the new Image feature: * beans (+ fixes streaming) * cast_vs_dogs (+ fixes streaming) * cifar10 * cifar100 * fashion_mnist * mnist * head_qa cc @nateraw
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Jeopardy _URL access denied
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[ "Just a side note: duplicate #3264" ]
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## Describe the bug http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz returns Access Denied now. However, https://drive.google.com/file/d/0BwT5wj_P7BKXb2hfM3d2RHU1ckE/view?usp=sharing from the original Reddit post https://www.reddit.com/r/datasets/comments/1uyd0t/200000_jeopardy_questions_in_a_json_file/ may work. ## Steps to reproduce the bug ```shell > python Python 3.7.12 (default, Sep 5 2021, 08:34:29) [Clang 11.0.3 (clang-1103.0.32.62)] on darwin Type "help", "copyright", "credits" or "license" for more information. ``` ```python >>> from datasets import load_dataset >>> load_dataset("jeopardy") ``` ## Expected results The download completes. ## Actual results ```shell Downloading: 4.18kB [00:00, 1.60MB/s] Downloading: 2.03kB [00:00, 1.04MB/s] Using custom data configuration default Downloading and preparing dataset jeopardy/default (download: 12.13 MiB, generated: 34.46 MiB, post-processed: Unknown size, total: 46.59 MiB) to /Users/mike/.cache/huggingface/datasets/jeopardy/default/0.1.0/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/load.py", line 1632, in load_dataset use_auth_token=use_auth_token, File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/builder.py", line 608, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/builder.py", line 675, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/Users/mike/.cache/huggingface/modules/datasets_modules/datasets/jeopardy/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810/jeopardy.py", line 72, in _split_generators filepath = dl_manager.download_and_extract(_DATA_URL) File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract return self.extract(self.download(url_or_urls)) File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 197, in download download_func, url_or_urls, map_tuple=True, num_proc=download_config.num_proc, disable_tqdm=False File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 197, in map_nested return function(data_struct) File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 305, in cached_path use_auth_token=download_config.use_auth_token, File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz ``` --- ```shell > curl http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz ``` ```xml <?xml version="1.0" encoding="UTF-8"?> <Error><Code>AccessDenied</Code><Message>Access Denied</Message><RequestId>70Y9R36XNPEQXMGV</RequestId><HostId>G6F5AK4qo7JdaEdKGMtS0P6gdLPeFOdEfSEfvTOZEfk9km0/jAfp08QLfKSTFFj1oWIKoAoBehM=</HostId></Error> ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.14.0 - Platform: macOS Catalina 10.15.7 - Python version: 3.7.12 - PyArrow version: 6.0.1
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Add The Pile USPTO subset
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Add: - USPTO subset of The Pile: "uspto" config Close bigscience-workshop/data_tooling#297. CC: @StellaAthena
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Add The Pile Free Law subset
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[ "@albertvillanova Is there a specific reason you’re adding the Pile under “the” instead of under “pile”? That does not appear to be consistent with other datasets.", "Hi @StellaAthena,\r\n\r\nI asked myself the same question, but at the end I decided to be consistent with previously added Pile subsets:\r\n- #2817\r\n\r\nI guess the reason is to stress that the definite article is always used before the name of the dataset (your site says: \"The Pile. An 800GB Dataset of Diverse Text for Language Modeling\"). Other datasets are not usually preceded by the definite article, like \"the SQuAD\" or \"the GLUE\" or \"the Common Voice\"...\r\n\r\nCC: @lhoestq ", "> I guess the reason is to stress that the definite article is always used before the name of the dataset (your site says: \"The Pile. An 800GB Dataset of Diverse Text for Language Modeling\").\r\n\r\nYes that's because of this that it starts with \"the\"" ]
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Add: - Free Law subset of The Pile: "free_law" config Close bigscience-workshop/data_tooling#75. CC: @StellaAthena
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add new field, and get errors
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[ "Hi, \r\n\r\ncould you please post this question on our [Forum](https://discuss.huggingface.co/) as we keep issues for bugs and feature requests? ", "> Hi,\r\n> \r\n> could you please post this question on our [Forum](https://discuss.huggingface.co/) as we keep issues for bugs and feature requests?\r\n\r\nok." ]
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after adding new field **tokenized_examples["example_id"]**, and get errors below, I think it is due to changing data to tensor, and **tokenized_examples["example_id"]** is string list **all fields** ``` ***************** train_dataset 1: Dataset({ features: ['attention_mask', 'end_positions', 'example_id', 'input_ids', 'start_positions', 'token_type_ids'], num_rows: 87714 }) ``` **Errors** ``` Traceback (most recent call last): File "/usr/local/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 705, in convert_to_tensors tensor = as_tensor(value) ValueError: too many dimensions 'str' ```
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Update languages in aeslc dataset card
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After having worked a bit with the dataset. As far as I know, it is solely in English (en-US). There are only a few mails in Spanish, French or German (less than a dozen I would estimate).
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to_tf_dataset() refactor
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[ "Also, please don't merge yet - I need to make sure all the code samples and notebooks have a collate_fn specified, since we're removing the ability for this method to work without one!", "Hi @lhoestq @mariosasko, the other PRs this was depending on in Transformers and huggingface/notebooks are now merged, so this is ready to go. Do you want to take one more look at it, or are you happy at this point?", "The documentation for the method is fine, it doesn't need to be changed, but the tutorial notebook definitely looks a little out of date. Let me see what I can do!", "@lhoestq I rewrote the last bit of the notebook - let me know what you think!", "Cool thank you ! It's much nicer that what we had :)\r\n\r\nI also spotted other things I'd like to update in the notebook (especially the beginning) but it can be fixed later" ]
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This is the promised cleanup to `to_tf_dataset()` now that the course is out of the way! The main changes are: - A collator is always required (there was way too much hackiness making things like labels work without it) - Lots of cleanup and a lot of code moved to `_get_output_signature` - Should now handle it gracefully when the data collator adds unexpected columns
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Extend support for streaming datasets that use pd.read_excel
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[ "TODO in the future: https://github.com/huggingface/datasets/pull/3355#discussion_r761138011\r\n- If we finally find a use case where the `pd.read_excel()` can work in streaming mode (using fsspec), that is, without using the `.read()`, I propose to try this first, catch the ValueError and then try with `.read`, but all implemented in `xpandas_read_excel`. " ]
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This PR fixes error: ``` ValueError: Cannot seek streaming HTTP file ``` CC: @severo
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Remove duplicate name from dataset cards
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Remove duplicate name from dataset card for: - ajgt_twitter_ar - emotone_ar
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add one field "example_id", but I can't see it in the "comput_loss" function
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[ "Hi ! Your function looks fine, I used to map `squad` locally and it indeed added the `example_id` field correctly.\r\n\r\nHowever I think that in the `compute_loss` method only a subset of the fields are available: the model inputs. Since `example_id` is not a model input (it's not passed as a parameter to the model), the data loader doesn't need to return it by default.\r\n\r\nHowever you can disable this behavior by setting `remove_unused_columns` to `False` to your training arguments. In this case in `compute_loss` you will get the full item with all the fields.\r\n\r\nNote that since the model doesn't take `example_id` as input, you will have to remove it from the inputs when `model(**inputs)` is called", "Hi, I have set **args.remove_unused_columns=False** and **training_args.remove_unused_columns=False**, but the field doesn't been contained yet.\r\n```\r\ndef main():\r\n argp = HfArgumentParser(TrainingArguments)\r\n # The HfArgumentParser object collects command-line arguments into an object (and provides default values for unspecified arguments).\r\n # In particular, TrainingArguments has several keys that you'll need/want to specify (when you call run.py from the command line):\r\n # --do_train\r\n # When included, this argument tells the script to train a model.\r\n # See docstrings for \"--task\" and \"--dataset\" for how the training dataset is selected.\r\n # --do_eval\r\n # When included, this argument tells the script to evaluate the trained/loaded model on the validation split of the selected dataset.\r\n # --per_device_train_batch_size <int, default=8>\r\n # This is the training batch size.\r\n # If you're running on GPU, you should try to make this as large as you can without getting CUDA out-of-memory errors.\r\n # For reference, with --max_length=128 and the default ELECTRA-small model, a batch size of 32 should fit in 4gb of GPU memory.\r\n # --num_train_epochs <float, default=3.0>\r\n # How many passes to do through the training data.\r\n # --output_dir <path>\r\n # Where to put the trained model checkpoint(s) and any eval predictions.\r\n # *This argument is required*.\r\n\r\n argp.add_argument('--model', type=str,\r\n default='google/electra-small-discriminator',\r\n help=\"\"\"This argument specifies the base model to fine-tune.\r\n This should either be a HuggingFace model ID (see https://huggingface.co/models)\r\n or a path to a saved model checkpoint (a folder containing config.json and pytorch_model.bin).\"\"\")\r\n argp.add_argument('--task', type=str, choices=['nli', 'qa'], required=True,\r\n help=\"\"\"This argument specifies which task to train/evaluate on.\r\n Pass \"nli\" for natural language inference or \"qa\" for question answering.\r\n By default, \"nli\" will use the SNLI dataset, and \"qa\" will use the SQuAD dataset.\"\"\")\r\n argp.add_argument('--dataset', type=str, default=None,\r\n help=\"\"\"This argument overrides the default dataset used for the specified task.\"\"\")\r\n argp.add_argument('--max_length', type=int, default=128,\r\n help=\"\"\"This argument limits the maximum sequence length used during training/evaluation.\r\n Shorter sequence lengths need less memory and computation time, but some examples may end up getting truncated.\"\"\")\r\n argp.add_argument('--max_train_samples', type=int, default=None,\r\n help='Limit the number of examples to train on.')\r\n argp.add_argument('--max_eval_samples', type=int, default=None,\r\n help='Limit the number of examples to evaluate on.')\r\n\r\n argp.remove_unused_columns = False\r\n training_args, args = argp.parse_args_into_dataclasses()\r\n args.remove_unused_columns=False\r\n training_args.remove_unused_columns=False\r\n```\r\n\r\n\r\n```\r\n**************** train_dataset: Dataset({\r\n features: ['id', 'title', 'context', 'question', 'answers'],\r\n num_rows: 87599\r\n})\r\n\r\n\r\n**************** train_dataset_featurized: Dataset({\r\n features: ['attention_mask', 'end_positions', 'input_ids', 'start_positions', 'token_type_ids'],\r\n num_rows: 87714\r\n})\r\n```", "Hi, I print the value, all are set to False, but don't work.\r\n```\r\n********************* training_args: TrainingArguments(\r\n_n_gpu=1,\r\nadafactor=False,\r\nadam_beta1=0.9,\r\nadam_beta2=0.999,\r\nadam_epsilon=1e-08,\r\ndataloader_drop_last=False,\r\ndataloader_num_workers=0,\r\ndataloader_pin_memory=True,\r\nddp_find_unused_parameters=None,\r\ndebug=[],\r\ndeepspeed=None,\r\ndisable_tqdm=False,\r\ndo_eval=False,\r\ndo_predict=False,\r\ndo_train=True,\r\neval_accumulation_steps=None,\r\neval_steps=None,\r\nevaluation_strategy=IntervalStrategy.NO,\r\nfp16=False,\r\nfp16_backend=auto,\r\nfp16_full_eval=False,\r\nfp16_opt_level=O1,\r\ngradient_accumulation_steps=1,\r\ngreater_is_better=None,\r\ngroup_by_length=False,\r\nignore_data_skip=False,\r\nlabel_names=None,\r\nlabel_smoothing_factor=0.0,\r\nlearning_rate=5e-05,\r\nlength_column_name=length,\r\nload_best_model_at_end=False,\r\nlocal_rank=-1,\r\nlog_level=-1,\r\nlog_level_replica=-1,\r\nlog_on_each_node=True,\r\nlogging_dir=./re_trained_model/runs/Dec01_14-15-08_399b9290604c,\r\nlogging_first_step=False,\r\nlogging_steps=500,\r\nlogging_strategy=IntervalStrategy.STEPS,\r\nlr_scheduler_type=SchedulerType.LINEAR,\r\nmax_grad_norm=1.0,\r\nmax_steps=-1,\r\nmetric_for_best_model=None,\r\nmp_parameters=,\r\nno_cuda=False,\r\nnum_train_epochs=3.0,\r\noutput_dir=./re_trained_model,\r\noverwrite_output_dir=False,\r\npast_index=-1,\r\nper_device_eval_batch_size=8,\r\nper_device_train_batch_size=8,\r\nprediction_loss_only=False,\r\npush_to_hub=False,\r\npush_to_hub_model_id=re_trained_model,\r\npush_to_hub_organization=None,\r\npush_to_hub_token=None,\r\nremove_unused_columns=False,\r\nreport_to=['tensorboard'],\r\nresume_from_checkpoint=None,\r\nrun_name=./re_trained_model,\r\nsave_on_each_node=False,\r\nsave_steps=500,\r\nsave_strategy=IntervalStrategy.STEPS,\r\nsave_total_limit=None,\r\nseed=42,\r\nsharded_ddp=[],\r\nskip_memory_metrics=True,\r\ntpu_metrics_debug=False,\r\ntpu_num_cores=None,\r\nuse_legacy_prediction_loop=False,\r\nwarmup_ratio=0.0,\r\nwarmup_steps=0,\r\nweight_decay=0.0,\r\n)\r\n```\r\n```\r\n********************* args: Namespace(dataset='squad', max_eval_samples=None, max_length=128, max_train_samples=None, model='google/electra-small-discriminator', remove_unused_columns=False, task='qa')\r\n2021-12-01 14:15:10,048 - WARNING - datasets.builder - Reusing dataset squad (/root/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\r\nSome weights of the model checkpoint at google/electra-small-discriminator were not used when initializing ElectraForQuestionAnswering: ['discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense.bias']\r\n- This IS expected if you are initializing ElectraForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\r\n- This IS NOT expected if you are initializing ElectraForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\r\nSome weights of ElectraForQuestionAnswering were not initialized from the model checkpoint at google/electra-small-discriminator and are newly initialized: ['qa_outputs.bias', 'qa_outputs.weight']\r\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\r\nPreprocessing data... (this takes a little bit, should only happen once per dataset)\r\n```", "Hmmm, it might be because the default data collator removes all the fields with `string` type:\r\n\r\nhttps://github.com/huggingface/transformers/blob/4c0dd199c8305903564c2edeae23d294edd4b321/src/transformers/data/data_collator.py#L107-L112\r\n\r\nI guess you also need a custom data collator that doesn't remove them.", "can you give a tutorial about how to do this?", "I overwrite **get_train_dataloader**, and remove **_remove_unused_columns**, but it doesn't work.\r\n\r\n```\r\n def get_train_dataloader(self) -> DataLoader:\r\n \"\"\"\r\n Returns the training :class:`~torch.utils.data.DataLoader`.\r\n\r\n Will use no sampler if :obj:`self.train_dataset` does not implement :obj:`__len__`, a random sampler (adapted\r\n to distributed training if necessary) otherwise.\r\n\r\n Subclass and override this method if you want to inject some custom behavior.\r\n \"\"\"\r\n if self.train_dataset is None:\r\n raise ValueError(\"Trainer: training requires a train_dataset.\")\r\n\r\n train_dataset = self.train_dataset\r\n # if is_datasets_available() and isinstance(train_dataset, datasets.Dataset):\r\n # train_dataset = self._remove_unused_columns(train_dataset, description=\"training\")\r\n\r\n if isinstance(train_dataset, torch.utils.data.IterableDataset):\r\n if self.args.world_size > 1:\r\n train_dataset = IterableDatasetShard(\r\n train_dataset,\r\n batch_size=self.args.train_batch_size,\r\n drop_last=self.args.dataloader_drop_last,\r\n num_processes=self.args.world_size,\r\n process_index=self.args.process_index,\r\n )\r\n\r\n return DataLoader(\r\n train_dataset,\r\n batch_size=self.args.train_batch_size,\r\n collate_fn=self.data_collator,\r\n num_workers=self.args.dataloader_num_workers,\r\n pin_memory=self.args.dataloader_pin_memory,\r\n )\r\n\r\n train_sampler = self._get_train_sampler()\r\n\r\n return DataLoader(\r\n train_dataset,\r\n batch_size=self.args.train_batch_size,\r\n sampler=train_sampler,\r\n collate_fn=self.data_collator,\r\n drop_last=self.args.dataloader_drop_last,\r\n num_workers=self.args.dataloader_num_workers,\r\n pin_memory=self.args.dataloader_pin_memory,\r\n )\r\n```", "Hi, it works now, thank you.\r\n1. **args.remove_unused_columns=False** and **training_args.remove_unused_columns=False**\r\n2. overwrite **get_train_dataloader**, and remove **_remove_unused_columns**\r\n3. add new fields, and can be got in **inputs**. " ]
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Hi, I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs ``` *********************** inputs: {'attention_mask': tensor([[1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], ..., [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25, 97, 93, 44, 25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390, ..., 0, 0, 0], [ 101, 2054, 2515, ..., 0, 0, 0], [ 101, 2054, 2106, ..., 0, 0, 0], ..., [ 101, 2339, 2001, ..., 0, 0, 0], [ 101, 2054, 2515, ..., 0, 0, 0], [ 101, 2054, 2003, ..., 0, 0, 0]], device='cuda:0'), 'start_positions': tensor([ 20, 90, 89, 41, 25, 96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0]], device='cuda:0')} ``` ``` # This function preprocesses a question answering dataset, tokenizing the question and context text # and finding the right offsets for the answer spans in the tokenized context (to use as labels). # Adapted from https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py def prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None): questions = [q.lstrip() for q in examples["question"]] max_seq_length = tokenizer.model_max_length # tokenize both questions and the corresponding context # if the context length is longer than max_length, we split it to several # chunks of max_length tokenized_examples = tokenizer( questions, examples["context"], truncation="only_second", max_length=max_seq_length, stride=min(max_seq_length // 2, 128), return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length" ) # Since one example might give us several features if it has a long context, # we need a map from a feature to its corresponding example. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # The offset mappings will give us a map from token to character position # in the original context. This will help us compute the start_positions # and end_positions to get the final answer string. offset_mapping = tokenized_examples.pop("offset_mapping") tokenized_examples["start_positions"] = [] tokenized_examples["end_positions"] = [] tokenized_examples["example_id"] = [] for i, offsets in enumerate(offset_mapping): input_ids = tokenized_examples["input_ids"][i] # We will label features not containing the answer the index of the CLS token. cls_index = input_ids.index(tokenizer.cls_token_id) sequence_ids = tokenized_examples.sequence_ids(i) # from the feature idx to sample idx sample_index = sample_mapping[i] # get the answer for a feature answers = examples["answers"][sample_index] tokenized_examples["example_id"].append(examples["id"][sample_index]) if len(answers["answer_start"]) == 0: tokenized_examples["start_positions"].append(cls_index) tokenized_examples["end_positions"].append(cls_index) else: # Start/end character index of the answer in the text. start_char = answers["answer_start"][0] end_char = start_char + len(answers["text"][0]) # Start token index of the current span in the text. token_start_index = 0 while sequence_ids[token_start_index] != 1: token_start_index += 1 # End token index of the current span in the text. token_end_index = len(input_ids) - 1 while sequence_ids[token_end_index] != 1: token_end_index -= 1 # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index). if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char): tokenized_examples["start_positions"].append(cls_index) tokenized_examples["end_positions"].append(cls_index) else: # Otherwise move the token_start_index and token_end_index to the two ends of the answer. # Note: we could go after the last offset if the answer is the last word (edge case). while token_start_index < len(offsets) and \ offsets[token_start_index][0] <= start_char: token_start_index += 1 tokenized_examples["start_positions"].append( token_start_index - 1) while offsets[token_end_index][1] >= end_char: token_end_index -= 1 tokenized_examples["end_positions"].append(token_end_index + 1) return tokenized_examples ``` _Originally posted by @yanllearnn in https://github.com/huggingface/datasets/issues/3333#issuecomment-983457161_
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PR_kwDODunzps4vO6uZ
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Make LABR dataset streamable
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Fix LABR dataset to make it streamable. Related to: #3350.
