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"NONE"
"**Fix #3257** Replaced _.format()_ and _%_ by f-strings in the following modules : - [x] **tests** - [x] **metrics** - [x] **benchmarks** - [x] **utils** - [x] **templates** Will follow in the next PR the modules left : - [ ] **src** Module **datasets** will not be edited as asked by @mariosasko PS : black and isort applied to files "
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"[#3264](https://github.com/huggingface/datasets/issues/3264)"
[ "There seems to be problems with datasets metadata, of which I dont have access to. I think one of the datasets is from reddit. Can anyone help?" ]
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"## Describe the bug Checksum failed when downloads kilt_tasks_wow. See error output for details. ## Steps to reproduce the bug ```python import datasets datasets.load_datasets('kilt_tasks','wow') ``` ## Expected results Download successful ## Actual results ``` Downloading and preparing dataset kilt_tasks/wow (download: 72.07 MiB, generated: 61.82 MiB, post-processed: Unknown size, total: 133.89 MiB) to /root/.cache/huggingface/datasets/kilt_tasks/wow/1.0.0/57dc8b2431e76637e0c6ef79689ca4af61ed3a330e2e0cd62c8971465a35db3a... 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 5121.25it/s] 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1527.42it/s] Traceback (most recent call last): File "kilt_wow.py", line 30, in <module> main() File "kilt_wow.py", line 27, in main train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/") File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data dataset = self.load_dataset() File "kilt_wow.py", line 21, in load_dataset return datasets.load_dataset('kilt_tasks','wow') File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 679, in _download_and_prepare verify_checksums( File "/opt/conda/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['http://dl.fbaipublicfiles.com/KILT/wow-train-kilt.jsonl', 'http://dl.fbaipublicfiles.com/KILT/wow-dev-kilt.jsonl'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.1 - Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10 - Python version: 3.8.3 - PyArrow version: 4.0.1 "
[ "Using `dataset = load_dataset(\"kilt_tasks\", \"wow\", ignore_verifications=True)` may fix it, but I am not think it is a elegant solution." ]
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"## Describe the bug - WikiAuto Manual The original manual datasets with the following downloading URL in this [repository](https://github.com/chaojiang06/wiki-auto) was [deleted](https://github.com/chaojiang06/wiki-auto/commit/0af9b066f2b4e02726fb8a9be49283c0ad25367f) by the author. ``` https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv ``` - jeopardy The downloading URL for jeopardy may move from ``` http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz ``` to ``` https://drive.google.com/file/d/0BwT5wj_P7BKXb2hfM3d2RHU1ckE/view?resourcekey=0-1abK4cJq-mqxFoSg86ieIg ``` - definite_pronoun_resolution The following downloading URL for definite_pronoun_resolution cannot be reached for some reasons. ``` http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt ``` ## Steps to reproduce the bug ```python import datasets datasets.load_datasets('wiki_auto','manual') datasets.load_datasets('jeopardy') datasets.load_datasets('definite_pronoun_resolution') ``` ## Expected results Download successfully ## Actual results - WikiAuto Manual ``` Downloading and preparing dataset wiki_auto/manual (download: 151.65 MiB, generated: 155.97 MiB, post-processed: Unknown size, total: 307.61 MiB) to /root/.cache/huggingface/datasets/wiki_auto/manual/1.0.0/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8... 0%| | 0/3 [00:00<?, ?it/s]Traceback (most recent call last): File "wiki_auto.py", line 43, in <module> main() File "wiki_auto.py", line 40, in main train, dev, test = dataset.generate_k_shot_data(k=16, seed=seed, path="../data/") File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 24, in generate_k_shot_data dataset = self.load_dataset() File "wiki_auto.py", line 34, in load_dataset return datasets.load_dataset('wiki_auto', 'manual') File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/root/.cache/huggingface/modules/datasets_modules/datasets/wiki_auto/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8/wiki_auto.py", line 193, in _split_generators data_dir = dl_manager.download_and_extract(my_urls) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract return self.extract(self.download(url_or_urls)) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download downloaded_path_or_paths = map_nested( File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested mapped = [ File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp> _single_map_nested((function, obj, types, None, True)) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested return function(data_struct) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path output_path = get_from_cache( File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 592, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv ``` - jeopardy ``` 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 /root/.cache/huggingface/datasets/jeopardy/default/0.1.0/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810... Traceback (most recent call last): File "jeopardy.py", line 45, in <module> main() File "jeopardy.py", line 42, in main train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/") File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data dataset = self.load_dataset() File "jeopardy.py", line 36, in load_dataset return datasets.load_dataset("jeopardy") File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/root/.cache/huggingface/modules/datasets_modules/datasets/jeopardy/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810/jeopardy.py", line 72, in _split_generators filepath = dl_manager.download_and_extract(_DATA_URL) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract return self.extract(self.download(url_or_urls)) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download downloaded_path_or_paths = map_nested( File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 206, in map_nested return function(data_struct) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path output_path = get_from_cache( File "/opt/conda/lib/python3.8/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 ``` - definite_pronoun_resolution ``` Downloading and preparing dataset definite_pronoun_resolution/plain_text (download: 222.12 KiB, generated: 239.12 KiB, post-processed: Unknown size, total: 461.24 KiB) to /root/.cache/huggingface/datasets/definite_pronoun_resolution/plain_text/1.0.0/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff... 0%| | 0/2 [00:00<?, ?it/s]Traceback (most recent call last): File "definite_pronoun_resolution.py", line 37, in <module> main() File "definite_pronoun_resolution.py", line 34, in main train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/") File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data dataset = self.load_dataset() File "definite_pronoun_resolution.py", line 28, in load_dataset return datasets.load_dataset('definite_pronoun_resolution') File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/root/.cache/huggingface/modules/datasets_modules/datasets/definite_pronoun_resolution/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff/definite_pronoun_resolution.py", line 76, in _split_generators files = dl_manager.download_and_extract( File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract return self.extract(self.download(url_or_urls)) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download downloaded_path_or_paths = map_nested( File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested mapped = [ File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp> _single_map_nested((function, obj, types, None, True)) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested return function(data_struct) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path output_path = get_from_cache( File "/opt/conda/lib/python3.8/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://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.1 - Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10 - Python version: 3.8.3 - PyArrow version: 4.0.1 "
[ "#take\r\nI am willing to fix this. Links can be replaced for WikiAuto Manual and jeopardy with new ones provided by authors.\r\n\r\nAs for the definite_pronoun_resolution URL, a certificate error seems to be preventing a download. I have the files on my local machine. I can include them in the dataset folder as the files are <1MB in size total.", "> #take I am willing to fix this. Links can be replaced for WikiAuto Manual and jeopardy.\r\n> \r\n> As for the definite_pronoun_resolution URL, a certificate error seems to be preventing a download. I have the files on my local machine. Anyone has opinions on whether it is preferable for me to host them somewhere (e.g. personal GDrive account) or upload them to the dataset folder directly and use github raw URLs? The files are <1MB in size.\r\n\r\nI am planning to fix it next few days. But my to-do list is full and I do not have the cache of definite_pronoun_resolution. I am glad that you can take this. Thanks a lot!", "No problem, buddy! Will submit a PR over this weekend." ]
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"## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - **Paper:** *link to the dataset paper if available* - **Data:** *link to the Github repository or current dataset location* - **Motivation:** *what are some good reasons to have 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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"Fixes for csv, json in io module and image_classification task with tests referenced in https://github.com/huggingface/datasets/issues/3171"
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"NONE"
"## Dataset viewer issue for '*Science Fiction TV Show Plots Corpus (Scifi_TV_Shows)*' **Link:** [link](https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows) I tried adding both a script (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/blob/main/Scifi_TV_Shows.py) and some dummy examples (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/tree/main/dummy), but the viewer still has a 404 error ("Not found. Maybe the cache is missing, or maybe the ressource does not exist."). I'm not sure what to try next. Thanks in advance! Am I the one who added this dataset? Yes "
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"Scifi_TV_Shows: Having trouble getting viewer to find appropriate files"
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"This PR: * allows 403 status code in HEAD requests to S3 buckets to fix the connection error in the Scielo dataset (instead of `url`, uses `response.url` to check the URL of the final endpoint) * makes the Scielo dataset streamable Fixes #3255. "
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"I was pleasantly surprised to find that someone had already added my dataset to the huggingface library, but some details were missing or incorrect. This PR fixes the documentation."
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"MEMBER"
"`load_from_disk` downloads the dataset to a temporary directory without checking if the dataset has already been downloaded once. It would be nice to have some sort of caching for datasets downloaded this way. This could leverage the fingerprint of the dataset that was saved in the `state.json` file."
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"CONTRIBUTOR"
"f-strings offer better readability/performance than `str.format` and `%`, so we should use them in all places in our codebase unless there is good reason to keep the older syntax. > **NOTE FOR CONTRIBUTORS**: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under `datasets/*`."
[ "Hi, I would be glad to help with this. Is there anyone else working on it?", "Hi, I would be glad to work on this too.", "#self-assign" ]
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"CONTRIBUTOR"
"I have replaced only a single assert in text_classification.py along with a unit test to verify an exception is raised based on https://github.com/huggingface/datasets/issues/3171 . I would like to first understand the code contribution workflow. So keeping the change to a single file rather than making too many changes. Once this gets approved, I will look into the rest. Thanks. "
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[ "Haha it looks like you got the chance of being reviewed twice at the same time and got the same suggestion twice x)\r\nAnyway it's all good now so we can merge !", "Thanks for the feedback. " ]
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"NONE"
"## Describe the bug I get `ConnectionError` when I am trying to load the SciELO dataset. When I try the URL with `requests` I get: ``` >>> requests.head("https://ndownloader.figstatic.com/files/14019287") <Response [302]> ``` And as far as I understand redirections in `datasets` are not supported for downloads. https://github.com/huggingface/datasets/blob/807341d0db0728073ab605c812c67f927d148f38/datasets/scielo/scielo.py#L45 ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("scielo", "en-es") ``` ## Expected results Download SciELO dataset and load Dataset object ## Actual results ``` Downloading and preparing dataset scielo/en-es (download: 21.90 MiB, generated: 68.45 MiB, post-processed: Unknown size, total: 90.35 MiB) to /Users/test/.cache/huggingface/datasets/scielo/en-es/1.0.0/7e05d55a20257efeb9925ff5de65bd4884fc6ddb6d765f1ea3e8860449d90e0e... Traceback (most recent call last): File "scielo.py", line 3, in <module> dataset = load_dataset("scielo", "en-es") File "../lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "../lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "../lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/Users/test/.cache/huggingface/modules/datasets_modules/datasets/scielo/7e05d55a20257efeb9925ff5de65bd4884fc6ddb6d765f1ea3e8860449d90e0e/scielo.py", line 77, in _split_generators data_dir = dl_manager.download_and_extract(_URLS[self.config.name]) File "../lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract return self.extract(self.download(url_or_urls)) File "../lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download downloaded_path_or_paths = map_nested( File "../lib/python3.8/site-packages/datasets/utils/py_utils.py", line 206, in map_nested return function(data_struct) File "../lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "../lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path output_path = get_from_cache( File "../lib/python3.8/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://ndownloader.figstatic.com/files/14019287 ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.1 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.12 - PyArrow version: 6.0.0 "
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"CONTRIBUTOR"
"This PR updates the checksums (as reported [here](https://discuss.huggingface.co/t/how-to-load-dataset-locally/11601/2)) of the `xcopa` dataset. Additionally, it adds new configs that hold the translated data of the original set of configs. This data was not available at the time of adding this dataset to the lib."
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[ "The CI failures are unrelated to the changes (missing fields in the readme and the CER metric error fixed in #3252)." ]
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"Update xcopa dataset (fix checksum issues + add translated data)"
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"NONE"
"## Describe the bug `GeneratorBasedBuilder` does not support `None` values. ## Steps to reproduce the bug See [this repository](https://github.com/pavel-lexyr/huggingface-datasets-bug-reproduction) for minimal reproduction. ## Expected results Dataset is initialized with a `None` value in the `value` column. ## Actual results ``` Traceback (most recent call last): File "main.py", line 3, in <module> datasets.load_dataset("./bad-data") File ".../datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File ".../datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File ".../datasets/builder.py", line 697, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File ".../datasets/builder.py", line 1103, in _prepare_split example = self.info.features.encode_example(record) File ".../datasets/features/features.py", line 1033, in encode_example return encode_nested_example(self, example) File ".../datasets/features/features.py", line 808, in encode_nested_example return { File ".../datasets/features/features.py", line 809, in <dictcomp> k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) File ".../datasets/features/features.py", line 855, in encode_nested_example return schema.encode_example(obj) File ".../datasets/features/features.py", line 299, in encode_example return float(value) TypeError: float() argument must be a string or a number, not 'NoneType' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.1 - Platform: Linux-5.4.0-81-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 6.0.0"
[ "Hi,\r\n\r\nthanks for reporting and providing a minimal reproducible example. \r\n\r\nThis line of the PR I've linked in our discussion on the Forum will add support for `None` values:\r\nhttps://github.com/huggingface/datasets/blob/a53de01842aac65c66a49b2439e18fa93ff73ceb/src/datasets/features/features.py#L835\r\n\r\nI expect that PR to be merged soon." ]
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"CONTRIBUTOR"
"Fixes the [failing CER metric test](https://app.circleci.com/pipelines/github/huggingface/datasets/8644/workflows/79816553-fa2f-4756-b022-d5937f00bf7b/jobs/53298) in CI by adding support for `jiwer==2.3.0`, which was released yesterday. Also, I verified that all the tests in `metrics/cer/test_cer.py` pass after the change, so the results should be the same irrespective of the `jiwer` version."
