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1,095,784,681
I_kwDODunzps5BUFjp
3,544
Ability to split a dataset in multiple files.
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1,641,510,145,000
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CONTRIBUTOR
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Hello, **Is your feature request related to a problem? Please describe.** My use case is that I have one writer that adds columns and multiple workers reading the same `Dataset`. Each worker should have access to columns added by the writer when they reload the dataset. I understand that we shouldn't overwrite an arrow file as this could cause Segfault and so on. Before 1.16, I was able to overwrite the dataset and that would work most of the time with some retries. **Describe the solution you'd like** I was thinking that if we could append `Dataset._data_files`, when the workers reload the Dataset, they would get the new columns. **Describe alternatives you've considered** I currently need to 1. Save multiple "versions" of the dataset and load the latest. 2. Try working with cache files to get the latest columns. **Additional context** I think this would be a great addition to HFDataset as Parquet supports multi-files input out of the box! I can make a PR myself with some pointers as needed :)
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1,095,226,438
I_kwDODunzps5BR9RG
3,543
Allow loading community metrics from the hub, just like datasets
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[ "Hi ! Thanks for your message :) This is a great idea indeed. We haven't started working on this yet though. For now I guess you can host your metric on the Hub (either with your model or your dataset) and use `hf_hub_download` to download it (docs [here](https://github.com/huggingface/huggingface_hub/blob/main/docs/hub/how-to-downstream.md#cached_download))", "This is a great solution in the meantime, thanks!" ]
1,641,468,386,000
1,641,488,206,000
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CONTRIBUTOR
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**Is your feature request related to a problem? Please describe.** Currently, I can load a metric implemented by me by providing the local path to the file in `load_metric`. However, there is no option to do it with the metric uploaded to the hub. This means that if I want to allow other users to use it, they must download it first which makes the usage less smooth. **Describe the solution you'd like** Load metrics from the hub just like datasets are loaded. In order to not break stuff, the convention can be to put the metric file in a "metrics" folder in the hub.
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1,095,088,485
PR_kwDODunzps4wmPIP
3,542
Update the CC-100 dataset card
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1,641,458,118,000
1,641,494,264,000
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CONTRIBUTOR
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* summary from the dataset homepage * more details about the data structure * this dataset does not contain annotations
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1,095,033,828
I_kwDODunzps5BROPk
3,541
Support 7-zip compressed data files
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1,641,453,063,000
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MEMBER
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**Is your feature request related to a problem? Please describe.** We should support 7-zip compressed data files: - in `extract` - in `iter_archive` both in streaming and non-streaming modes.
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1,094,900,336
I_kwDODunzps5BQtpw
3,540
How to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset?
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1,641,435,222,000
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Hi, I use torch.utils.data.Dataset to define my own data, but I need to use the 'map' function of datasets.arrow_dataset.Dataset later, so I hope to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset. Here is an example. ``` from torch.utils.data import Dataset from datasets.arrow_dataset import Dataset as HFDataset class ADataset(Dataset): def __init__(self, data): super().__init__() self.data = data def __getitem__(self, index): return self.data[index] def __len__(self): return self.len class MDataset(): def __init__(self, tokenizer: AutoTokenizer, data_args, training_args): self.train_dataset = ADataset(data_args) self.tokenizer = tokenizer self.data_args = data_args self.train_dataset = self.train_dataset.map( self.process_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on train dataset", ) def process_function(self, examples): sentences = [" ".join(sample[0][3]) for sample in examples] tokenized = self.tokenizer( sentences, max_length=self.max_seq_len, padding=self.padding, truncation=True) ``` But it would raise an ERROR, AttributeError: 'ADataset' object has no attribute 'map'. so how to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset? Thanks in advance!
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PR_kwDODunzps4wlXU4
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Research wording for nc licenses
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[ "The CI failure is about some missing tags or sections in the dataset cards, and is unrelated to the part about non commercial use of this PR. Merging" ]
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CONTRIBUTOR
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Noticing that the recent commit throws a lot of errors in the automatic checks. It looks to me that those errors are simply errors that were already there (metadata issues), unrelated to what I've just changed, but worth another look to make sure.
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added PII statements and license links to data cards
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1,641,420,157,000
CONTRIBUTOR
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Updates for the following datacards: multilingual_librispeech openslr speech commands superb timit_asr vctk
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update `pretty_name` for all datasets
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CONTRIBUTOR
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This PR updates `pretty_name` for all datasets. Previous PR #3498 had done this for only first 200 datasets
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Add SVHN dataset
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Add the SVHN dataset. Additional notes: * compared to the TFDS implementation, exposes additional the "full numbers" config * adds the streaming support for `os.path.splitext` and `scipy.io.loadmat` * adds `h5py` to the requirements list for the dummy data test
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Update wiki_dpr README.md
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MEMBER
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Some infos of wiki_dpr were missing as noted in https://github.com/huggingface/datasets/issues/3510, I added them and updated the tags and the examples
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Task search function on hub not working correctly
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null
[ "known issue due to https://github.com/huggingface/datasets/pull/2362 (and [internal](https://github.com/huggingface/moon-landing/issues/946)) , will be solved soon" ]
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MEMBER
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When I want to look at all datasets of the category: `speech-processing` *i.e.* https://huggingface.co/datasets?task_categories=task_categories:speech-processing&sort=downloads , then the following dataset doesn't show up for some reason: - https://huggingface.co/datasets/speech_commands even thought it's task tags seem correct: https://raw.githubusercontent.com/huggingface/datasets/master/datasets/speech_commands/README.md
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PR_kwDODunzps4wi1ft
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Give clearer instructions to add the YAML tags
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[ "this is great, maybe just put all of it in one line?\r\n\r\n> TODO: Add YAML tags here. Copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging" ]
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MEMBER
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Fix #3531. CC: @julien-c @VictorSanh
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Give clearer instructions to add the YAML tags
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MEMBER
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## Describe the bug As reported by @julien-c, many community datasets contain the line `YAML tags:` at the top of the YAML section in the header of the README file. See e.g.: https://huggingface.co/datasets/bigscience/P3/commit/a03bea08cf4d58f268b469593069af6aeb15de32 Maybe we should give clearer instruction/hints in the README template.
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Removing reference to "Common Voice" in Personal and Sensitive Information section. Adding link to license. Correct license type in metadata.
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Updating licensing information & personal and sensitive information.
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Updating license with appropriate capitalization & a link. Updating Personal and Sensitive Information to address PII concern.
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1,641,339,581,000
1,641,342,230,000
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CONTRIBUTOR
null
Adding licensing information.
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1,093,833,446
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Update README.md
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CONTRIBUTOR
null
Not entirely sure, following the links here, but it seems the relevant license is at https://github.com/soskek/bookcorpus/blob/master/LICENSE
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1,093,831,268
PR_kwDODunzps4wiL8p
3,525
Adding license information for Openbookcorpus
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[ "Thanks, @meg-huggingface for the updates!\r\n\r\nThanks also for setting me as a reviewer, but I'm totally not a specialist of the datasets themselves, so I prefer to just lurk and let @lhoestq @albertvillanova @mariosasko or @patrickvonplaten review these changes (https://github.com/huggingface/datasets/pulls/assigned/meg-huggingface).", "The MIT license seems to be for the crawling code, no ? Then maybe we can also redirect users to the [terms of smashwords.com](https://www.smashwords.com/about/tos) regarding copyrights, in particular the paragraph 10 for end-users. In particular it seems that end users can download and use the content \"for their personal enjoyment in any reasonable non-commercial manner in compliance with copyright law\" and the smashwords end-users agreement.\r\n\r\nIt should be the same for https://github.com/huggingface/datasets/pull/3526 as well" ]
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CONTRIBUTOR
null
Not entirely sure, following the links here, but it seems the relevant license is at https://github.com/soskek/bookcorpus/blob/master/LICENSE
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1,093,826,723
PR_kwDODunzps4wiK_v
3,524
Adding link to license.