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Add VCTK dataset
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[ "Hello @patrickvonplaten, I hope it's okay to ping you with a (dumb) question!\r\n\r\nI've been trying to get `dl_manager.download_and_extract(_DL_URL)` to work with no avail. I verified that this is a problem on two different machines (lab server, GCP), so I doubt it's an issue with network connectivity. Here is the full trace.\r\n\r\n```\r\n(venv) (base) jaketae@jake-gpu1:~/documents/datasets$ datasets-cli test datasets/vctk --save_infos --all_configs\r\nTesting builder 'main' (1/1)\r\nDownloading and preparing dataset vctk/main to /home/jaketae/.cache/huggingface/datasets/vctk/main/0.9.2/2bfa52a93469fa9d6d4b1831c6511db5442b9f4e48620aef2bc3890d7a5268a8...\r\nTraceback (most recent call last):\r\n File \"/home/jaketae/documents/datasets/venv/bin/datasets-cli\", line 33, in <module>\r\n sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())\r\n File \"/home/jaketae/documents/datasets/src/datasets/commands/datasets_cli.py\", line 33, in main\r\n service.run()\r\n File \"/home/jaketae/documents/datasets/src/datasets/commands/test.py\", line 146, in run\r\n builder.download_and_prepare(\r\n File \"/home/jaketae/documents/datasets/src/datasets/builder.py\", line 593, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/jaketae/documents/datasets/src/datasets/builder.py\", line 659, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/home/jaketae/.cache/huggingface/modules/datasets_modules/datasets/vctk/2bfa52a93469fa9d6d4b1831c6511db5442b9f4e48620aef2bc3890d7a5268a8/vctk.py\", line 76, in _split_generators\r\n root_path = dl_manager.download_and_extract(_DL_URL)\r\n File \"/home/jaketae/documents/datasets/src/datasets/utils/download_manager.py\", line 283, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/jaketae/documents/datasets/src/datasets/utils/download_manager.py\", line 195, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/home/jaketae/documents/datasets/src/datasets/utils/py_utils.py\", line 234, in map_nested\r\n return function(data_struct)\r\n File \"/home/jaketae/documents/datasets/src/datasets/utils/download_manager.py\", line 216, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/home/jaketae/documents/datasets/src/datasets/utils/file_utils.py\", line 298, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/jaketae/documents/datasets/src/datasets/utils/file_utils.py\", line 608, in get_from_cache\r\n raise ConnectionError(f\"Couldn't reach {url}\")\r\nConnectionError: Couldn't reach https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip\r\n```\r\n\r\nOn my local, however, the URL correctly points to the download zip file. My admittedly naive guess is that the website is web-crawler or scraper proof (requiring specific headers, etc.), but I also think I might have just missed a very basic step in the process.\r\n\r\nApologies for the delayed PR, and TIA for the help!", "Hey @jaketae, \r\n\r\nHmm, yeah I don't know really either - the link also works correctly for me when doing:\r\n\r\n```\r\nwget https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip\r\n```\r\n\r\nI think however that I had a similar problem previously with Edinburgh's (`.ed.ac.uk`) websites which I solved with the following hack. Not sure if this could be the same problem here...\r\nhttps://github.com/huggingface/datasets/blob/e1104ad5d3e83f8b1571e0d6fef4fdabf0a1fde5/datasets/ami/ami.py#L364\r\n\r\n", "The AMI dataset is stored under a different website though it seems: `\"https://groups.inf.ed.ac.uk/ami/AMICorpusMirror//amicorpus/{}/audio/{}\"`\r\n\r\nso not 100p sure if this solves the problem", "Hi @patrickvonplaten,\r\n\r\nThanks for the feedback! Sadly, disabling multi-processing didn't cut it for me. \r\n\r\nI've been looking at VCTK code in [`torchaudio`](https://pytorch.org/audio/stable/_modules/torchaudio/datasets/vctk.html) and [`tfds`](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/audio/vctk.py). I don't think they're using a hack to accomplish this, so I'll try to look into it to see if I can pinpoint the cause. I'll keep you in the loop here. Thank you!", "Hi @patrickvonplaten, \r\n\r\nAfter more investigation, I found that simply increasing `etag_timeout` in `get_from_cache` from 10 to 100 solved it. However, unless I'm missing something, an issue is that `etag_timeout` is basically hard-coded as a default parameter because `cached_path`, which calls `get_from_cache` has no way of modifying the default. \r\n\r\nhttps://github.com/huggingface/datasets/blob/b25ac1d62670e7b339ed552ecc37846d2abd30c7/src/datasets/utils/file_utils.py#L298-L310\r\n\r\nhttps://github.com/huggingface/datasets/blob/b25ac1d62670e7b339ed552ecc37846d2abd30c7/src/datasets/utils/file_utils.py#L497-L510\r\n\r\n\r\nI can think of two solutions.\r\n\r\n* Simply increase the default to 100\r\n* Allow `etag_timeout` to be modifiable on a per-dataset basis by integrating it to `download_config` (maybe this is already supported?)\r\n\r\nThank you!", "I think in this case we can increase the `etag_timeout` - what do you think @lhoestq @albertvillanova ?", "Yes let's increase it to 100 for the moment. Later we can see if it really needed to move it into `download_config` or not", "Thanks for the feedback @patrickvonplaten @lhoestq, I'll continue working on this in that direction!", "Hello @patrickvonplaten, VCTK is ready for review! \r\n\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> ds = load_dataset(\"vctk\")\r\nUsing the latest cached version of the module from /home/lily/jt856/.cache/huggingface/modules/datasets_modules/datasets/vctk/b7aa278182de3a7aa2897cbd12c1e19f1af9840a2ead69a6d710fdbc1d2df02a (last modified on Sat Dec 25 00:47:31 2021) since it couldn't be found locally at vctk., or remotely on the Hugging Face Hub.\r\nReusing dataset vctk (/home/lily/jt856/.cache/huggingface/datasets/vctk/main/0.9.2/b7aa278182de3a7aa2897cbd12c1e19f1af9840a2ead69a6d710fdbc1d2df02a)\r\n100%|████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 198.35it/s]\r\n>>> len(ds[\"train\"])\r\n88156\r\n>>> ds[\"train\"][0]\r\n{'speaker_id': 'p225', 'audio': {'path': '/home/lily/jt856/.cache/huggingface/datasets/downloads/extracted/8ed7dad05dfffdb552a3699777442af8e8ed11e656feb277f35bf9aea448f49e/wav48_silence_trimmed/p225/p225_001_mic1.flac', 'array': array([0.00485229, 0.00689697, 0.00619507, ..., 0.00811768, 0.00836182,\r\n 0.00854492], dtype=float32), 'sampling_rate': 48000}, 'file': '/home/lily/jt856/.cache/huggingface/datasets/downloads/extracted/8ed7dad05dfffdb552a3699777442af8e8ed11e656feb277f35bf9aea448f49e/wav48_silence_trimmed/p225/p225_001_mic1.flac', 'text': 'Please call Stella.', 'text_id': '001', 'age': '23', 'gender': 'F', 'accent': 'English', 'region': 'Southern England', 'comment': ''}\r\n```\r\nA number of tests are failing on CircleCI, but from my limited knowledge they appear to be complaining about `conda` and `pip`/`wheel`-related incompatibilities. But if I'm reading them wrong and it's an issue with this PR, please let me know and I'll try to fix them.\r\n\r\nBelated merry Christmas and a happy new year!" ]
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Fixes #1837.
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Avoid content-encoding issue while streaming datasets
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This PR will fix streaming of datasets served with gzip content-encoding: ``` ClientPayloadError: 400, message='Can not decode content-encoding: gzip' ``` Fix #2918. CC: @severo
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raise exception instead of using assertions.
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[ "@mariosasko - Thanks for the review & suggestions. Updated as per the suggestions. ", "@mariosasko - Hello, Are there any additional changes required from my end??. Wondering if this PR can be merged or still pending on additional steps.", "@mariosasko - The approved changes in the PR now has conflicts with the master branch. Would you like me to resolve the conflicts??. Let me know. ", "@mariosasko @lhoestq - Gentle reminder about my previous question. ", "Hi ! Thanks for the heads up :)\r\nI just resolved the conflicts, it should be alright now", "Merging, thanks for the help @manisnesan !" ]
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fix for the remaining files https://github.com/huggingface/datasets/issues/3171
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BLEURT: Match key names to correspond with filename
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[ "Thanks for the suggestion! I think the current checked-in `CHECKPOINT_URLS` is already not working. I believe anyone who tried using the new ckpts (`BLEURT-20-X`) can't unless this fix is in. The zip file from bleurt side unzips to directory name matching the filename (capitalized for new ones). For example without current changes I'd get the following error\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nAssertionError Traceback (most recent call last)\r\n<ipython-input-5-f6832fe20f84> in <module>()\r\n 1 predictions = [\"hello there\", \"general kenobi\"]\r\n 2 references = [\"hello there\", \"general kenobi\"]\r\n----> 3 bleurt = datasets.load_metric(\"bleurt\", \"bleurt-20\")\r\n 4 results = bleurt.compute(predictions=predictions, references=references)\r\n\r\n4 frames\r\n/usr/local/lib/python3.7/dist-packages/bleurt/checkpoint.py in read_bleurt_config(path)\r\n 84 \"\"\"Reads and checks config file from a BLEURT checkpoint.\"\"\"\r\n 85 assert tf.io.gfile.exists(path), \\\r\n---> 86 \"Could not find BLEURT checkpoint {}\".format(path)\r\n 87 config_path = os.path.join(path, CONFIG_FILE)\r\n 88 assert tf.io.gfile.exists(config_path), \\\r\n\r\nAssertionError: Could not find BLEURT checkpoint /root/.cache/huggingface/metrics/bleurt/bleurt-20/downloads/extracted/e34c60f1a05394ecda54e253a10413ca7b5d59f9a23f3cc73258c6b78ffa2f50/bleurt-20\r\n```\r\ninspecting specified path I see that directory name is `BLEURT-20` instead of `bleurt-20`. \r\nOther solution similar to your suggestion is meddle with `dl_manager.download_and_extract` to unzip to paths with lowering all the paths but I imagine this will affect other parts of the library. ", "Indeed, good catch ! Your solution that fixes `CHECKPOINT_URLS ` is simple and works well, thanks :)\r\n\r\nFurthermore to avoid breaking changes though we could also keep the support for the lowercase one:\r\n```python\r\n if self.config_name.lower() in CHECKPOINT_URLS:\r\n checkpoint_name = self.config_name.lower()\r\n elif self.config_name.upper() in CHECKPOINT_URLS:\r\n checkpoint_name = self.config_name.upper()\r\n else:\r\n raise KeyError(\r\n f\"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}\"\r\n )\r\n```\r\nand then we can use `checkpoint_name` instead of `self.config_name` to download and instantiate the model:\r\n```python\r\n model_path = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])\r\n self.scorer = score.BleurtScorer(os.path.join(model_path, checkpoint_name))\r\n```\r\n\r\nPlease let me know if that sounds reasonable to you !", "Thanks for the suggestion! I believe your suggestion should work to make keys case insensitive. Changes are committed to the PR now. " ]
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In order to properly locate downloaded ckpt files key name needs to match filename. Correcting change introduced in #3235
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iter_archive for zip files
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[ "And also don't always try streaming with Google Drive - it can have issues because of how Google Drive works (with quotas, restrictions, etc.) and it can indeed cause `BlockSizeError`.\r\n\r\nFeel free to host your test data elsewhere, such as in a dataset repository on https://huggingface.co (see [here](https://huggingface.co/docs/datasets/upload_dataset.html#upload-your-files) for a tutorial on how to upload files)" ]
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* In this PR, I added the option to iterate through zipfiles for `download_manager.py` only. * Next PR will be the same applied to `streaming_download_manager.py`. * Related issue #3272. ## Comments : * There is no `.isreg()` equivalent in zipfile library to check if file is Regular so I used `.is_dir()` instead to skip directories. * For now I got `streaming_download_manager.py` working for local zip files, but not for urls. I get the following error when I test it on an archive in google drive, so still working on it. `BlockSizeError: Got more bytes so far (>2112) than requested (22)` ## Tasks : - [x] download_manager.py - [ ] streaming_download_manager.py
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Failed to convert `string` with pyarrow for QED since 1.15.0
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[ "Scratch that, probably the old and incompatible usage of dataset builder from promptsource.", "Actually, re-opening this issue cause the error persists\r\n\r\n```python\r\n>>> load_dataset(\"qed\")\r\nDownloading and preparing dataset qed/qed (download: 13.43 MiB, generated: 9.70 MiB, post-processed: Unknown size, total: 23.14 MiB) to /home/victor_huggingface_co/.cache/huggingface/datasets/qed/qed/1.0.0/47d8b6f033393aa520a8402d4baf2d6bdc1b2fbde3dc156e595d2ef34caf7d75...\r\n100%|███████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2228.64it/s]\r\nTraceback (most recent call last): \r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/load.py\", line 1669, in load_dataset\r\n use_auth_token=use_auth_token,\r\n File \"/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py\", line 594, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py\", line 681, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py\", line 1083, in _prepare_split\r\n num_examples, num_bytes = writer.finalize()\r\n File \"/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/arrow_writer.py\", line 468, in finalize\r\n self.write_examples_on_file()\r\n File \"/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/arrow_writer.py\", line 339, in write_examples_on_file\r\n pa_array = pa.array(typed_sequence)\r\n File \"pyarrow/array.pxi\", line 229, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/arrow_writer.py\", line 125, in __arrow_array__\r\n out = pa.array(cast_to_python_objects(self.data, only_1d_for_numpy=True), type=type)\r\n File \"pyarrow/array.pxi\", line 315, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 143, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 99, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Could not convert 'in' with type str: tried to convert to boolean\r\n```\r\n\r\nEnvironment (datasets and pyarrow):\r\n\r\n```bash\r\n(promptsource) victor_huggingface_co@victor-dev:~/promptsource$ datasets-cli env\r\n\r\nCopy-and-paste the text below in your GitHub issue.\r\n\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-5.0.0-1020-gcp-x86_64-with-debian-buster-sid\r\n- Python version: 3.7.11\r\n- PyArrow version: 6.0.1\r\n```\r\n```bash\r\n(promptsource) victor_huggingface_co@victor-dev:~/promptsource$ pip show pyarrow\r\nName: pyarrow\r\nVersion: 6.0.1\r\nSummary: Python library for Apache Arrow\r\nHome-page: https://arrow.apache.org/\r\nAuthor: \r\nAuthor-email: \r\nLicense: Apache License, Version 2.0\r\nLocation: /home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages\r\nRequires: numpy\r\nRequired-by: streamlit, datasets\r\n```" ]
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## Describe the bug Loading QED was fine until 1.15.0. related: bigscience-workshop/promptsource#659, bigscience-workshop/promptsource#670 Not sure where the root cause is, but here are some candidates: - #3158 - #3120 - #3196 - #2891 ## Steps to reproduce the bug ```python load_dataset("qed") ``` ## Expected results Loading completed. ## Actual results ```shell ArrowInvalid: Could not convert in with type str: tried to convert to boolean Traceback: File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/streamlit/script_runner.py", line 354, in _run_script exec(code, module.__dict__) File "/Users/s0s0cr3/Documents/GitHub/promptsource/promptsource/app.py", line 260, in <module> dataset = get_dataset(dataset_key, str(conf_option.name) if conf_option else None) File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/streamlit/caching.py", line 543, in wrapped_func return get_or_create_cached_value() File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/streamlit/caching.py", line 527, in get_or_create_cached_value return_value = func(*args, **kwargs) File "/Users/s0s0cr3/Documents/GitHub/promptsource/promptsource/utils.py", line 49, in get_dataset builder_instance.download_and_prepare() File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/datasets/builder.py", line 697, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/datasets/builder.py", line 1106, in _prepare_split num_examples, num_bytes = writer.finalize() File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/datasets/arrow_writer.py", line 456, in finalize self.write_examples_on_file() File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/datasets/arrow_writer.py", line 325, in write_examples_on_file pa_array = pa.array(typed_sequence) File "pyarrow/array.pxi", line 222, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/Users/s0s0cr3/Library/Python/3.9/lib/python/site-packages/datasets/arrow_writer.py", line 121, in __arrow_array__ out = pa.array(cast_to_python_objects(self.data, only_1d_for_numpy=True), type=type) File "pyarrow/array.pxi", line 305, in pyarrow.lib.array File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.0, 1.16.1 - Platform: macOS 1.15.7 or above - Python version: 3.7.12 and 3.9 - PyArrow version: 3.0.0, 5.0.0, 6.0.1
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I_kwDODunzps4_oqIn
3,345
Failed to download species_800 from Google Drive zip file
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[ "Hi,\r\n\r\nthe dataset is downloaded normally on my machine. Maybe the URL was down at the time of your download. Could you try again?", "> Hi,\r\n> \r\n> the dataset is downloaded normally on my machine. Maybe the URL was down at the time of your download. Could you try again?\r\n\r\nI have tried that many times with both load_dataset() and a browser almost simultaneously. The browser always works for me while load_dataset() fails.", "@mariosasko \r\n> the dataset is downloaded normally on my machine. Maybe the URL was down at the time of your download. Could you try again?\r\n\r\nI've tried yet again just a moment ago. This time I realize that, the step `(... post-processed: Unknown size, total: 20.89 MiB) to /Users/mike/.cache/huggingface/datasets/species800/species_800/1.0.0/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976...` and the one after seem unstable. If I want to retry, I will have to delete it (and probably other cache lock files). It **_sometimes_** works.\r\n\r\nBut I didn't try `download_mode=\"force_redownload\"` yet.\r\n\r\nAnyway, I suppose this isn't really a pressing issue for the time being, so I'm going to close this. Thank you.\r\n\r\n" ]
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CONTRIBUTOR
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## Describe the bug One can manually download the zip file on Google Drive, but `load_dataset()` cannot. related: #3248 ## Steps to reproduce the bug ```shell > python Python 3.7.12 (default, Sep 5 2021, 08:34:29) [Clang 11.0.3 (clang-1103.0.32.62)] on darwin Type "help", "copyright", "credits" or "license" for more information. ``` ```python >>> from datasets import load_dataset >>> s800 = load_dataset("species_800") ``` ## Expected results species_800 downloaded. ## Actual results ```shell Downloading: 5.68kB [00:00, 1.22MB/s] Downloading: 2.70kB [00:00, 691kB/s] Downloading and preparing dataset species800/species_800 (download: 17.36 MiB, generated: 3.53 MiB, post-processed: Unknown size, total: 20.89 MiB) to /Users/mike/.cache/huggingface/datasets/species800/species_800/1.0.0/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976... 0%| | 0/1 [00:00<?, ?it/s]Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/load.py", line 1632, in load_dataset use_auth_token=use_auth_token, File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/builder.py", line 608, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/builder.py", line 675, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/Users/mike/.cache/huggingface/modules/datasets_modules/datasets/species_800/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976/species_800.py", line 104, in _split_generators downloaded_files = dl_manager.download_and_extract(urls_to_download) File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract return self.extract(self.download(url_or_urls)) File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 197, in download download_func, url_or_urls, map_tuple=True, num_proc=download_config.num_proc, disable_tqdm=False File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 209, in map_nested for obj in utils.tqdm(iterable, disable=disable_tqdm) File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 209, in <listcomp> for obj in utils.tqdm(iterable, disable=disable_tqdm) File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 143, in _single_map_nested return function(data_struct) File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 305, in cached_path use_auth_token=download_config.use_auth_token, File "/Users/mike/Library/Caches/pypoetry/virtualenvs/promptsource-hsdAcWsQ-py3.7/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://drive.google.com/u/0/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download/ ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.14,0 1.15.0, 1.16.1 - Platform: macOS Catalina 10.15.7 - Python version: 3.7.12 - PyArrow version: 6.0.1
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Add ArrayXD docs
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Documents support for dynamic first dimension in `ArrayXD` from #2891, and explain the `ArrayXD` feature in general. Let me know if I'm missing anything @lhoestq :)
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Better error message when download fails
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From our discussions in https://github.com/huggingface/datasets/issues/3269 and https://github.com/huggingface/datasets/issues/3282 it would be nice to have better messages if a download fails. In particular the error now shows: - the error from the HEAD request if there's one - otherwise the response code of the HEAD request I also added an error to tell users to pass `use_auth_token` when the Hugging Face Hub returns 401 (Unauthorized). While paying around with this I also fixed a minor issue with the `force_download` parameter that was not always taken into account
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Fix ASSET dataset data URLs
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[ "> Hi @tianjianjiang, thanks for the fix.\r\n> The links should also be updated in the `dataset_infos.json` file.\r\n> The failing tests are due to the missing tag in the header of the `README.md` file:\r\n\r\nHi @albertvillanova, thank you for the info! My apologies for the messy PR.\r\n" ]
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Change the branch name "master" to "main" in the data URLs, since facebookresearch has changed that.