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"Fix failing CER metric test in CI after update"
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"NONE"
"This PR adds the ETHICS dataset, including all 5 sub-datasets. From https://arxiv.org/abs/2008.02275"
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"MEMBER"
"To be compatible with streaming, this dataset must use `dl_manager.iter_archive` since the data are in a .tgz file"
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"Streaming from Google Drive is a bit more challenging than the other host we've been supporting: - the download URL must be updated to add the confirm token obtained by HEAD request - it requires to use cookies to keep the connection alive - the URL doesn't tell any information about whether the file is compressed or not Therefore I did two things: - I added a step for URL and headers/cookies preparation in the StreamingDownloadManager - I added automatic compression type inference by reading the [magic number](https://en.wikipedia.org/wiki/List_of_file_signatures) This allows to do do fancy things like ```python from datasets.utils.streaming_download_manager import StreamingDownloadManager, xopen, xjoin, xglob # zip file containing a train.tsv file url = "https://drive.google.com/uc?export=download&id=1k92sUfpHxKq8PXWRr7Y5aNHXwOCNUmqh" extracted = StreamingDownloadManager().download_and_extract(url) for inner_file in xglob(xjoin(extracted, "*.tsv")): with xopen(inner_file) as f: # streaming starts here for line in f: print(line) ``` This should make around 80 datasets streamable. It concerns those hosted on Google Drive but also any dataset for which the URL doesn't give any information about compression. Here is the full list: ``` amazon_polarity, ami, arabic_billion_words, ascent_kb, asset, big_patent, billsum, capes, cmrc2018, cnn_dailymail, code_x_glue_cc_code_completion_token, code_x_glue_cc_code_refinement, code_x_glue_cc_code_to_code_trans, code_x_glue_tt_text_to_text, conll2002, craigslist_bargains, dbpedia_14, docred, ehealth_kd, emo, euronews, germeval_14, gigaword, grail_qa, great_code, has_part, head_qa, health_fact, hope_edi, id_newspapers_2018, igbo_english_machine_translation, irc_disentangle, jfleg, jnlpba, journalists_questions, kor_ner, linnaeus, med_hop, mrqa, mt_eng_vietnamese, multi_news, norwegian_ner, offcombr, offenseval_dravidian, para_pat, peoples_daily_ner, pn_summary, poleval2019_mt, pubmed_qa, qangaroo, reddit_tifu, refresd, ro_sts_parallel, russian_super_glue, samsum, sberquad, scielo, search_qa, species_800, spider, squad_adversarial, tamilmixsentiment, tashkeela, ted_talks_iwslt, trec, turk, turkish_ner, twi_text_c3, universal_morphologies, web_of_science, weibo_ner, wiki_bio, wiki_hop, wiki_lingua, wiki_summary, wili_2018, wisesight1000, wnut_17, yahoo_answers_topics, yelp_review_full, yoruba_text_c3 ``` Some of them may not work if the host doesn't support HTTP range requests for example Fix https://github.com/huggingface/datasets/issues/2742 Fix https://github.com/huggingface/datasets/issues/3188"
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[ "I just tried some datasets and noticed that `spider` is not working for some reason (the compression type is not recognized), resulting in FileNotFoundError. I can take a look tomorrow", "I'm fixing the remaining files based on TAR archives" ]
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"## Describe the bug When trying to create a dataset from a json file with around 25MB, the following error is raised `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct` Splitting the big file into smaller ones and then loading it with the `load_dataset` method did also not work. Creating a pandas dataframe from it and then loading it with `Dataset.from_pandas` works ## Steps to reproduce the bug ```python load_dataset("json", data_files="test.json") ``` test.json ~25MB ```json {"a": {"c": 8, "b": 5}} {"a": {"b": 7, "c": 6}} {"a": {"c": 8, "b": 5}} {"a": {"b": 7, "c": 6}} {"a": {"c": 8, "b": 5}} ... ``` working.json ~160bytes ```json {"a": {"c": 8, "b": 5}} {"a": {"b": 7, "c": 6}} {"a": {"c": 8, "b": 5}} {"a": {"b": 7, "c": 6}} {"a": {"c": 8, "b": 5}} ``` ## Expected results It should load the dataset from the json file without error. ## Actual results It raises Exception `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct` ``` Traceback (most recent call last): File "/Users/m/workspace/xxx/project/main.py", line 60, in <module> dataset = load_dataset("json", data_files="result.json") File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/load.py", line 1627, in load_dataset builder_instance.download_and_prepare( File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 697, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 1159, in _prepare_split writer.write_table(table) File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/arrow_writer.py", line 428, in write_table pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) File "pyarrow/table.pxi", line 1685, in pyarrow.lib.Table.from_arrays File "pyarrow/table.pxi", line 630, in pyarrow.lib._sanitize_arrays File "pyarrow/array.pxi", line 338, in pyarrow.lib.asarray File "pyarrow/table.pxi", line 304, in pyarrow.lib.ChunkedArray.cast File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/pyarrow/compute.py", line 309, in cast return call_function("cast", [arr], options) File "pyarrow/_compute.pyx", line 528, in pyarrow._compute.call_function File "pyarrow/_compute.pyx", line 327, in pyarrow._compute.Function.call File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 120, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct ``` ## Environment info - `datasets` version: 1.14.0 - Platform: macOS-12.0.1-arm64-arm-64bit - Python version: 3.9.7 - PyArrow version: 6.0.0 "
[ "Hi,\r\n\r\nthis issue is similar to https://github.com/huggingface/datasets/issues/3093, so you can either use the solution provided there or try to load the data in one chunk (you can control the chunk size by specifying the `chunksize` parameter (`int`) in `load_dataset`).\r\n\r\n@lhoestq Is this worth opening an issue on Jira? Basically, PyArrow doesn't allow casts that change the order of the struct fields because they treat `pa.struct` as an ordered sequence. Reordering fields manually in Python is probably too slow, so I think this needs to be fixed by them to be usable on our side.", "I agree I would expect PyArrow to be able to handle this, do you want to open the issue @mariosasko ?\r\nAlthough maybe it's possible to fix struct casting on our side without hurting performance too much, if it's simply a matter of reordering the arrays in the StructArray" ]
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"[tiny] fix typo in stream docs"
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"`load_from_disk` uses `tempfile.TemporaryDirectory()` instead of our `get_temporary_cache_files_directory()` function. This can cause the temporary directory to be deleted before the dataset object is garbage collected. In practice, it prevents anyone from using methods like `shuffle` on a dataset loaded this way, because it can't write the shuffled indices in a directory that doesn't exist anymore. In this PR I switch to using `get_temporary_cache_files_directory()` and I update the tests. cc @mariosasko since you worked on `get_temporary_cache_files_directory()`"
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"Fix filter method for batched=True"
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"`isort` can place modules by itself from [version 5.0.0](https://pycqa.github.io/isort/docs/upgrade_guides/5.0.0.html#module-placement-changes-known_third_party-known_first_party-default_section-etc) onwards, making the `known_first_party` and `known_third_party` fields in `setup.cfg` redundant (this is why our CI works, even though we haven't touched these options in a while)."
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[ "Adding ANERcorp dataset\r\n\r\n## Adding a Dataset\r\n- **Name:** *ANERcorp-CAMeLLab*\r\n- **Description:** *Since its creation in 2008, the ANERcorp dataset (Benajiba & Rosso, 2008) has been a standard reference used by Arabic named entity recognition researchers around the world. However, over time, this dataset was copied over from user to user, modified slightly here and there, and split in many different configurations that made it hard to compare fairly across papers and systems.\r\n\r\nIn 2020, a group of researchers from CAMeL Lab (Habash, Alhafni and Oudah), and Mind Lab (Antoun and Baly) met with the creator of the corpus, Yassine Benajiba, to consult with him and collectively agree on an exact split, and accepted minor corrections from the original dataset. Bashar Alhafni from CAMeL Lab working with Nizar Habash implemented the decisions provided in this release.*\r\n\r\n- **Paper:** *(a) Benajiba, Yassine, Paolo Rosso, and José Miguel Benedí Ruiz. \"Anersys: An Arabic named entity recognition system based on maximum entropy.\" In International Conference on Intelligent Text Processing and Computational Linguistics, pp. 143-153. Springer, Berlin, Heidelberg, 2007.\r\n\r\n(b)Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, and Nizar Habash. \"CAMeL Tools: An Open Source Python Toolkit, for Arabic Natural Language Processing.\" In Proceedings of the Conference on Language Resources and Evaluation (LREC 2020), Marseille, 2020.*\r\n- **Data:** *https://camel.abudhabi.nyu.edu/anercorp/*\r\n- **Motivation:** This is the standard dataset for evaluating NER performance in Arabic*\r\n\r\nInstructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)." ]
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"## Describe the bug Following command gives an ConnectionError. ## Steps to reproduce the bug ```python disaster = load_dataset('disaster_response_messages') ``` ## Error ``` ConnectionError: Couldn't reach https://datasets.appen.com/appen_datasets/disaster_response_data/disaster_response_messages_training.csv ``` ## Expected results It should load dataset without an error ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: Google Colab - Python version: 3.7 - PyArrow version: "
[ "It looks like the dataset isn't available anymore on appen.com\r\n\r\nThe CSV files appear to still be available at https://www.kaggle.com/landlord/multilingual-disaster-response-messages though. It says that the data are under the CC0 license so I guess we can host the dataset elsewhere instead ?" ]
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"## Describe the bug When downloaded from macine 1 the dataset is downloaded and parsed correctly. When downloaded from machine two (which has a different cache directory), the following script: import datasets from datasets import load_dataset raw_dataset_elkhair_1 = load_dataset('arabic_billion_words', 'Alittihad', split="train",download_mode='force_redownload') gives the following error: **Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: 1.49 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/arabic_billion_words/Alittihad/1.1.0/687a1f963284c8a766558661375ea8f7ab3fa3633f8cd9c9f42a53ebe83bfe17... Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 348M/348M [00:24<00:00, 14.0MB/s] Traceback (most recent call last): File ".../why_mismatch.py", line 3, in <module> File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 709, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/opt/conda/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits raise NonMatchingSplitsSizesError(str(bad_splits)) datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=1601790302, num_examples=349342, dataset_name='arabic_billion_words'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='arabic_billion_words')}]** Note that the package versions of datasets (1.15.1) and rarfile (4.0) are identical. ## Steps to reproduce the bug import datasets from datasets import load_dataset raw_dataset_elkhair_1 = load_dataset('arabic_billion_words', 'Alittihad', split="train",download_mode='force_redownload') # Sample code to reproduce the bug ## Expected results Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: 1.49 GiB, post-processed: Unknown size, total: 1.82 GiB) to .../.cache/huggingface/datasets/arabic_billion_words/Alittihad/1.1.0/687a1f963284c8a766558661375ea8f7ab3fa3633f8cd9c9f42a53ebe83bfe17... Downloading: 100%|███████████████████████████| 348M/348M [00:22<00:00, 15.8MB/s] Dataset arabic_billion_words downloaded and prepared to .../.cache/huggingface/datasets/arabic_billion_words/Alittihad/1.1.0/687a1f963284c8a766558661375ea8f7ab3fa3633f8cd9c9f42a53ebe83bfe17. Subsequent calls will reuse this data. ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> Machine 1: - `datasets` version: 1.15.1 - Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 4.0.1 Machine 2 (the bugged one) - `datasets` version: 1.15.1 - Platform: Linux-4.4.0-210-generic-x86_64-with-glibc2.10 - Python version: 3.8.8 - PyArrow version: 6.0.0 "
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"``## Adding a Dataset - **Name:** *Reuters21578* - **Description:** *ConnectionError: Couldn't reach https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz* - **Data:** *https://huggingface.co/datasets/reuters21578* `from datasets import load_dataset` `dataset = load_dataset("reuters21578", 'ModLewis')` ConnectionError: Couldn't reach https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz And I try to request the link as follow: `import requests` `requests.head('https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz')` SSLError: HTTPSConnectionPool(host='kdd.ics.uci.edu', port=443): Max retries exceeded with url: /databases/reuters21578/reuters21578.tar.gz (Caused by SSLError(SSLError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:852)'),)) This problem likes #575 What should I do ? "
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[ "Hi ! The URL works fine on my side today, could you try again ?", "thank you @lhoestq \r\nit works" ]
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"## Describe the bug Descriptions of the wikitext datasests are wrong. ## Steps to reproduce the bug Please see: https://github.com/huggingface/datasets/blob/f6dcafce996f39b6a4bbe3a9833287346f4a4b68/datasets/wikitext/wikitext.py#L50 ## Expected results The descriptions for raw-v1 and v1 should be switched. "
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"CONTRIBUTOR"
"## Describe the bug Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples: ```python DatasetDict({ train: Dataset({ features: ['id', 'title', 'abstract', 'full_text', 'qas'], num_rows: 0 }) validation: Dataset({ features: ['id', 'title', 'abstract', 'full_text', 'qas'], num_rows: 0 }) }) ``` ## Steps to reproduce the bug Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110: ```python from datasets import load_dataset load_dataset("qasper") # The problem only started with the commit of #3110 load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780") ``` ## Expected results ```python DatasetDict({ train: Dataset({ features: ['id', 'title', 'abstract', 'full_text', 'qas'], num_rows: 888 }) validation: Dataset({ features: ['id', 'title', 'abstract', 'full_text', 'qas'], num_rows: 281 }) }) ``` Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110: ```python from datasets import load_dataset load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d") ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.2.dev0 (master) - Python version: 3.8.10 - PyArrow version: 3.0.0 "
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[ "Hi @eladsegal, thanks for reporting.\r\n\r\nI am sorry, but I can't reproduce the bug:\r\n```\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"qasper\")\r\nDownloading: 5.11kB [00:00, ?B/s]\r\nDownloading and preparing dataset qasper/qasper (download: 9.88 MiB, generated: 35.11 MiB, post-processed: Unknown size, total: 44.99 MiB) to .cache\\qasper\\qasper\\0.1.0\\b99154d2a15aa54bfc669f82b2eda715a2e342e81023d39613b0e2920fdb3ad8...\r\nDataset qasper downloaded and prepared to .cache\\qasper\\qasper\\0.1.0\\b99154d2a15aa54bfc669f82b2eda715a2e342e81023d39613b0e2920fdb3ad8. Subsequent calls will reuse this data.\r\n100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<?, ?it/s]\r\n\r\nIn [3]: ds\r\nOut[3]:\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n num_rows: 888\r\n })\r\n validation: Dataset({\r\n features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n num_rows: 281\r\n })\r\n})\r\n``` \r\n\r\nThis makes me suspect that the origin of the problem might be the cache: I didn't have this dataset in my cache, although I guess you already had it, before the code change introduced by #3110.\r\n\r\n@lhoestq might it be possible that the code change introduced by #3110 makes \"inaccessible\" all previously cached TAR-based datasets?\r\n- Before the caching system downloaded and extracted the tar dataset\r\n- Now it only downloads the tar dataset (no extraction is done)", "I can't reproduce either in my environment (macos, python 3.7).\r\n\r\nIn your case it generates zero examples. This can only happen if the extraction of the TAR archive doesn't output the right filenames. Indeed if the `qasper` script can't find the right file to load, it's currently ignored and it returns zero examples. This case was not even considered when #3110 was developed since we considered the file names to be deterministic - and not depend on your environment.\r\n\r\nTherefore here is my hypothesis:\r\n- either the cache is corrupted somehow with an empty TAR archive\r\n- OR I suspect that the issue comes from python 3.8\r\n", "I just tried again on python 3.8 and I was able to reproduce the issue. Let me work on a fix", "Ok I found the issue. It's not related to python 3.8 in itself though. This issue happens because your local installation of `datasets` is outdated compared to the changes to datasets in #3110\r\n\r\nTo fix this you just have to pull the latest changes from `master` :)\r\n\r\nLet me know if that helps !\r\n\r\n--------------\r\n\r\nHere are more details about my investigation:\r\n\r\nIt's possible to reproduce this issue if you use `datasets<=1.15.1` or before b6469baa22c174b3906c631802a7016fedea6780 and if you load the dataset after revision b6469baa22c174b3906c631802a7016fedea6780. This is because `dl_manager.iter_archive` had issues at that time (and it was not used anywhere anyway).\r\n\r\nIn particular it was returning the absolute path to extracted files instead of the relative path of the file inside the archive. This was an issue because `dl_manager.iter_archive` isn't supposed to extract the TAR archive. Instead, it iterates over all the files inside the archive, without creating a directory with the extracted content.\r\n\r\nTherefore if you want to use the datasets on `master`, make sure that you have an up-to-date local installation of `datasets` as well, or you may face incompatibilities like this.", "Thanks!\r\nBut what about code that is already using older version of datasets? \r\nThe reason I encountered this issue was that suddenly one of my repos with version 1.12.1 started getting 0 examples.\r\nI handled it by adding `revision` to `load_dataset`, but I guess it would still be an issue for other users who doesn't know this.", "Hi, in 1.12.1 it uses the dataset scripts from that time, not the one on master.\r\n\r\nIt only uses the datasets from master if you installed `datasets` from source, or if the dataset isn't available in your local version (in this case it shows a warning and it loads from master).\r\n", "OK, I understand the issue a bit better now.\r\nI see I wasn't on 1.12.1, but on 1.12.1.dev0 and since it is a dev version it uses master.\r\nSo users that use an old dev version must specify revision or else they'll encounter this problem.\r\n\r\nBTW, when I opened the issue I installed the latest master version with\r\n```\r\npip install git+git://github.com/huggingface/datasets@master#egg=datasets\r\n```\r\nand also used `download_mode=\"force_redownload\"`, and it still returned 0 examples.\r\nNow I deleted all of the cache and ran the code again, and it worked.\r\nI'm not sure what exactly happened here, but looks like it was due to a mix of an unofficial version and its cache.\r\n\r\nThanks again!" ]
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"CONTRIBUTOR"
"Adds options to use newer recommended checkpoint (as of 2021/10/8) bleurt-20 and its distilled versions. Updated checkpoints are described in https://github.com/google-research/bleurt/blob/master/checkpoints.md#the-recommended-checkpoint-bleurt-20 This change won't affect the default behavior of metrics/bleurt. It only adds option to load newer checkpoints as `datasets.load_metric('bleurt', 'bleurt-20')` `bluert-20` generates scores roughly between 0 and 1, which wasn't the case for the previous checkpoints. "
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"Addd options to use updated bleurt checkpoints"
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"CONTRIBUTOR"
"Adds a new variable, `DISABLE_PYARROW_TYPES_OPTIMIZATION`, to `config.py` for easier control of the Arrow type optimization. Fix #2206 "
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[ "That's good to have a way to disable this easily :)\r\nI just find it a bit unfortunate that users would have to experience the error once and then do `DISABLE_PYARROW_TYPES_OPTIMIZATION=1`. Do you know if there's a way to simply fallback on disabling it automatically when it fails ?", "@lhoestq Actually, I agree a fallback makes more sense. The current approach is not very practical indeed and would require a mention in the docs.\r\n", "Replaced the env variable with a fallback!", "Hmm if the fallback automatically happens without the user knowing it, then I don't think we really need to mention it. But if you really wanted to, I think the [Improve performance](https://huggingface.co/docs/datasets/cache.html#improve-performance) section would be a great place for it! ", "Yea I think this could just end up in a note that says that `datasets` automatically picks the most optimized integer precision for your tokenized text data to save you disk space. Maybe later if we have a page on text processing we can add this note, but for now I agree it doesn't fit well into the doc.\r\n\r\nIn particular in the \"Improve performance\" section we mention what users can do to speed up their computations, while this behavior is just some internal feature that users don't have control over anyway." ]
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"Continuation of the documentation started in https://github.com/huggingface/datasets/pull/3221, taking into account @stevhliu 's comments"
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"NONE"
"## Describe the bug The download Link of the Xsum dataset provided in the repository is [Link](http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download. ## Steps to reproduce the bug ```python load_dataset('xsum') ``` ## Actual results ``` python raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz ``` "
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[ "Hi ! On my side the URL is working fine, could you try again ?", "> Hi ! On my side the URL is working fine, could you try again ?\r\n\r\nI try it again and cannot download the file (might because of my location). Could you please provide another download link(such as google drive)? :>", "I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example.", "> I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example.\r\n\r\n:> ok. Thanks for your reply." ]
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"By grouping tests by test file, we make sure that all the tests in `test_load.py` are sent to the same worker. Therefore, the fixture `hf_token` will be called only once (and from the same worker). Related to: #3200. Fix #3219."