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3,523
Added links to licensing and PII message in vctk dataset
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wmt19 is broken (zh-en)
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NONE
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## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("wmt19", 'zh-en') ``` ## Expected results The dataset should download. ## Actual results `ConnectionError: Couldn't reach ftp://cwmt-wmt:cwmt-wmt@datasets.nju.edu.cn/parallel/casia2015.zip` ## Environment info - `datasets` version: 1.15.1 - Platform: Linux - Python version: 3.8
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Vivos license update
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Updated the license information with the link to the license text
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Audio datacard update - first pass
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[ "I'm not sure that we want to change the tags at the top of the cards by hand. Those are used to create the tags in the hub. Although looking at all the tags now, we might want to normalize the current tags again (hyphens or no, \".0\" or no). Maybe we could add a binary tag for public domain or not?", "> \r\n\r\nThat's a good point, I didn't realize these were auto-populated.\r\nAt the same time, some of them are wrong -- how/where are they auto-populated? Seems like we should fix it at that source for the future.\r\nIn the mean time, I see that \"cc0-1.0\" is the desired tag for public domain, so I will change that for now." ]
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CONTRIBUTOR
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Filling out data card "Personal and Sensitive Information" for speech datasets to note PII concerns
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CC100: Using HTTPS for the data source URL fixes load_dataset()
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Without this change the following script (with any lang parameter) consistently fails. After changing to the HTTPS URL, the script works as expected. ```python from datasets import load_dataset dataset = load_dataset("cc100", lang="en") ``` This is the error produced by the previous script: ```sh Using custom data configuration en-lang=en Downloading and preparing dataset cc100/en to /home/antti/.cache/huggingface/datasets/cc100/en-lang=en/0.0.0/526ac20780de5e074cf73a7466e868cb67f960b48f6de42ff6a6c4e71910d71b... Traceback (most recent call last): File "/home/antti/tmp/cc100/cc100.py", line 3, in <module> dataset = load_dataset("cc100", lang="en") File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/load.py", line 1694, in load_dataset builder_instance.download_and_prepare( File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/builder.py", line 595, in download_and_prepare self._download_and_prepare( File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/builder.py", line 661, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/antti/.cache/huggingface/modules/datasets_modules/datasets/cc100/526ac20780de5e074cf73a7466e868cb67f960b48f6de42ff6a6c4e71910d71b/cc100.py", line 117, in _split_generators path = dl_manager.download_and_extract(download_url) File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 308, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 196, in download downloaded_path_or_paths = map_nested( File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 251, in map_nested return function(data_struct) File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 298, in cached_path output_path = get_from_cache( File "/home/antti/tmp/cc100/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})") ConnectionError: Couldn't reach http://data.statmt.org/cc-100/en.txt.xz (error 503) ``` Note that I get the same behavior also using curl on the command line. The plain HTTP "curl -L http://data.statmt.org/cc-100/en.txt.xz" fails with "503 Service unavailable", but the with the HTTPS version of the URL curl starts downloading the file. My guess is that the server does overly aggressive rate-limitting. When a client requests an HTTP URL, it (sensibly) gets redirected to the HTTPS equivalent, but now the server notices two requests coming from the same client (the original HTTP and the redirected HTTPS) during a brief time windows and rate-limitter kicks in and blocks the second request! If the client initally uses the HTTPS URL there's only one incoming request which the rate-limitter allows.
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Add PubMed Central Open Access dataset
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[ "In the framework of BigScience:\r\n- bigscience-workshop/data_tooling#121\r\n\r\nI have created this dataset as a community dataset: https://huggingface.co/datasets/albertvillanova/pmc_open_access\r\n\r\nHowever, I was wondering that it may be more appropriate to move it under an org namespace: `pubmed_central` or `pmc`\r\nThis way, we could add other datasets I'm also working on: Author Manuscript Dataset, Historical OCR Dataset, LitArch Open Access Subset.\r\n\r\nWhat do you think? @lhoestq @mariosasko ", "Why not ! Having them under such namespaces would also help people searching for this kind of datasets.\r\nWe can also invite people from pubmed at one point" ]
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MEMBER
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## Adding a Dataset - **Name:** PubMed Central Open Access - **Description:** The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under license terms that allow reuse. - **Paper:** *link to the dataset paper if available* - **Data:** https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/ - **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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Add CPPE-5 dataset
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Adds the recently released CPPE-5 dataset.
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dataset `asset` - change to raw.githubusercontent.com URLs
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1,641,228,237,000
1,641,231,542,000
1,641,231,541,000
MEMBER
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Changed the URLs to the ones it was automatically re-directing. Before, the download was failing
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1,092,624,695
I_kwDODunzps5BICE3
3,515
`ExpectedMoreDownloadedFiles` for `evidence_infer_treatment`
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MEMBER
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## Describe the bug I am trying to load a dataset called `evidence_infer_treatment`. The first subset (`1.1`) works fine but the second returns an error (`2.0`). It downloads a file but crashes during the checksums. ## Steps to reproduce the bug ```python >>> from datasets import load_dataset >>> load_dataset("evidence_infer_treatment", "2.0") Downloading and preparing dataset evidence_infer_treatment/2.0 (download: 34.84 MiB, generated: 91.46 MiB, post-processed: Unknown size, total: 126.30 MiB) to /home/victor_huggingface_co/.cache/huggingface/datasets/evidence_infer_treatment/2.0/2.0.0/6812655bfd26cbaa58c84eab098bf6403694b06c6ae2ded603c55681868a1e24... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/load.py", line 1669, in load_dataset use_auth_token=use_auth_token, File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 594, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 664, in _download_and_prepare self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files" File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 33, in verify_checksums raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'http://evidence-inference.ebm-nlp.com/v2.0.tar.gz'} ``` I did try to pass the argument `ignore_verifications=True` but run into an error when trying to build the dataset: ```python >>> load_dataset("evidence_infer_treatment", "2.0", ignore_verifications=True, download_mode="force_redownload") Downloading and preparing dataset evidence_infer_treatment/2.0 (download: 34.84 MiB, generated: 91.46 MiB, post-processed: Unknown size, total: 126.30 MiB) to /home/victor_huggingface_co/.cache/huggingface/datasets/evidence_infer_treatment/2.0/2.0.0/6812655bfd26cbaa58c84eab098bf6403694b06c6ae2ded603c55681868a1e24... Downloading: 164MB [00:23, 6.98MB/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/load.py", line 1669, in load_dataset use_auth_token=use_auth_token, File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 594, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 681, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 1080, in _prepare_split example = self.info.features.encode_example(record) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 1032, in encode_example return encode_nested_example(self, example) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 807, in encode_nested_example k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 807, in <dictcomp> k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 829, in encode_nested_example list_dict[k] = [encode_nested_example(dict_tuples[0], o) for o in dict_tuples[1:]] File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 829, in <listcomp> list_dict[k] = [encode_nested_example(dict_tuples[0], o) for o in dict_tuples[1:]] File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 828, in encode_nested_example for k, dict_tuples in utils.zip_dict(schema.feature, *obj): File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 136, in zip_dict yield key, tuple(d[key] for d in dicts) File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 136, in <genexpr> yield key, tuple(d[key] for d in dicts) KeyError: '' ``` ## Environment info - `datasets` version: 1.16.1 - Platform: Linux-5.0.0-1020-gcp-x86_64-with-debian-buster-sid - Python version: 3.7.11 - PyArrow version: 6.0.1
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1,092,606,383
PR_kwDODunzps4weN9W
3,514
Fix to_tf_dataset references in docs
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[ "The code snippet in [this section](https://huggingface.co/docs/datasets/master/use_dataset.html?highlight=to_tf_dataset#tensorflow) is missing an import (`DataCollatorWithPadding`) and doesn't initialize the TF model before the `model.fit` call." ]
1,641,223,899,000
1,641,408,768,000
1,641,408,768,000
CONTRIBUTOR
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Fix the `to_tf_dataset` references in the docs. The currently failing example of usage will be fixed by #3338.
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PR_kwDODunzps4weGWl
3,513
Add desc parameter to filter
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CONTRIBUTOR
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Fix #3317
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I_kwDODunzps5BHBcl
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No Data format found
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[ "Hi, which dataset is giving you an error?" ]
1,641,202,871,000
1,641,202,972,000
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NONE
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## Dataset viewer issue for '*name of the dataset*' **Link:** *link to the dataset viewer page* *short description of the issue* Am I the one who added this dataset ? Yes-No
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I_kwDODunzps5BGTKr
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Dataset
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[ "Can you reopen with the correct dataset name (if relevant)?\r\n\r\nThanks", "The dataset viewer was down tonight. It works again." ]
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NONE
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## Dataset viewer issue for '*name of the dataset*' **Link:** *link to the dataset viewer page* *short description of the issue* Am I the one who added this dataset ? Yes-No
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1,091,997,004
I_kwDODunzps5BFo1M
3,510
`wiki_dpr` details for Open Domain Question Answering tasks
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[ "Hi ! According to the DPR paper, the wikipedia dump is the one from Dec. 20, 2018.\r\nEach instance contains a paragraph of at most 100 word, as well as the title of the wikipedia page it comes from and the DPR embedding (a 768-d vector)." ]
1,641,121,441,000
1,641,388,565,000
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Hey guys! Thanks for creating the `wiki_dpr` dataset! I am currently trying to use the dataset for context retrieval using DPR on NQ questions and need details about what each of the files and data instances mean, which version of the Wikipedia dump it uses, etc. Please respond at your earliest convenience regarding the same! Thanks a ton! P.S.: (If one of @thomwolf @lewtun @lhoestq could respond, that would be even better since they have the first-hand details of the dataset. If anyone else has those, please reach out! Thanks!)