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Mirror the canonical datasets to the Hugging Face Hub
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[ "I created a GitHub project to keep track of what needs to be done:\r\nhttps://github.com/huggingface/datasets/projects/3\r\n\r\nI also store my code in a (private for now) repository at https://github.com/huggingface/mirror_canonical_datasets_on_hub", "I understand that the datasets are mirrored on the Hub now, right? Might I close @lhoestq @SBrandeis?" ]
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- [ ] create a repo on https://hf.co/datasets for every canonical dataset - [ ] on every commit related to a dataset, update the hf.co repo See https://github.com/huggingface/moon-landing/pull/1562 @SBrandeis: I let you edit this description if needed to precise the intent.
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Fix JSON ClassLabel casting for integers
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Loading a JSON dataset with ClassLabel feature types currently fails if the JSON data already has integers. Indeed currently it tries to convert the strings to integers without even checking if the data are not integers already. For example this currently fails: ```python from datasets import load_dataset, Features, ClassLabel path = "data.json" f = Features({"a": ClassLabel(names=["neg", "pos"])}) d = load_dataset("json", data_files=path, features=f) ``` data.json ```json {"a": 0} {"a": 1} ``` I fixed that by adding a line that checks the type of the JSON data before trying to convert them cc @albertvillanova let me know if it sounds good to you
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to_tf_dataset fails on TPU
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[ "This might be related to https://github.com/tensorflow/tensorflow/issues/38762 , what do you think @Rocketknight1 ?\r\n> Dataset.from_generator is expected to not work with TPUs as it uses py_function underneath which is incompatible with Cloud TPU 2VM setup. If you would like to read from large datasets, maybe try to materialize it on disk and use TFRecordDataest instead.", "Hi @lhoestq @nbroad1881, I think it's very similar, yes. Unfortunately `to_tf_dataset` uses `tf.numpy_function` which can't be compiled - this is a necessary evil to load from the underlying Arrow dataset. We need to update the notebooks/examples to clarify that this won't work, or to identify a workaround. You may be able to get it to work on an actual cloud TPU VM, but those are quite new and we haven't tested it yet. ", "Thank you for the explanation. I didn't realize the nuances of `tf.numpy_function`. In this scenario, would it be better to use `export(format='tfrecord')` ? It's not quite the same, but for very large datasets that don't fit in memory it looks like it is the only option. I haven't used `export` before, but I do recall reading that there are suggestions for how big and how many tfrecords there should be to not bottleneck the TPU. It might be nice if there were a way for the `export` method to split the files up into appropriate chunk sizes depending on the size of the dataset and the number of devices. And if that is too much, it would be nice to be able to specify the number of files that would be created when using `export`. Well... maybe the user should just do the chunking themselves and call `export` a bunch of times. Whatever the case, you have been helpful. Thanks Tensorflow boy ;-) ", "Yeah, this is something we really should have a proper guide on. I'll make a note to test some things and make a 'TF TPU best practices' notebook at some point, but in the meantime I think your solution of exporting TFRecords will probably work. ", "Also: I knew that tweet would haunt me" ]
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Using `to_tf_dataset` to create a dataset and then putting it in `model.fit` results in an internal error on TPUs. I've only tried on Colab and Kaggle TPUs, not GCP TPUs. ## Steps to reproduce the bug I made a colab to show the error. https://colab.research.google.com/drive/12x_PFKzGouFxqD4OuWfnycW_1TaT276z?usp=sharing ## Expected results dataset from `to_tf_dataset` works in `model.fit` Right below the first error in the colab I use `tf.data.Dataset.from_tensor_slices` and `model.fit` works just fine. This is the desired outcome. ## Actual results ``` InternalError: 5 root error(s) found. (0) INTERNAL: {{function_node __inference_train_function_30558}} failed to connect to all addresses Additional GRPC error information from remote target /job:localhost/replica:0/task:0/device:CPU:0: :{"created":"@1638231897.932218653","description":"Failed to pick subchannel","file":"third_party/grpc/src/core/ext/filters/client_channel/client_channel.cc","file_line":3151,"referenced_errors":[{"created":"@1638231897.932216754","description":"failed to connect to all addresses","file":"third_party/grpc/src/core/lib/transport/error_utils.cc","file_line":161,"grpc_status":14}]} [[{{node StatefulPartitionedCall}}]] [[MultiDeviceIteratorGetNextFromShard]] Executing non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic. [[RemoteCall]] [[IteratorGetNextAsOptional]] [[tpu_compile_succeeded_assert/_14023832043698465348/_7/_439]] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.16.1 - Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 3.0.0 - Tensorflow 2.7.0 - `transformers` 4.12.5
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[WIP] Add doctests for tutorials
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[ "I manage to remove the mentions of ellipsis in the code by launching the command as follows:\r\n\r\n```\r\npython -m doctest -v docs/source/load_hub.rst -o=ELLIPSIS\r\n```\r\n\r\nThe way you put your ellipsis will only work on mac, I've adapted it for linux as well with the following:\r\n\r\n```diff\r\n >>> from datasets import load_dataset_builder\r\n >>> dataset_builder = load_dataset_builder('imdb')\r\n- >>> print(dataset_builder.cache_dir) #doctest: +ELLIPSIS\r\n- /Users/.../.cache/huggingface/datasets/imdb/plain_text/1.0.0/...\r\n+ >>> print(dataset_builder.cache_dir)\r\n+ /.../.cache/huggingface/datasets/imdb/plain_text/1.0.0/...\r\n```\r\n\r\nThis passes on my machine:\r\n\r\n```\r\nTrying:\r\n print(dataset_builder.cache_dir)\r\nExpecting:\r\n /.../.cache/huggingface/datasets/imdb/plain_text/1.0.0/...\r\nok\r\n```\r\n\r\nI'm getting a last error:\r\n\r\n```py\r\nExpected:\r\n DatasetDict({\r\n train: Dataset({\r\n features: ['sentence1', 'sentence2', 'label', 'idx'],\r\n num_rows: 3668\r\n })\r\n validation: Dataset({\r\n features: ['sentence1', 'sentence2', 'label', 'idx'],\r\n num_rows: 408\r\n })\r\n test: Dataset({\r\n features: ['sentence1', 'sentence2', 'label', 'idx'],\r\n num_rows: 1725\r\n })\r\n })\r\nGot:\r\n DatasetDict({\r\n train: Dataset({\r\n features: ['idx', 'label', 'sentence1', 'sentence2'],\r\n num_rows: 3668\r\n })\r\n validation: Dataset({\r\n features: ['idx', 'label', 'sentence1', 'sentence2'],\r\n num_rows: 408\r\n })\r\n test: Dataset({\r\n features: ['idx', 'label', 'sentence1', 'sentence2'],\r\n num_rows: 1725\r\n })\r\n })\r\n```\r\n\r\nBut this is due to `doctest` looking for an exact match and the list having an unordered print order. I wish `doctest` would be a bit more flexible with that." ]
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Opening a PR as discussed with @LysandreJik for some help with doctest issues. The goal is to add doctests for each of the tutorials in the documentation to make sure the code samples work as shown. ### Issues A doctest has been added in the docstring of the `load_dataset_builder` function in `load.py` to handle variable outputs with the `ELLIPSIS` directive. When I run doctest on the `load_hub.rst` file, doctest should recognize the expected output from the docstring, and the corresponding code sample in `load_hub.rst` should pass. I am having the same issue with handling tracebacks in the `load_dataset` function. From the docstring: ``` >>> dataset_builder.cache_dir #doctest: +ELLIPSIS /Users/.../.cache/huggingface/datasets/imdb/plain_text/1.0.0/... ``` Test result: ``` Failed example: dataset_builder.cache_dir Expected: /Users/.../.cache/huggingface/datasets/imdb/plain_text/1.0.0/... Got: /Users/steven/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1 ``` I am able to get the doctest to pass by adding the doctest directives (`ELLIPSIS` and `NORMALIZE_WHITESPACE`) to the code samples in the `rst` file directly. But my understanding is that these directives should also work in the docstrings of the functions. I am running the test from the root of the directory: ``` python -m doctest -v docs/source/load_hub.rst ```
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Typing of Dataset.__getitem__ could be improved.
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[ "Hi ! Thanks for the suggestion, I didn't know about this decorator.\r\n\r\nIf you are interesting in contributing, feel free to open a pull request to add the overload methods for each typing combination :) To assign you to this issue, you can comment `#self-assign` in this thread.\r\n\r\n`Dataset.__getitem__` is defined right here: https://github.com/huggingface/datasets/blob/e6f1352fe19679de897f3d962e616936a17094f5/src/datasets/arrow_dataset.py#L1840", "#self-assign" ]
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## Describe the bug The newly added typing for Dataset.__getitem__ is Union[Dict, List]. This makes tools like mypy a bit awkward to use as we need to check the type manually. We could use type overloading to make this easier. [Documentation](https://docs.python.org/3/library/typing.html#typing.overload) ## Steps to reproduce the bug Let's have a file `test.py` ```python from typing import List, Dict, Any from datasets import Dataset ds = Dataset.from_dict({ 'a': [1,2,3], 'b': ["1", "2", "3"] }) one_colum: List[str] = ds['a'] some_index: Dict[Any, Any] = ds[1] ``` ## Expected results Running `mypy test.py` should not give any error. ## Actual results ``` test.py:10: error: Incompatible types in assignment (expression has type "Union[Dict[Any, Any], List[Any]]", variable has type "List[str]") test.py:11: error: Incompatible types in assignment (expression has type "Union[Dict[Any, Any], List[Any]]", variable has type "Dict[Any, Any]") Found 2 errors in 1 file (checked 1 source file) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.13.3 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.8 - PyArrow version: 6.0.1
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Add support for multiple dynamic dimensions and to_pandas conversion for dynamic arrays
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Add support for multiple dynamic dimensions (e.g. `(None, None, 3)` for arbitrary sized images) and `to_pandas()` conversion for dynamic arrays. TODOs: * [ ] Cleaner code * [ ] Formatting issues (if NumPy doesn't allow broadcasting even though dtype is np.object) * [ ] Fix some issues with zero-dim tensors * [ ] Tests
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add Speech commands dataset
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[ "@anton-l ping", "@lhoestq \r\nHi Quentin! Thank you for your feedback and suggestions! 🤗\r\n\r\nYes, that was actually what I wanted to do next - I mean the steaming stuff :)\r\nAlso, I need to make some changes to the readme (to account for the updated features set).\r\n\r\nHopefully, I will be done by tomorrow afternoon if that's ok. \r\n", "@lhoestq Hi Quentin!\r\n\r\nI've implemented (hopefully, correctly) the streaming compatibility but the problem with the current approach is that we first need to iterate over the full archive anyway to get the list of filenames for train and validation sets (see [this](https://github.com/huggingface/datasets/pull/3335/files#diff-aeea540d136025e30a842856779e9c6485a5dc6fc9eb7fd6d3be2acd2f49b8e3R186), the same approach is implemented in TFDS version). Only after that, we can generate examples, so we cannot stream the dataset before the first iteration ends and it takes some time. It's probably not the most effective way. \r\n\r\nIf the streaming mode is turned off, this approach (with two iterations) is actually slower than the previous implementation (with archive extraction). \r\n\r\nMy suggestion is to host separate archives for each split prepared in advance. That way there would be no need for iterating over the common archive to collect train and validation filenames. @anton-l suggested to make AWS mirrors for them. I've prepared these archives, for now you can take a look at them [here](https://drive.google.com/drive/folders/1oMrZHzPgHAKprKJuvih91CM8KMSzh_pL?usp=sharing). I simplified their structure a bit so if we switch to using them, the code then should be changed (and simplified) a bit too.\r\n", "Hi ! Thanks for the changes :)\r\n\r\n> My suggestion is to host separate archives for each split prepared in advance. That way there would be no need for iterating over the common archive to collect train and validation filenames. @anton-l suggested to make AWS mirrors for them. I've prepared these archives, for now you can take a look at them here. I simplified their structure a bit so if we switch to using them, the code then should be changed (and simplified) a bit too.\r\n\r\nI agree, I just uploaded them on AWS\r\n\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.01/v0.01_test.tar.gz\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.01/v0.01_train.tar.gz\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.01/v0.01_validation.tar.gz\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.02/v0.02_test.tar.gz\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.02/v0.02_validation.tar.gz\r\n\r\nNote that in the future we can move those files to actual repositories on the Hugging Face Hub, since we are migrating the datasets from this repository to the Hugging Face Hub (as mirrors), to make them more accessible to the community.", "@lhoestq Thank you! Gonna look at this tomorrow :)", "@lhoestq I've modified the code to fit new data format, now it works for v0.01 but doesn't work for v0.02 as the training archive is missing. Could you please create a mirror for that one too? You can find it [here](https://drive.google.com/file/d/1mPjnVMYb-VhPprGlOX8v9TBT1GT-rtcp/view?usp=sharing)\r\n\r\nAnd when it's done I'll need to regenerate all the meta / dummy stuff, and this version will be ready for a review :)", "Here you go :)\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.02/v0.02_train.tar.gz", "FYI I juste merged a fix for the Windows CI error on `master`, feel free to merge `master` again into your branch", "All green ! I had to fix some minor stuff in the CI but it's good now\r\n\r\nNext step is to mark it as ready for review, and I think it's all good so we can merge 🚀 ", "@lhoestq 🤗", ":tada: " ]
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closes #3283
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Integrate Polars library
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[ "If possible, a neat API could be something like `Dataset.to_polars()`, as well as `Dataset.set_format(\"polars\")`", "Note they use a \"custom\" implementation of Arrow: [Arrow2](https://github.com/jorgecarleitao/arrow2).", "Polars has grown rapidly in popularity over the last year - could you consider integrating the Polars functionality again?\r\n\r\nI don't think the \"custom\" implementation should be a barrier, it still conforms to the Arrow specification " ]
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Check potential integration of the Polars library: https://github.com/pola-rs/polars - Benchmark: https://h2oai.github.io/db-benchmark/ CC: @thomwolf @lewtun
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load JSON files, get the errors