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"CONTRIBUTOR"
"Even though it is possible to manually get the tagset list with ```python dset.features[field_name].feature.names ``` I think it is useful to have an overview of the used tagset on the dataset card. This is particularly useful in light of the **dataset viewer**: the tags are encoded, so it is not immediately obvious what they are for a given sample. Adding a label-int mapping should make it easier for visitors to get a grasp of what they mean. From user-experience perspective, I would urge the full tagsets to always be available in the README's but I understand that that would take a lot of work, probably. Perhaps it can be automated? closes #3189 "
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[ "I also added the missing `pretty_name` tag in the dataset card to fix the CI" ]
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"MEMBER"
"Currently the BibTeX citation parsed from the CITATION file has wrong URL (it shows the repo URL instead of the proceedings paper URL): ``` @inproceedings{Lhoest_Datasets_A_Community_2021, author = {Lhoest, Quentin and Villanova del Moral, Albert and von Platen, Patrick and Wolf, Thomas and Šaško, Mario and Jernite, Yacine and Thakur, Abhishek and Tunstall, Lewis and Patil, Suraj and Drame, Mariama and Chaumond, Julien and Plu, Julien and Davison, Joe and Brandeis, Simon and Sanh, Victor and Le Scao, Teven and Canwen Xu, Kevin and Patry, Nicolas and Liu, Steven and McMillan-Major, Angelina and Schmid, Philipp and Gugger, Sylvain and Raw, Nathan and Lesage, Sylvain and Lozhkov, Anton and Carrigan, Matthew and Matussière, Théo and von Werra, Leandro and Debut, Lysandre and Bekman, Stas and Delangue, Clément}, booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, month = {11}, pages = {175--184}, publisher = {Association for Computational Linguistics}, title = {{Datasets: A Community Library for Natural Language Processing}}, url = {https://github.com/huggingface/datasets}, year = {2021} } ```"
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"Add CITATION file."
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"NONE"
"## Describe the bug When a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails. ## Steps to reproduce the bug Create a folder that contains the following: ``` . ├── testdata │   └── mydata.json └── test.py ``` Please download [this file](https://github.com/huggingface/datasets/files/7491797/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide) :exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`! `test.py` simply contains: ```python from datasets import load_dataset my_dataset = load_dataset("testdata") ``` To reproduce the error, simply run ``` python test.py ``` ## Expected results The data should load correctly without error. ## Actual results The dataset builder fails with: ``` Using custom data configuration testdata-d490389b8ab4fd82 Downloading and preparing dataset json/testdata to /home/junshern.chan/.cache/huggingface/datasets/json/testdata-d490389b8ab4fd82/0.0.0/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34... 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2264.74it/s] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 447.01it/s] Failed to read file '/home/junshern.chan/hf-json-bug/testdata/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0 Traceback (most recent call last): File "test.py", line 28, in <module> my_dataset = load_dataset("testdata") File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 697, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 1156, in _prepare_split for key, table in utils.tqdm( File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/tqdm/std.py", line 1168, in __iter__ for obj in iterable: File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 146, in _generate_tables raise ValueError( ValueError: Not able to read records in the JSON file at /home/junshern.chan/hf-json-bug/testdata/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.15.1 - Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 6.0.0 "
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[ "I have additionally identified the source of the error, being that [this condition](https://github.com/huggingface/datasets/blob/fc46bba66ba4f432cc10501c16a677112e13984c/src/datasets/packaged_modules/json/json.py#L124-L126) in the file\r\n`python3.8/site-packages/datasets/packaged_modules/json/json.py` is not being entered correctly:\r\n```python\r\n if (\r\n isinstance(e, pa.ArrowInvalid)\r\n and \"straddling\" not in str(e)\r\n or block_size > len(batch)\r\n ):\r\n```\r\n\r\nFrom what I can tell, in my case the block_size simply needs to be increased, but the error message does not contain \"straddling\" so the condition does trigger correctly and we fail to reach [the line to increase block_size](https://github.com/huggingface/datasets/blob/fc46bba66ba4f432cc10501c16a677112e13984c/src/datasets/packaged_modules/json/json.py#L135).\r\n\r\nChanging the condition above to simply\r\n```python\r\n if (\r\n block_size > len(batch)\r\n ):\r\n```\r\n\r\nFixes the error for me. I'm happy to create a PR containing this fix if the developers deem the other conditions unnecessary.", "Hi ! I think the issue comes from the fact that your JSON file is not a valid JSON Lines file.\r\nEach example should be on one single line.\r\n\r\nCan you try fixing the format to have one line per example and try again ?", ":open_mouth: you're right, that did it! I just put everything on a single line (my file only has a single example) and that fixed the error. Thank you so much!" ]
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"Fix paper BibTeX citation with proceedings reference."
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"CONTRIBUTOR"
"Tatoeba's latest version is v2021-07-22"
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[ "How about this? @lhoestq @abhishekkrthakur ", "Hi ! I think it would be nice if people could still be able to load the old version.\r\nMaybe this can be a parameter ? For example to load the old version they could do\r\n```python\r\nload_dataset(\"tatoeba\", lang1=\"en\", lang2=\"mr\", date=\"v2020-11-09\")\r\n```\r\n\r\nIf it sounds good to you, we can add this parameter to the TatoebaConfig:\r\n```python\r\nclass TatoebaConfig(datasets.BuilderConfig):\r\n def __init__(self, *args, lang1=None, lang2=None, date=\"v2021-07-22\", **kwargs):\r\n self.date = date\r\n```\r\nand then pass the date to the URL\r\n```python\r\n_BASE_URL = \"https://object.pouta.csc.fi/OPUS-Tatoeba/{}/moses/{}-{}.txt.zip\"\r\n```\r\n```python\r\n def _base_url(lang1, lang2, date):\r\n return _BASE_URL.format(date, lang1, lang2)\r\n```\r\n\r\nWhat do you think ?", "`_DATE = \"v\" + \"-\".join(s.zfill(2) for s in _VERSION.split(\".\"))` seems rather tricky but works well. How about this? @lhoestq \r\n", "The CI is only failing because of the missing sections in the dataset card, and because of an issue with the CER metric that is unrelated to this PR" ]
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"CONTRIBUTOR"
"This is a continuation of the now closed PR in https://github.com/huggingface/datasets/pull/3206. The discussion there has shaped a new approach to do this. In this PR, behavior of `pklregister` and `Pickler` is extended. Earlier, users were already able to register custom pickle functions. That is useful if they have objects that are not easily picklable with default methods. When one registers a custom function to a type, an object of that type will be pickled with the given function by `Pickler` which looks up the type in its `dispatch` table. The downside of this method, and of `pickle` in general, is that it is limited to direct type-matching and does not allow sub-classes. In many, default, cases that is not an issue. But when you are using external libraries where classes (e.g. parsers, models) are sub-classed this is not ideal. ```python from datasets.fingerprint import Hasher from datasets.utils.py_utils import pklregister class BaseParser: pass class EnglishParser(BaseParser): pass @pklregister(BaseParser) def custom_pkl_func(pickler, obj): print(f"Called the custom pickle function for type {type(obj)}!") # do something with the obj and ultimately save with the pickler base = BaseParser() en = EnglishParser() # Hasher.hash uses the Pickler behind the scenes # `custom_pkl_func` called for base Hasher.hash(base) # `custom_pkl_func` not called for en :-( Hasher.hash(en) ``` In the example above we'd want to sub-class `EnglishParser` to be handled in the same way as its super-class `BaseParser`. This PR solves that by allowing for a keyword-argument `allow_subclasses` in `pklregister` (default: `False`). ```python @pklregister(BaseParser, allow_subclasses=True) ``` When this option is enabled, we not only save the function in `Pickler.dispatch` but also save it in a custom table `Pickler.subclass_dispatch` **which allows us to dynamically add sub-classes of that class to the real dispatch table**. Then, if we want to pickle an object `obj` with `Pickler.dump()` (which ultimately will call `Pickler.save()`) we _first_ check whether any of the object's super-classes exist in `Pickler.sublcass_dispatch` and get the related custom pickle function. If we find one, we add the type of `obj` alongside the function to `Pickler.dispatch`. All of this happens at the start of the call to `Pickler.save()`. _Only then_ dill.Pickler's `save` will be called, which in turn will call `pickle._Pickler.save` which handles everything. Here, the `Pickler.dispatch` table will be used to look up custom pickler functions - and it now also includes the function for `obj`, which was copied from its super-class, which we added at the very start of our custom `Pickler.save()`. For edge cases and, especially, for testing, a contextmanager class `TempPickleRegistry` is included that resets the pickle registry on exit to its previous state. ```python with TempPickleRegistry(): @pklregister(MyObjClass) def pickle_registry_test_false(pickler, obj): pickler.save(obj.fancy_method()) some_obj = MyObjClass() dumps(some_obj) # `MyObjClass` is in Pickler.dispatch # ... `MyObjClass` is _not_ in Pickler.dispatch anymore ``` closes https://github.com/huggingface/datasets/issues/3178 To Do ==== - [x] Write tests - [ ] Write documentation/examples?"