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1,091,214,808
I_kwDODunzps5BCp3Y
3,507
Discuss whether support canonical datasets w/o dataset_infos.json and/or dummy data
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[ "IMO, the data streaming test is good enough of a test that the dataset works correctly (assuming that we can more or less ensure that if streaming works then the non-streaming case will also work), so that for datasets that have a working dataset preview, we can remove the dummy data IMO. On the other hand, it seems like not all datasets have streaming enabled yet and for those datasets (if they are used a lot), I think it would be nice to continue testing some dummy data.\r\n\r\nI don't really have an opinion regarding the JSON metadata as I don't know enough about it.\r\n\r\n", "I don't know all the details, but generally I'd be in favor of unifying the metadata formats into YAML inside .md (and so deprecating the dataset_infos.json) \r\n\r\n(Ultimately the CI can run on \"HuggingFace Actions\" instead of on GitHub)", "The dataset_infos.json file currently has these useful infos for each dataset configuration, that I think can be moved to the dataset tags:\r\n- Size of the dataset in MB: download size, arrow file size, and total size (sum of download + arrow)\r\n- Size of each split in MB and number of examples. Again this can be moved to the dataset tags\r\n- Feature type of each column\r\n- supported task templates (it defines what columns correspond to the features and labels for example)\r\n\r\nBut it also has this, which I'm not sure if it should be in the tags or not:\r\n- Checksums of the downloaded files for integrity verifications\r\n\r\nSo ultimately this file could probably be deprecated in favor of having the infos in the tags.\r\n\r\n> Also note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nTo get the exact number of examples and size in MB of the dataset, one needs to download and generate it completely. IMO these infos are very important when someone considers using a dataset. Though using streaming we could do some extrapolation to have approximate values instead.\r\n\r\nFor the integrity verifications we also need the number of examples and the checksums of the downloaded files, so it requires the dataset to be fully downloaded once. This can be optional though.\r\n\r\n> IMO, the data streaming test is good enough of a test that the dataset works correctly (assuming that we can more or less ensure that if streaming works then the non-streaming case will also work)\r\n\r\nI agree with this. Usually if a dataset works in streaming mode, then it works in non-streaming mode (the other way around is not true though).\r\n\r\n> On the other hand, it seems like not all datasets have streaming enabled yet and for those datasets (if they are used a lot), I think it would be nice to continue testing some dummy data.\r\n\r\nYes indeed, or at least make sure that it was tested on the true data.", "(note that if we wanted to display sizes, etc we could also pretty easily parse the `dataset_infos.json` on the hub side)", "I agree that we can move the relevant parts of `dataset_infos.json` to the YAML tags.\r\n\r\n> On the other hand, it seems like not all datasets have streaming enabled yet and for those datasets (if they are used a lot), I think it would be nice to continue testing some dummy data. <\r\n> > Yes indeed, or at least make sure that it was tested on the true data.\r\n\r\nI like the idea of testing streaming and falling back to the dummy data test if streaming does not work. Generating dummy data can be very tedious, so this would be a nice incentive for the contributors to make their datasets streamable. " ]
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MEMBER
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I open this PR to have a public discussion about this topic and make a decision. As previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)? On the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However: - the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though) - we are migrating canonical datasets to the Hub Do we really need to continue testing them in out CI? Also note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data). Feel free to ping other people for the discussion. CC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw
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Allows DatasetDict.filter to have batching option
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- Related to: #3244 - Fixes: #3503 We extends `.filter( ... batched: bool)` support to DatasetDict.
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cast_column function not working with map function in streaming mode for Audio features
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[ "Hi! This is probably due to the fact that `IterableDataset.map` sets `features` to `None` before mapping examples. We can fix the issue by passing the old `features` dict to the map generator and performing encoding/decoding there (before calling the map transform function)." ]
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## Describe the bug I am trying to use Audio class for loading audio features using custom dataset. I am able to cast 'audio' feature into 'Audio' format with cast_column function. On using map function, I am not getting 'Audio' casted feature but getting path of audio file only. I am getting features of 'audio' of string type with load_dataset call. After using cast_column 'audio' feature is converted into 'Audio' type. But in map function I am not able to get Audio type for audio feature & getting string type data containing path of file only. So I am not able to use processor in encode function. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import load_dataset, Audio from transformers import Wav2Vec2Processor def encode(batch, processor): print("Audio: ",batch['audio']) batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values return batch def print_ds(ds): iterator = iter(ds) for d in iterator: print("Data: ",d) break processor = Wav2Vec2Processor.from_pretrained(pretrained_model_path) dataset = load_dataset("custom_dataset.py","train",data_files={'train':'train_path.txt'}, data_dir="data", streaming=True, split="train") print("Features: ",dataset.features) print_ds(dataset) dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000)) print("Features: ",dataset.features) print_ds(dataset) dataset = dataset.map(lambda x: encode(x,processor)) print("Features: ",dataset.features) print_ds(dataset) ``` ## Expected results map function not printing Audio type features be used with processor function and getting error in processor call due to this. ## Actual results # after load_dataset call Features: {'sentence': Value(dtype='string', id=None), 'audio': Value(dtype='string', id=None)} Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': 'data/0116_003.wav'} # after cast_column call Features: {'sentence': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None)} Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': {'path': 'data/0116_003.wav', 'array': array([ 1.2662281e-06, 1.0264218e-06, -1.3615092e-06, ..., 1.3017889e-02, 1.0085563e-02, 4.8155054e-03], dtype=float32), 'sampling_rate': 16000}} # after map call Features: None Audio: data/0116_003.wav Traceback (most recent call last): File "demo2.py", line 36, in <module> print_ds(dataset) File "demo2.py", line 11, in print_ds for d in iterator: File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 341, in __iter__ for key, example in self._iter(): File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 338, in _iter yield from ex_iterable File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 192, in __iter__ yield key, self.function(example) File "demo2.py", line 32, in <lambda> dataset = dataset.map(lambda x: batch_encode(x,processor)) File "demo2.py", line 6, in batch_encode batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values TypeError: string indices must be integers ## Environment info - `datasets` version: 1.17.0 - Platform: Linux-4.14.243 with-debian-bullseye-sid - Python version: 3.7.9 - PyArrow version: 6.0.1
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Unable to download PUBMED_title_abstracts_2019_baseline.jsonl.zst
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[ "Hi @ToddMorrill, thanks for reporting.\r\n\r\nThree weeks ago I contacted the team who created the Pile dataset to report this issue with their data host server: https://the-eye.eu\r\n\r\nThey told me that unfortunately, the-eye was heavily affected by the recent tornado catastrophe in the US. They hope to have their data back online asap." ]
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## Describe the bug I am unable to download the PubMed dataset from the link provided in the [Hugging Face Course (Chapter 5 Section 4)](https://huggingface.co/course/chapter5/4?fw=pt). https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import load_dataset # This takes a few minutes to run, so go grab a tea or coffee while you wait :) data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst" pubmed_dataset = load_dataset("json", data_files=data_files, split="train") pubmed_dataset ``` I also tried with `wget` as follows. ``` wget https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst ``` ## Expected results I expect to be able to download this file. ## Actual results Traceback ``` --------------------------------------------------------------------------- timeout Traceback (most recent call last) /usr/lib/python3/dist-packages/urllib3/connection.py in _new_conn(self) 158 try: --> 159 conn = connection.create_connection( 160 (self._dns_host, self.port), self.timeout, **extra_kw /usr/lib/python3/dist-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 83 if err is not None: ---> 84 raise err 85 /usr/lib/python3/dist-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 73 sock.bind(source_address) ---> 74 sock.connect(sa) 75 return sock timeout: timed out During handling of the above exception, another exception occurred: ConnectTimeoutError Traceback (most recent call last) /usr/lib/python3/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 664 # Make the request on the httplib connection object. --> 665 httplib_response = self._make_request( 666 conn, /usr/lib/python3/dist-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw) 375 try: --> 376 self._validate_conn(conn) 377 except (SocketTimeout, BaseSSLError) as