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[ "Hi ! The message you're getting is not an error. It simply says that your JSON dataset is being prepared to a location in `/root/.cache/huggingface/datasets`", "> \r\n\r\nbut I want to load local JSON file by command\r\n`python3 run.py --do_train --task qa --dataset squad-retrain-data/train-v2.0.json --output_dir ./re_trained_model/`\r\n\r\n**squad-retrain-data/train-v2.0.json** is the local JSON file, how to load it and map it to a special structure?", "You can load it with `dataset = datasets.load_dataset('json', data_files=args.dataset)` as you said.\r\nThen if you need to apply additional processing to map it to a special structure, you can use rename columns or use `dataset.map`. For more information, you can check the documentation here: https://huggingface.co/docs/datasets/process.html\r\n\r\nAlso feel free to share your `run.py` code so we can take a look", "```\r\n# Dataset selection\r\n if args.dataset.endswith('.json') or args.dataset.endswith('.jsonl'):\r\n dataset_id = None\r\n # Load from local json/jsonl file\r\n dataset = datasets.load_dataset('json', data_files=args.dataset)\r\n # By default, the \"json\" dataset loader places all examples in the train split,\r\n # so if we want to use a jsonl file for evaluation we need to get the \"train\" split\r\n # from the loaded dataset\r\n eval_split = 'train'\r\n else:\r\n default_datasets = {'qa': ('squad',), 'nli': ('snli',)}\r\n dataset_id = tuple(args.dataset.split(':')) if args.dataset is not None else \\\r\n default_datasets[args.task]\r\n # MNLI has two validation splits (one with matched domains and one with mismatched domains). Most datasets just have one \"validation\" split\r\n eval_split = 'validation_matched' if dataset_id == ('glue', 'mnli') else 'validation'\r\n # Load the raw data\r\n dataset = datasets.load_dataset(*dataset_id)\r\n```\r\n\r\nI want to load JSON squad dataset instead `dataset = datasets.load_dataset('squad')` to retrain the model. \r\n", "If your JSON has the same format as the SQuAD dataset, then you need to pass `field=\"data\"` to `load_dataset`, since the SQuAD format is one big JSON object in which the \"data\" field contains the list of questions and answers.\r\n```python\r\ndataset = datasets.load_dataset('json', data_files=args.dataset, field=\"data\")\r\n```\r\n\r\nLet me know if that helps :)\r\n\r\n", "Yes, code works. but the format is not as expected.\r\n```\r\ndataset = datasets.load_dataset('json', data_files=args.dataset, field=\"data\")\r\n```\r\n```\r\npython3 run.py --do_train --task qa --dataset squad --output_dir ./re_trained_model/\r\n```\r\n************ train_dataset: Dataset({\r\n features: ['id', 'title', 'context', 'question', 'answers'],\r\n num_rows: 87599\r\n})\r\n\r\n\r\n```\r\npython3 run.py --do_train --task qa --dataset squad-retrain-data/train-v2.0.json --output_dir ./re_trained_model/\r\n```\r\n************ train_dataset: Dataset({\r\n features: ['title', 'paragraphs'],\r\n num_rows: 442\r\n})\r\n\r\nI want the JSON to have the same format as before features. https://github.com/huggingface/datasets/blob/master/datasets/squad_v2/squad_v2.py is the script dealing with **squad** but how can I apply it by using JSON? ", "Ok I see, you have the paragraphs so you just need to process them to extract the questions and answers. I think you can process the SQuAD-like data this way:\r\n```python\r\ndef process_squad(articles):\r\n out = {\r\n \"title\": [],\r\n \"context\": [],\r\n \"question\": [],\r\n \"id\": [],\r\n \"answers\": [],\r\n }\r\n for title, paragraphs in zip(articles[\"title\"], articles[\"paragraphs\"]):\r\n for paragraph in paragraphs:\r\n for qa in paragraph[\"qas\"]:\r\n out[\"title\"].append(title)\r\n out[\"context\"].append(paragraph[\"context\"])\r\n out[\"question\"].append(qa[\"question\"])\r\n out[\"id\"].append(qa[\"id\"])\r\n out[\"answers\"].append({\r\n \"answer_start\": [answer[\"answer_start\"] for answer in qa[\"answers\"]],\r\n \"text\": [answer[\"text\"] for answer in qa[\"answers\"]],\r\n })\r\n return out\r\n\r\ndataset = dataset.map(process_squad, batched=True, remove_columns=[\"paragraphs\"])\r\n```\r\n\r\nI adapted the code from [squad.py](https://github.com/huggingface/datasets/blob/master/datasets/squad/squad.py). The code takes as input a batch of articles (title + paragraphs) and gets all the questions and answers from the JSON structure.\r\n\r\nThe output is a dataset with `features: ['answers', 'context', 'id', 'question', 'title']`\r\n\r\nLet me know if that helps !\r\n", "Yes, this works. But how to get the training output during training the squad by **Trainer** \r\nfor example https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/trainer_qa.py \r\nI want the training inputs, labels, outputs for every epoch and step to produce the training dynamic graph", "I think you may need to implement your own Trainer, from the `QuestionAnsweringTrainer` for example.\r\nThis way you can have the flexibility of saving all the inputs/output used at each step", "does there have any function to be overwritten to do this?", "> does there have any function to be overwritten to do this?\r\n\r\nok, I overwrote the compute_loss, thank you.", "Hi, I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs\r\n\r\n```\r\n*********************** inputs: {'attention_mask': tensor([[1, 1, 1, ..., 0, 0, 0],\r\n [1, 1, 1, ..., 0, 0, 0],\r\n [1, 1, 1, ..., 0, 0, 0],\r\n ...,\r\n [1, 1, 1, ..., 0, 0, 0],\r\n [1, 1, 1, ..., 0, 0, 0],\r\n [1, 1, 1, ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25, 97, 93, 44, 25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390, ..., 0, 0, 0],\r\n [ 101, 2054, 2515, ..., 0, 0, 0],\r\n [ 101, 2054, 2106, ..., 0, 0, 0],\r\n ...,\r\n [ 101, 2339, 2001, ..., 0, 0, 0],\r\n [ 101, 2054, 2515, ..., 0, 0, 0],\r\n [ 101, 2054, 2003, ..., 0, 0, 0]], device='cuda:0'), 'start_positions': tensor([ 20, 90, 89, 41, 25, 96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0, ..., 0, 0, 0],\r\n [0, 0, 0, ..., 0, 0, 0],\r\n [0, 0, 0, ..., 0, 0, 0],\r\n ...,\r\n [0, 0, 0, ..., 0, 0, 0],\r\n [0, 0, 0, ..., 0, 0, 0],\r\n [0, 0, 0, ..., 0, 0, 0]], device='cuda:0')} \r\n```\r\n\r\n```\r\n# This function preprocesses a question answering dataset, tokenizing the question and context text\r\n# and finding the right offsets for the answer spans in the tokenized context (to use as labels).\r\n# Adapted from https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py\r\ndef prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):\r\n questions = [q.lstrip() for q in examples[\"question\"]]\r\n max_seq_length = tokenizer.model_max_length\r\n # tokenize both questions and the corresponding context\r\n # if the context length is longer than max_length, we split it to several\r\n # chunks of max_length\r\n tokenized_examples = tokenizer(\r\n questions,\r\n examples[\"context\"],\r\n truncation=\"only_second\",\r\n max_length=max_seq_length,\r\n stride=min(max_seq_length // 2, 128),\r\n return_overflowing_tokens=True,\r\n return_offsets_mapping=True,\r\n padding=\"max_length\"\r\n )\r\n\r\n # Since one example might give us several features if it has a long context,\r\n # we need a map from a feature to its corresponding example.\r\n sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n # The offset mappings will give us a map from token to character position\r\n # in the original context. This will help us compute the start_positions\r\n # and end_positions to get the final answer string.\r\n offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n tokenized_examples[\"start_positions\"] = []\r\n tokenized_examples[\"end_positions\"] = []\r\n\r\n tokenized_examples[\"example_id\"] = []\r\n\r\n for i, offsets in enumerate(offset_mapping):\r\n input_ids = tokenized_examples[\"input_ids\"][i]\r\n # We will label features not containing the answer the index of the CLS token.\r\n cls_index = input_ids.index(tokenizer.cls_token_id)\r\n sequence_ids = tokenized_examples.sequence_ids(i)\r\n # from the feature idx to sample idx\r\n sample_index = sample_mapping[i]\r\n # get the answer for a feature\r\n answers = examples[\"answers\"][sample_index]\r\n\r\n tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n if len(answers[\"answer_start\"]) == 0:\r\n tokenized_examples[\"start_positions\"].append(cls_index)\r\n tokenized_examples[\"end_positions\"].append(cls_index)\r\n else:\r\n # Start/end character index of the answer in the text.\r\n start_char = answers[\"answer_start\"][0]\r\n end_char = start_char + len(answers[\"text\"][0])\r\n\r\n # Start token index of the current span in the text.\r\n token_start_index = 0\r\n while sequence_ids[token_start_index] != 1:\r\n token_start_index += 1\r\n\r\n # End token index of the current span in the text.\r\n token_end_index = len(input_ids) - 1\r\n while sequence_ids[token_end_index] != 1:\r\n token_end_index -= 1\r\n\r\n # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n if not (offsets[token_start_index][0] <= start_char and\r\n offsets[token_end_index][1] >= end_char):\r\n tokenized_examples[\"start_positions\"].append(cls_index)\r\n tokenized_examples[\"end_positions\"].append(cls_index)\r\n else:\r\n # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n # Note: we could go after the last offset if the answer is the last word (edge case).\r\n while token_start_index < len(offsets) and \\\r\n offsets[token_start_index][0] <= start_char:\r\n token_start_index += 1\r\n tokenized_examples[\"start_positions\"].append(\r\n token_start_index - 1)\r\n while offsets[token_end_index][1] >= end_char:\r\n token_end_index -= 1\r\n tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n return tokenized_examples\r\n```" ]
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Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command `!python3 run.py --do_train --task qa --dataset squad-retrain-data/train-v2.0.json --output_dir ./re_trained_model/` change the dateset to load json by refering to https://huggingface.co/docs/datasets/loading.html `dataset = datasets.load_dataset('json', data_files=args.dataset)` Errors: `Downloading and preparing dataset json/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/json/default-c1e124ad488911b8/0.0.0/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264... ` _Originally posted by @yanllearnn in https://github.com/huggingface/datasets/issues/730#issuecomment-981095050_
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Fix error message and add extension fallback
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Fix the error message raised if `infered_module_name` is `None` in `CommunityDatasetModuleFactoryWithoutScript.get_module` and make `infer_module_for_data_files` more robust. In the linked issue, `infer_module_for_data_files` returns `None` because `json` is the second most common extension due to the suffix ordering. Now, we go from the most common to the least common extension and try to map it or return `None`. Fix #3331
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AttributeError: 'CommunityDatasetModuleFactoryWithoutScript' object has no attribute 'path'
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[ "Hi,\r\n\r\nthe fix was merged and will be available in the next release of `datasets`.\r\nIn the meantime, you can use it by installing `datasets` directly from master as follows:\r\n```\r\npip install git+https://github.com/huggingface/datasets.git\r\n```" ]
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## Describe the bug I add a new question answering dataset to huggingface datasets manually. Here is the link: [luozhouyang/question-answering-datasets](https://huggingface.co/datasets/luozhouyang/question-answering-datasets) But when I load the dataset, an error raised: ```bash AttributeError: 'CommunityDatasetModuleFactoryWithoutScript' object has no attribute 'path' ``` ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("luozhouyang/question-answering-datasets", data_files=["dureader_robust.train.json"]) ``` ## Expected results Load dataset successfully without any error. ## Actual results ```bash Traceback (most recent call last): File "/mnt/home/zhouyang.lzy/github/naivenlp/naivenlp/tests/question_answering_tests/dataset_test.py", line 89, in test_load_dataset_with_hf data_files=["dureader_robust.train.json"], File "/mnt/home/zhouyang.lzy/.conda/envs/naivenlp/lib/python3.6/site-packages/datasets/load.py", line 1616, in load_dataset **config_kwargs, File "/mnt/home/zhouyang.lzy/.conda/envs/naivenlp/lib/python3.6/site-packages/datasets/load.py", line 1443, in load_dataset_builder path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files File "/mnt/home/zhouyang.lzy/.conda/envs/naivenlp/lib/python3.6/site-packages/datasets/load.py", line 1157, in dataset_module_factory raise e1 from None File "/mnt/home/zhouyang.lzy/.conda/envs/naivenlp/lib/python3.6/site-packages/datasets/load.py", line 1144, in dataset_module_factory download_mode=download_mode, File "/mnt/home/zhouyang.lzy/.conda/envs/naivenlp/lib/python3.6/site-packages/datasets/load.py", line 798, in get_module raise FileNotFoundError(f"No data files or dataset script found in {self.path}") AttributeError: 'CommunityDatasetModuleFactoryWithoutScript' object has no attribute 'path' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.1 - Platform: linux - Python version: 3.6.13 - PyArrow version: 6.0.1
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3,330
Change TriviaQA license (#3313)
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Fixes (#3313)
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Map function: Type error on iter #999
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[ "Hi, thanks for reporting.\r\n\r\nIt would be really helpful if you could provide the actual code of the `text_numbers_to_int` function so we can reproduce the error.", "```\r\ndef text_numbers_to_int(text, column=\"\"):\r\n \"\"\"\r\n Convert text numbers to int.\r\n\r\n :param text: text numbers\r\n :return: int\r\n \"\"\"\r\n try:\r\n numbers = find_numbers(text)\r\n if not numbers:\r\n return text\r\n result = \"\"\r\n i, j = 0, 0\r\n while i < len(text):\r\n if j < len(numbers) and i == numbers[j][1]:\r\n n = int(numbers[j][0]) if numbers[j][0] % 1 == 0 else float(numbers[j][0])\r\n result += str(n)\r\n i = numbers[j][2] #end\r\n j += 1\r\n else:\r\n result += text[i]\r\n i += 1\r\n if column:\r\n return{column: result}\r\n else:\r\n return {column: result}\r\n except Exception as e:\r\n print(e)\r\n return {column: result}\r\n```", "Maybe this is because of the `return text` line ? I think it should return a dictionary rather than a string", "Yes that was it, good catch! Thanks" ]
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## Describe the bug Using the map function, it throws a type error on iter #999 Here is the code I am calling: ``` dataset = datasets.load_dataset('squad') dataset['validation'].map(text_numbers_to_int, input_columns=['context'], fn_kwargs={'column': 'context'}) ``` text_numbers_to_int returns the input text with numbers replaced in the format {'context': text} It happens at ` File "C:\Users\lonek\anaconda3\envs\ai\Lib\site-packages\datasets\arrow_writer.py", line 289, in <listcomp> [row[0][col] for row in self.current_examples], type=col_type, try_type=col_try_type, col=col ` The issue is that the list comprehension expects self.current_examples to be type tuple(dict, str), but for some reason 26 out of 1000 of the sefl.current_examples are type tuple(str, str) Here is an example of what self.current_examples should be ({'context': 'Super Bowl 50 was an...merals 50.'}, '') Here is an example of what self.current_examples are when it throws the error: ('The Panthers used th... Marriott.', '')
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3,328
Quick fix error formatting
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While working on a dataset, I got the error ``` TypeError: Provided `function` which is applied to all elements of table returns a `dict` of types {[type(x) for x in processed_inputs.values()]}. When using `batched=True`, make sure provided `function` returns a `dict` of types like `{allowed_batch_return_types}`. ``` This PR should fix the formatting of this error
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"Shape of query is incorrect, it has to be either a 1D array or 2D (1, N)"
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## Describe the bug Passing a correctly shaped Numpy-Array to get_nearest_examples leads to the Exception "Shape of query is incorrect, it has to be either a 1D array or 2D (1, N)" Probably the reason for this is a wrongly converted assertion. 1.15.1: `assert len(query.shape) == 1 or (len(query.shape) == 2 and query.shape[0] == 1)` 1.16.1: ``` if len(query.shape) != 1 or (len(query.shape) == 2 and query.shape[0] != 1): raise ValueError("Shape of query is incorrect, it has to be either a 1D array or 2D (1, N)") ``` ## Steps to reproduce the bug follow the steps described here: https://huggingface.co/course/chapter5/6?fw=tf ```python question_embedding.shape # (1, 768) scores, samples = embeddings_dataset.get_nearest_examples( "embeddings", question_embedding, k=5 # Error ) # "Shape of query is incorrect, it has to be either a 1D array or 2D (1, N)" ``` ## Expected results Should work without exception ## Actual results Throws exception ## Environment info - `datasets` version: 1.15.1 - Platform: Darwin-20.6.0-x86_64-i386-64bit - Python version: 3.7.12 - PyArrow version: 6.0.
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Fix import `datasets` on python 3.10
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In python 3.10 it's no longer possible to use `functools.wraps` on a method decorated with `classmethod`. To fix this I inverted the order of the `inject_arrow_table_documentation` and `classmethod` decorators Fix #3324
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Update conda dependencies
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Some dependencies minimum versions were outdated. For example `pyarrow` and `huggingface_hub`
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Can't import `datasets` in python 3.10
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When importing `datasets` I'm getting this error in python 3.10: ```python Traceback (most recent call last): File "<string>", line 1, in <module> File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/__init__.py", line 34, in <module> from .arrow_dataset import Dataset, concatenate_datasets File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/arrow_dataset.py", line 47, in <module> from .arrow_reader import ArrowReader File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/arrow_reader.py", line 33, in <module> from .table import InMemoryTable, MemoryMappedTable, Table, concat_tables File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/table.py", line 334, in <module> class InMemoryTable(TableBlock): File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/table.py", line 361, in InMemoryTable def from_pandas(cls, *args, **kwargs): File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/table.py", line 24, in wrapper out = wraps(arrow_table_method)(method) File "/Users/quentinlhoest/.pyenv/versions/3.10.0/lib/python3.10/functools.py", line 61, in update_wrapper wrapper.__wrapped__ = wrapped AttributeError: readonly attribute ``` This makes the conda build fail. I'm opening a PR to fix this and do a patch release 1.16.1
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3,323
Fix wrongly converted assert
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[ "Closes #3327 " ]
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Seems like this assertion was replaced by an exception but the condition got wrongly converted.