[ "@lhoestq Feel free to have a look. The implementation is slightly different from what you suggested. I have opted to overwrite `save` instead of meddling with `save_global`. `save_global` is called very late down in dill/pickle so it is hard to control for what is happening there. I might be wrong. Pickling is more complex than I thought! \r\n\r\nThe linked issue (`map` with spaCy) also works now!\r\n\r\n```python\r\nimport pickle\r\nimport spacy\r\nfrom spacy import Language\r\nfrom datasets import load_dataset\r\nfrom datasets.utils.py_utils import dumps, pklregister\r\n\r\n@pklregister(Language, allow_subclasses=True)\r\ndef hash_spacy_language(pickler, nlp: Language):\r\n pickler.save(nlp.to_bytes())\r\n\r\ndef main():\r\n fin = r\"large/file.txt\"\r\n nlp = spacy.load(\"en_core_web_sm\")\r\n\r\n def tokenize(l):\r\n return {\"tok\": [t.text for t in nlp(l[\"text\"])]}\r\n\r\n ds = load_dataset(\"text\", data_files=fin)\r\n ds = ds[\"train\"].map(tokenize)\r\n\r\n # Sanity check: load NLP from pickle created with our own `dumps`\r\n config = nlp.config\r\n lang_cls = spacy.util.get_lang_class(config[\"nlp\"][\"lang\"])\r\n nlp2 = lang_cls.from_config(config)\r\n nlp2.from_bytes(pickle.loads(dumps(nlp)))\r\n\r\n assert isinstance(nlp2, type(nlp))\r\n assert dumps(nlp) == dumps(nlp2)\r\n\r\nif __name__ == '__main__':\r\n main()\r\n```\r\n\r\nIf this all looks good to you, I'll start writing on some documentation and examples.\r\n", "One more thing. This is a reduction function for SpaCy Language that should work with the new API:\r\n```python\r\n@pklregister(Language, allow_subclasses=True)\r\ndef hash_spacy_language(pickler, obj):\r\n def create_language(config, bytes_data):\r\n lang_cls = spacy.util.get_lang_class(config[\"nlp\"][\"lang\"])\r\n nlp = lang_cls.from_config(config)\r\n return nlp.from_bytes(bytes_data)\r\n\r\n args = (obj.config, obj.to_bytes())\r\n pickler.save_reduce(create_language, args, obj=obj)\r\n```\r\nso IMO we are missing a test with `pickler.save_reduce`. ", "> One more thing. This is a reduction function for SpaCy Language that should work with the new API:\r\n> \r\n> ```python\r\n> @pklregister(Language, allow_subclasses=True)\r\n> def hash_spacy_language(pickler, obj):\r\n> def create_language(config, bytes_data):\r\n> lang_cls = spacy.util.get_lang_class(config[\"nlp\"][\"lang\"])\r\n> nlp = lang_cls.from_config(config)\r\n> return nlp.from_bytes(bytes_data)\r\n> \r\n> args = (obj.config, obj.to_bytes())\r\n> pickler.save_reduce(create_language, args, obj=obj)\r\n> ```\r\n> \r\n> so IMO we are missing a test with `pickler.save_reduce`.\r\n\r\nSure that seems a good idea, but I do not quite understand what `save_reduce` does. Could you give some more info about what reduce functions do and how they differ from regular `save` and `save_global`? I've read about it but the docs nor the built-in `pickle` code seem really helpful.", "I'm no pickle expect, but here is my understanding. I believe the pickler uses the reduce function when you do `loads` to reconstructs the original object from the parameters/arguments that were saved with `dumps`.\r\n\r\nFor example your sanity check could be simplified from\r\n```python\r\n config = nlp.config\r\n lang_cls = spacy.util.get_lang_class(config[\"nlp\"][\"lang\"])\r\n nlp2 = lang_cls.from_config(config)\r\n nlp2 = nlp2.from_bytes(pickle.loads(dumps(nlp)))\r\n```\r\nto\r\nEDIT: <s>pickle.loads(pickle.dumps(nlp))</s>\r\n```python\r\n nlp2 = loads(dumps(nlp)) # using our custom pickler\r\n```\r\n\r\nThough note that while it can be convenient for tests, we actually don't care about the reconstruction of the object since we're only using the pickler for `dumps` to compute hashes.", "> I'm no pickle expect, but here is my understanding. I believe the pickler uses the reduce function when you do `loads` to reconstructs the original object from the parameters/arguments that were saved with `dumps`.\r\n> \r\n> For example your sanity check could be simplified from\r\n> \r\n> ```python\r\n> config = nlp.config\r\n> lang_cls = spacy.util.get_lang_class(config[\"nlp\"][\"lang\"])\r\n> nlp2 = lang_cls.from_config(config)\r\n> nlp2 = nlp2.from_bytes(pickle.loads(dumps(nlp)))\r\n> ```\r\n> \r\n> to\r\n> \r\n> ```python\r\n> nlp2 = pickle.loads(pickle.dumps(nlp))\r\n> ```\r\n> \r\n> Though note that while it can be convenient for tests, we actually don't care about the reconstruction of the object since we're only using the pickler for `dumps` to compute hashes.\r\n\r\nYes, the sanity check can be simplified like that _if_ we use `pickle.dumps` - but that would not test our own `dumps` functionality and would do a naive dump instead of using `to_bytes`. It won't work if we use our own `dumps`, exactly because of the reason that we want custom pickling and being able to call `to_bytes`. To reconstruct the object from the pickled bytes from `to_bytes` we need `from_bytes`. The result of pickle/dill loads will therefore always be a `bytes` object and not a `Language` object.\r\n\r\nBut `save_reduce` is called when saving, right? Not when loading, AFAICT. I am just not sure what exactly it is saving. It is _potentially_ called [at the end of `save`](https://github.com/python/cpython/blob/24af9a40a8f85af813ea89998aa4e931fcc78cd9/Lib/pickle.py#L603) but only if we haven't returned by then. I just can't figure out what that base case is.", "I don't think we expect users to write the reduce function that isn't going to be used anyway. So maybe let's stick with `save` ?", "@BramVanroy \r\nAs I understand `save_reduce` is very similar to `copyreg.pickle`, so I'd suggest you to check the following links:\r\n* https://docs.python.org/3/library/copyreg.html#copyreg.pickle\r\n* https://docs.python.org/3/library/pickle.html#object.__reduce__\r\n\r\n\r\n@lhoestq \r\n> I don't think we expect users to write the reduce function that isn't going to be used anyway. So maybe let's stick with save ?\r\n\r\nI agree. \r\n\r\n`save_reduce` is very similar to `copyreg.pickle` and `object.__reduce__`, which are part of public API (and `save` isn't), so I expect more advanced users to know how to write their own reduction functions. But, as you say, `pklregister` should also work with `save` (even though I think `save` is a bit lower-level, and harder to understand than `save_reduce`).\r\n\r\nAll our examples in `py_utils` that use `pklregister` also use `save_reduce` in the last step, so my reduction for SpaCy is meant to be added there, and not to be written by users (because SpaCy is very popular, so the official support by us makes sense :)).\r\n\r\nAnd in the tests, let's ignore the reconstruction part of pickle/dill, because it's not important for us, and focus on the generated dumps. What do you think?", "@mariosasko What exactly do you mean with \"isn't part of the public API\"? It is [a public method](https://github.com/python/cpython/blob/24af9a40a8f85af813ea89998aa4e931fcc78cd9/Lib/pickle.py#L535) in base pickle, just like `dump` is but maybe you mean something else.", "@BramVanroy Oh sorry, it's public (not prefixed with `\"_\"`) but it's not documented in the docs. `save_reduce` is also not in the docs, but its signature/functionality is similar to `copyreg.pickle` and I see it more often being used in the projects on GH, so it's seems \"more public\" to me. ", "Unfortunately I feel that pickle in general is under-documented. 😄 \r\n\r\nFor the documentation, I can add a brief example, maybe under \"How-to Guides\"? The only thing that isn't immediately obvious to me is how I can add that doc page to the TOC?", "Yes great idea ! To add that doc page to the TOC, you just have to add it to the index.rst file in the \"How-to guides\" TOC section", "@mariosasko @lhoestq Feel free to make any edits or suggestions in the text!", "Hi @mariosasko. I wish you'd told me sooner, as I spent quite some time writing on this.\r\n\r\nI'm also not sure whether it is too advanced to have in the documentation. The spaCy use-case seems potentially frequent. Or do you wish to add that case to the defaults, and whenever new issues come up that seem like frequent/obvious cases, add those internally as well?" ]
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"CONTRIBUTOR"
"This PR adds documentation for the `Audio` feature. It describes: - The difference between loading `path` and `audio`, as well as use-cases/best practices for each of them. - Resampling audio files with `cast_column`, and then calling `ds[0]["audio"]` to automatically decode and resample to the desired sampling rate. - Resampling with `map`. Preview [here](https://52969-250213286-gh.circle-artifacts.com/0/docs/_build/html/audio_process.html), let me know if I'm missing anything!"
[ "Nice ! love it this way. I guess you can set this PR to \"ready for review\" ?" ]
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"As discussed in https://github.com/huggingface/datasets/issues/3027 we should automatically infer what file is supposed to go to what split automatically, based on filenames. I added the support for different kinds of patterns, for both dataset repositories and local directories: ``` Input structure: my_dataset_repository/ ├── README.md └── dataset.csv Output patterns: {"train": ["*"]} ``` ``` Input structure: my_dataset_repository/ ├── README.md ├── train.csv └── test.csv my_dataset_repository/ ├── README.md └── data/ ├── train.csv └── test.csv my_dataset_repository/ ├── README.md ├── train_0.csv ├── train_1.csv ├── train_2.csv ├── train_3.csv ├── test_0.csv └── test_1.csv Output patterns: {"train": ["*train*"], "test": ["*test*"]} ``` ``` Input structure: my_dataset_repository/ ├── README.md └── data/ ├── train/ │ ├── shard_0.csv │ ├── shard_1.csv │ ├── shard_2.csv │ └── shard_3.csv └── test/ ├── shard_0.csv └── shard_1.csv Output patterns: {"train": ["*train*/*", "*train*/**/*"], "test": ["*test*/*", "*test*/**/*"]} ``` and also this pattern that allows to have custom split names, and that is the structure used by #3098 for `push_to_hub` (cc @LysandreJik ): ``` Input structure: my_dataset_repository/ ├── README.md └── data/ ├── train-00000-of-00003.csv ├── train-00001-of-00003.csv ├── train-00002-of-00003.csv ├── test-00000-of-00001.csv ├── random-00000-of-00003.csv ├── random-00001-of-00003.csv └── random-00002-of-00003.csv Output patterns: { "train": ["data/train-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"], "test": ["data/test-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"], "random": ["data/random-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"], } ``` You can check the documentation about structuring your repository [here](https://52640-250213286-gh.circle-artifacts.com/0/docs/_build/html/repository_structure.html). cc @stevhliu Fix https://github.com/huggingface/datasets/issues/3027 Fix https://github.com/huggingface/datasets/issues/3212 In the future we can also add support for dataset configurations."
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[ "Really cool!\r\nWhen splitting by folder, what do we use for validation set (\"valid\", \"validation\" or both)?", "> When splitting by folder, what do we use for validation set (\"valid\", \"validation\" or both)?\r\n\r\nBoth are fine :) As soon as it has \"valid\" in it", "Merging for now, if you have comments about the documentation we can address them in subsequent PRs :)", "Thanks for the comments @stevhliu :) I just opened https://github.com/huggingface/datasets/pull/3233 to take them into account" ]
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"Resolve data_files by split name"
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"Add to the docs more details about the dataset viewer feature in the Hub. CC: @julien-c "
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"MEMBER"
"## Describe the bug From time to time, there appear Invalid Token errors with private datasets: - https://app.circleci.com/pipelines/github/huggingface/datasets/8520/workflows/d44629f2-4749-40f8-a657-50931d0b3434/jobs/52534 ``` ____________ ERROR at setup of test_load_streaming_private_dataset _____________ ValueError: Invalid token passed! ____ ERROR at setup of test_load_streaming_private_dataset_with_zipped_data ____ ValueError: Invalid token passed! =========================== short test summary info ============================ ERROR tests/test_load.py::test_load_streaming_private_dataset - ValueError: I... ERROR tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data ``` - https://app.circleci.com/pipelines/github/huggingface/datasets/8557/workflows/a8383181-ba6d-4487-9d0a-f750b6dcb936/jobs/52763 ``` ____ ERROR at setup of test_load_streaming_private_dataset_with_zipped_data ____ [gw1] linux -- Python 3.6.15 /home/circleci/.pyenv/versions/3.6.15/bin/python3.6 hf_api = <huggingface_hub.hf_api.HfApi object at 0x7f4899bab908> hf_token = 'vgNbyuaLNEBuGbgCEtSBCOcPjZnngJufHkTaZvHwkXKGkHpjBPwmLQuJVXRxBuaRzNlGjlMpYRPbthfHPFWXaaEDTLiqTTecYENxukRYVAAdpeApIUPxcgsowadkTkPj' zip_csv_path = PosixPath('/tmp/pytest-of-circleci/pytest-0/popen-gw1/data16/dataset.csv.zip') @pytest.fixture(scope="session") def hf_private_dataset_repo_zipped_txt_data_(hf_api: HfApi, hf_token, zip_csv_path): repo_name = "repo_zipped_txt_data-{}".format(int(time.time() * 10e3)) hf_api.create_repo(token=hf_token, name=repo_name, repo_type="dataset", private=True) repo_id = f"{USER}/{repo_name}" hf_api.upload_file( token=hf_token, path_or_fileobj=str(zip_csv_path), path_in_repo="data.zip", repo_id=repo_id, > repo_type="dataset", ) tests/hub_fixtures.py:68: ... ValueError: Invalid token passed! =========================== short test summary info ============================ ERROR tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data ``` "
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"Fix trailing whitespace. Fix #3217."
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"## Describe the bug ``` datasets/riddle_sense/riddle_sense.py:36:21: W291 trailing whitespace ```"
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[ "To give more context: https://github.com/psf/black/issues/318. `black` doesn't treat this as a bug, but `flake8` does. \r\n" ]
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"Once `tensorflow` version 2.6.2 is released: - https://github.com/tensorflow/tensorflow/commit/c1867f3bfdd1042f694df7a9870be51ba80543cb - https://pypi.org/project/tensorflow/2.6.2/ with the patch: - tensorflow/tensorflow#52927 we can remove the temporary fix we introduced in: - #3208 Fix #3209."
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"CONTRIBUTOR"
"I added a little more description about `to_tf_dataset` compared to just setting the format"
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[ "@stevhliu Accepted both suggestions, thanks for the review!" ]
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"Small updates to to_tf_dataset documentation"
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"CONTRIBUTOR"
"## Adding a Dataset - **Name:** *ACAV100M* - **Description:** *contains 100 million videos with high audio-visual correspondence, ideal for self-supervised video representation learning.* - **Paper:** *https://arxiv.org/abs/2101.10803* - **Data:** *https://github.com/sangho-vision/acav100m* - **Motivation:** *The largest dataset (to date) for audio-visual learning.* 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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"Fix #3204 "
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"Fix tuple_ie download url"
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"CONTRIBUTOR"
"When loading a dataset that consists of several files (e.g. `my_data/data_001.json`, `my_data/data_002.json` etc.) they are not loaded in order when using `load_dataset("my_data")`. This could lead to counter-intuitive results if, for example, the data files are sorted by date or similar since they would appear in different order in the `Dataset`. The straightforward solution is to sort the list of files alphabetically before loading them. cc @lhoestq "
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[ "This will be fixed by https://github.com/huggingface/datasets/pull/3221" ]
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"MEMBER"
"Currently the `disable_nullable` parameter is not consistent across all dataset transforms. For example it is `False` in `map` but `True` in `flatten_indices`. This creates unexpected behaviors like this ```python from datasets import Dataset, concatenate_datasets d1 = Dataset.from_dict({"a": [0, 1, 2, 3]}) d2 = d1.filter(lambda x: x["a"] < 2).flatten_indices() d1.data.schema == d2.data.schema # False ``` This can cause issues when concatenating datasets for example. For consistency I set `disable_nullable` to `False` in `flatten_indices` and I fixed some docstrings cc @SBrandeis "
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"NONE"
"when I use python examples/pytorch/translation/run_translation.py --model_name_or_path examples/pytorch/translation/opus-mt-en-ro --do_train --do_eval --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config_name ro-en --output_dir /tmp/tst-translation --per_device_train_batch_size=4 --per_device_eval_batch_size=4 --overwrite_output_dir --predict_with_generate to finetune translation model on huggingface, I get the issue"ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py".But I can open the https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py by using website. What should I do to solve the issue?"
[ "Hi ! Do you have some kind of proxy in your browser that gives you access to internet ?\r\n\r\nMaybe you're having this error because you don't have access to this URL from python ?" ]
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"Related to: - #3208"
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"Unpin keras once TF fixes its release"
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"Fix #3207."