e: /usr/lib/python3/dist-packages/urllib3/connectionpool.py in _validate_conn(self, conn) 995 if not getattr(conn, "sock", None): # AppEngine might not have `.sock` --> 996 conn.connect() 997 /usr/lib/python3/dist-packages/urllib3/connection.py in connect(self) 313 # Add certificate verification --> 314 conn = self._new_conn() 315 hostname = self.host /usr/lib/python3/dist-packages/urllib3/connection.py in _new_conn(self) 163 except SocketTimeout: --> 164 raise ConnectTimeoutError( 165 self, ConnectTimeoutError: (<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)') During handling of the above exception, another exception occurred: MaxRetryError Traceback (most recent call last) /usr/lib/python3/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 438 if not chunked: --> 439 resp = conn.urlopen( 440 method=request.method, /usr/lib/python3/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 718 --> 719 retries = retries.increment( 720 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2] /usr/lib/python3/dist-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace) 435 if new_retry.is_exhausted(): --> 436 raise MaxRetryError(_pool, url, error or ResponseError(cause)) 437 MaxRetryError: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)')) During handling of the above exception, another exception occurred: ConnectTimeout Traceback (most recent call last) /tmp/ipykernel_15104/606583593.py in <module> 3 # This takes a few minutes to run, so go grab a tea or coffee while you wait :) 4 data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst" ----> 5 pubmed_dataset = load_dataset("json", data_files=data_files, split="train") 6 pubmed_dataset ~/.local/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1655 1656 # Create a dataset builder -> 1657 builder_instance = load_dataset_builder( 1658 path=path, 1659 name=name, ~/.local/lib/python3.8/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs) 1492 download_config = download_config.copy() if download_config else DownloadConfig() 1493 download_config.use_auth_token = use_auth_token -> 1494 dataset_module = dataset_module_factory( 1495 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files 1496 ) ~/.local/lib/python3.8/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs) 1116 # Try packaged 1117 if path in _PACKAGED_DATASETS_MODULES: -> 1118 return PackagedDatasetModuleFactory( 1119 path, data_files=data_files, download_config=download_config, download_mode=download_mode 1120 ).get_module() ~/.local/lib/python3.8/site-packages/datasets/load.py in get_module(self) 773 else get_patterns_locally(str(Path().resolve())) 774 ) --> 775 data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token) 776 module_path, hash = _PACKAGED_DATASETS_MODULES[self.name] 777 builder_kwargs = {"hash": hash, "data_files": data_files} ~/.local/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token) 576 for key, patterns_for_key in patterns.items(): 577 out[key] = ( --> 578 DataFilesList.from_local_or_remote( 579 patterns_for_key, 580 base_path=base_path, ~/.local/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token) 545 base_path = base_path if base_path is not None else str(Path().resolve()) 546 data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions) --> 547 origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token) 548 return cls(data_files, origin_metadata) 549 ~/.local/lib/python3.8/site-packages/datasets/data_files.py in _get_origin_metadata_locally_or_by_urls(data_files, max_workers, use_auth_token) 492 data_files: List[Union[Path, Url]], max_workers=64, use_auth_token: Optional[Union[bool, str]] = None 493 ) -> Tuple[str]: --> 494 return thread_map( 495 partial(_get_single_origin_metadata_locally_or_by_urls, use_auth_token=use_auth_token), 496 data_files, ~/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py in thread_map(fn, *iterables, **tqdm_kwargs) 92 """ 93 from concurrent.futures import ThreadPoolExecutor ---> 94 return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs) 95 96 ~/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py in _executor_map(PoolExecutor, fn, *iterables, **tqdm_kwargs) 74 map_args.update(chunksize=chunksize) 75 with PoolExecutor(**pool_kwargs) as ex: ---> 76 return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs)) 77 78 ~/.local/lib/python3.8/site-packages/tqdm/notebook.py in __iter__(self) 252 def __iter__(self): 253 try: --> 254 for obj in super(tqdm_notebook, self).__iter__(): 255 # return super(tqdm...) will not catch exception 256 yield obj ~/.local/lib/python3.8/site-packages/tqdm/std.py in __iter__(self) 1171 # (note: keep this check outside the loop for performance) 1172 if self.disable: -> 1173 for obj in iterable: 1174 yield obj 1175 return /usr/lib/python3.8/concurrent/futures/_base.py in result_iterator() 617 # Careful not to keep a reference to the popped future 618 if timeout is None: --> 619 yield fs.pop().result() 620 else: 621 yield fs.pop().result(end_time - time.monotonic()) /usr/lib/python3.8/concurrent/futures/_base.py in result(self, timeout) 442 raise CancelledError() 443 elif self._state == FINISHED: --> 444 return self.__get_result() 445 else: 446 raise TimeoutError() /usr/lib/python3.8/concurrent/futures/_base.py in __get_result(self) 387 if self._exception: 388 try: --> 389 raise self._exception 390 finally: 391 # Break a reference cycle with the exception in self._exception /usr/lib/python3.8/concurrent/futures/thread.py in run(self) 55 56 try: ---> 57 result = self.fn(*self.args, **self.kwargs) 58 except BaseException as exc: 59 self.future.set_exception(exc) ~/.local/lib/python3.8/site-packages/datasets/data_files.py in _get_single_origin_metadata_locally_or_by_urls(data_file, use_auth_token) 483 if isinstance(data_file, Url): 484 data_file = str(data_file) --> 485 return (request_etag(data_file, use_auth_token=use_auth_token),) 486 else: 487 data_file = str(data_file.resolve()) ~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in request_etag(url, use_auth_token) 489 def request_etag(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> Optional[str]: 490 headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token) --> 491 response = http_head(url, headers=headers, max_retries=3) 492 response.raise_for_status() 493 etag = response.headers.get("ETag") if response.ok else None ~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries) 474 headers = copy.deepcopy(headers) or {} 475 headers["user-agent"] = get_datasets_user_agent(user_agent=headers.get("user-agent")) --> 476 response = _request_with_retry( 477 method="HEAD", 478 url=url, ~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params) 407 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err: 408 if tries > max_retries: --> 409 raise err 410 else: 411 logger.info(f"{method} request to {url} timed out, retrying... [{tries/max_retries}]") ~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params) 403 tries += 1 404 try: --> 405 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params) 406 success = True 407 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err: /usr/lib/python3/dist-packages/requests/api.py in request(method, url, **kwargs) 58 # cases, and look like a memory leak in others. 59 with sessions.Session() as session: ---> 60 return session.request(method=method, url=url, **kwargs) 61 62 /usr/lib/python3/dist-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json) 531 } 532 send_kwargs.update(settings) --> 533 resp = self.send(prep, **send_kwargs) 534 535 return resp /usr/lib/python3/dist-packages/requests/sessions.py in send(self, request, **kwargs) 644 645 # Send the request --> 646 r = adapter.send(request, **kwargs) 647 648 # Total elapsed time of the request (approximately) /usr/lib/python3/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 502 # TODO: Remove this in 3.0.0: see #2811 503 if not isinstance(e.reason, NewConnectionError): --> 504 raise ConnectTimeout(e, request=request) 505 506 if isinstance(e.reason, ResponseError): ConnectTimeout: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)')) ``` ## Environment info - `datasets` version: 1.17.0 - Platform: Linux-5.11.0-43-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 6.0.1
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I_kwDODunzps5A_0sf
3,503
Batched in filter throws error
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I hope this is really a bug, I could not find it among the open issues ## Describe the bug using `batched=False` in DataSet.filter throws error ```python TypeError: filter() got an unexpected keyword argument 'batched' ``` but in the docs it is lister as an argument. ## Steps to reproduce the bug ```python task = "mnli" max_length = 128 tokenizer = AutoTokenizer.from_pretrained("./pretrained_models/pretrained_models_drozd/sl250.m.gsic.titech.ac.jp:8000/21.11.17_06.30.32_roberta-base_a0057/checkpoints/smpl_400M/hf/") dataset = load_dataset("glue", task) task_to_keys = { "cola": ("sentence", None), "mnli": ("premise", "hypothesis"), "mnli-mm": ("premise", "hypothesis"), "mrpc": ("sentence1", "sentence2"), "qnli": ("question", "sentence"), "qqp": ("question1", "question2"), "rte": ("sentence1", "sentence2"), "sst2": ("sentence", None), "stsb": ("sentence1", "sentence2"), "wnli": ("sentence1", "sentence2"), } ##### tokenization_parameters sentence1_key, sentence2_key = task_to_keys[task] def preprocess_function(examples, max_length): if sentence2_key is None: return tokenizer( examples[sentence1_key], truncation=True, max_length=max_length ) return tokenizer( examples[sentence1_key], examples[sentence2_key], truncation=False, padding="max_length", max_length=max_length, ) encoded_dataset = dataset.map( lambda x: preprocess_function(x, max_length=max_length), batched=False ) encoded_dataset.filter(lambda x: len(x['input_ids']) <= max_length, batched=False) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.16.1, 1.17.0 - Platform: ubuntu - Python version: 3.8.12
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Add QuALITY
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Fixes #3441.
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Update pib dataset card
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MEMBER
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Related to #3496
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Docs: Add VCTK dataset description
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This PR is a very minor followup to #1837, with only docs changes (single comment string).