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Add missing tags to XTREME
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Add missing tags to the XTREME benchmark for better discoverability.
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3,321
Update URL of tatoeba subset of xtreme
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[ "<s>To be more precise: `os.path.join` is replaced on-the-fly by `xjoin` anyway with patching, to extend it to remote files</s>", "Oh actually just ignore what I said: they were used to concatenate URLs, which is not recommended. Let me fix that again by appending using `+`" ]
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Updates the URL of the tatoeba subset of xtreme. Additionally, replaces `os.path.join` with `xjoin` to correctly join the URL segments on Windows. Fix #3320
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3,320
Can't get tatoeba.rus dataset
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## Describe the bug It gives an error. > FileNotFoundError: Couldn't find file at https://github.com/facebookresearch/LASER/raw/master/data/tatoeba/v1/tatoeba.rus-eng.rus ## Steps to reproduce the bug ```python data=load_dataset("xtreme","tatoeba.rus", split="validation") ``` ## Solution The library tries to access the **master** branch. In the github repo of facebookresearch, it is in the **main** branch.
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Add push_to_hub docs
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[ "Looks good to me! :)\r\n\r\nMaybe we can mention that users can also set the `private` argument if they want to keep their dataset private? It would lead nicely into the next section on Privacy.", "Thanks for your comments, I fixed the capitalization for consistency and added an passage to mention the `private` parameter and to have a nice transition to the Privacy section :)\r\n\r\nI also added the login instruction that was missing before the user can actually upload a dataset." ]
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Since #3098 it's now possible to upload a dataset on the Hub directly from python using the `push_to_hub` method. I just added a section in the "Upload a dataset to the Hub" tutorial. I kept the section quite simple but let me know if it sounds good to you @LysandreJik @stevhliu :)
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Finish transition to PyArrow 3.0.0
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Finish transition to PyArrow 3.0.0 that was started in #3098.
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Add desc parameter to Dataset filter method
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[ "Hi,\r\n\r\n`Dataset.map` allows more generic transforms compared to `Dataset.filter`, which purpose is very specific (to filter examples based on a condition). That's why I don't think we need the `desc` parameter there for consistency. #3196 has added descriptions to the `Dataset` methods that call `.map` internally, but not for the `filter` method, so we should do that.\r\n\r\nDo you have a description in mind? Maybe `\"Filtering the dataset\"` or `\"Filtering the indices\"`? If yes, feel free to open a PR.", "I'm personally ok with adding the `desc` parameter actually. Let's say you have different filters, it can be nice to differentiate between the different filters when they're running no ?", "@mariosasko the use case is filtering of a dataset prior to tokenization and subsequent training. As the dataset is huge it's just a matter of giving a user (model trainer) some feedback on what's going on. Otherwise, feedback is given for all steps in training preparation and not for filtering and the filtering in my use case lasts about 4-5 minutes. And yes, if there are more filtering stages, as @lhoestq pointed out, it would be nice to give some feedback. I thought desc is there already and got confused when I got the script error. ", "I don't have a strong opinion on that, so having `desc` as a parameter is also OK." ]
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**Is your feature request related to a problem? Please describe.** As I was filtering very large datasets I noticed the filter method doesn't have the desc parameter which is available in the map method. Why don't we add a desc parameter to the filter method both for consistency and it's nice to give some feedback to users during long operations on Datasets? **Describe the solution you'd like** Add desc parameter to Dataset filter method **Describe alternatives you've considered** N/A **Additional context** N/A
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Add RedCaps dataset
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## Adding a Dataset - **Name:** RedCaps - **Description:** Web-curated image-text data created by the people, for the people - **Paper:** https://arxiv.org/abs/2111.11431 - **Data:** https://redcaps.xyz/ - **Motivation:** Multimodal image-text dataset: 12M+ Image-text pairs Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Proposed by @patil-suraj
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Removing query params for dynamic URL caching
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[ "IMO it makes more sense to have `ignore_url_params` as an attribute of `DownloadConfig` to avoid defining a new argument in `DownloadManger`'s methods.", "@mariosasko that would make sense to me too, but it seems like `DownloadConfig` wasn't intended to be modified from a dataset loading script. @lhoestq wdyt?", "We can expose `DownloadConfig` as a property of `DownloadManager`, and then in the script before the download call we could do: `dl_manager.download_config.ignore_url_params = True`. But yes, let's hear what Quentin thinks.", "Oh indeed that's a great idea. This parameter is similar to others like `download_config.use_etag` that defines the behavior of the download and caching, so it's better if we have it there, and expose the `download_config`", "Implemented it via `dl_manager.download_config.ignore_url_params` now, and also added a usage example above :) " ]
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The main use case for this is to make dynamically generated private URLs (like the ones returned by CommonVoice API) compatible with the datasets' caching logic. Usage example: ```python import datasets class CommonVoice(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo() def _split_generators(self, dl_manager): dl_manager.download_config.ignore_url_params = True HUGE_URL = "https://mozilla-common-voice-datasets.s3.dualstack.us-west-2.amazonaws.com/cv-corpus-7.0-2021-07-21/cv-corpus-7.0-2021-07-21-ab.tar.gz?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIAQ3GQRTO3IU5JYB5K%2F20211125%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20211125T131423Z&X-Amz-Expires=43200&X-Amz-Security-Token=FwoGZXIvYXdzEL7%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaDLsZw7Nj0d9h4rgheyKSBJJ6bxo1JdWLXAUhLMrUB8AXfhP8Ge4F8dtjwXmvGJgkIvdMT7P4YOEE1pS3mW8AyKsz7Z7IRVCIGQrOH1AbxGVVcDoCMMswXEOqL3nJFihKLf99%2F6l8iJVZdzftRUNgMhX5Hz0xSIL%2BzRDpH5nYa7C6YpEdOdW81CFVXybx7WUrX13wc8X4ZlUj7zrWcWf5p2VEIU5Utb7YHVi0Y5TQQiZSDoedQl0j4VmMuFkDzoobIO%2BvilgGeE2kIX0E62X423mEGNu4uQV5JsOuLAtv3GVlemsqEH3ZYrXDuxLmnvGj5HfMtySwI4vKv%2BlnnirD29o7hxvtidXiA8JMWhp93aP%2Fw7sod%2BPPbb5EqP%2B4Qb2GJ1myClOKcLEY0cqoy7XWm8NeVljLJojnFJVS5mNFBAzCCTJ%2FidxNsj8fflzkRoAzYaaPBuOTL1dgtZCdslK3FAuEvw0cik7P9A7IYiULV33otSHKMPcVfNHFsWQljs03gDztsIUWxaXvu6ck5vCcGULsHbfe6xoMPm2bR9jtKLONsslPcnzWIf7%2Fch2w%2F%2BjtTCd9IxaH4kytyJ6mIjpV%2FA%2F2h9qeDnDFsCphnMjAzPQn6tqCgTtPcyJ2b8c94ncgUnE4mepx%2FDa%2FanAEsrg9RPdmbdoPswzHn1IClh91IfSN74u95DZUxlPeZrHG5HxVCN3dKO6j%2Ft1xd20L0hEtazDdKOr8%2FYwGMirp8rp%2BII0pYOwQOrYHqH%2FREX2dRJctJtwE86Qj1eU8BAdXuFIkLC4NWXw%3D&X-Amz-Signature=1b8108d29b0e9c2bf6c7246e58ca8d5749a83de0704757ad8e8a44d78194691f&X-Amz-SignedHeaders=host" dl_path = dl_manager.download_and_extract(HUGE_URL) print(dl_path) HUGE_URL += "&some_new_or_changed_param=12345" dl_path = dl_manager.download_and_extract(HUGE_URL) print(dl_path) dl_manager = datasets.DownloadManager(dataset_name="common_voice") CommonVoice()._split_generators(dl_manager) ``` Output: ``` /home/user/.cache/huggingface/datasets/downloads/6ef2a377398ff3309554be040caa78414e6562d623dbd0ce8fc262459a7f8ec6 /home/user/.cache/huggingface/datasets/downloads/6ef2a377398ff3309554be040caa78414e6562d623dbd0ce8fc262459a7f8ec6 ```
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Adding arg to pass process rank to `map`
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[ "Some commits seem to be there twice (made the mistake of rebasing because I wasn't sure whether the doc had changed), is this an issue @lhoestq ?" ]
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This PR adds a `with_rank` argument to `map` that gives the user the possibility to pass the rank of each process to their function. This is mostly designed for multi-GPU map (each process can be sent to a different device thanks to the rank). I've also added tests. I'm putting the PR up so you can check the code, I'll add a multi-GPU example to the doc (+ write a bit in the doc for the new arg)
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TriviaQA License Mismatch
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[ "Hi ! You're completely right, this must be mentioned in the dataset card.\r\nIf you're interesting in contributing, feel free to open a pull request to mention this in the `trivia_qa` dataset card in the \"Licensing Information\" section at https://github.com/huggingface/datasets/blob/master/datasets/trivia_qa/README.md" ]
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## Describe the bug TriviaQA Webpage at http://nlp.cs.washington.edu/triviaqa/ says they do not own the copyright to the data. However, Huggingface datasets at https://huggingface.co/datasets/trivia_qa mentions that the dataset is released under Apache License Is the License Information on HuggingFace correct?
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add bl books genre dataset
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[ "To fix the CI, feel free to run the `make style` command to format the code.\r\n\r\nThen it also looks like the dummy_data.zip archives are all empty, which makes the tests fail. Can you try regenerating them ? They should have one file inside which is a dummy version of the file at https://bl.iro.bl.uk/downloads/36c7cd20-c8a7-4495-acbe-469b9132c6b1?locale=en", "@lhoestq, thanks for that feedback. \r\n\r\nI should have made most of these changes now. The `--auto_generate` flag wasn't working because the file wasn't downloaded with a `.csv` extension. I used `--match_text_files \"*\"` to get around this. Because there is a lot of data that isn't annotated using the default line number for the dummy data causes the `annotated_raw` and the `title_genre_classifiction` configs to fail because they don't generate any examples — bumping the line numbers to `250` fixes this. This does make the dummy data a bit bigger, though. \r\n\r\nThe total directory size for the dataset is now `150kb`. Is this okay, or do you want me to generate the dummy data manually instead? ", "Hi ! yes 150kB is fine :)\r\nFeel free to push your new dummy_data.zip files (I think the current one are still the empty ones)", "@lhoestq I've pushed those dummy files now and added your other suggestions.", "The CI failure is unrelated to this PR, merging :)", "@lhoestq, thanks for all your help with this pull request 😀" ]
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First of all thanks for the fantastic library/collection of datasets 🤗 This pull request adds a dataset of metadata from digitised (mostly 19th Century) books from the British Library The [data](https://bl.iro.bl.uk/concern/datasets/1e1ccb46-65b4-4481-b6f8-b8129d5da053) contains various metadata about the books. In addition, a subset of the data includes 'genre' information which can be used for supervised text classification tasks. I hope that this offers easier access to a dataset for doing text classification on GLAM (galleries, libraries, archives and museums) data. I have tried to create three configurations that provide both an 'easy' version of the dataset if you want to use it for training a genre classification model and a more 'raw' version of the data for other potential use cases for the data. I am open to suggestions if this doesn't make sense. Similarly, for some of the arrow datatypes, I have had to fall back to strings since there are missing values for some fields/rows but I may have missed a more elegant way of dealing with it.
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Add WebSRC
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## Adding a Dataset - **Name:** WebSRC - **Description:** WebSRC is a novel Web-based Structural Reading Comprehension dataset. It consists of 0.44M question-answer pairs, which are collected from 6.5K web pages with corresponding HTML source code, screenshots and metadata. - **Paper:** https://arxiv.org/abs/2101.09465 - **Data:** https://x-lance.github.io/WebSRC/dashboard.html# - **Motivation:** Currently adding MarkupLM to HuggingFace Transformers, which achieves SOTA on this 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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Fatal error condition occurred in aws-c-io
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[ "Hi ! Are you having this issue only with this specific dataset, or it also happens with other ones like `squad` ?", "@lhoestq It happens also on `squad`. It successfully downloads the whole dataset and then crashes on: \r\n\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\nI tested it on Ubuntu and its working OK. Didn't test on non-preview version of Windows 11, `Windows-10-10.0.22504-SP0` is a preview version, not sure if this is causing it.", "I see the same error in Windows-10.0.19042 as of a few days ago:\r\n\r\n`Fatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS`\r\n\r\npython 3.8.12 h7840368_2_cpython conda-forge\r\nboto3 1.20.11 pyhd8ed1ab_0 conda-forge\r\nbotocore 1.23.11 pyhd8ed1ab_0 conda-forge\r\n\r\n...but I am not using `datasets` (although I might take a look now that I know about it!)\r\n\r\nThe error has occurred a few times over the last two days, but not consistently enough for me to get it with DEBUG. If there is any interest I can report back here, but it seems not unique to `datasets`.", "I'm not sure what `datasets` has to do with a crash that seems related to `aws-c-io`, could it be an issue with your environment ?", "> I'm not sure what `datasets` has to do with a crash that seems related to `aws-c-io`, could it be an issue with your environment ?\r\n\r\nAgreed, this issue is not likely a bug in datasets, since I get the identical error without datasets installed.", "Will close this issue. Bug in `aws-c-io` shouldn't be in `datasets` repo. Nevertheless, it can be useful to know that it happens. Thanks @leehaust @lhoestq ", "I have also had this issue since a few days, when running scripts using PyCharm in particular, but it does not seem to affect the script from running, only reporting this error at the end of the run.", "I also get this issue, It appears after my script has finished running. I get the following error message\r\n```\r\nFatal error condition occurred in /home/conda/feedstock_root/build_artifacts/aws-c-io_1637179816120/work/source/event_loop.c:72: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n################################################################################\r\nStack trace:\r\n################################################################################\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_backtrace_print+0x59) [0x2aabe0479579]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_fatal_assert+0x48) [0x2aabe04696c8]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././././libaws-c-io.so.1.0.0(+0x13ad3) [0x2aabe0624ad3]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././././libaws-c-io.so.1.0.0(+0x113ca) [0x2aabe06223ca]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-crt-cpp.so(_ZN3Aws3Crt2Io15ClientBootstrapD1Ev+0x3a) [0x2aabe041cf5a]\r\n/home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././libaws-cpp-sdk-core.so(+0x5f570) [0x2aabe00eb570]\r\n/lib64/libc.so.6(+0x39ce9) [0x2aaaab835ce9]\r\n/lib64/libc.so.6(+0x39d37) [0x2aaaab835d37]\r\n/lib64/libc.so.6(__libc_start_main+0xfc) [0x2aaaab81e55c]\r\npython(+0x1c721d) [0x55555571b21d]\r\nAborted\r\n```\r\nI don't get this issue when running my code in a container, and it seems more relevant to PyArrow but thought a more complete stack trace might be helpful to someone\r\n", "I created an issue on JIRA:\r\nhttps://issues.apache.org/jira/browse/ARROW-15141", "@CallumMcMahon Do you have a small reproducer for this problem on Linux? I can reproduce this on Windows but sadly not with linux.", "Any updates on this issue? I started receiving the same error a few days ago on the amazon reviews", "Hi,\r\n\r\nI also ran into this issue, Windows only. It caused our massive binary to minidump left and right, very annoying.\r\nWhen the program is doing an exit, the destructors in the exit-handlers want to do cleanup, leading to code in event_loop.c, on line 73-ish:\r\n\r\nAWS_FATAL_ASSERT(\r\n aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) ==\r\n AWS_OP_SUCCESS);\r\n\r\nThe fatal_assert end in an abort/minidump.\r\n\r\nDigging through the code, I found that aws_thread_launch in the Windows version (aws-c-common/source/windows/thread.c) has only ONE reason to return anything other than AWS_OP_SUCCESS:\r\n\r\nreturn aws_raise_error(AWS_ERROR_THREAD_INSUFFICIENT_RESOURCE);\r\n\r\non line 263, when CreateThread fails. Our conclusion was that, apparently, Windows dislikes launching a new thread while already handling the exit-handlers. And while I appreciate the the fatal_assert is there in case of problems, the cure here is worse than the problem.\r\n\r\nI \"fixed\" this in our (Windows) environment by (bluntly) removing the AWS_FATAL_ASSERT. If Windows cannot start a thread, the program is in deep trouble anyway and the chances of that actually happening are acceptable (to us).\r\nThe exit is going to clean up all resources anyway.\r\n\r\nA neater fix would probably be to detect somehow that the program is actually in the process of exiting and then not bother (on windows, anyway) to start a cleanup thread. Alternatively, try to start the thread but not fatal-assert when it fails during exit. Or perhaps Windows can be convinced somehow to start the thread under these circumstances?\r\n\r\n@xhochy : The problem is Windows-only, the aws_thread_launch has two implementations (posix and windows). The problem is in the windows CreateThread which fails.\r\n", "I also encountered the same problem, but I made an error in the multi gpu training environment on Linux, and the single gpu training environment will not make an error.\r\ni use accelerate package to do multi gpu training.", "> I also get this issue, It appears after my script has finished running. I get the following error message\r\n> \r\n> ```\r\n> Fatal error condition occurred in /home/conda/feedstock_root/build_artifacts/aws-c-io_1637179816120/work/source/event_loop.c:72: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\n> Exiting Application\r\n> ################################################################################\r\n> Stack trace:\r\n> ################################################################################\r\n> /home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_backtrace_print+0x59) [0x2aabe0479579]\r\n> /home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_fatal_assert+0x48) [0x2aabe04696c8]\r\n> /home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././././libaws-c-io.so.1.0.0(+0x13ad3) [0x2aabe0624ad3]\r\n> /home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]\r\n> /home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././././libaws-c-io.so.1.0.0(+0x113ca) [0x2aabe06223ca]\r\n> /home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]\r\n> /home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../../././libaws-crt-cpp.so(_ZN3Aws3Crt2Io15ClientBootstrapD1Ev+0x3a) [0x2aabe041cf5a]\r\n> /home/user_name/conda_envs/env_name/lib/python3.7/site-packages/pyarrow/../../.././libaws-cpp-sdk-core.so(+0x5f570) [0x2aabe00eb570]\r\n> /lib64/libc.so.6(+0x39ce9) [0x2aaaab835ce9]\r\n> /lib64/libc.so.6(+0x39d37) [0x2aaaab835d37]\r\n> /lib64/libc.so.6(__libc_start_main+0xfc) [0x2aaaab81e55c]\r\n> python(+0x1c721d) [0x55555571b21d]\r\n> Aborted\r\n> ```\r\n> \r\n> I don't get this issue when running my code in a container, and it seems more relevant to PyArrow but thought a more complete stack trace might be helpful to someone\r\n\r\nAny updates for your issue because I'm getting the same one ", "Potentially related AWS issue: https://github.com/aws/aws-sdk-cpp/issues/1809\r\n\r\nRan into this issue today while training a BPE tokenizer on a dataset.\r\n\r\nTrain code:\r\n\r\n```python\r\n\"\"\"Train a ByteLevelBPETokenizer based on a given dataset. The dataset must be on the HF Hub.