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"## Describe the bug Release of TensorFlow 2.7.0 contains an incompatibility with Keras. See: - keras-team/keras#15579 This breaks our CI test suite: https://app.circleci.com/pipelines/github/huggingface/datasets/8493/workflows/055c7ae2-43bc-49b4-9f11-8fc71f35a25c/jobs/52363 "
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"Inspired by the discussion on hashing in https://github.com/huggingface/datasets/issues/3178#issuecomment-959016329, @lhoestq suggested that it would be neat to allow users more control over the hashing process. Specifically, it would be great if users can specify specific hashing functions depending on the **class** of the object. As an example, we found in the linked topic that loaded spaCy models (`Language` objects) have different hashes when `dump`'d, but their byte representation with `Language.to_bytes()` _is_ deterministic. It would therefore be great if we could specify that for `Language` objects, the hasher should hash the objects `to_bytes()` return value instead of the object itself. This PR adds a new, but tiny, dependency to manage the registry, namely [`catalogue`](https://github.com/explosion/catalogue). Two files have been changed (apart from the added dependency in `setup.py`) and one file has been added. **utils.registry** (added) This file defines our custom Registry and builds a registry called "hashers". A Registry is basically dictionary from names (str) to functions. A function can be added to the registry by a decorator, e.g. ```python @hashers.register(spacy.Language) def hash_spacy_language(nlp): return Hasher.hash(nlp.to_bytes()) ``` You'll notice that `spacy.Language` is not a string, even though the registry holds a str->func mapping. To accomplish this with classes in a dynamic way, catalogue.Registry needed to be subclassed and modified as `DatasetsRegistry`. All methods that use a name as an input are now modified so that classes are deterministically converted in strings in such a way that we can later retrieve the actual class from the string (below). **utils.py_utils** (modified) Added two functions to deal with classes and their qualified names, that is, their full descriptive name including the module. On the one hand it allows us to retrieve a string from a given class, e.g. given `Module` class, return `torch.nn.Module` str. Conversly, a function is added to convert such a full qualified name into a class. For instance, given the string `torch.nn.Module`, return the `Module` class. These straightforward methods allow us to interchangeably use classes and strings without any needed user interaction - they can just register a class, and behind the scenes `DatasetsRegistry` converts these to deterministic strings. **fingerprint** (modified) Updated Hasher.hash so that if the object to hash is an instance of a class in the registry, the registered function is used to hash the object instead of the default behavior. To do so we iterate over the registry `hashers` and convert its keys (strings) into classes, and then we can use `isinstance`. ```python # Check if the current object is an instance that is # applicable to the user-defined hashers. If so, hash # with the user-defined function for full_module_name, func in hashers.get_all().items(): registered_cls = get_cls_from_qualname(full_module_name) if isinstance(value, registered_cls): return func(value) ``` **Putting it all together** To test this, you can try the following example with spaCy. First install spaCy from source and checkout a specific commit. ```shell git clone https://github.com/explosion/spaCy.git cd spaCy/ git checkout cab9209c3dfcd1b75dfe5657f10e52c4d847a3cf cd .. git clone https://github.com/BramVanroy/datasets.git cd datasets git checkout registry pip install -e . pip install ../spaCy spacy download en_core_web_sm ``` Now you can run the following script. By default it will use the custom hasher function for the Language object. You can enable the default behavior by commenting out `@hashers.register...`. ```python import spacy from datasets.fingerprint import Hasher from datasets.utils.registry import hashers # Register a function so that when the Hasher encounters a spacy.Language object # it uses this custom function to hash instead of the default @hashers.register(spacy.Language) def hash_spacy_language(nlp): return Hasher.hash(nlp.to_bytes()) def main(): print(hashers.get_all()) nlp = spacy.load("en_core_web_sm") dump1 = Hasher.hash(nlp) nlp = spacy.load("en_core_web_sm") dump2 = Hasher.hash(nlp) print(dump1) # succeeds when using the registered custom function # fails if using the default assert dump1 == dump2 if __name__ == '__main__': main() ``` To do ==== - The above is just a proof-of-concept. I am open to changes/suggestions - Tests still need to be written - We should consider whether we can make `DatasetsRegistry` very restrictive and ONLY allowing classes. That would make testing easier - otherwise we also need to test for other sorts of objects. - Maybe the `hashers` definition is better suited in `fingerprint`? - Documentation/examples need to be updated - Not sure why the logger is not working in `hash()` - `get_cls_from_qualname` might need a fail-safe: is it possible for a full_qualname to not have a module, and if so how do we deal with that? "
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[ "Hi @BramVanroy, thanks for your PR.\r\n\r\nThere was a bug in TensorFlow/Keras. We have made a temporary fix in master branch. Please, merge master into your PR branch, so that the CI tests pass.\r\n\r\n```\r\ngit checkout registry\r\ngit fetch upstream master\r\ngit merge upstream/master\r\n```", "@albertvillanova Done. Although new tests will need to be added. I am looking for some feedback on my initial proposal in this PR. Reviews and ideas welcome!", "Hi ! Thanks for diving into this :)\r\n\r\nWith this approach you get the right hash when doing `Hasher.hash(nlp)` but if you try to hash an object that has `nlp` as one of its attributes for example you will get different hashes every time.\r\n\r\nThis is because `Hasher.hash` is not recursive itself. Indeed what happens when you try to hash an object is that:\r\n1. it is dumped with our custom `dill` pickler (which is recursive)\r\n2. the bytes of the dump are hashed\r\n\r\nTo fix this we must integrate the custom hashing as a custom pickler dumping instead.\r\n\r\nNote that we're only using the `pickler.dumps` method and not `pickler.loads` since we only use it to get hashes, so it doesn't matter if `loads` doesn't reconstruct the object exactly. What's important it only to capture all the necessary information that defines how the object transforms the data (here `nlp.to_bytes()` determines how the spacy pipeline transforms the text).\r\n\r\nOur pickler already has a registry and you can register new dump functions with:\r\n```python\r\nimport dill\r\nimport spacy\r\nfrom datasets.utils.py_utils import pklregister\r\n\r\n@pklregister(spacy.Language)\r\ndef _save_spacy_language(pickler, nlp):\r\n pickler.save_reduce(...) # I think we can use nlp.to_bytes() here\r\n dill._dill.log.info(...)\r\n```\r\n\r\nYou can find some examples of custom dump functions in `py_utils.py`", "Ah, darn it. Completely missed that register. Time wasted, unfortunately. \r\n\r\nTo better understand what you mean, I figured I'd try the basis of your snippet and I've noticed quite an annoying side-effect of how the pickle dispatch table seems to work. It explicitly uses an object's [`type()`](https://github.com/python/cpython/blob/87032cfa3dc975d7442fd57dea2c6a56d31c911a/Lib/pickle.py#L557-L558), which makes sense for pickling some (primitive) types it is not ideal for more complex ones, I think. `Hasher.hash` has the same issue as far as I can tell.\r\n\r\nhttps://github.com/huggingface/datasets/blob/d21ce54f2c2782f854f975eb1dc2be6f923b4314/src/datasets/fingerprint.py#L187-L191\r\n\r\nThis is very restrictive, and won't work for subclasses. In the case of spaCy, for instance, we register `Language`, but `nlp` is an instance of `English`, which is a _subclass_ of `Language`. These are different types, and so they will not match in the dispatch table. Maybe this is more general approach to cover such cases? Something like this is a start but too broad, but ideally a hierarchy is constructed and traversed of all classes in the table and the lowest class is selected to ensure that the most specific class function is dispatched.\r\n\r\n```python\r\n def hash(cls, value: Any) -> str:\r\n # Try to match the exact type\r\n if type(value) in cls.dispatch:\r\n return cls.dispatch[type(value)](cls, value)\r\n\r\n # Try to match instance (superclass)\r\n for type_cls, func in cls.dispatch.items():\r\n if isinstance(value, type_cls):\r\n return cls.dispatch[type_cls](cls, value)\r\n\r\n return cls.hash_default(value)\r\n```\r\n\r\nThis does not solve the problem for pickling, though. That is quite unfortunate IMO because that implies that users always have to specify the most specific class, which is not always obvious. (For instance, `spacy.load`'s signature returns `Language`, but as said before a subclass might be returned.)\r\n\r\nSecond, I am trying to understand `save_reduce` but I can find very little documentation about it, only the source code which is quite cryptic. Can you explain it a bit? The required arguments are not very clear to me and there is no docstring.\r\n\r\n```python\r\n def save_reduce(self, func, args, state=None, listitems=None, dictitems=None, obj=None):\r\n```", "Here is an example illustrating the problem with sub-classes.\r\n\r\n```python\r\nimport spacy\r\n\r\nfrom spacy import Language\r\nfrom spacy.lang.en import English\r\n\r\nfrom datasets.utils.py_utils import Pickler, pklregister\r\n\r\n# Only useful in the registry (matching with `nlp`)\r\n# if you swap it out for very specific `English`\r\n@pklregister(English)\r\ndef hash_spacy_language(pickler, nlp):\r\n pass\r\n\r\n\r\ndef main():\r\n print(Pickler.dispatch)\r\n nlp = spacy.load(\"en_core_web_sm\")\r\n print(f\"NLP type {type(nlp)} in dispatch table? \", type(nlp) in Pickler.dispatch)\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n```", "Indeed that's not ideal.\r\nMaybe we could integrate all the subclasses directly in `datasets`. That's simple to do but the catch is that if users have new subclasses of `Language` it won't work.\r\n\r\nOtherwise we can see how to make the API simpler for users by allowing subclasses\r\n```python\r\n# if you swap it out for very specific `English`\r\n@pklregister(Language, allow_subclasses=True)\r\ndef hash_spacy_language(pickler, nlp):\r\n pass\r\n```\r\n\r\nHere is an idea how to make this work, let me know what you think:\r\n\r\nWhen `Pickler.dumps` is called, it uses `Pickler.save_global` which is a method that is going to be called recursively on all the objects. We can customize this part, and make it work as we want when it encounters a subclass of `Language`.\r\n\r\nFor example when it encounters a subclass of `Language`, we can dynamically register the hashing function for the subclass (`English` for example) in `Pickler.save_global`, right before calling the actual `dill.Pickler.save_global(self, obj, name=name)`:\r\n```python\r\npklregister(type(obj))(hash_function_registered_for_parent_class)\r\ndill.Pickler.save_global(self, obj, name=name)\r\n```\r\n\r\nIn practice that means we can have an additional dispatch dictionary (similar to `Pickler.dispatch`) to store the hashing functions when `allow_subclasses=True`, and use this dictionary in `Pickler.save_global` to check if we need to use a hashing function registered with `allow_subclasses=True` and get `hash_function_registered_for_parent_class`.", "If I understood you correctly, I do not think that that is enough because you are only doing this for a type and its direct parent class. You could do this for all superclasses (so traverse all ancestors and find the registered function for the first that is encountered). I can work on that, if you agree. The one thing that I am not sure about is how you want to create the secondary dispatch table. An empty dict as class variable in Pickler? (It doesn't have to be a true dispatcher, I think.)\r\n\r\nI do not think that dynamic registration is the ideal situation (it feels a bit hacky). An alternative would be to subclass Pickle and Dill to make sure that instead of just type() checking in the dispatch table also superclasses are considered. But that is probably overkill.", "> You could do this for all superclasses (so traverse all ancestors and find the registered function for the first that is encountered)\r\n\r\nThat makes sense indeed !\r\n\r\n> The one thing that I am not sure about is how you want to create the secondary dispatch table. An empty dict as class variable in Pickler? (It doesn't have to be a true dispatcher, I think.)\r\n\r\nSure, let's try to not use too complicated stuff\r\n\r\n> I do not think that dynamic registration is the ideal situation (it feels a bit hacky). An alternative would be to subclass Pickle and Dill to make sure that instead of just type() checking in the dispatch table also superclasses are considered. But that is probably overkill.\r\n\r\nIndeed that would feel less hacky, but maybe it's too complex just for this. I feel like this part of the library is already hard to understand when you're not familiar with pickle. IMO having only a few changes that are simpler to understand is better than having a rewrite of `dill`'s core code.\r\n\r\nThanks a lot for your insights, it looks like we're going to have something that works well and that unlocks some nice flexibility for users :) Feel free to ping me anytime if I can help on this", "Sure, thanks for brainstorming! I'll try to work on it this weekend. Will also revert the current changes in this PR and rename it. ", "It seems like this is going in the right direction :). \r\n\r\n@BramVanroy Just one small suggestion for future contributions: instead of using `WIP` in the PR title, you can create a draft PR if you're still working on it.", "Maybe I should just create a new (draft) PR then, seeing that I'll have to rename and revert the changes anyway? I'll link to this PR so that the discussion is at least referenced.", "I can convert this PR to a draft PR. Let me know what would you prefer.", "I think reverting my previous commits would make for a dirty (or confusing) commit history, so I'll just create a new one. Thanks." ]
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"This PR adds the MultiDoc2Dial dataset introduced in this [paper](https://arxiv.org/pdf/2109.12595v1.pdf )"
[ "@songfeng cc", "Hi @sivasankalpp, thanks for your PR.\r\n\r\nThere was a bug in TensorFlow/Keras. We have made a temporary fix in our master branch. Please, merge master into your PR branch, so that the CI tests pass.\r\n\r\n```\r\ngit checkout multidoc2dial\r\ngit fetch upstream master\r\ngit merge upstream/master\r\n```", "Hi @albertvillanova, I have merged master into my PR branch. All tests are passing. \r\nPlease take a look when you get a chance, thanks! \r\n" ]
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"NONE"
"Hi, `dataset = datasets.load_dataset('tuple_ie', 'all')` returns a FileNotFound error. Is the data not available? Many thanks. "
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[ "@mariosasko @lhoestq Could you give me an update on how to load the dataset after the fix?\r\nThanks.", "Hi @arda-vianai,\r\n\r\nfirst, you can try:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset('tuple_ie', 'all', revision=\"master\")\r\n```\r\nIf this doesn't work, your version of `datasets` is missing some features that are required to run the dataset script, so install the master version with the following command:\r\n```\r\npip install git+https://github.com/huggingface/datasets.git\r\n```\r\nand then:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset('tuple_ie', 'all')\r\n```\r\nshould work (even without `revision`).", "@mariosasko \r\nThanks, it is working now. I actually did that before but I didn't restart the kernel. I restarted it and it works now. My bad!!!\r\nMany thanks and great job!\r\n-arda" ]
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"CONTRIBUTOR"
"It seems that DaNLP has updated their download URLs and it therefore also needs to be updated in here..."
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[ "Actually it looks like the old URL is still working, and it's also the one that is mentioned in https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md\r\n\r\nWhat makes you think we should use the new URL ?", "@lhoestq Sorry! I might have jumped to conclusions a bit too fast here... \r\n\r\nI was working in Google Colab and got an error that it was unable to use the URL. I then forked the project, updated the URL, ran it locally and it worked. I therefore assumed that my URL update fixed the issue, however, I see now that it might rather be a Google Colab issue... \r\n\r\nStill - this seems to be the official URL for downloading the dataset, and I think that it will be most beneficial to use. :-) ", "It looks like they're using these new urls for their new datasets. Maybe let's change to the new URL in case the old one stops working at one point. Thanks" ]
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"NONE"
"**Is your feature request related to a problem? Please describe.** Recently, some semantic segmentation models were added to HuggingFace Transformers, including [SegFormer](https://huggingface.co/transformers/model_doc/segformer.html) and [BEiT](https://huggingface.co/transformers/model_doc/beit.html). Semantic segmentation (which is the task of labeling every pixel of an image with a corresponding class) is typically evaluated using the Mean Intersection and Union (mIoU). Together with the upcoming Image Feature, adding this metric could be very handy when creating example scripts to fine-tune any Transformer-based model on a semantic segmentation dataset. An implementation can be found [here](https://github.com/open-mmlab/mmsegmentation/blob/504965184c3e6bc9ec43af54237129ef21981a5f/mmseg/core/evaluation/metrics.py#L132) for instance. "
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"Add mIoU metric"
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"NONE"
"## Adding a Dataset - **Name:** GSM8K (short for Grade School Math 8k) - **Description:** GSM8K is a dataset of 8.5K high quality linguistically diverse grade school math word problems created by human problem writers. - **Paper:** https://openai.com/blog/grade-school-math/ - **Data:** https://github.com/openai/grade-school-math - **Motivation:** The dataset is useful to investigate the reasoning abilities of large Transformer models, such as GPT-3. 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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"MEMBER"
"The staging back end sometimes returns invalid token errors when trying to delete a repo. I modified the fixture in the test that uses staging to ignore this error"
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"MEMBER"
"huggingface_hub just released its first minor version, so we need to update the dependency It was supposed to be part of 1.15.0 but I'm adding it for 1.15.1"
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"MEMBER"
"Add https://www.openslr.org/94/"
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"Add Multi-Lingual LibriSpeech"
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"MEMBER"
"Hi ! #3124 introduced a regression that made the benchmarks CI fail because of a bad array comparison when checking the first encoded element. This PR fixes this by making sure that encoding is applied on all sequence types except lists. cc @eladsegal fyi (no big deal)"
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"CONTRIBUTOR"
"This PR: * automatically calls `flatten_indices` where needed: in `unique` and `save_to_disk` to avoid saving the indices file * adds descriptions to the map calls Fix #3040 "
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"CONTRIBUTOR"
"PyArrow has explicit support for `null` values, so it makes sense to support Nones on our side as well. Changes: * allow None for the features types with special encoding (`ClassLabel, TranslationVariableLanguages, Value, _ArrayXD`) * handle None in `class_encode_column` * support None sorting in `sort` (use pandas for that) TODO: - [ ] check how the Audio features behaves with Nones - [ ] Better None handling in `concatenate_datasets`/`add_item` - [ ] Fix formatting with Nones - [ ] Tests Fix #3181 "
[ "I also created a PR regarding `disable_nullable` that must be always `False` by default, in order to always allow None values\r\nhttps://github.com/huggingface/datasets/pull/3211" ]
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"MEMBER"
"Once datasets-tagging has been transferred to Spaces: - huggingface/datasets-tagging#22 We should update the link in Datasets."