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Adjusting chunk size for streaming datasets
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[ "Hi ! Data streaming uses `fsspec` to read the data files progressively. IIRC the block size for buffering is 5MiB by default. So every time you finish iterating over a block, it downloads the next one. You can still try to increase the `fsspec` block size for buffering if it can help. To do so you just need to increase `fsspec.spec.AbstractBufferedFile.DEFAULT_BLOCK_SIZE `\r\n\r\nCurrently this is unfortunately done in a single thread, so it blocks the processing to download and uncompress the next block. At one point it would be nice to be able to do that in parallel !" ]
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**Is your feature request related to a problem? Please describe.** I want to use mc4 which I cannot save locally, so I stream it. However, I want to process the entire dataset and filter some documents from it. With the current chunk size of around 1000 documents (right?) I hit a performance bottleneck because of the frequent decompressing. **Describe the solution you'd like** I would appreciate a parameter in the load_dataset function, that allows me to set the chunksize myself (to a value like 100'000 in my case). Like that, I hope to improve the processing time.
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update `pretty_name` for first 200 datasets
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I made a script some time back to fetch `pretty_names` from `papers_with_code` dataset along with some other rules incase that dataset wasn't available on `papers_with_code`. Updating them in the `README` of `datasets`. Took only the first 200 datasets into consideration and after some eyeballing, most of them were looking good to me!
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Changing sampling rate in audio dataset and subsequently mapping with `num_proc > 1` leads to weird bug
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[ "Same error occures when using max samples with https://github.com/huggingface/transformers/blob/master/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py", "I'm seeing this too, when using preprocessing_num_workers with \r\nhttps://github.com/huggingface/transformers/blob/master/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py" ]
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Running: ```python from datasets import load_dataset, DatasetDict import datasets from transformers import AutoFeatureExtractor raw_datasets = DatasetDict() raw_datasets["train"] = load_dataset("common_voice", "ab", split="train") feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base") raw_datasets = raw_datasets.cast_column( "audio", datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate) ) num_workers = 16 def prepare_dataset(batch): sample = batch["audio"] inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"]) batch["input_values"] = inputs.input_values[0] batch["input_length"] = len(batch["input_values"]) return batch raw_datasets.map( prepare_dataset, remove_columns=next(iter(raw_datasets.values())).column_names, num_proc=16, desc="preprocess datasets", ) ``` gives ```bash File "/home/patrick/experiments/run_bug.py", line 25, in <module> raw_datasets.map( File "/home/patrick/python_bin/datasets/dataset_dict.py", line 492, in map { File "/home/patrick/python_bin/datasets/dataset_dict.py", line 493, in <dictcomp> k: dataset.map( File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2139, in map shards = [ File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2140, in <listcomp> self.shard(num_shards=num_proc, index=rank, contiguous=True, keep_in_memory=keep_in_memory) File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 3164, in shard return self.select( File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 485, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/patrick/python_bin/datasets/fingerprint.py", line 411, in wrapper out = func(self, *args, **kwargs) File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2756, in select return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint) File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2667, in _new_dataset_with_indices return Dataset( File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 659, in __init__ raise ValueError( ValueError: External features info don't match the dataset: Got {'client_id': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None), 'down_votes': Value(dtype='int64', id=None), 'age': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'accent': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None)} with type struct<client_id: string, path: string, audio: string, sentence: string, up_votes: int64, down_votes: int64, age: string, gender: string, accent: string, locale: string, segment: string> but expected something like {'client_id': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'audio': {'path': Value(dtype='string', id=None), 'bytes': Value(dtype='binary', id=None)}, 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None), 'down_votes': Value(dtype='int64', id=None), 'age': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'accent': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None)} with type struct<client_id: string, path: string, audio: struct<path: string, bytes: binary>, sentence: string, up_votes: int64, down_votes: int64, age: string, gender: string, accent: string, locale: string, segment: string> ``` Versions: ```python - `datasets` version: 1.16.2.dev0 - Platform: Linux-5.15.8-76051508-generic-x86_64-with-glibc2.33 - Python version: 3.9.7 - PyArrow version: 6.0.1 ``` and `transformers`: ``` - `transformers` version: 4.16.0.dev0 - Platform: Linux-5.15.8-76051508-generic-x86_64-with-glibc2.33 - Python version: 3.9.7 ```
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Update version of pib dataset and make it streamable
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[ "It seems like there is still an error: `Message: 'TarContainedFile' object has no attribute 'readable'`\r\n\r\nhttps://huggingface.co/datasets/pib/viewer", "@severo I was wondering about that...\r\n\r\nIt works fine when I run it in streaming mode in my terminal:\r\n```python\r\nIn [3]: from datasets import load_dataset; ds = load_dataset(\"pib\", \"gu-pa\", split=\"train\", streaming=True); item = next(iter(ds))\r\n\r\nIn [4]: item\r\nOut[4]: \r\n{'translation': {'gu': 'એવો નિર્ણય લેવાયો હતો કે ખંતપૂર્વકની કામગીરી હાથ ધરવા, કાયદેસર અને ટેકનિકલ મૂલ્યાંકન કરવા, વેન્ચર કેપિટલ ઇન્વેસ્ટમેન્ટ સમિતિની બેઠક યોજવા વગેરે એઆઇએફને કરવામાં આવેલ પ્રતિબદ્ધતાના 0.50 ટકા સુધી અને બાકીની રકમ એફએફએસને પૂર્ણ કરવામાં આવશે.',\r\n 'pa': 'ਇਹ ਵੀ ਫੈਸਲਾ ਕੀਤਾ ਗਿਆ ਕਿ ਐੱਫਆਈਆਈ ਅਤੇ ਬਕਾਏ ਲਈ ਕੀਤੀਆਂ ਗਈਆਂ ਵਚਨਬੱਧਤਾਵਾਂ ਦੇ 0.50 % ਦੀ ਸੀਮਾ ਤੱਕ ਐੱਫਈਐੱਸ ਨੂੰ ਮਿਲਿਆ ਜਾਏਗਾ, ਇਸ ਨਾਲ ਉੱਦਮ ਪੂੰਜੀ ਨਿਵੇਸ਼ ਕਮੇਟੀ ਦੀ ਬੈਠਕ ਦਾ ਆਯੋਜਨ ਉਚਿਤ ਸਾਵਧਾਨੀ, ਕਾਨੂੰਨੀ ਅਤੇ ਤਕਨੀਕੀ ਮੁੱਲਾਂਕਣ ਲਈ ਸੰਚਾਲਨ ਖਰਚ ਆਦਿ ਦੀ ਪੂਰਤੀ ਹੋਵੇਗੀ।'}}\r\n```", "OK, it works now!\r\n\r\n<img width=\"794\" alt=\"Capture d’écran 2022-01-03 à 15 41 44\" src=\"https://user-images.githubusercontent.com/1676121/147943676-6199d1a9-f288-4350-af96-a7c297ebb743.png\">\r\n" ]
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This PR: - Updates version of pib dataset: from 0.0.0 to 1.3.0 - Makes the dataset streamable Fix #3491. CC: @severo
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Add VoxLingua107
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## Adding a Dataset - **Name:** VoxLingua107 - **Description:** VoxLingua107 is a speech dataset for training spoken language identification models. - **Paper:** https://arxiv.org/abs/2011.12998 - **Data:** http://bark.phon.ioc.ee/voxlingua107/ - **Motivation:** 107 languages, totaling 6628 hours for the train split. 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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Clone full repo to detect new tags when mirroring datasets on the Hub
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[ "Good catch !!", "The CI fail is unrelated to this PR and fixed on master, merging :)" ]
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The new releases of `datasets` were not detected because the shallow clone in the CI wasn't getting the git tags. By cloning the full repository we can properly detect a new release, and tag all the dataset repositories accordingly cc @SBrandeis
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Fix VCTK encoding
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utf-8 encoding was missing in the VCTK dataset builder added in #3351
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Add `gzip` for `to_json`
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(Partially) closes #3480. I have added `gzip` compression for `to_json`. I realised we can run into this compression problem with `to_csv` as well. `IOHandler` can be used for `to_csv` too. Please let me know if any changes are required.
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Update version of pib dataset
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On the Hub we have v0, while there exists v1.3. Related to bigscience-workshop/data_tooling#130
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Does datasets support load text from HDFS?
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[ "Hi ! `datasets` currently supports reading local files or files over HTTP. We may add support for other filesystems (cloud storages, hdfs...) at one point though :)" ]
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The raw text data is stored on HDFS due to the dataset's size is too large to store on my develop machine, so I wander does datasets support read data from hdfs?