\r\nThis script is adaptated from the Transformers example in https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling\r\n\"\"\"\r\nfrom os import PathLike\r\nfrom pathlib import Path\r\nfrom typing import Sequence, Union\r\n\r\nfrom datasets import load_dataset\r\nfrom tokenizers import ByteLevelBPETokenizer\r\n\r\n\r\ndef train_tokenizer(dataset_name: str = \"oscar\", dataset_config_name: str = \"unshuffled_deduplicated_nl\",\r\n dataset_split: str = \"train\", dataset_textcol: str = \"text\",\r\n vocab_size: int = 50265, min_frequency: int = 2,\r\n special_tokens: Sequence[str] = (\"<s>\", \"<pad>\", \"</s>\", \"<unk>\", \"<mask>\"),\r\n dout: Union[str, PathLike] = \".\"):\r\n # load dataset\r\n dataset = load_dataset(dataset_name, dataset_config_name, split=dataset_split)\r\n # Instantiate tokenizer\r\n tokenizer = ByteLevelBPETokenizer()\r\n\r\n def batch_iterator(batch_size=1024):\r\n for i in range(0, len(dataset), batch_size):\r\n yield dataset[i: i + batch_size][dataset_textcol]\r\n\r\n # Customized training\r\n tokenizer.train_from_iterator(batch_iterator(), vocab_size=vocab_size, min_frequency=min_frequency,\r\n special_tokens=special_tokens)\r\n\r\n # Save to disk\r\n pdout = Path(dout).resolve()\r\n pdout.mkdir(exist_ok=True, parents=True)\r\n tokenizer.save_model(str(pdout))\r\n\r\n\r\ndef main():\r\n import argparse\r\n cparser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter)\r\n\r\n cparser.add_argument(\"dataset_name\", help=\"Name of dataset to use for tokenizer training\")\r\n cparser.add_argument(\"--dataset_config_name\", default=None,\r\n help=\"Name of the config to use for tokenizer training\")\r\n cparser.add_argument(\"--dataset_split\", default=None,\r\n help=\"Name of the split to use for tokenizer training (typically 'train')\")\r\n cparser.add_argument(\"--dataset_textcol\", default=\"text\",\r\n help=\"Name of the text column to use for tokenizer training\")\r\n cparser.add_argument(\"--vocab_size\", type=int, default=50265, help=\"Vocabulary size\")\r\n cparser.add_argument(\"--min_frequency\", type=int, default=2, help=\"Minimal frequency of tokens\")\r\n cparser.add_argument(\"--special_tokens\", nargs=\"+\", default=[\"<s>\", \"<pad>\", \"</s>\", \"<unk>\", \"<mask>\"],\r\n help=\"Special tokens to add. Useful for specific training objectives. Note that if you wish\"\r\n \" to use this tokenizer with a default transformers.BartConfig, then make sure that the\"\r\n \" order of at least these special tokens are correct: BOS (0), padding (1), EOS (2)\")\r\n cparser.add_argument(\"--dout\", default=\".\", help=\"Path to directory to save tokenizer.json file\")\r\n\r\n train_tokenizer(**vars(cparser.parse_args()))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\nCommand:\r\n\r\n```sh\r\n$WDIR=\"your_tokenizer\"\r\npython prepare_tokenizer.py dbrd --dataset_config_name plain_text --dataset_split unsupervised --dout $WDIR\r\n```\r\n\r\nOutput:\r\n\r\n```\r\nReusing dataset dbrd (cache/datasets/dbrd/plain_text/3.0.0/2b12e31348489dfe586c2d0f40694e5d9f9454c9468457ac9f1b51abf686eeb3)\r\n[00:00:30] Pre-processing sequences ████████ 0 / 0\r\n[00:00:00] Tokenize words ████████ 333319 / 333319\r\n[00:01:06] Count pairs ████████ 333319 / 333319\r\n[00:00:03] Compute merges ████████ 50004 / 50004\r\n\r\nFatal error condition occurred in /opt/vcpkg/buildtrees/aws-c-io/src/9e6648842a-364b708815.clean/source/event_loop.c:72: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n################################################################################\r\nStack trace:\r\n################################################################################\r\nvenv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x200af06) [0x155106589f06]\r\nvenv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x20028e5) [0x1551065818e5]\r\nvenv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x1f27e09) [0x1551064a6e09]\r\nvenv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x200ba3d) [0x15510658aa3d]\r\nvenv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x1f25948) [0x1551064a4948]\r\nvenv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x200ba3d) [0x15510658aa3d]\r\nvenv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x1ee0b46) [0x15510645fb46]\r\nvenv/lib/python3.9/site-packages/pyarrow/libarrow.so.900(+0x194546a) [0x155105ec446a]\r\n/lib64/libc.so.6(+0x39b0c) [0x1551075b8b0c]\r\n/lib64/libc.so.6(on_exit+0) [0x1551075b8c40]\r\n/lib64/libc.so.6(__libc_start_main+0xfa) [0x1551075a249a]\r\npython(_start+0x2e) [0x4006ce]\r\nAborted (core dumped)\r\n```\r\n\r\nRunning on datasets==2.4.0 and pyarrow==9.0.0 on RHEL 8.\r\n", "There is also a discussion here https://issues.apache.org/jira/browse/ARROW-15141 where it is suggested for conda users to use an older version of aws-sdk-cpp: `aws-sdk-cpp=1.8.186`", "Downgrading pyarrow to 6.0.1 solves the issue for me.\r\n\r\n`pip install pyarrow==6.0.1`", "First of all, I’d never call a downgrade a solution, at most a (very) temporary workaround.\r\nFurthermore: This bug also happens outside pyarrow, I incorporate AWS in a standalone Windows C-program and that crashes during exit.\r\n\r\nFrom: Bo-Ru (Roy) Lu ***@***.***>\r\nSent: Thursday, 15 September 2022 01:12\r\nTo: huggingface/datasets ***@***.***>\r\nCc: Ruurd Beerstra ***@***.***>; Comment ***@***.***>\r\nSubject: Re: [huggingface/datasets] Fatal error condition occurred in aws-c-io (Issue #3310)\r\n\r\nSent by an external sender. Please be cautious about clicking on links and opening attachments.\r\n--------------------------------------------------------------------------------------------------------------------------------\r\n\r\n\r\nDowngrading pyarrow to 6.0.1 solves the issue.\r\n\r\n—\r\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/3310#issuecomment-1247390774>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AKYUE3WBCSMHKJOOA2RQELLV6JLSVANCNFSM5IQ3WG7Q>.\r\nYou are receiving this because you commented.Message ID: ***@***.******@***.***>>\r\n", "> First of all, I’d never call a downgrade a solution, at most a (very) temporary workaround.\r\n\r\nVery much so! It looks like an apparent fix for the underlying problem [might](https://github.com/awslabs/aws-c-io/pull/515) have landed, but it sounds like it might still be a bit of a [lift](https://github.com/aws/aws-sdk-cpp/issues/1809#issuecomment-1289859795) to get it into aws-sdk-cpp.\r\n\r\n> Downgrading pyarrow to 6.0.1 solves the issue for me.\r\n\r\nSidenote: On conda-forge side, all recent pyarrow releases (all the way up to v9 and soon v10) have carried the respective pin and will not run into this issue.\r\n\r\n```\r\nconda install -c conda-forge pyarrow\r\n```\r\n\r\n", "For pip people, I confirmed that installing the nightly version of pyarrow also solves this by: `pip install --extra-index-url https://pypi.fury.io/arrow-nightlies/ --prefer-binary --pre pyarrow --upgrade`. (See https://arrow.apache.org/docs/python/install.html#installing-nightly-packages)\r\nAny version after https://github.com/apache/arrow/pull/14157 would work fine.", "> Furthermore: This bug also happens outside pyarrow, I incorporate AWS in a standalone Windows C-program and that crashes during exit.\r\n\r\nDo you have a reproducer you could share? I'd like to test if the new versions that supposedly solve this actually do, but we don't have a way to test it...", "Hi,\r\n\r\nNo – sorry. It is part of a massive eco-system which cannot easily be shared.\r\nBut I think the problem was summarized quite clearly: Windows does not allow a CreateThread while doing ExitProcess.\r\nThe cleanup that gets called as part of the exit handler code tries to start a thread, the fatal-assert on that causes the crash, and in windows we get a very big dump file.\r\nThe fix I applied simply removes that fatal assert, that solves the problem for me.\r\nI did not delve into the what the thread was trying to achieve and if that might cause issues when not executed during exit of the process. We did not notice anything of the kind.\r\nHowever, we *did* notice the many, many gigabytes of accumulated dumps of hundreds of processes 😊\r\n\r\nI’ll try and upgrade to the latest AWS version and report my findings, but that will be after I return from a month of vacationing…\r\n\r\n\r\n * Regards – Ruurd Beerstra\r\n\r\n\r\nFrom: h-vetinari ***@***.***>\r\nSent: Friday, 28 October 2022 02:09\r\nTo: huggingface/datasets ***@***.***>\r\nCc: Ruurd Beerstra ***@***.***>; Comment ***@***.***>\r\nSubject: Re: [huggingface/datasets] Fatal error condition occurred in aws-c-io (Issue #3310)\r\n\r\nSent by an external sender. Please be cautious about clicking on links and opening attachments.\r\n--------------------------------------------------------------------------------------------------------------------------------\r\n\r\n\r\nFurthermore: This bug also happens outside pyarrow, I incorporate AWS in a standalone Windows C-program and that crashes during exit.\r\n\r\nDo you have a reproducer you could share? I'd like to test if the new versions that supposedly solve this actually do, but we don't have a way to test it...\r\n\r\n—\r\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/3310#issuecomment-1294251331>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AKYUE3SHHPC5AT7KQ4GDAJDWFMKRTANCNFSM5IQ3WG7Q>.\r\nYou are receiving this because you commented.Message ID: ***@***.******@***.***>>\r\n", "> No – sorry. It is part of a massive eco-system which cannot easily be shared.\r\n\r\nOK, was worth a try...\r\n\r\n> The fix I applied simply removes that fatal assert, that solves the problem for me.\r\n\r\nThis seems to be what https://github.com/awslabs/aws-c-io/pull/515 did upstream.\r\n\r\n> I’ll try and upgrade to the latest AWS version and report my findings, but that will be after I return from a month of vacationing…\r\n\r\ncaution: aws-sdk-cpp hasn't yet upgraded its bundled(?) aws-c-io and hence doesn't contain the fix AFAICT", "Hi, I also encountered the same problem, but I made an error on Ubuntu without using `datasets` as @Crabzmatic he wrote.\r\n\r\nAt that time, I find my version of pyarrow is 9.0.0, which is different from as follow:\r\n> https://github.com/huggingface/datasets/issues/3310#issuecomment-1247390774\r\n> Downgrading pyarrow to 6.0.1 solves the issue for me.\r\n> \r\n> `pip install pyarrow==6.0.1`\r\n\r\nAs it happens, I found this error message when I introduced the [`Trainer`](https://huggingface.co/docs/transformers/main_classes/trainer) of HuggingFace\r\n\r\nFor example, I write following code:\r\n```python\r\nfrom transformers import Trainer\r\nprint('Hugging Face')\r\n```\r\n I get the following error message:\r\n```python\r\nHugging Face\r\nFatal error condition occurred in /opt/vcpkg/buildtrees/aws-c-io/src/9e6648842a-364b708815.clean/source/event_loop.c:72: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n################################################################################\r\nStack trace:\r\n################################################################################\r\n/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x200af06) [0x7fa9add1df06]\r\n/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x20028e5) [0x7fa9add158e5]\r\n/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x1f27e09) [0x7fa9adc3ae09]\r\n/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x200ba3d) [0x7fa9add1ea3d]\r\n/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x1f25948) [0x7fa9adc38948]\r\n/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x200ba3d) [0x7fa9add1ea3d]\r\n/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x1ee0b46) [0x7fa9adbf3b46]\r\n/home/ubuntu/anaconda3/envs/pytorch38/lib/python3.8/site-packages/pyarrow/libarrow.so.900(+0x194546a) [0x7fa9ad65846a]\r\n/lib/x86_64-linux-gnu/libc.so.6(+0x468d7) [0x7faa2fcfe8d7]\r\n/lib/x86_64-linux-gnu/libc.so.6(on_exit+0) [0x7faa2fcfea90]\r\n/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xfa) [0x7faa2fcdc0ba]\r\n/home/ubuntu/anaconda3/envs/pytorch38/bin/python(+0x1f9ad7) [0x5654571d1ad7]\r\n```\r\nBut, when I remove the `Trainer` module from transformers, **everthing is OK**.\r\n\r\nSo Why ?\r\n\r\n**Environment info**\r\n- Platform: Ubuntu 18\r\n- Python version: 3.8\r\n- PyArrow version: 9.0.0\r\n- transformers: 4.22.1\r\n- simpletransformers: 0.63.9", "> I get the following error message:\r\n\r\nNot sure what's going on, but that shouldn't happen, especially as we're pinning to a version that should avoid this.\r\n\r\nCan you please open an issue https://github.com/conda-forge/arrow-cpp-feedstock, including the requested output of `conda list` & `conda info`?", "pyarrow 10.0.1 was just released in conda-forge, which is the first release where we're building against aws-sdk-cpp 1.9.* again after more than a year. Since we cannot test the failure reported here on our infra, I'd be very grateful if someone could verify that the problem does or doesn't reappear. 🙃 \r\n\r\n```\r\nconda install -c conda-forge pyarrow=10\r\n```", "> pyarrow 10.0.1 was just released in conda-forge, which is the first release where we're building against aws-sdk-cpp 1.9.* again after more than a year. Since we cannot test the failure reported here on our infra, I'd be very grateful if someone could verify that the problem does or doesn't reappear. 🙃\r\n> \r\n> ```\r\n> conda install -c conda-forge pyarrow=10\r\n> ```\r\n\r\nThe problem is gone after I install the new version. Thanks!\r\npip install pyarrow==10", "@liuchaoqun, with `pip install pyarrow` you don't get aws-bindings, they're too complicated to package into wheels as far as I know. And even if they're packaged, at the time of the release of pyarrow 10 it would have still been pinned to aws 1.8 for the same reasons as in this issue." ]
1,637,584,074,000
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NONE
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## Describe the bug Fatal error when using the library ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('wikiann', 'en') ``` ## Expected results No fatal errors ## Actual results ``` Fatal error condition occurred in D:\bld\aws-c-io_1633633258269\work\source\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS Exiting Application ``` ## Environment info - `datasets` version: 1.15.2.dev0 - Platform: Windows-10-10.0.22504-SP0 - Python version: 3.8.12 - PyArrow version: 6.0.0
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3,309
fix: files counted twice in inferred structure
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[ "I see it creates some errors in the tests.\r\n\r\nAnother solution if needed is to add something like `data_files = list(set(data_files))` after [this line](https://github.com/huggingface/datasets/blob/8555197a3fe826e98bd0206c2d031c4488c53c5c/src/datasets/data_files.py#L511)", "Hi ! Thanks for the correction :)\r\n\r\nYour change seems right, let me look at the errors and try to fix this", "Not sure if it's due to this change but I tested `load_dataset('dalle-mini/encoded-vqgan_imagenet_f16_16384', streaming=True)` and the `validation` set is empty.", "So indeed there was an issue with the patterns `*` and `**/*` that would return some files twice. This issue came from the fact that we were not using the right `glob`.\r\n\r\nIndeed we were using `Path.rglob` for local files and `Path.match` for remote files. Since these two methods don't have the same behavior for such patterns, I decided to change that.\r\n\r\nIn particular, we now use `glob.glob` (same as `fsspec` glob) as a reference for data files resolution from patterns. This is the same as dask for example.\r\n\r\n/!\\ Here are some behaviors specific to `glob.glob` that are different from Path.glob, Path.match or fnmatch:\r\n- '*' matches only first level files\r\n- '**/*' matches only at least second level files\r\n\r\nThis way we have a consistent behavior with respect to other python data libraries and there's no overlap anymore between the two patterns.\r\n\r\nSome implementations details:\r\n\r\nTo ensure that we have the same behavior for local files and for files in a remote dataset repository, I decided to use `fsspec` glob for both. This was made possible by implementing the `HfFileSystem` class as a `fsspec` filesystem.\r\n\r\nI pushed those changes directly to your PR - I hope you don't mind. I'm still fixing the remaining tests.\r\nPlease let me know if that solves your problem, and then we can merge !", "There's still an issue with fsspec's glob - I'll take a look this afternoon", "I just found out that actually glob.glob and fsspec glob are different haha\r\nglob.glob needs `**/*` and recursive=True to look into deep subdirectories, while fsspec only requires `**`\r\n\r\nI think we can go with fsspec glob for consistency with dask and since it's our main tool for filesystems management", "To recap:\r\n```\r\nWe use fsspec glob as a reference for data files resolution from patterns.\r\nThis is the same as dask for example.\r\n\r\n/!\\ Here are some behaviors specific to fsspec glob that are different from glob.glob, Path.glob, Path.match or fnmatch:\r\n- '*' matches only first level items\r\n- '**' matches all items\r\n- '**/*' matches all at least second level items\r\n\r\nMore generally:\r\n- `*`` matches any character except a forward-slash (to match just the file or directory name)\r\n- `**`` matches any character including a forward-slash /\r\n```", "lol Windows… Maybe `Pathlib` for the tests?\r\n\r\nI tested streaming a repo and it worked perfectly now!" ]
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Files were counted twice in a structure like: ``` my_dataset_local_path/ ├── README.md └── data/ ├── train/ │ ├── shard_0.csv │ ├── shard_1.csv │ ├── shard_2.csv │ └── shard_3.csv └── valid/ ├── shard_0.csv └── shard_1.csv ``` The reason is that they were matching both `*train*/*` and `*train*/**/*`. This PR fixes it. @lhoestq
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I_kwDODunzps4_IvWZ
3,308
"dataset_infos.json" missing for chr_en and mc4
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[ "Hi ! Thanks for reporting :) \r\nWe can easily add the metadata for `chr_en` IMO, but for mC4 it will take more time, since it requires to count the number of examples in each language", "No problem. I am trying to do some analysis on the metadata of all available datasets. Is reading `metadata_infos.json` for each dataset the correct way to go? \r\n\r\nI noticed that the same information is also available as special variables inside .py file of each dataset. So, I was wondering if `metadata_infos.json` has been deprecated?\r\n\r\n![image](https://user-images.githubusercontent.com/8587189/142914413-a95a1abf-6f3e-4fbe-96e5-16d3ca39c831.png)\r\n", "The `dataset_infos.json` files have more information and are made to be used to analyze the datasets without having to run/parse the python scripts. Moreover some datasets on the Hugging face don't even have a python script, and for those ones we'll make tools to generate the JSON file automatically :)" ]
1,637,453,242,000
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## Describe the bug In the repository, every dataset has its metadata in a file called`dataset_infos.json`. But, this file is missing for two datasets: `chr_en` and `mc4`. ## Steps to reproduce the bug Check [chr_en](https://github.com/huggingface/datasets/tree/master/datasets/chr_en) and [mc4](https://github.com/huggingface/datasets/tree/master/datasets/mc4)
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PR_kwDODunzps4uzlWa
3,307
Add IndoNLI dataset
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[ "@lhoestq thanks for the review! I've modified the labels to follow other NLI datasets.\r\nPlease review my change and let me know if I miss anything." ]
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This PR adds IndoNLI dataset, from https://aclanthology.org/2021.emnlp-main.821/
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nested sequence feature won't encode example if the first item of the outside sequence is an empty list
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[ "knock knock", "Hi, thanks for reporting! I've linked a PR that should fix the issue.", "I've checked the PR and it looks great, thanks a lot!" ]
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## Describe the bug As the title, nested sequence feature won't encode example if the first item of the outside sequence is an empty list. ## Steps to reproduce the bug ```python from datasets import Features, Sequence, ClassLabel features = Features({ 'x': Sequence(Sequence(ClassLabel(names=['a', 'b']))), }) print(features.encode_batch({ 'x': [ [['a'], ['b']], [[], ['b']], ] })) ``` ## Expected results print `{'x': [[[0], [1]], [[], ['1']]]}` ## Actual results print `{'x': [[[0], [1]], [[], ['b']]]}` ## Environment info - `datasets` version: 1.15.1 - Platform: Linux-5.13.0-21-generic-x86_64-with-glibc2.34 - Python version: 3.9.7 - PyArrow version: 6.0.0 ## Additional information I think the issue stems from [here](https://github.com/huggingface/datasets/blob/8555197a3fe826e98bd0206c2d031c4488c53c5c/src/datasets/features/features.py#L847-L848).