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"CONTRIBUTOR"
"While running the tests, I found that the multiprocessing examples fail on Windows, or rather they do not complete: they cause a deadlock. I haven't dug deep into it, but they do not seem to work as-is. I currently have no time to tests this in detail but at least the tests seem not to run correctly (deadlocking). ## Steps to reproduce the bug ```shell pytest tests/test_arrow_dataset.py -k "test_filter_multiprocessing" pytest tests/test_arrow_dataset.py -k "test_map_multiprocessing" ``` ## Expected results The functionality to work on all platforms. ## Actual results Deadlock. ## Environment info - `datasets` version: 1.14.1.dev0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.9.2, also tested with 3.7.9 - PyArrow version: 4.0.1 "
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"NONE"
"## Dataset viewer issue for '*compguesswhat*' **Link:** https://huggingface.co/datasets/compguesswhat File not found Am I the one who added this dataset ? No "
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"CONTRIBUTOR"
"## Describe the bug Hi, I would like to shuffle a dataset, then filter it based on each existing label. however, the combination of `filter`, `shuffle` seems to results in a bug. In the minimal example below, as you see in the filtered results, the filtered labels are not unique, meaning filter has not worked. Any suggestions as a temporary fix is appreciated @lhoestq. Thanks. Best regards Rabeeh ## Steps to reproduce the bug ```python import numpy as np import datasets datasets = datasets.load_dataset('super_glue', 'rte', script_version="master") shuffled_data = datasets["train"].shuffle(seed=42) for label in range(2): print("label ", label) data = shuffled_data.filter(lambda example: int(example['label']) == label) print("length ", len(data), np.unique(data['label'])) ``` ## Expected results Filtering per label, should only return the data with that specific label. ## Actual results As you can see, filtered data per label, has still two labels of [0, 1] ``` label 0 length 1249 [0 1] label 1 length 1241 [0 1] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: linux - Python version: 3.7.11 - PyArrow version: 5.0.0 "
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[ "I cannot reproduce this on master and pyarrow==4.0.1.\r\n", "Hi ! There was a regression in `datasets` 1.12 that introduced this bug. It has been fixed in #3019 in 1.13\r\n\r\nCan you try to update `datasets` and try again ?", "Thanks a lot, fixes with 1.13" ]
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"CONTRIBUTOR"
"In the [conll2003](https://huggingface.co/datasets/conll2003#data-fields) README, the labels are described as follows > - `id`: a `string` feature. > - `tokens`: a `list` of `string` features. > - `pos_tags`: a `list` of classification labels, with possible values including `"` (0), `''` (1), `#` (2), `$` (3), `(` (4). > - `chunk_tags`: a `list` of classification labels, with possible values including `O` (0), `B-ADJP` (1), `I-ADJP` (2), `B-ADVP` (3), `I-ADVP` (4). > - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4) `B-LOC` (5), `I-LOC` (6) `B-MISC` (7), `I-MISC` (8). First of all, it would be great if we can get a list of ALL possible pos_tags. Second, the chunk tags labels cannot be correct. The description says the values go from 0 to 4 whereas the data shows values from at least 11 to 21 and 0. EDIT: not really a bug, sorry for mistagging."
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[ "Hi @BramVanroy,\r\n\r\nsince these fields are of type `ClassLabel` (you can check this with `dset.features`), you can inspect the possible values with:\r\n```python\r\ndset.features[field_name].feature.names # .feature because it's a sequence of labels\r\n```\r\n\r\nand to find the mapping between names and integers, use: \r\n```python\r\ndset.features[field_name].feature.int2str(value_or_values_list) # map integer value to string value\r\n# or\r\ndset.features[field_name].feature.str2int(value_or_values_list) # map string value to integer value\r\n```\r\n\r\n" ]
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"conll2003 incorrect label explanation"
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"CONTRIBUTOR"
"**Link:** https://huggingface.co/datasets/conll2002 The dataset viewer throws a server error when trying to preview the dataset. ``` Message: Extraction protocol 'train' for file at 'https://raw.githubusercontent.com/teropa/nlp/master/resources/corpora/conll2002/esp.train' is not implemented yet ``` In addition, the "point of contact" has encoding issues and does not work when clicked. Am I the one who added this dataset ? No, @lhoestq did"
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[ "Hi ! Thanks for reporting :)\r\n\r\nThis is related to https://github.com/huggingface/datasets/issues/2742, I'm working on it. It should fix the viewer for around 80 datasets.\r\n", "Ah, hadn't seen that sorry.\r\n\r\nThe scrambled \"point of contact\" is a separate issue though, I think.", "@lhoestq The \"point of contact\" is still an issue." ]
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"CONTRIBUTOR"
"Similar to my [PR for TER](https://github.com/huggingface/datasets/pull/3153), it feels only right to also include ChrF and friends. These are present in Sacrebleu and are therefore very similar to implement as TER and sacrebleu. I tested the implementation with sacrebleu's tests to verify. You can try this below for yourself ```python import datasets EPSILON = 1e-4 chrf = datasets.load_metric(r"path\to\datasets\metrics\chrf") test_cases = [ (["abcdefg"], ["hijklmnop"], 0.0), (["a"], ["b"], 0.0), ([""], ["b"], 0.0), ([""], ["ref"], 0.0), ([""], ["reference"], 0.0), (["aa"], ["ab"], 8.3333), (["a", "b"], ["a", "c"], 8.3333), (["a"], ["a"], 16.6667), (["a b c"], ["a b c"], 50.0), (["a b c"], ["abc"], 50.0), ([" risk assessment must be made of those who are qualified and expertise in the sector - these are the scientists ."], ["risk assessment has to be undertaken by those who are qualified and expert in that area - that is the scientists ."], 63.361730), ([" Die Beziehung zwischen Obama und Netanjahu ist nicht gerade freundlich. "], ["Das Verhältnis zwischen Obama und Netanyahu ist nicht gerade freundschaftlich."], 64.1302698), (["Niemand hat die Absicht, eine Mauer zu errichten"], ["Niemand hat die Absicht, eine Mauer zu errichten"], 100.0), ] for hyp, ref, score in test_cases: # Note the reference transformation which is different from scarebleu's input format results = chrf.compute(predictions=hyp, references=[[r] for r in ref], char_order=6, word_order=0, beta=3, eps_smoothing=True) if abs(score - results["score"]) > EPSILON: print(f"expected {score}, got {results['score']} for {hyp} - {ref}") test_cases_effective_order = [ (["a"], ["a"], 100.0), ([""], ["reference"], 0.0), (["a b c"], ["a b c"], 100.0), (["a b c"], ["abc"], 100.0), ([""], ["c"], 0.0), (["a", "b"], ["a", "c"], 50.0), (["aa"], ["ab"], 25.0), ] for hyp, ref, score in test_cases_effective_order: # Note the reference transformation which is different from scarebleu's input format results = chrf.compute(predictions=hyp, references=[[r] for r in ref], char_order=6, word_order=0, beta=3, eps_smoothing=False) if abs(score - results["score"]) > EPSILON: print(f"expected {score}, got {results['score']} for {hyp} - {ref}") test_cases_keep_whitespace = [ ( ["Die Beziehung zwischen Obama und Netanjahu ist nicht gerade freundlich."], ["Das Verhältnis zwischen Obama und Netanyahu ist nicht gerade freundschaftlich."], 67.3481606, ), ( ["risk assessment must be made of those who are qualified and expertise in the sector - these are the scientists ."], ["risk assessment has to be undertaken by those who are qualified and expert in that area - that is the scientists ."], 65.2414427, ), ] for hyp, ref, score in test_cases_keep_whitespace: # Note the reference transformation which is different from scarebleu's input format results = chrf.compute(predictions=hyp, references=[[r] for r in ref], char_order=6, word_order=0, beta=3, whitespace=True) if abs(score - results["score"]) > EPSILON: print(f"expected {score}, got {results['score']} for {hyp} - {ref}") predictions = ["The relationship between Obama and Netanyahu is not exactly friendly."] references = [["The ties between Obama and Netanyahu are not particularly friendly."]] print(chrf.compute(predictions=predictions, references=references)) ```"
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"Add ChrF(++) (as implemented in sacrebleu)"
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"CONTRIBUTOR"
"## Dataset viewer issue for '*nli_tr*' **Link:** https://huggingface.co/datasets/nli_tr Hello, Thank you for the new dataset preview feature that will help the users to view the datasets online. We just noticed that the dataset viewer widget in the `nli_tr` dataset shows the error below. The error must be due to a temporary problem that may have blocked access to the dataset through the dataset viewer. But the dataset is currently accessible through the link in the error message. May we kindly ask if it would be possible to rerun the job so that it can access the dataset for the dataset viewer function? Thank you. Emrah ------------------------------------------ Server Error Status code: 404 Exception: FileNotFoundError Message: [Errno 2] No such file or directory: 'zip://snli_tr_1.0_train.jsonl::https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/snli_tr_1.0.zip ------------------------------------------ Am I the one who added this dataset ? Yes "
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"NONE"
"## Dataset viewer issue for dataset 'samsum' **Link:** https://huggingface.co/datasets/samsum Server Error Status code: 400 Exception: NotImplementedError Message: Extraction protocol '7z' for file at 'https://arxiv.org/src/1911.12237v2/anc/corpus.7z' is not implemented yet "
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"7z dataset preview not implemented?"
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"CONTRIBUTOR"
"Hi, as we've recently finished with the new RONEC (Romanian Named Entity Corpus), we'd like to update the dataset here as well. It's actually essential as links to V1 are no longer valid. In reality we'd like to replace completely v1, as v2 is a full re-annotation of v1 with additional data (up to 2x size vs v1). I've run the make style and all the dummy and real data test, and they passed. I hope it's okay to merge the new RONEC v2 in the datasets. Thanks!"
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[ "@lhoestq Thanks for the review. I totally understand what you are saying. Normally, I would definitely agree with you, but in this particular case, the quality of v1 is poor, and the dataset itself is small (at the time we created v1 it was the only RO NER dataset, and its size was limited by the available resources). \r\n\r\nThis is why we worked to build a larger one, with much better inter-annotator agreement. Fact is, models trained on v1 will be of very low quality and I would not recommend to anybody to use/do that. That's why I'd strongly suggest we replace v1 with v2, and kindof make v1 vanish :) \r\n\r\nWhat do you think? If you insist on having v1 accessible, I'll add the required code. Thanks!\r\n\r\n", "Ok I see ! I think it's fine then, no need to re-add V1" ]
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"CONTRIBUTOR"
"Document the `use_etag` and `num_proc` attributes in `DownloadConig`."
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"When hashing an object that has several times the same string, the hashing could return a different hash if the identical strings share the same python `id()` or not. Here is an example code that shows how the issue can affect the caching: ```python import json import pyarrow as pa from datasets.features import Features from datasets.fingerprint import Hasher schema = pa.schema([pa.field("some_string", pa.string()), pa.field("another_string", pa.string())]) features_from_schema = Features.from_arrow_schema(schema) Hasher.hash(features_from_schema) # dffa9dca9a73fd8c features_dict = json.loads('{"some_string": {"dtype": "string", "id": null, "_type": "Value"}, "another_string": {"dtype": "string", "id": null, "_type": "Value"}}') features_from_json = Features.from_dict(features_dict) Hasher.hash(features_from_json) # 3812e76b15e6420e features_from_schema == features_from_json # True ``` This is because in `features_dict`, some strings like "dtype" are repeated but don't share the same id, contrary to the ones in `features_from_schema`. I fixed that by disabling memoization for strings. This could be optimized in the future by implementing a smarter memoization with a special handling for strings."