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Avoid unnecessary list creations
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[ "@bryant1410 Thanks for working on this. Could you please split the PR into 4 or 5 smaller PRs (ideally one PR for each bullet point from your description) because it's not practical to review such a large PR, especially if the changes are not interrelated?" ]
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Like in `join([... for s in ...])`. Also changed other things that I saw: * Use a `with` statement for many `open` that missed them, so the files don't remain open. * Remove unused variables. * Many HTTP links converted into HTTPS (verified). * Remove unnecessary "r" mode arg in `open` (double-checked it was actually the default in each case). * Remove Python 2 style of using `super`. * Run `pyupgrade $(find . -name "*.py" -type f) --py36-plus` (which already does some of the previous points). * Run `dos2unix $(find . -name "*.py" -type f)` (CRLF to LF line endings). * Fix typos.
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URL query parameters are set as path in the compression hop for fsspec
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[ "I think the test passes because it simply ignore what's after `gzip://`.\r\n\r\nThe returned urlpath is expected to look like `gzip://filename::url`, and the filename is currently considered to be what's after the final `/`, hence the result.\r\n\r\nWe can decide to change this and simply have `gzip://::url`, this way we don't need to guess the filename, what do you think ?" ]
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## Describe the bug There is an ssue with `StreamingDownloadManager._extract`. I don't know how the test `test_streaming_gg_drive_gzipped` passes: For ```python TEST_GG_DRIVE_GZIPPED_URL = "https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz" urlpath = StreamingDownloadManager().download_and_extract(TEST_GG_DRIVE_GZIPPED_URL) ``` gives `urlpath`: ```python 'gzip://uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz::https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz' ``` The gzip path makes no sense: `gzip://uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz` ## Steps to reproduce the bug ```python from datasets.utils.streaming_download_manager import StreamingDownloadManager dl_manager = StreamingDownloadManager() urlpath = dl_manager.extract("https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz") print(urlpath) ``` ## Expected results The query parameters should not be set as part of the path.
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Update ADD_NEW_DATASET.md
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fixed make style prompt for Windows Terminal
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Fix weird spacing in ManualDownloadError message
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`textwrap.dedent` works based on the spaces at the beginning. Before this change, there wasn't any space.
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skip columns which cannot set to specific format when set_format
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[ "You can add columns that you wish to set into `torch` format using `dataset.set_format(\"torch\", ['id', 'abc'])` so that input batch of the transform only contains those columns", "Sorry, I miss `output_all_columns` args and thought after `dataset.set_format(\"torch\", columns=columns)` I can only get specific columns I assigned." ]
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**Is your feature request related to a problem? Please describe.** When using `dataset.set_format("torch")`, I must make sure every columns in datasets can convert to `torch`, however, sometimes I want to keep some string columns. **Describe the solution you'd like** skip columns which cannot set to specific format when set_format instead of raise an error.
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make shape verification to use ArrayXD instead of nested lists for map
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[ "Hi! \r\n\r\nYes, this makes sense for numeric values, but first I have to finish https://github.com/huggingface/datasets/pull/3336 because currently ArrayXD only allows the first dimension to be dynamic." ]
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As describe in https://github.com/huggingface/datasets/issues/2005#issuecomment-793716753 and mentioned by @mariosasko in [image feature example](https://colab.research.google.com/drive/1mIrTnqTVkWLJWoBzT1ABSe-LFelIep1c#scrollTo=ow3XHDvf2I0B&line=1&uniqifier=1), IMO make shape verifcaiton to use ArrayXD instead of nested lists for map can help user reduce unnecessary cast. I notice datasets have done something special for `input_ids` and `attention_mask` which is also unnecessary after this feature added.
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Remove unused phony rule from Makefile
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[ "The CI failure is unrelated to this PR and fixed on master, merging !" ]
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Fix duplicate keys in NewsQA
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* Fix duplicate keys in NewsQA when loading from CSV files. * Fix s/narqa/newsqa/ in the download manually error message. * Make the download manually error message show nicely when printed. Otherwise, is hard to read due to spacing issues. * Fix the format of the license text. * Reformat the code to make it simpler.
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Fix overriding of filesystem info
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Previously, `BaseCompressedFileFileSystem.info` was overridden and transformed from function to dict. This generated a bug for filesystem methods that use `self.info()`, like e.g. `fs.isfile()`. This PR: - Adds tests for `fs.isfile` (that use `fs.info`). - Fixes custom `BaseCompressedFileFileSystem.info` by removing its overriding.
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the compression format requested when saving a dataset in json format is not respected
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[ "Thanks for reporting @SaulLu.\r\n\r\nAt first sight I think the problem is caused because `pandas` only takes into account the `compression` parameter if called with a non-null file path or buffer. And in our implementation, we call pandas `to_json` with `None` `path_or_buf`.\r\n\r\nWe should fix this:\r\n- either handling directly the `compression` parameter ourselves\r\n- or refactoring to pass non-null path or buffer to pandas\r\n\r\nCC: @lhoestq", "I was thinking if we can handle the `compression` parameter by ourselves? Compression types will be similar to what `pandas` offer. Initially, we can try this with 2-3 compression types and see how good/bad it is? Let me know if it sounds good, I can raise a PR for this next week", "Hi ! Thanks for your help @bhavitvyamalik :)\r\nMaybe let's start with `gzip` ? I think it's the most common use case, then if we're fine with it we can add other compression methods" ]
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## Describe the bug In the documentation of the `to_json` method, it is stated in the parameters that > **to_json_kwargs – Parameters to pass to pandas’s pandas.DataFrame.to_json. however when we pass for example `compression="gzip"`, the saved file is not compressed. Would you also have expected compression to be applied? :relaxed: ## Steps to reproduce the bug ```python my_dict = {"a": [1, 2, 3], "b": [1, 2, 3]} ``` ### Result with datasets ```python from datasets import Dataset dataset = Dataset.from_dict(my_dict) dataset.to_json("dic_with_datasets.jsonl.gz", compression="gzip") !cat dic_with_datasets.jsonl.gz ``` output ``` {"a":1,"b":1} {"a":2,"b":2} {"a":3,"b":3} ``` Note: I would expected to see binary data here ### Result with pandas ```python import pandas as pd df = pd.DataFrame(my_dict) df.to_json("dic_with_pandas.jsonl.gz", lines=True, orient="records", compression="gzip") !cat dic_with_pandas.jsonl.gz ``` output ``` 4��a�dic_with_pandas.jsonl��VJT�2�QJ��\� ��g��yƵ���������)��� ``` Note: It looks like binary data ## Expected results I would have expected that the saved result with datasets would also be a binary file ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.16.1 - Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17 - Python version: 3.8.11 - PyArrow version: 5.0.0
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Dataset preview is not available (I think for all Hugging Face datasets)
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[ "You're right, we have an issue today with the datasets preview. We're investigating.", "It should be fixed now. Thanks for reporting.", "Down again. ", "Fixed for good." ]
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## Dataset viewer issue for '*french_book_reviews*' **Link:** https://huggingface.co/datasets/Abirate/french_book_reviews **short description of the issue** For my dataset, the dataset preview is no longer functional (it used to work: The dataset had been added the day before and it was fine...) And, after looking over the datasets, I discovered that this issue affects all Hugging Face datasets (as of yesterday, December 23, 2021, around 10 p.m. (CET)). **Am I the one who added this dataset** : Yes **Note**: here a screenshot showing the issue ![Dataset preview is not available for my dataset](https://user-images.githubusercontent.com/66887439/147333078-60734578-420d-4e91-8691-a90afeaa8948.jpg) **And here for glue dataset :** ![Dataset preview is not available for other Hugging Face datasets(glue)](https://user-images.githubusercontent.com/66887439/147333492-26fa530c-befd-4992-8361-70c51397a25a.jpg)
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Extend support for streaming datasets that use os.walk
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[ "Nice. I'll update the dataset viewer once merged, and test on these four datasets" ]
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This PR extends the support in streaming mode for datasets that use `os.walk`, by patching that function. This PR adds support for streaming mode to datasets: 1. autshumato 1. code_x_glue_cd_code_to_text 1. code_x_glue_tc_nl_code_search_adv 1. nchlt CC: @severo