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asserts replaced with exception for ``fingerprint.py``, ``search.py``, ``arrow_writer.py`` and ``metric.py``
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Addresses #3171 Fixes exception for ``fingerprint.py``, ``search.py``, ``arrow_writer.py`` and ``metric.py`` and modified tests
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Dataset object has no attribute `to_tf_dataset`
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[ "The issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.\r\n\r\n```\r\n# upgrade transformers and datasets to latest versions\r\n!pip install --upgrade transformers\r\n!pip install --upgrade datasets\r\n```\r\n\r\nRegards!" ]
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I am following HuggingFace Course. I am at Fine-tuning a model. Link: https://huggingface.co/course/chapter3/2?fw=tf I use tokenize_function and `map` as mentioned in the course to process data. `# define a tokenize function` `def Tokenize_function(example):` ` return tokenizer(example['sentence'], truncation=True)` `# tokenize entire data` `tokenized_data = raw_data.map(Tokenize_function, batched=True)` I get Dataset object at this point. When I try converting this to a TF dataset object as mentioned in the course, it throws the following error. `# convert to TF dataset` `train_data = tokenized_data["train"].to_tf_dataset( ` ` columns = ['attention_mask', 'input_ids', 'token_type_ids'], ` ` label_cols = ['label'], ` ` shuffle = True, ` ` collate_fn = data_collator, ` ` batch_size = 8 ` `)` Output: `---------------------------------------------------------------------------` `AttributeError Traceback (most recent call last)` `/tmp/ipykernel_42/103099799.py in <module>` ` 1 # convert to TF dataset` `----> 2 train_data = tokenized_data["train"].to_tf_dataset( \` ` 3 columns = ['attention_mask', 'input_ids', 'token_type_ids'], \` ` 4 label_cols = ['label'], \` ` 5 shuffle = True, \` `AttributeError: 'Dataset' object has no attribute 'to_tf_dataset'` When I look for `dir(tokenized_data["train"])`, there is no method or attribute in the name of `to_tf_dataset`. Why do I get this error? And how to clear this? Please help me.
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[ "\r\n> \r\n> Input:\r\n> \r\n> ```\r\n> tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n> data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\r\n> ```\r\n> \r\n> Output:\r\n> \r\n> ```\r\n> TypeError Traceback (most recent call last)\r\n> /tmp/ipykernel_42/1563280798.py in <module>\r\n> 1 checkpoint = 'bert-base-uncased'\r\n> 2 tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n> ----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"pt\")\r\n> TypeError: __init__() got an unexpected keyword argument 'return_tensors'\r\n> ```\r\n> \r\n\r\nThe issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.\r\n\r\n`# upgrade transformers and datasets to latest versions`\r\n`!pip install --upgrade transformers`\r\n`!pip install --upgrade datasets`\r\n\r\nCheers!" ]
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Hi, I am following the HuggingFace course. I am now at Fine-tuning [https://huggingface.co/course/chapter3/3?fw=tf](https://huggingface.co/course/chapter3/3?fw=tf). When I set up `DataCollatorWithPadding` as following I got an error while trying to reproduce the course code in Kaggle. This error occurs with either a CPU-only-device or a GPU-device. Input: ```checkpoint = 'bert-base-uncased' tokenizer = AutoTokenizer.from_pretrained(checkpoint) data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf") ``` Output: ```--------------------------------------------------------------------------- TypeError Traceback (most recent call last) /tmp/ipykernel_42/1563280798.py in <module> 1 checkpoint = 'bert-base-uncased' 2 tokenizer = AutoTokenizer.from_pretrained(checkpoint) ----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="pt") TypeError: __init__() got an unexpected keyword argument 'return_tensors' ``` When I call `help` method, it too confirms that there is no argument `return_tensors`. Input: ``` help(DataCollatorWithPadding.__init__) ``` Output: ``` Help on function __init__ in module transformers.data.data_collator: __init__(self, tokenizer: transformers.tokenization_utils_base.PreTrainedTokenizerBase, padding: Union[bool, str, transformers.file_utils.PaddingStrategy] = True, max_length: Union[int, NoneType] = None, pad_to_multiple_of: Union[int, NoneType] = None) -> None ``` But, the source file *[Data Collator - docs](https://huggingface.co/transformers/main_classes/data_collator.html#datacollatorwithpadding)* says that there is such an argument. By default, it returns Pytorch tensors while I need TF tensors. Where do I miss? Please help me.
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fix old_val typo in f-string
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This PR is to correct a typo in #3277 that @Carlosbogo revieled in a comment. Related closed issue : #3257 Sorry about that 😅.
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Add the missing tags to the wikipedia dataset card. I also added the missing languages code in our language codes list. This should also fix the code snippet that is presented on the Hub to load the dataset: fix https://github.com/huggingface/datasets/issues/3292
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❓ Dataset loading script from Hugging Face Hub
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[ "Hi ! In the next version of `datasets`, your train and test splits will be correctly separated (changes from #3027) if you create a dataset repository with only your CSV files.\r\n\r\nAlso it seems that you overwrite the `data_files` and `data_dir` arguments in your code, when you instantiate the AGNewsConfig objects. Those parameters are not necessary since you already know which files you want to load.\r\n\r\nYou can find an example on how to specify which file the dataset has to download in this [example script](https://huggingface.co/datasets/lhoestq/custom_squad/blob/main/custom_squad.py#L101-L107):\r\n```python\r\n_URLS = {\r\n \"train\": \"train-v1.1.json\", # you can use a URL or a relative path from the python script to your file in the repository\r\n \"dev\": \"dev-v1.1.json\",\r\n}\r\n```\r\n```python\r\n def _split_generators(self, dl_manager):\r\n downloaded_files = dl_manager.download_and_extract(_URLS)\r\n\r\n return [\r\n datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": downloaded_files[\"train\"]}),\r\n datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={\"filepath\": downloaded_files[\"dev\"]}),\r\n ]\r\n```", "Also I think the viewer will be updated when you fix the dataset script, let me know if it doesn't", "Hi @lhoestq,\r\n\r\nThanks a lot for the super quick answer!\r\n\r\nYour suggestion solves my issue. I am now able to load the dataset properly 🚀 \r\nHowever, the dataviewer is not working yet.\r\n\r\nReally, thanks a lot for your help and consideration!\r\n\r\nBest,\r\nPietro", "Great ! We'll take a look at the viewer to fix it", "@lhoestq I think I am having a related problem.\r\nMy call to load_dataset() looks like this:\r\n\r\n```\r\n datasets = load_dataset(\r\n os.path.abspath(layoutlmft.data.datasets.xfun.__file__),\r\n f\"xfun.{data_args.lang}\",\r\n additional_langs=data_args.additional_langs,\r\n keep_in_memory=True,\r\n )\r\n\r\n```\r\n\r\nMy _split_generation code is:\r\n\r\n```\r\n def _split_generators(self, dl_manager):\r\n \"\"\"Returns SplitGenerators.\"\"\"\r\n\r\n downloaded_file = dl_manager.download_and_extract(\"https://guillaumejaume.github.io/FUNSD/dataset.zip\")\r\n return [\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": f\"{downloaded_file}/dataset/training_data/\"}\r\n ),\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TEST, gen_kwargs={\"filepath\": f\"{downloaded_file}/dataset/testing_data/\"}\r\n ),\r\n ]\r\n\r\n```\r\nHowever I get the error \"TypeError: _generate_examples() got an unexpected keyword argument 'filepath'\"\r\nThe path looks right and I see the data in the path so I think the only problem I have is that it doesn't like the key \"filepath\". However, the documentation (example [here](https://huggingface.co/datasets/lhoestq/custom_squad/blob/main/custom_squad.py#L101-L107)) seems to show that this is the correct parameter. \r\n\r\nHere is the full stack trace:\r\n\r\n```\r\nDownloading and preparing dataset xfun/xfun.en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/caseygre/.cache/huggingface/datasets/xfun/xfun.en/0.0.0/96b8cb7c57f6f822f0ab37ae3be7b82d84ac57062e774c9361ccf0a4b9ef61cc...\r\nTraceback (most recent call last):\r\n File \"/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py\", line 574, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py\", line 652, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/Users/caseygre/PycharmProjects/aegis-ml-new/unilm/venv-LayoutLM/lib/python3.9/site-packages/datasets/builder.py\", line 975, in _prepare_split\r\n generator = self._generate_examples(**split_generator.gen_kwargs)\r\nTypeError: _generate_examples() got an unexpected keyword argument 'filepath'\r\npython-BaseException\r\n```", "Hi ! The `gen_kwargs` dictionary is passed to `_generate_examples`, so in your case it must be defined this way:\r\n```python\r\ndef _generate_examples(self, filepath):\r\n ...\r\n```\r\n\r\nAnd here is an additional tip: you can use `os.path.join(downloaded_file, \"dataset/testing_data\")` instead of `f\"downloaded_file}/dataset/testing_data/\"` to get compatibility with Windows and streaming.\r\n\r\nIndeed Windows uses a backslash separator, not a slash, and streaming uses chained URLs (like `zip://dataset/testing_data::https://https://guillaumejaume.github.io/FUNSD/dataset.zip` for example)", "Thanks for you quick reply @lhoestq and so sorry for my very delayed response.\r\nWe have gotten around the error another way but I will try to duplicate this when I can. We may have had \"filepaths\" instead of \"filepath\" in our def of _generate_examples() and not noticed the difference. If I find a more useful answer for others I will add to this ticket so they know what the issue was.\r\nNote: we do have our own _generate_examples() defined with the same def as Quentin has. (But one version does have \"filepaths\".)\r\n", "Fixed in the viewer: https://huggingface.co/datasets/pietrolesci/ag_news" ]
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Hi there, I am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below. Issues I have encountered: - Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations - Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this ```python load_dataset("pietrolesci/ag_news", name="my_configuration") ``` Apparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following ``` ag_news.py train.csv test.csv ``` and the loading script I specify `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({"train": "train.csv", "test": "test.csv"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub. Any suggestion is very much appreciated. Best, Pietro Link to the hub repo: https://huggingface.co/datasets/pietrolesci/ag_news BONUS: how can I make the data viewer work in this specific case? :)
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I_kwDODunzps4_F7TF
3,299
Add option to find unique elements in nested sequences when calling `Dataset.unique`
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[ "Hi @mariosasko!\r\n\r\nHas this been patched into any of the releases?", "Hi! Not yet, would you be interested in contributing a PR? I can give you some pointers if needed. ", "@mariosasko did this ever get implemented? Willing to help if you are still up for it.", "@dcruiz01 No, but here is an example of how to do this with the existing API:\r\n\r\n\r\n```python\r\nds = Dataset.from_dict({\"tokens\": [[\"a\", \"b\"], [\"c\", \"a\"], [\"c\", \"e\"]]})\r\n\r\ndef flatten_tokens(pa_table):\r\n return pa.table([pc.list_flatten(pa_table[\"tokens\"])], [\"flat_tokens\"])\r\n\r\nds = ds.with_format(\"arrow\")\r\nds = ds.map(flatten_tokens, batched=True)\r\nds = ds.with_format(None)\r\n\r\nunique_tokens = ds.unique(\"flat_tokens\")\r\n```\r\n\r\nWhen I think about it, `.unique` on `Sequence(Value(...))` should return unique sequences/arrays, not unique elements of these sequences..." ]
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CONTRIBUTOR
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It would be nice to have an option to flatten nested sequences to find unique elements stored in them when calling `Dataset.unique`. ~~Currently, `Dataset.unique` only supports finding unique sequences and not unique elements in that situation.~~
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Agnews dataset viewer is not working
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[ "Hi ! Thanks for reporting\r\nWe've already fixed the code that generates the preview for this dataset, we'll release the fix soon :)", "Hi @lhoestq, thanks for your feedback!", "Fixed in the viewer.\r\n\r\nhttps://huggingface.co/datasets/ag_news" ]
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## Dataset viewer issue for '*name of the dataset*' **Link:** https://huggingface.co/datasets/ag_news Hi there, the `ag_news` dataset viewer is not working. Am I the one who added this dataset? No
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I_kwDODunzps4_E9Mz
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.map() cache is wrongfully reused - only happens when the mapping function is imported
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[ "Hi ! Thanks for reporting. Indeed this is a current limitation of the usage we have of `dill` in `datasets`. I'd suggest you use your workaround for now until we find a way to fix this. Maybe functions that are not coming from a module not installed with pip should be dumped completely, rather than only taking their locations into account", "I agree. Sounds like a solution for it would be pretty dirty, even [cloudpickle](https://stackoverflow.com/a/16891169) doesn't help in this case.\r\nIn the meanwhile I think that adding a warning and the workaround somewhere in the documentation can be helpful.", "For anyone interested, I see that with `dill==0.3.6` the workaround I suggested doesn't work anymore.\r\nI opened an issue about it: https://github.com/uqfoundation/dill/issues/572.\r\n\r\n " ]
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CONTRIBUTOR
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## Describe the bug When `.map` is used with a mapping function that is imported, the cache is reused even if the mapping function has been modified. The reason for this is that `dill` that is used for creating the fingerprint [pickles imported functions by reference](https://stackoverflow.com/a/67851411). I guess it is not a widespread case, but it can still lead to unwanted results unnoticeably. ## Steps to reproduce the bug Create files `a.py` and `b.py`: ```python # a.py from datasets import load_dataset def main(): squad = load_dataset("squad") squad.map(mapping_func, batched=True) def mapping_func(examples): ID_LENGTH = 4 examples["id"] = [id_[:ID_LENGTH] for id_ in examples["id"]] return examples if __name__ == "__main__": main() ``` ```python # b.py from datasets import load_dataset from a import mapping_func def main(): squad = load_dataset("squad") squad.map(mapping_func, batched=True) if __name__ == "__main__": main() ``` Run `python b.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see "Loading cached processed dataset at...". Now change `ID_LENGTH` to another number in order to change the mapping function, and run `python b.py` again. You'll see that `.map` loads from the cache the result of the previous mapping function. ## Expected results Run `python a.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see "Loading cached processed dataset at...". Now change `ID_LENGTH` to another number in order to change the mapping function, and run `python a.py` again. You'll see that the dataset is being processed and that there's no reuse of the previous mapping function result. ## Workaround Put the mapping function inside a dummy class as a static method: ```python # a.py class MappingFuncClass: @staticmethod def mapping_func(examples): ID_LENGTH = 4 examples["id"] = [id_[:ID_LENGTH] for id_ in examples["id"]] return examples ``` ```python # b.py from datasets import load_dataset from a import MappingFuncClass def main(): squad = load_dataset("squad") squad.map(MappingFuncClass.mapping_func, batched=True) if __name__ == "__main__": main() ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.1 - Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17 - Python version: 3.8.10 - PyArrow version: 4.0.1
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Fix temporary dataset_path creation for URIs related to remote fs
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[ "Hi ! Thanks for the fix :) \r\n\r\nI think this should be `extract_path_from_uri` 's responsibility to strip the extra `/` from a badly formatted path like `hdfs:///absolute/path` (or raise an error). Do you think you can simply do the changes in `extract_path_from_uri` ? This way this fix will be available for all the other parts of the lib that need to extract the inner path from an URI of a remote filesystem\r\n\r\nThen we can also keep your test cases but simply apply them to `extract_path_from_uri` instead", "Hi @lhoestq! No problem! Thanks for your interest! :)\r\n\r\nI think stripping the 3rd `/` in `hdfs:///absolute/path` inside `extract_path_from_uri` is not the solution. When I provide `hdfs:///absolute/path` to `extract_path_from_uri` we want `/absolute/path` to be returned, as it does now (at least in the case of URIs with `hdfs` schemas, for `s3` is different as it should start with a bucket name).\r\n\r\nThe problem comes in line 1041 in the original code below:\r\n\r\nhttps://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1038-L1042\r\n\r\nLets assume the following parameters for line 1041 after `extract_path_from_uri` has removed the `hdfs` schema part and the `://` from `hdfs:///absolute/path`, and `get_temporary_cache_files_directory()` returns `/tmp/a1b2b3c4`, as it is shown below: \r\n\r\n```python\r\nsrc_dataset_path = '/absolute/path'\r\ntmp_dir = '/tmp/a1b2b3c4'\r\ndataset_path = Path(tmp_dir, src_dataset_path)\r\n```\r\n\r\nAfter passing those paths to the `Path` object, `dataset_path` contains only `/absolute/path`; that is, it has lost the temporary directory path. This is because, when two (or more) absolute paths are passed to the `Path` function, only the last one is taken. However, if the contents of those variables are:\r\n\r\n```python\r\nsrc_dataset_path = 'relative/path'\r\ntmp_dir = '/tmp/a1b2b3c4'\r\ndataset_path = Path(tmp_dir, src_dataset_path)\r\n```\r\n\r\nthen `dataset_path` contains `/tmp/a1b2b3c4/relative/path` as expected.\r\n\r\nAbsolute paths are allowed in hdfs URIs, so that's why I added the extra function `build_local_temp_path` in the PR; so in case the second argument is an absolute path, it still will create the correct absolute path by concatenating the temp dir and the path passed by converting it to a relative path (and it also works for windows paths too.) It also allows to add the tests, checking that the main combinations are ok.\r\n\r\nI've checked all the places where the result of `extract_path_from_uri` is used, and as far as I've seen this is the only place where it is concatenated with another possible absolute path, so no need to add `build_local_temp_path` anywhere else. \r\n" ]
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This aims to close #3295
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Temporary dataset_path for remote fs URIs not built properly in arrow_dataset.py::load_from_disk
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[ "Hi ! Good catch and thanks for opening a PR :)\r\n\r\nI just responded in your PR" ]
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## Describe the bug When trying to build a temporary dataset path from a remote URI in this block of code: https://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1038-L1042 the result is not the expected when passing an absolute path in an URI like `hdfs:///absolute/path`. ## Steps to reproduce the bug ```python dataset_path = "hdfs:///absolute/path" src_dataset_path = extract_path_from_uri(dataset_path) tmp_dir = get_temporary_cache_files_directory() dataset_path = Path(tmp_dir, src_dataset_path) print(dataset_path) ``` ## Expected results With the code above, we would expect a value in `dataset_path` similar to: `/tmp/tmpnwxyvao5/absolute/path` ## Actual results However, we get a `dataset_path` value like: `/absolute/path` This is because this line here: https://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1041 returns the last absolute path when two absolute paths (the one in `tmp_dir` and the one extracted from the URI in `src_dataset_path`) are passed as arguments. ## Environment info - `datasets` version: 1.13.3 - Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33 - Python version: 3.9.7 - PyArrow version: 5.0.0
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Add Natural Adversarial Objects dataset
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## Adding a Dataset - **Name:** Natural Adversarial Objects (NAO) - **Description:** Natural Adversarial Objects (NAO) is a new dataset to evaluate the robustness of object detection models. NAO contains 7,934 images and 9,943 objects that are unmodified and representative of real-world scenarios, but cause state-of-the-art detection models to misclassify with high confidence. - **Paper:** https://arxiv.org/abs/2111.04204v1 - **Data:** https://drive.google.com/drive/folders/15P8sOWoJku6SSEiHLEts86ORfytGezi8 - **Motivation:** interesting object detection dataset useful for miscclassifications cc @NielsRogge 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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Pin version exclusion for Markdown
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As Markdown version 3.3.5 has a bug, it is better to exclude it in case the users have it previously installed in their environment. Related to #3289, #3286.