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"CONTRIBUTOR"
"## Describe the bug When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`. ## Steps to reproduce the bug ```python from datasets import load_dataset qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists") print(qasper[60]["full_text"]["section_name"]) ``` When installing version 1.1.40, the output is `[None, 'Introduction', 'Benchmark Datasets', ...]` When installing from the master branch, the output is `['None', 'Introduction', 'Benchmark Datasets', ...]` Notice how the first element was changed from `NoneType` to `str`. ## Expected results `None` should stay as is. ## Actual results `None` is converted to a string. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: master - Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17 - Python version: 3.8.10 - PyArrow version: 4.0.1 "
[ "Hi @eladsegal, thanks for reporting.\r\n\r\n@mariosasko I saw you are already working on this, but maybe my comment will be useful to you.\r\n\r\nAll values are casted to their corresponding feature type (including `None` values). For example if the feature type is `Value(\"bool\")`, `None` is casted to `False`.\r\n\r\nIt is true that strings were an exception, but this was recently fixed by @lhoestq (see #3158).", "Thanks for reporting.\r\n\r\nThis is actually a breaking change that I think can cause issues when users preprocess their data. String columns used to be nullable. Maybe we can correct https://github.com/huggingface/datasets/pull/3158 to keep the None values and avoid this breaking change ?\r\n\r\nEDIT: the other types (bool, int, etc) can also become nullable IMO", "So what would be the best way to handle a feature that can have a null value in some of the instances? So far I used `None`.\r\nUsing the empty string won't be a good option, as it can be an actual value in the data and is not the same as not having a value at all.", "Hi @eladsegal,\r\n\r\nUse `None`. As @albertvillanova correctly pointed out, this change in conversion was introduced (by mistake) in #3158. To avoid it, install the earlier revision with:\r\n```\r\npip install git+https://github.com/huggingface/datasets.git@8107844ec0e7add005db0585c772ee20adc01a5e\r\n```\r\n\r\nI'm making all the feature types nullable as we speak, and the fix will be merged probably early next week." ]
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"MEMBER"
"Fixing label mapping for hlgd. 0 correponds to same event and 1 corresponds to different event <img width="642" alt="Capture d’écran 2021-10-29 à 10 39 58 AM" src="https://user-images.githubusercontent.com/16107619/139454810-1f225e3d-ad48-44a8-b8b1-9205c9533839.png"> <img width="638" alt="Capture d’écran 2021-10-29 à 10 40 09 AM" src="https://user-images.githubusercontent.com/16107619/139454813-93066a3c-7d33-4f56-b133-2f1a7661e438.png"> nt"
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[ "heck, test failings. moving to draft. will come back to this later today hopefully", "Thanks for fixing this :)\r\nI just updated the dataset_infos.json and added the missing `pretty_name` tag to the dataset card", "thank you @lhoestq! running around as always it felt through as a lower priority..." ]
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"CONTRIBUTOR"
"## Describe the bug After https://github.com/huggingface/datasets/pull/3159, we can get the config name of "Check/region_1", which is "Check___region_1". But now we cannot load the dataset (not sure it's related to the above PR though). It's the case for all the similar datasets, listed in https://github.com/huggingface/datasets-preview-backend/issues/78 ## Steps to reproduce the bug ```python >>> from datasets import get_dataset_config_names >>> get_dataset_config_names("Check/region_1") ['Check___region_1'] >>> load_dataset("Check/region_1") Using custom data configuration Check___region_1-d2b3bc48f11c9be2 Downloading and preparing dataset json/Check___region_1 to /home/slesage/.cache/huggingface/datasets/json/Check___region_1-d2b3bc48f11c9be2/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426... 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 4443.12it/s] 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1277.19it/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset builder_instance.download_and_prepare( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare self._download_and_prepare( File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 697, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1159, in _prepare_split writer.write_table(table) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 442, in write_table pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 442, in <listcomp> pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema) File "pyarrow/table.pxi", line 1249, in pyarrow.lib.Table.__getitem__ File "pyarrow/table.pxi", line 1825, in pyarrow.lib.Table.column File "pyarrow/table.pxi", line 1800, in pyarrow.lib.Table._ensure_integer_index KeyError: 'Field "builder_name" does not exist in table schema' ``` Loading in streaming mode also returns something strange: ```python >>> list(load_dataset("Check/region_1", streaming=True, split="train")) Using custom data configuration Check___region_1-d2b3bc48f11c9be2 [{'builder_name': None, 'citation': '', 'config_name': None, 'dataset_size': None, 'description': '', 'download_checksums': None, 'download_size': None, 'features': {'speech': {'feature': {'dtype': 'float64', 'id': None, '_type': 'Value'}, 'length': -1, 'id': None, '_type': 'Sequence'}, 'sampling_rate': {'dtype': 'int64', 'id': None, '_type': 'Value'}, 'label': {'dtype': 'string', 'id': None, '_type': 'Value'}}, 'homepage': '', 'license': '', 'post_processed': None, 'post_processing_size': None, 'size_in_bytes': None, 'splits': None, 'supervised_keys': None, 'task_templates': None, 'version': None}, {'_data_files': [{'filename': 'dataset.arrow'}], '_fingerprint': 'f1702bb5533c549c', '_format_columns': ['speech', 'sampling_rate', 'label'], '_format_kwargs': {}, '_format_type': None, '_indexes': {}, '_indices_data_files': None, '_output_all_columns': False, '_split': None}] ``` ## Expected results The dataset should be loaded ## Actual results An error occurs ## Environment info - `datasets` version: 1.14.1.dev0 - Platform: Linux-5.11.0-1020-aws-x86_64-with-glibc2.31 - Python version: 3.9.6 - PyArrow version: 4.0.1 "
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"CONTRIBUTOR"
"## Describe the bug I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable. ## Steps to reproduce the bug Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below). ```python import pickle from datasets import load_dataset import spacy class Processor: def __init__(self): self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"]) @staticmethod def collate(batch): return [d["en"] for d in batch] def parse(self, batch): batch = batch["translation"] return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]} def process(self): ds = load_dataset("wmt16", "de-en", split="train[:10%]") ds = ds.map(self.parse, batched=True, num_proc=6) if __name__ == '__main__': pr = Processor() # succeeds with open("temp.pkl", "wb") as f: pickle.dump(pr, f) print("Successfully pickled!") pr.process() ``` --- Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object. ```python from datasets.fingerprint import Hasher import pickle from datasets import load_dataset import spacy class Processor: def __init__(self): self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"]) @staticmethod def collate(batch): return [d["en"] for d in batch] def parse(self, batch): batch = batch["translation"] return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]} def process(self): ds = load_dataset("wmt16", "de-en", split="train[:10]") return ds.map(self.parse, batched=True) if __name__ == '__main__': pr = Processor() # succeeds with open("temp.pkl", "wb") as f: pickle.dump(pr, f) print("Successfully pickled class instance!") # succeeds with open("temp.pkl", "wb") as f: pickle.dump(pr.nlp, f) print("Successfully pickled nlp!") # fails print(Hasher.hash(pr.nlp)) pr.process() ``` ## Expected results This to be picklable, working (fingerprinted), and no warning. ## Actual results In the first snippet, I get this warning Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed. In the second, I get this traceback which directs to the `Hasher.hash` line. ``` Traceback (most recent call last): File " \Python\Python36\lib\pickle.py", line 918, in save_global obj2, parent = _getattribute(module, name) File " \Python\Python36\lib\pickle.py", line 266, in _getattribute .format(name, obj)) AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0> During handling of the above exception, another exception occurred: Traceback (most recent call last): File " scratch_4.py", line 40, in <module> print(Hasher.hash(pr.nlp)) File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash return cls.hash_default(value) File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default return cls.hash_bytes(dumps(value)) File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps dump(obj, file) File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump Pickler(file, recurse=True).dump(obj) File " \lib\site-packages\dill\_dill.py", line 498, in dump StockPickler.dump(self, obj) File " \Python\Python36\lib\pickle.py", line 409, in dump self.save(obj) File " \Python\Python36\lib\pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File " \Python\Python36\lib\pickle.py", line 634, in save_reduce save(state) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict StockPickler.save_dict(pickler, obj) File " \Python\Python36\lib\pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems save(v) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \Python\Python36\lib\pickle.py", line 781, in save_list self._batch_appends(obj) File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends save(x) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \Python\Python36\lib\pickle.py", line 736, in save_tuple save(element) File " \Python\Python36\lib\pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File " \Python\Python36\lib\pickle.py", line 634, in save_reduce save(state) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \Python\Python36\lib\pickle.py", line 736, in save_tuple save(element) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict StockPickler.save_dict(pickler, obj) File " \Python\Python36\lib\pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems save(v) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0 pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj) File " \Python\Python36\lib\pickle.py", line 610, in save_reduce save(args) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \Python\Python36\lib\pickle.py", line 736, in save_tuple save(element) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function obj=obj, File " \Python\Python36\lib\pickle.py", line 610, in save_reduce save(args) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \Python\Python36\lib\pickle.py", line 751, in save_tuple save(element) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict StockPickler.save_dict(pickler, obj) File " \Python\Python36\lib\pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems save(v) File " \Python\Python36\lib\pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File " \Python\Python36\lib\pickle.py", line 605, in save_reduce save(cls) File " \Python\Python36\lib\pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File " \lib\site-packages\dill\_dill.py", line 1439, in save_type StockPickler.save_global(pickler, obj, name=name) File " \Python\Python36\lib\pickle.py", line 922, in save_global (obj, module_name, name)) _pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes ``` ## Environment info Tried on both Linux and Windows - `datasets` version: 1.14.0 - Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12 - PyArrow version: 6.0.0 "
[ "After some digging, I found that this is caused by `dill` and using `recurse=True)` when trying to dump the object. The problem also occurs without multiprocessing. I can only find [the following information](https://dill.readthedocs.io/en/latest/dill.html#dill._dill.dumps) about this:\r\n\r\n> If recurse=True, then objects referred to in the global dictionary are recursively traced and pickled, instead of the default behavior of attempting to store the entire global dictionary. This is needed for functions defined via exec().\r\n\r\nIn the utils, this is explicitly enabled\r\n\r\nhttps://github.com/huggingface/datasets/blob/df63614223bf1dd1feb267d39d741bada613352c/src/datasets/utils/py_utils.py#L327-L330\r\n\r\nIs this really necessary? Is there a way around it? Also pinging the spaCy team in case this is easy to solve on their end. (I hope so.)", "Hi ! Thanks for reporting\r\n\r\nYes `recurse=True` is necessary to be able to hash all the objects that are passed to the `map` function\r\n\r\nEDIT: hopefully this object can be serializable soon, but otherwise we can consider adding more control to the user on how to hash objects that are not serializable (as mentioned in https://github.com/huggingface/datasets/issues/3044#issuecomment-948818210)", "I submitted a PR to spacy that should fix this issue (linked above). I'll leave this open until that PR is merged. ", "@lhoestq After some testing I find that even with the updated spaCy, no cache files are used. I do not get any warnings though, but I can see that map is run every time I run the code. Do you have thoughts about why? If you want to try the tests below, make sure to install spaCy from [here](https://github.com/BramVanroy/spaCy) and installing the base model with `python -m spacy download en_core_web_sm`.\r\n\r\n```python\r\nfrom functools import partial\r\nfrom pathlib import Path\r\n\r\nimport spacy\r\nfrom datasets import Dataset\r\nimport datasets\r\ndatasets.logging.set_verbosity_debug()\r\n\r\ndef tokenize(nlp, l):\r\n return {\"tok\": [t.text for t in nlp(l[\"text\"])]}\r\n\r\ndef main():\r\n fin = r\"some/file/with/many/lines\"\r\n lines = Path(fin).read_text(encoding=\"utf-8\").splitlines()\r\n nlp = spacy.load(\"en_core_web_sm\")\r\n ds = Dataset.from_dict({\"text\": lines, \"text_id\": list(range(len(lines)))})\r\n tok = partial(tokenize, nlp)\r\n ds = ds.map(tok, load_from_cache_file=True)\r\n print(ds[0:2])\r\n\r\nif __name__ == '__main__':\r\n main()\r\n```\r\n\r\n... or with load_dataset (here I get the message that `load_dataset` can reuse the dataset, but still I see all samples being processed via the tqdm progressbar):\r\n\r\n```python\r\nfrom functools import partial\r\n\r\nimport spacy\r\nfrom datasets import load_dataset\r\nimport datasets\r\ndatasets.logging.set_verbosity_debug()\r\n\r\ndef tokenize(nlp, sample):\r\n return {\"tok\": [t.text for t in nlp(sample[\"text\"])]}\r\n\r\ndef main():\r\n fin = r\"some/file/with/many/lines\"\r\n nlp = spacy.load(\"en_core_web_sm\")\r\n tok_func = partial(tokenize, nlp)\r\n ds = load_dataset('text', data_files=fin)\r\n ds = ds[\"train\"].map(tok_func)\r\n print(ds[0:2])\r\n\r\nif __name__ == '__main__':\r\n main()\r\n```", "It looks like every time you load `en_core_web_sm` you get a different python object:\r\n```python\r\nimport spacy\r\nfrom datasets.fingerprint import Hasher\r\n\r\nnlp1 = spacy.load(\"en_core_web_sm\")\r\nnlp2 = spacy.load(\"en_core_web_sm\")\r\nHasher.hash(nlp1), Hasher.hash(nlp2)\r\n# ('f6196a33882fea3b', 'a4c676a071f266ff')\r\n```\r\nHere is a list of attributes that have different hashes for `nlp1` and `nlp2`:\r\n- tagger\r\n- parser\r\n- entity\r\n- pipeline (it's the list of the three attributes above)\r\n\r\nI just took a look at the tagger for example and I found subtle differences (there may be other differences though):\r\n```python\r\nnlp1.tagger.model.tok2vec.embed.id, nlp2.tagger.model.tok2vec.embed.id\r\n# (1721, 2243)\r\n```\r\n\r\nWe can try to find all the differences and find the best way to hash those objects properly", "Thanks for searching! I went looking, and found that this is an implementation detail of thinc\r\n\r\nhttps://github.com/explosion/thinc/blob/68691e303ae68cae4bc803299016f1fc064328bf/thinc/model.py#L96-L98\r\n\r\nPresumably (?) exactly to distinguish between different parts in memory when multiple models are loaded. Do not think that this can be changed on their end - but I will ask what exactly it is for (I'm curious).\r\n\r\nDo you think it is overkill to write something into the hasher explicitly to deal with spaCy models? It seems like something that is beneficial to many, but I do not know if you are open to adding third-party-specific ways to deal with this. If you are, I can have a look for this specific case how we can ignore `thinc.Model.id` from the hasher.", "It can be even simpler to hash the bytes of the pipeline instead\r\n```python\r\nnlp1.to_bytes() == nlp2.to_bytes() # True\r\n```\r\n\r\nIMO we should integrate the custom hashing for spacy models into `datasets` (we use a custom Pickler for that).\r\nWhat could be done on Spacy's side instead (if they think it's nice to have) is to implement a custom pickling for these classes using `to_bytes`/`from_bytes` to have deterministic pickle dumps.\r\n\r\nFinally I think it would be nice in the future to add an API to let `datasets` users control this kind of things. Something like being able to define your own hashing if you use complex objects.\r\n```python\r\n@datasets.register_hash(spacy.language.Language)\r\ndef hash_spacy_language(nlp):\r\n return Hasher.hash(nlp.to_bytes())\r\n```", "I do not quite understand what you mean. as far as I can tell, using `to_bytes` does a pickle dump behind the scene (with `srsly`), recursively using `to_bytes` on the required objects. Therefore, the result of `to_bytes` is a deterministic pickle dump AFAICT. Or do you mean that you wish that using your own pickler and running `dumps(nlp)` should also be deterministic? I guess that would require `__setstate__` and `__getstate__` methods on all the objects that have to/from_bytes. I'll have a listen over at spaCy what they think, and if that would solve the issue. I'll try this locally first, if I find the time.\r\n\r\nI agree that having the option to use a custom hasher would be useful. I like your suggestion!\r\n\r\nEDIT: after trying some things and reading through their API, it seems that they explicitly do not want this. https://spacy.io/usage/saving-loading#pipeline\r\n\r\n> When serializing the pipeline, keep in mind that this will only save out the binary data for the individual components to allow spaCy to restore them – not the entire objects. This is a good thing, because it makes serialization safe. But it also means that you have to take care of storing the config, which contains the pipeline configuration and all the relevant settings.\r\n\r\nBest way forward therefore seems to implement the ability to specify a hasher depending on the objects that are pickled, as you suggested. I can work on this if that is useful. I could use some pointers as to how you would like to implement the `register_hash` functionality though. I assume using `catalogue` over at Explosion might be a good starting point.\r\n\r\n", "Interestingly, my PR does not solve the issue discussed above. The `tokenize` function hash is different on every run, because for some reason `nlp.__call__` has a different hash every time. The issue therefore seems to run much deeper than I thought. If you have any ideas, I'm all ears.\r\n\r\n```shell\r\ngit clone https://github.com/explosion/spaCy.git\r\ncd spaCy/\r\ngit checkout cab9209c3dfcd1b75dfe5657f10e52c4d847a3cf\r\ncd ..\r\n\r\ngit clone https://github.com/BramVanroy/datasets.git\r\ncd datasets\r\ngit checkout registry\r\npip install -e .\r\npip install ../spaCy\r\nspacy download en_core_web_sm\r\n```\r\n\r\n```python\r\nimport spacy\r\n\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\nfrom datasets.utils.registry import hashers\r\n\r\n@hashers.register(spacy.Language)\r\ndef hash_spacy_language(nlp):\r\n return Hasher.hash(nlp.to_bytes())\r\n\r\ndef main():\r\n fin = r\"your/large/file\"\r\n nlp = spacy.load(\"en_core_web_sm\")\r\n # This is now always the same yay!\r\n print(Hasher.hash(nlp))\r\n\r\n def tokenize(l):\r\n return {\"tok\": [t.text for t in nlp(l[\"text\"])]}\r\n\r\n ds = load_dataset(\"text\", data_files=fin)\r\n # But this is not...\r\n print(Hasher.hash(tokenize))\r\n # ... because of this\r\n print(Hasher.hash(nlp.__call__))\r\n ds = ds[\"train\"].map(tokenize)\r\n print(ds[0:2])\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n```", "Hi ! I just answered in your PR :) In order for your custom hashing to be used for nested objects, you must integrate it into our recursive pickler that we use for hashing." ]
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"CONTRIBUTOR"
"It would help with the clutter in my terminal if tqdm is only shown for rank 0 when using `num_proces>0` in the map and filter methods of datasets. ```python dataset.map(lambda examples: tokenize(examples["text"]), batched=True, num_proc=6) ``` The above snippet leads to a lot of TQDM bars and depending on your terminal, these will not overwrite but keep pushing each other down. ``` #0: 0%| | 0/13 [00:00<?, ?ba/s] #1: 0%| | 0/13 [00:00<?, ?ba/s] #2: 0%| | 0/13 [00:00<?, ?ba/s] #3: 0%| | 0/13 [00:00<?, ?ba/s] #4: 0%| | 0/13 [00:00<?, ?ba/s] #5: 0%| | 0/13 [00:00<?, ?ba/s] #0: 8%| | 1/13 [00:00<?, ?ba/s] #1: 8%| | 1/13 [00:00<?, ?ba/s] ... ``` Instead, it would be welcome if we had the option to only show the progress of rank 0."