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Use `iter_files` instead of `str(Path(...)` in image dataset
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[ "`iter_archive` is about to support ZIP archives. I think we should use this no ?\r\n\r\nsee #3347 https://github.com/huggingface/datasets/pull/3379", "I was interested in the support for isfile/dir in remote.\r\n\r\nAnyway, `iter_files` will be available for community users.", "I'm not a big fan of having two functions that do the same thing. What do you think ?", "They do not do the same thing:\r\n- One iterates over files in a directory\r\n- The other I guess will iterate over the members of an archive", "Makes sense ! Sounds good then - sorry for my misunderstanding\r\n\r\nNote that `iter_archive` will be more performant for data streaming that `iter_files` thanks to the buffering so maybe in the future we can `iter_archive` for some of these datasets", "Yes, @lhoestq I agree with you: once `iter_archive` supports zip files, it will be more suitable than `iter_files` for these 2 datasets.\r\n\r\nAnyway, this PR also implements `isfile`/`isdir` in streaming mode, besides fixing `iter_files`. And I'm interested in having those in master.\r\n\r\nMaybe, could we merge this PR into master and take note to refactor the datasets to use `iter_archive` once zip is supported?\r\nOther option could be to split this PR into 2..." ]
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Use `iter_files` in the `beans` and the `cats_vs_dogs` dataset scripts as suggested by @albertvillanova. Additional changes: * Fix `iter_files` in `MockDownloadManager` (see this [CI error](https://app.circleci.com/pipelines/github/huggingface/datasets/9247/workflows/2657ff8a-b531-4fd9-a9fc-6541a72e8d83/jobs/57028)) * Add support for `os.path.isdir` and `os.path.isfile` in streaming (`os.path.isfile` is needed in `StreamingDownloadManager`'s `iter_files` to make `cats_vs_dogs` streamable) TODO: - [ ] add tests for `xisdir` and `xisfile`
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Extend support for streaming datasets that use ET.parse
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This PR extends the support in streaming mode for datasets that use `ET.parse`, by patching the function. This PR adds support for streaming mode to datasets: 1. ami 1. assin 1. assin2 1. counter 1. enriched_web_nlg 1. europarl_bilingual 1. hyperpartisan_news_detection 1. polsum 1. qa4mre 1. quail 1. ted_talks_iwslt 1. udhr 1. web_nlg 1. winograd_wsc CC: @severo
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The rotten_tomatoes dataset of movie reviews contains some reviews in Spanish
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[ "Hi @puzzler10, thanks for reporting.\r\n\r\nPlease note this dataset is not hosted on Hugging Face Hub. See: \r\nhttps://github.com/huggingface/datasets/blob/c8f914473b041833fd47178fa4373cdcb56ac522/datasets/rotten_tomatoes/rotten_tomatoes.py#L42\r\n\r\nIf there are issues with the source data of a dataset, you should contact the data owners/creators instead. In the homepage associated with this dataset (http://www.cs.cornell.edu/people/pabo/movie-review-data/), you can find the authors of the dataset and how to contact them:\r\n> If you have any questions or comments regarding this site, please send email to Bo Pang or Lillian Lee.\r\n\r\nP.S.: Please also note that the example you gave of non-English review is in Portuguese (not Spanish). ;)", "Maybe best to just put a quick sentence in the dataset description that highlights this? " ]
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## Describe the bug See title. I don't think this is intentional and they probably should be removed. If they stay the dataset description should be at least updated to make it clear to the user. ## Steps to reproduce the bug Go to the [dataset viewer](https://huggingface.co/datasets/viewer/?dataset=rotten_tomatoes) for the dataset, set the offset to 4160 for the train dataset, and scroll through the results. I found ones at index 4166 and 4173. There's others too (e.g. index 2888) but those two are easy to find like that. ## Expected results English movie reviews only. ## Actual results Example of a Spanish movie review (4173): > "É uma pena que , mais tarde , o próprio filme abandone o tom de paródia e passe a utilizar os mesmos clichês que havia satirizado "
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Decode images when iterating
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If I iterate over a vision dataset, the images are not decoded, and the dictionary with the bytes is returned. This PR enables image decoding in `Dataset.__iter__` Close https://github.com/huggingface/datasets/issues/3473
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Iterating over a vision dataset doesn't decode the images
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[ "As discussed, I remember I set `decoded=False` here to avoid decoding just by iterating over examples of dataset. We wanted to decode only if the \"audio\" field (for Audio feature) was accessed.", "> I set decoded=False here to avoid decoding just by iterating over examples of dataset. We wanted to decode only if the \"audio\" field (for Audio feature) was accessed\r\n\r\nhttps://github.com/huggingface/datasets/pull/3430 will add more control to decoding, so I think it's OK to enable decoding in `__iter__` for now. After we merge the linked PR, the user can easily disable it again.", "@mariosasko I wonder why there is no issue in `Audio` feature with decoding disabled in `__iter__`, whereas there is in `Image` feature.\r\n\r\nEnabling decoding in `__iter__` will make fail Audio regressions tests: https://github.com/huggingface/datasets/runs/4608657230?check_suite_focus=true\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/features/test_audio.py::test_dataset_with_audio_feature_map_is_not_decoded\r\nFAILED tests/features/test_audio.py::test_dataset_with_audio_feature_map_is_decoded\r\n========================= 2 failed, 15 passed in 8.37s =========================", "Please also note that the regression tests were implemented in accordance with the specifications:\r\n- when doing a `map` (wich calls `__iter__`) of a function that doesn't access the audio field, the decoding should be disabled; this is why the decoding is disabled in `__iter__` (and only enabled in `__getitem__`).", "> I wonder why there is no issue in Audio feature with decoding disabled in __iter__, whereas there is in Image feature.\r\n\r\n@albertvillanova Not sure if I understand this part. Currently, both the Image and the Audio feature don't decode data in `__iter__`, so their behavior is aligned there.\r\n", "Therefore, this is not an issue, neither for Audio nor Image feature.\r\n\r\nCould you please elaborate more on the expected use case? @lhoestq @NielsRogge \r\n\r\nThe expected use cases (in accordance with the specs: see #2324):\r\n- decoding should be enabled when accessing a specific item (`__getitem__`)\r\n- decoding should be disabled while iterating (`__iter__`) to allow preprocessing of non-audio/image features (like label or text, for example) using `.map`\r\n- decoding should be enabled in a `.map` only if the `.map` function accesses the audio/image feature (implemented using `LazyDict`)", "For me it's not an issue, actually. I just (mistakenly) tried to iterate over a PyTorch Dataset instead of a PyTorch DataLoader, \r\n\r\ni.e. I did this:\r\n\r\n`batch = next(iter(train_ds)) `\r\n\r\nwhereas I actually wanted to do\r\n\r\n`batch = next(iter(train_dataloader))`\r\n\r\nand then it turned out that in the first case, the image was a string of bytes rather than a Pillow image, hence Quentin opened an issue.", "Thanks @NielsRogge for the context.\r\n\r\nSo IMO everything is working as expected.\r\n\r\nI'm closing this issue. Feel free to reopen it again if further changes of the specs should be addressed.", "Thanks for the details :)\r\n\r\nI still think that it's unexpected to get different results when doing\r\n```python\r\nfor i in range(len(dataset)):\r\n sample = dataset[i]\r\n```\r\nand\r\n```python\r\nfor sample in dataset:\r\n pass\r\n```\r\neven though I understand that if you don't need to decode the data, then decoding image or audio data when iterating is a waste of time and resources.\r\n\r\nBut in this case users can still drop the column that need decoding to get the full speed back no ?" ]
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## Describe the bug If I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned. ## Steps to reproduce the bug ```python from datasets import load_dataset import PIL mnist = load_dataset("mnist", split="train") first_image = mnist[0]["image"] assert isinstance(first_image, PIL.PngImagePlugin.PngImageFile) # passes first_image = next(iter(mnist))["image"] assert isinstance(first_image, PIL.PngImagePlugin.PngImageFile) # fails ``` ## Expected results The image should be decoded, as a PIL Image ## Actual results We get a dictionary ``` {'bytes': b'\x89PNG\r\n\x1a\n\x00..., 'path': None} ``` ## Environment info - `datasets` version: 1.17.1.dev0 - Platform: Darwin-20.6.0-x86_64-i386-64bit - Python version: 3.7.2 - PyArrow version: 6.0.0 The bug also exists in 1.17.0 ## Investigation I think the issue is that decoding is disabled in `__iter__`: https://github.com/huggingface/datasets/blob/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6/src/datasets/arrow_dataset.py#L1651-L1661 Do you remember why it was disabled in the first place @albertvillanova ? Also cc @mariosasko @NielsRogge
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Fix `str(Path(...))` conversion in streaming on Linux
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Fix `str(Path(...))` conversion in streaming on Linux. This should fix the streaming of the `beans` and `cats_vs_dogs` datasets.
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Fix Tashkeela dataset to yield stripped text
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This PR: - Yields stripped text - Fix path for Windows - Adds license - Adds more info in dataset card Close bigscience-workshop/data_tooling#279
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Fix rendering of docs
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Minor fix in docs. Currently, `ClassLabel` docstring rendering is not right.