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Not able to load 'wikipedia' dataset
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[ "Hi ! Indeed it looks like the code snippet on the Hugging face Hub doesn't show the second parameter\r\n\r\n![image](https://user-images.githubusercontent.com/42851186/142649237-45ba55c5-1a64-4c30-8692-2c8120572f92.png)\r\n\r\nThanks for reporting, I'm taking a look\r\n" ]
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## Describe the bug I am following the instruction for loading the wikipedia dataset using datasets. However getting the below error. ## Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset("wikipedia") ``` ## Expected results A clear and concise description of the expected results. ## Actual results ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs) 339 "Config name is missing." 340 "\nPlease pick one among the available configs: %s" % list(self.builder_configs.keys()) --> 341 + "\nExample of usage:\n\t`{}`".format(example_of_usage) 342 ) 343 builder_config = self.BUILDER_CONFIGS[0] ValueError: Config name is missing. Please pick one among the available configs: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu'] Example of usage: `load_dataset('wikipedia', '20200501.aa')` I think the other parameter is missing in the load_dataset function that is not shown in the instruction.
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Use f-strings in the dataset scripts
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Uses f-strings to format the .py files in the dataset folder
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Make several audio datasets streamable
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[ "Reading FLAC (for `librispeech_asr`) works OK for me (`soundfile` version: `0.10.3`):\r\n```python\r\nIn [2]: ds = load_dataset(\"datasets/librispeech_asr/librispeech_asr.py\", \"clean\", streaming=True, split=\"train.100\")\r\n\r\nIn [3]: item = next(iter(ds))\r\n\r\nIn [4]: item.keys()\r\nOut[4]: dict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])\r\n\r\nIn [5]: item[\"file\"]\r\nOut[5]: '374-180298-0000.flac'\r\n\r\nIn [6]: item[\"audio\"].keys()\r\nOut[6]: dict_keys(['path', 'array', 'sampling_rate'])\r\n\r\nIn [7]: item[\"audio\"][\"sampling_rate\"]\r\nOut[7]: 16000\r\n\r\nIn [8]: item[\"audio\"][\"path\"]\r\nOut[8]: '374-180298-0000.flac'\r\n\r\nIn [9]: item[\"audio\"][\"array\"].shape\r\nOut[9]: (232480,)\r\n```", "Oh cool ! I think this might have come from an issue with my local `soundfile` installation then", "I'll do `multilingual_librispeech` in a separate PR since it requires the data to be in another format (in particular separate the train/dev/test splits in different files)", "@lhoestq @albertvillanova - think it would have been nice to have added a big message at the top stating that this is a breaking change and ping `transformers` people a bit more here." ]
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<s>Needs https://github.com/huggingface/datasets/pull/3129 to be merged first</s> Make those audio datasets streamable: - [x] common_voice - [x] openslr - [x] vivos - [x] librispeech_asr <s>(still has some issues to read FLAC)</s> *actually it's ok* - [ ] <s>multilingual_librispeech (yet to be converted)</S> *TODO in a separate PR*
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Unpin markdown for build_docs now that it's fixed
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`markdown`'s bug has been fixed, so this PR reverts #3286
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Allow datasets with indices table when concatenating along axis=1
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Calls `flatten_indices` on the datasets with indices table in `concatenate_datasets` to fix issues when concatenating along `axis=1`. cc @lhoestq: I decided to flatten all the datasets instead of flattening all the datasets except the largest one in the end. The latter approach fails on the following example: ```python a = Dataset.from_dict({"a": [10, 20, 30, 40]}) b = Dataset.from_dict({"b": [10, 20, 30, 40, 50, 60]}) # largest dataset a = a.select([1, 2, 3]) b = b.select([1, 2, 3]) concatenate_datasets([a, b], axis=1) # fails at line concat_tables(...) because the real length of b's data is 6 and a's length is 3 after flattening (was 4 before flattening) ``` Also, it requires additional re-ordering of indices to prepare them for working with the indices table of the largest dataset. IMO not worth when we save only one `flatten_indices` call. (feel free to check the code of that approach at https://github.com/huggingface/datasets/commit/6acd10481c70950dcfdbfd2bab0bf0c74ad80bcb if you are interested) Fixes #3273
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Add The Pile dataset and PubMed Central subset
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Add: - The complete final version of The Pile dataset: "all" config - PubMed Central subset of The Pile: "pubmed_central" config Close #1675, close bigscience-workshop/data_tooling#74. CC: @StellaAthena, @lewtun
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Fix build_docs CI
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Because of https://github.com/Python-Markdown/markdown/issues/1196 we have to temporarily pin `markdown` to 3.3.4 for the docs to build without issues
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Add IEMOCAP dataset
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[ "The IEMOCAP dataset is private and available only on request.\r\n```\r\nTo obtain the IEMOCAP data you just need to fill out an electronic release form below.\r\n```\r\n\r\n- [Request form](https://sail.usc.edu/iemocap/release_form.php)\r\n- [License ](https://sail.usc.edu/iemocap/Data_Release_Form_IEMOCAP.pdf)\r\n\r\n\r\n> We do not share the dataset for commercial purposes due to privacy concerns surrounding the participants of the research. The login details will only be emailed to the given academic email address.\r\n\r\nI think it won't be possible to add this dataset to 🤗 datasets.", "Hi @dnaveenr ! We can contact the authors to see if they are interested in hosting the dataset on the Hub. In the meantime, feel free to work on a script with manual download.", "Hi @mariosasko . Thanks for your response. Sure, I will mail them and find out if they're open to this.\r\n\r\nWork on a script with manual download ? This is new to me, any guidelines would be helpful here.\r\n", "> Thanks for your response. Sure, I will mail them and find out if they're open to this.\r\n\r\nIt's best to leave this part to us because we have to explain how login would work and (potentially) set up a custom verification for the dataset.\r\n\r\n> Work on a script with manual download ? This is new to me, any guidelines would be helpful here.\r\n\r\nFor instance, this is one of the scripts with manual download: https://huggingface.co/datasets/arxiv_dataset. Compared to the standard dataset, it has the `manual_download_instructions` attribute and uses `dl_manager.manual_dir` (derived from `load_dataset(..., data_dir=\"path/to/data\")`) to access the dataset's data files.", "> It's best to leave this part to us because we have to explain how login would work and (potentially) set up a custom verification for the dataset.\r\n\r\nYes. That would be perfect. Thanks.\r\n\r\n----\r\nOkay. Thanks for giving a reference. This is helpful. I will go through it.\r\n\r\n", "@mariosasko has this been solved? I would like to use login and custom verification for training on my private dataset.", "@flckv I think the [gating mechanism](https://huggingface.co/docs/hub/datasets-gated) is what you are looking for. ", "@mariosasko Thanks, but no. I would like to keep my HuggingFace Dataset private and train a model on it. Is this possible?" ]
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## Adding a Dataset - **Name:** IEMOCAP - **Description:** acted, multimodal and multispeaker database - **Paper:** https://sail.usc.edu/iemocap/Busso_2008_iemocap.pdf - **Data:** https://sail.usc.edu/iemocap/index.html - **Motivation:** Useful multimodal dataset cc @anton-l 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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Add VoxLingua107 dataset
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## Adding a Dataset - **Name:** VoxLingua107 - **Description:** VoxLingua107 is a speech dataset for training spoken language identification models. The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives. - **Paper:** https://arxiv.org/abs/2011.12998 - **Data:** http://bark.phon.ioc.ee/voxlingua107/ - **Motivation:** Nice audio classification dataset cc @anton-l 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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Add Speech Commands dataset
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## Adding a Dataset - **Name:** Speech commands - **Description:** A Dataset for Limited-Vocabulary Speech Recognition - **Paper:** https://arxiv.org/abs/1804.03209 - **Data:** https://www.tensorflow.org/datasets/catalog/speech_commands, Available: http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz - **Motivation:** Nice dataset for audio classification training cc @anton-l 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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ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
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[ "Hi ! Thanks for reporting :)\r\nI think this is because the dataset is behind an access page. We can fix the dataset viewer\r\n\r\nIf you also have this error when you use the `datasets` library in python, you should probably pass `use_auth_token=True` to the `load_dataset()` function to use your account to access the dataset.", "Ah ok, I didn't realise about the login page. I'll try `use_auth_token=True` and see if that solves it.\r\n\r\nRegards!", "Hi, \r\n\r\nUsing `use_auth_token=True` and downloading the credentials with `huggingface-cli login` (stored in .huggingface/token) solved the issue.\r\n\r\nShould I leave the issue open until you fix the Dataset viewer issue?", "Cool ! Yes let's keep this issue open until the viewer is fixed - I'll close it when this is fixed. Thanks", "The error I get when trying to load OSCAR 21.09 is this\r\n```\r\nConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py\r\n```\r\n\r\nThe URL I get in the browser is this\r\n```\r\nhttps://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py\r\n```\r\n\r\nMaybe URL is the issue? (resolve vs blob)", "> The error I get when trying to load OSCAR 21.09 is this\r\n> \r\n> ```\r\n> ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py\r\n> ```\r\n> \r\n> The URL I get in the browser is this\r\n> \r\n> ```\r\n> https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py\r\n> ```\r\n> \r\n> Maybe URL is the issue? (resolve vs blob)\r\n\r\nYou need to download your login credentials. See `huggingface-cli login` documentation and when loading the dataset use `use_auth_token=True`:\r\n`\r\nload_dataset(corpus, language, split=None, use_auth_token=True, cache_dir=cache_folder)`", "Fixed.\r\n\r\n<img width=\"1542\" alt=\"Capture d’écran 2022-04-12 à 13 57 24\" src=\"https://user-images.githubusercontent.com/1676121/162957585-af96d19c-f86c-47fe-80c4-2b071083cee4.png\">\r\n" ]
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## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)* *The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.* ``` raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py ``` Am I the one who added this dataset ? No Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library.
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[Datasets] Improve Covost 2
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[ "I am trying to use `load_dataset` with the French dataset(common voice corpus 1) which is downloaded from a common voice site and the target language is English (using colab)\r\n\r\nSteps I have followed:\r\n\r\n**1. untar:**\r\n`!tar xvzf fr.tar -C data_dir`\r\n\r\n**2. load data:**\r\n`load_dataset('covost2', 'fr_en', data_dir=\"/content/data_dir\")`\r\n\r\n0 rows are loading as shown below:\r\n```\r\nUsing custom data configuration fr_en-data_dir=%2Fcontent%2Fdata_dir\r\nReusing dataset covost2 (/root/.cache/huggingface/datasets/covost2/fr_en-data_dir=%2Fcontent%2Fdata_dir/1.0.0/bba950aae1ffa5a14b876b7e09c17b44de2c3cf60e7bd5d459640beffc78e35b)\r\n100%\r\n3/3 [00:00<00:00, 54.98it/s]\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['client_id', 'file', 'audio', 'sentence', 'translation', 'id'],\r\n num_rows: 0\r\n })\r\n validation: Dataset({\r\n features: ['client_id', 'file', 'audio', 'sentence', 'translation', 'id'],\r\n num_rows: 0\r\n })\r\n test: Dataset({\r\n features: ['client_id', 'file', 'audio', 'sentence', 'translation', 'id'],\r\n num_rows: 0\r\n })\r\n})\r\n```\r\n\r\nCan you please provide a sample working example code to load the dataset?", "Hi ! I think it only works with the subsets of Common Voice Corpus 4, not Common Voice Corpus 1" ]
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It's currently quite confusing to understand the manual data download instruction of Covost and not very user-friendly. Currenty the user has to: 1. Go on Common Voice website 2. Find the correct dataset which is **not** mentioned in the error message 3. Download it 4. Untar it 5. Create a language id folder (why? this folder does not exist in the `.tar` downloaded file) 6. pass the folder containing the created language id folder This PR improves this to: 1. Go on Common Voice website 2. Find the correct dataset which **is** mentioned in the error message 3. Download it 4. Untar it 5. pass the untared folder **Note**: This PR is not at all time-critical
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Fix bookcorpusopen RAM usage
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Each document is a full book, so the default arrow writer batch size of 10,000 is too big, and it can fill up RAM quickly before flushing the first batch on disk. I changed its batch size to 256 to use maximum 100MB of memory Fix #3167.
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Minor Typo Fix - Precision to Recall
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Proposed update to the documentation for WER
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I wanted to submit a minor update to the description of WER for your consideration. Because of the possibility of insertions, the numerator in the WER formula can be larger than N, so the value of WER can be greater than 1.0: ``` >>> from datasets import load_metric >>> metric = load_metric("wer") >>> metric.compute(predictions=["hello how are you"], references=["hello"]) 3.0 ``` and similarly from the underlying jiwer module's `wer` function: ``` >>> from jiwer import wer >>> wer("hello", "hello how are you") 3.0 ```
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f-string formatting
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[ "Hello @lhoestq, ```make style``` is applied as asked. :)" ]
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**Fix #3257** Replaced _.format()_ and _%_ by f-strings in the following modules : - [x] **tests** - [x] **metrics** - [x] **benchmarks** - [x] **utils** - [x] **templates** - [x] **src/Datasets/\*.py** Modules in **_src/Datasets/_**: - [x] **commands** - [x] **features** - [x] **formatting** - [x] **io** - [x] **tasks** - [x] **utils** Module **datasets** will not be edited as asked by @mariosasko -A correction of the first PR (#3267)-
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Update KILT metadata JSON
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Fix #3265.
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