[ "Hi,\r\n\r\nIt's hard to provide an API that would cover all use-cases with tqdm in this project.\r\n\r\nHowever, you can make it work by defining a custom decorator (a bit hacky tho) as follows:\r\n```python\r\nimport datasets\r\n\r\ndef progress_only_on_rank_0(func):\r\n def wrapper(*args, **kwargs):\r\n rank = kwargs.get(\"rank\")\r\n disable_tqdm = kwargs.get(\"disable_tqdm\", False)\r\n disable_tqdm = True if rank is not None and rank > 0 else disable_tqdm\r\n kwargs[\"disable_tqdm\"] = disable_tqdm\r\n return func(*args, **kwargs)\r\n return wrapper\r\n \r\ndatasets.Dataset._map_single = progress_only_on_rank_0(datasets.Dataset._map_single)\r\n``` \r\n\r\nEDIT: Ups, closed by accident.\r\n\r\nThanks for the provided links. `Trainer` requires this for training in multi-node distributed setting. However, `Dataset.map` doesn't support that yet.\r\n\r\nDo you have an API for this in mind? `Dataset.map` is already bloated with the arguments, so IMO it's not a good idea to add a new arg there.\r\n\r\n", "Inspiration may be found at `transformers`.\r\n\r\nhttps://github.com/huggingface/transformers/blob/4a394cf53f05e73ab9bbb4b179a40236a5ffe45a/src/transformers/trainer.py#L1231-L1233\r\n\r\nTo get unique IDs for each worker, see https://stackoverflow.com/a/10192611/1150683" ]
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"CONTRIBUTOR"
"Fixed #3168. The SLR38 indices are CSV files and there wasn't any code in openslr.py to process these files properly. The end result was an empty table. I've added code to properly process these CSV files."
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[ "Also fix #3125." ]
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"CONTRIBUTOR"
"This PR adds some documentation for new features released in v1.13.0, with the main addition being `to_tf_dataset`: - Show how to use `to_tf_dataset` in the tutorial, and move `set_format(type='tensorflow'...)` to the Process section (let me know if I'm missing anything @Rocketknight1 😅). - Add an example for loading dataset from multiple zipped CSV files to the Load section. - Add an example for removing columns for an `IterableDataset`. - Add graphic for visualizing streaming."
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[ "This looks great, thank you!", "Thanks !\r\n\r\nFor some reason the new GIF is 6MB, which is a bit heavy for an image on a website. The previous one was around 200KB though which is perfect. For a good experience we usually expect images to be less than 500KB - otherwise for users with poor connection it takes too long to load. Could you try to reduce its size ? Than I think we can merge :)" ]
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"I've replaced two asserts with their proper exceptions following the guidelines described in issue #3171 by following the contributing guidelines. PS: This is one of my first PRs, hoping I don't break anything!"
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[ "Your first PR went smoothly, well done!\r\nYou are welcome to continue contributing to this project.\r\nGràcies, @joseporiolayats! 😉 " ]
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"Infer max filename length in filelock on Unix-like systems. Should fix problems on encrypted filesystems such as eCryptfs. Fix #2924 cc: @lmmx"
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"## Describe the bug I use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow. The exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup. In my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`. I could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome. ```python class Dataset() ... def __del__(self): if hasattr(self, "_data"): _ = self._data # <- ugly trick that allows avoiding the issue. del self._data if hasattr(self, "_indices"): del self._indices ``` ## Steps to reproduce the bug ```python # Unfortunately I couldn't isolate the bug. ``` ## Expected results Calling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception/traceback. ## Actual results ``` Exception ignored in: <function Dataset.__del__ at 0x7f7cec179160>███████████████████████████████████████████████████| 5/5 [00:05<00:00, 1.17ba/s] Traceback (most recent call last): File ".../python3.8/site-packages/datasets/arrow_dataset.py", line 906, in __del__ del self._data File ".../python3.8/site-packages/ray/worker.py", line 1033, in sigterm_handler sys.exit(signum) SystemExit: 15 ``` ## Environment info Tested on 2 environments: **Environment 1.** - `datasets` version: 1.14.0 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.8 - PyArrow version: 6.0.0 **Environment 2.** - `datasets` version: 1.14.0 - Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.7 - PyArrow version: 6.0.0 "
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[ "NB: even if the error is raised, the dataset is successfully cached. So restarting the script after every `map()` allows to ultimately run the whole preprocessing. But this prevents to realistically run the code over multiple nodes.", "Hi,\r\n\r\nIt's not easy to debug the problem without the script. I may be wrong since I'm not very familiar with PyTorch Lightning, but shouldn't you preprocess the data in the `prepare_data` function of `LightningDataModule` and not in the `setup` function.\r\nAs you can't modify the module state in `prepare_data` (according to the docs), use the `cache_file_name` argument in `Dataset.map` there, and reload the processed data in `setup` with `Dataset.from_file(cache_file_name)`. If `num_proc>1`, check the docs on the `suffix_template` argument of `Dataset.map` to get an idea what the final `cache_file_names` are going to be.\r\n\r\nLet me know if this helps.", "Hi @mariosasko, thank you for the hint, that helped me to move forward with that issue. \r\n\r\nI did a major refactoring of my project to disentangle my `LightningDataModule` and `Dataset`. Just FYI, it looks like:\r\n\r\n```python\r\nclass Builder():\r\n def __call__() -> DatasetDict:\r\n # load and preprocess the data\r\n return dataset\r\n\r\nclass DataModule(LightningDataModule):\r\n def prepare_data():\r\n self.builder()\r\n def setup():\r\n self.dataset = self.builder()\r\n```\r\n\r\nUnfortunately, the entanglement between `LightningDataModule` and `Dataset` was not the issue.\r\n\r\nThe culprit was `hydra` and a slight adjustment of the structure of my project solved this issue. The problematic project structure was:\r\n\r\n```\r\nsrc/\r\n | - cli.py\r\n | - training/\r\n | -experiment.py\r\n\r\n# code in experiment.py\r\ndef run_experiment(config):\r\n # preprocess data and run\r\n \r\n# code in cli.py\r\n@hydra.main(...)\r\ndef run(config):\r\n return run_experiment(config)\r\n```\r\n\r\nMoving `run()` from `clip.py` to `training.experiment.py` solved the issue with `SystemError 15`. No idea why. \r\n\r\nEven if the traceback was referring to `Dataset.__del__`, the problem does not seem to be primarily related to `datasets`, so I will close this issue. Thank you for your help!" ]
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"`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`"
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"CONTRIBUTOR"
"Motivated by https://github.com/huggingface/transformers/issues/12789 in Transformers, one welcoming change would be replacing assertions with proper exceptions. The only type of assertions we should keep are those used as sanity checks. Currently, there is a total of 87 files with the `assert` statements (located under `datasets` and `src/datasets`), so when working on this, to manage the PR size, only modify 4-5 files at most before submitting a PR."
[ "Adding the remaining tasks for this issue to help new code contributors. \r\n$ cd src/datasets && ack assert -lc \r\n- [ ] splits.py:4\r\n- [ ] commands/convert.py:1\r\n- [ ] arrow_reader.py:3\r\n- [ ] load.py:7\r\n- [ ] utils/patching.py:1\r\n- [ ] utils/py_utils.py:2\r\n- [ ] features/features.py:9\r\n- [ ] arrow_writer.py:7\r\n- [ ] data_files.py:1\r\n- [ ] search.py:6\r\n- [ ] table.py:1\r\n- [ ] metric.py:3\r\n- [ ] tasks/image_classification.py:1\r\n- [ ] arrow_dataset.py:17\r\n- [ ] fingerprint.py:6\r\n- [ ] io/json.py:1\r\n- [ ] io/csv.py:1" ]
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"Replace `set` with the `unique_values` generator in `zip_dict`. This PR fixes the problem with the different ordering of the example keys across different Python sessions caused by the `zip_dict` call in `Features.decode_example`. "
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"Resolve #2924 (https://github.com/huggingface/datasets/issues/2924#issuecomment-952330956) wherein the assumption of file lock maximum filename length to be 255 raises an OSError on encrypted drives (ecryptFS on Linux uses part of the lower filename, reducing the maximum filename size to 143). Allowing this limit to be set in the config module allows this to be modified by users. Will not affect Windows users, as their class passes 255 on init explicitly. Reproduced with the following example ([the first few lines of a script from Lightning Flash](https://lightning-flash.readthedocs.io/en/latest/reference/speech_recognition.html), fine-tuning a HF model): ```py import torch import flash from flash.audio import SpeechRecognition, SpeechRecognitionData from flash.core.data.utils import download_data # 1. Create the DataModule download_data("https://pl-flash-data.s3.amazonaws.com/timit_data.zip", "./data") datamodule = SpeechRecognitionData.from_json( input_fields="file", target_fields="text", train_file="data/timit/train.json", test_file="data/timit/test.json", ) ``` Which gave this traceback: ```py Traceback (most recent call last): File "lf_ft.py", line 10, in <module> datamodule = SpeechRecognitionData.from_json( File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py", line 1005, in from_json return cls.from_data_source( File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py", line 571, in from_data_source train_dataset, val_dataset, test_dataset, predict_dataset = data_source.to_datasets( File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py", line 307, in to_datasets train_dataset = self.generate_dataset(train_data, RunningStage.TRAINING) File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py", line 344, in generate_dataset data = load_data(data, mock_dataset) File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/audio/speech_recognition/data.py", line 103, in load_data dataset_dict = load_dataset(self.filetype, data_files={stage: str(file)}) File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/load.py", line 1599, in load_dataset builder_instance = load_dataset_builder( File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/load.py", line 1457, in load_dataset_builder builder_instance: DatasetBuilder = builder_cls( File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/builder.py", line 285, in __init__ with FileLock(lock_path): File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py", line 323, in __enter__ self.acquire() File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py", line 272, in acquire self._acquire() File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py", line 403, in _acquire fd = os.open(self._lock_file, open_mode) OSError: [Errno 36] File name too long: '/home/louis/.cache/huggingface/datasets/_home_louis_.cache_huggingface_datasets_json_default-98e6813a547f72fa_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426.lock' ``` Note the filename is 145 chars long: ``` >>> len("_home_louis_.cache_huggingface_datasets_json_default-98e6813a547f72fa_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426.lock") 145 ``` After installing datasets as an editable local package and modifying the script I was running to first include: ```py import datasets datasets.config.MAX_DATASET_CONFIG_ID_READABLE_LENGTH = 143 ``` The error goes away. If I instead deliberately set the value incorrectly as 144, the OSError returns: ``` Traceback (most recent call last): File "lf_ft.py", line 14, in <module> datamodule = SpeechRecognitionData.from_json( File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py", line 1005, in from_json return cls.from_data_source( File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py", line 571, in from_data_source train_dataset, val_dataset, test_dataset, predict_dataset = data_source.to_datasets( File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py", line 307, in to_datasets train_dataset = self.generate_dataset(train_data, RunningStage.TRAINING) File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py", line 344, in generate_dataset data = load_data(data, mock_dataset) File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/audio/speech_recognition/data.py", line 103, in load_data dataset_dict = load_dataset(self.filetype, data_files={stage: str(file)}) File "/home/louis/dev/hf_datasets/src/datasets/load.py", line 1605, in load_dataset builder_instance = load_dataset_builder( File "/home/louis/dev/hf_datasets/src/datasets/load.py", line 1463, in load_dataset_builder builder_instance: DatasetBuilder = builder_cls( File "/home/louis/dev/hf_datasets/src/datasets/builder.py", line 285, in __init__ with FileLock(lock_path): File "/home/louis/dev/hf_datasets/src/datasets/utils/filelock.py", line 326, in __enter__ self.acquire() File "/home/louis/dev/hf_datasets/src/datasets/utils/filelock.py", line 275, in acquire self._acquire() File "/home/louis/dev/hf_datasets/src/datasets/utils/filelock.py", line 406, in _acquire fd = os.open(self._lock_file, open_mode) OSError: [Errno 36] File name too long: '/home/louis/.cache/huggingface/datasets/_home_louis_.cache_huggingface_datasets_json_default-32c812b5c1272d64_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279...-5794079643713042223.lock' ```"
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[ "I've also added environment variable configuration so that this can be configured once per machine (e.g. in a `.bashrc` file), as is already done for a few other config variables here.", "Cancelling PR in favour of @mariosasko's in #3173" ]
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"CONTRIBUTOR"
"## Describe the bug As the summary says, openslr / SLR83 / train is empty. The dataset returned after loading indicates there are **zero** rows. The correct number should be **17877**. ## Steps to reproduce the bug ```python import datasets datasets.load_dataset('openslr', 'SLR83') ``` ## Expected results ``` DatasetDict({ train: Dataset({ features: ['path', 'audio', 'sentence'], num_rows: 17877 }) }) ``` ## Actual results ``` DatasetDict({ train: Dataset({ features: ['path', 'audio', 'sentence'], num_rows: 0 }) }) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.14.1.dev0 (master HEAD) - Platform: Ubuntu 20.04 - Python version: 3.7.10 - PyArrow version: 3.0.0 "
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[ "Hi @tyrius02, thanks for reporting. I see you self-assigned this issue: are you working on this?", "@albertvillanova Yes. Figured I introduced the broken config, I should fix it too.\r\n\r\nI've got it working, but I'm struggling with one of the tests. I've started a PR so I/we can work through it.", "Looks like the tests all passed on the PR." ]
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"## Describe the bug When using the latest version of datasets (1.14.0), I cannot use the `bookcorpusopen` dataset. The process blocks always around `9924 examples [00:06, 1439.61 examples/s]` when preparing the dataset. I also noticed that after half an hour the process is automatically killed because of the RAM usage (the machine has 1TB of RAM...). This did not happen with 1.4.1. I tried also `rm -rf ~/.cache/huggingface` but did not help. Changing python version between 3.7, 3.8 and 3.9 did not help too. ## Steps to reproduce the bug ```python import datasets d = datasets.load_dataset('bookcorpusopen') ``` ## Expected results A clear and concise description of the expected results. ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.14.0 - Platform: Linux-5.4.0-1054-aws-x86_64-with-glibc2.27 - Python version: 3.9.7 - PyArrow version: 4.0.1 "
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