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Fix METEOR missing NLTK's omw-1.4
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[ "I also modified the doctest call to raise the exception that doctest may catch, instead of `doctest.UnexpectedException`.\r\nThis will make debugging easier if it happens again" ]
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NLTK 3.6.6 now requires `omw-1.4` to be downloaded for METEOR to work. This should fix the CI on master
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Add COCO dataset
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[ "The CI failures other than a missing dummy data file and missing fields in the card are unrelated to this PR. ", "Thanks a lot for this great work and fixing TFDS based script @mariosasko 🤗 will generate the dummy dataset and write the model card tomorrow!", "@mariosasko I added the dataset card, I'm on the dummy data rn. ", "@merveenoyan Let me know if you need any help with the dummy data.\r\n\r\nI plan to split the current script/dataset into 4 smaller scripts/datasets to make sure they are properly indexed by Papers With Code later on. In this format:\r\n* the `*_image_captioning` configs will form the [COCO Captions](https://paperswithcode.com/sota/image-captioning-on-coco-captions) dataset (also present in TFDS, but only the 2017 version)\r\n* the `stuff_segmentation` config will form the [COCO Stuff](https://paperswithcode.com/dataset/coco-stuff) dataset\r\n* the `desnepose` config will form the [DensePose-COCO](https://paperswithcode.com/dataset/densepose) dataset\r\n* the rest will be [COCO](https://paperswithcode.com/dataset/coco) (+ will add the `minival` and the `valminusminival` splits to COCO 2014)\r\n\r\nAlso, if I find the time, I'll add preprocessing examples that rely on `pycocotools` to the README files." ]
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This PR adds the MS COCO dataset. Compared to the [TFDS](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/object_detection/coco.py) script, this implementation adds 8 additional configs to cover the tasks other than object detection. Some notes: * the data exposed by TFDS is contained in the `2014`, `2015`, `2017` and `2017_panoptic_segmentation` configs here * I've updated `encode_nested_example` for easier handling of missing values (cc @lhoestq @albertvillanova; will add tests if you are OK with the changes in `features.py`) * this implementation should fix https://github.com/huggingface/datasets/pull/3377#issuecomment-985559427 TODOs: - [x] dataset card - [ ] dummy data cc @merveenoyan Closes #2526
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Push dataset infos.json to Hub
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[ "The change from `___` to `--` was allowed by https://github.com/huggingface/moon-landing/pull/1657" ]
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When doing `push_to_hub`, the feature types are lost (see issue https://github.com/huggingface/datasets/issues/3394). This PR fixes this by also pushing a `dataset_infos.json` file to the Hub, that stores the feature types. Other minor changes: - renamed the `___` separator to `--`, since `--` is now disallowed in a name in the back-end. I tested this feature with datasets like conll2003 that has feature types like `ClassLabel` that were previously lost. Close https://github.com/huggingface/datasets/issues/3394 I would like to include this in today's release (though not mandatory), so feel free to comment/suggest changes
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Add CRASS dataset
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[ "Hi Albert,\r\nThank you for your comments.\r\nI hope I have uploaded my local git repo to include the dummy files and style reworkings.\r\nAdded YAML in Readme as well.\r\n\r\nPlease check again.\r\n\r\nHope it works now :)" ]
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Added crass dataset
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Unable to load 'cnn_dailymail' dataset
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[ "Hi @talha1503, thanks for reporting.\r\n\r\nIt seems there is an issue with one of the data files hosted at Google Drive:\r\n```\r\nGoogle Drive - Quota exceeded\r\n\r\nSorry, you can't view or download this file at this time.\r\n\r\nToo many users have viewed or downloaded this file recently. Please try accessing the file again later. If the file you are trying to access is particularly large or is shared with many people, it may take up to 24 hours to be able to view or download the file. If you still can't access a file after 24 hours, contact your domain administrator.\r\n```\r\n\r\nAs you probably know, Hugging Face does not host the data, and in this case the data owner decided to host their data at Google Drive, which has quota limits.\r\n\r\nIs there anything we could do, @lhoestq @mariosasko?", "This looks related to https://github.com/huggingface/datasets/issues/996" ]
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## Describe the bug I wanted to load cnn_dailymail dataset from huggingface datasets on Google Colab, but I am getting an error while loading it. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('cnn_dailymail', '3.0.0', ignore_verifications = True) ``` ## Expected results Expecting to load 'cnn_dailymail' dataset. ## Actual results `NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.16.1 - Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 3.0.0
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struct.error: 'i' format requires -2147483648 <= number <= 2147483647
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[ "Hi ! Can you try setting `datasets.config.MAX_TABLE_NBYTES_FOR_PICKLING` to a smaller value than `4 << 30` (4GiB), for example `500 << 20` (500MiB) ? It should reduce the maximum size of the arrow table being pickled during multiprocessing.\r\n\r\nIf it fixes the issue, we can consider lowering the default value for everyone." ]
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## Describe the bug A clear and concise description of what the bug is. using latest datasets=datasets-1.16.1-py3-none-any.whl process my own multilingual dataset by following codes, and the number of rows in all dataset is 306000, the max_length of each sentence is 256: ![image](https://user-images.githubusercontent.com/30341159/146865779-3d25d011-1f42-4026-9e1b-76f6e1d172e9.png) then I get this error: ![image](https://user-images.githubusercontent.com/30341159/146865844-e60a404c-5f3a-403c-b2f1-acd943b5cdb8.png) I have seen the issue in #2134 and #2150, so I don't understand why latest repo still can't deal with big dataset. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: linux docker - Python version: 3.6
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Update swahili_news dataset
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Update dataset with latest verion data files. Fix #3462. Close bigscience-workshop/data_tooling#107
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Update swahili_news dataset
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Please note also: the HuggingFace version at https://huggingface.co/datasets/swahili_news is outdated. An updated version, with deduplicated text and official splits, can be found at https://zenodo.org/record/5514203. ## Adding a Dataset - **Name:** swahili_news Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Related to: - bigscience-workshop/data_tooling#107
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Fix links in metrics description
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Remove Markdown syntax for links in metrics description, as it is not properly rendered. Related to #3437.
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Don't encode lists as strings when using `Value("string")`
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Following https://github.com/huggingface/datasets/pull/3456#event-5792250497 it looks like `datasets` can silently convert lists to strings using `str()`, instead of raising an error. This PR fixes this and should fix the issue with WER showing low values if the input format is not right.
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dataset.filter overwriting previously set dataset._indices values, resulting in the wrong elements being selected.
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[ "I think this is a duplicate of [#3190](https://github.com/huggingface/datasets/issues/3190)?", "Upgrading the datasets version as per #3190 fixes this bug. \r\nI'm Marking as closed." ]
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## Describe the bug When using dataset.select to select a subset of a dataset, dataset._indices are set to indicate which elements are now considered in the dataset. The same thing happens when you shuffle the dataset; dataset._indices are set to indicate what the new order of the data is. However, if you then use a dataset.filter, that filter interacts with those dataset._indices values in a non-intuitive manner. https://huggingface.co/docs/datasets/_modules/datasets/arrow_dataset.html#Dataset.filter Effectively, it looks like the original set of _indices were discared and overwritten by the set created during the filter operation. I think this is actually an issue with how the map function handles dataset._indices. Ideally it should use the _indices it gets passed, and then return an updated _indices which reflect the map transformation applied to the starting _indices. ## Steps to reproduce the bug ```python dataset = load_dataset('imdb', split='train', keep_in_memory=True) dataset = dataset.shuffle(keep_in_memory=True) dataset = dataset.select(range(0, 10), keep_in_memory=True) print("initial 10 elements") print(dataset['label']) # -> [1, 1, 0, 1, 0, 0, 0, 1, 0, 0] dataset = dataset.filter(lambda x: x['label'] == 0, keep_in_memory=True) print("filtered 10 elements looking for label 0") print(dataset['label']) # -> [1, 1, 1, 1, 1, 1] ``` ## Actual results ``` $ python indices_bug.py initial 10 elements [1, 1, 0, 1, 0, 0, 0, 1, 0, 0] filtered 10 elements looking for label 0 [1, 1, 1, 1, 1, 1] ``` This code block first shuffles the dataset (to get a mix of label 0 and label 1). Then it selects just the first 10 elements (the number of elements does not matter, 10 is just easy to visualize). The important part is that you select some subset of the dataset. Finally, a filter is applied to pull out just the elements with `label == 0`. The bug is that you cannot combine any dataset operation which sets the dataset._indices with filter. In this case I have 2, shuffle and subset. If you just use a single dataset._indices operation (in this case shuffle) the bug still shows up. The shuffle sets the dataset._indices and then filter uses those indices in the map, then overwrites dataset._indices with the filter results. ```python dataset = load_dataset('imdb', split='train', keep_in_memory=True) dataset = dataset.shuffle(keep_in_memory=True) dataset = dataset.filter(lambda x: x['label'] == 0, keep_in_memory=True) dataset = dataset.select(range(0, 10), keep_in_memory=True) print(dataset['label']) # -> [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] ``` ## Expected results In an ideal world, the dataset filter would respect any dataset._indices values which had previously been set. If you use dataset.filter with the base dataset (where dataset._indices has not been set) then the filter command works as expected. ## Environment info Here are the commands required to rebuild the conda environment from scratch. ``` # create a virtual environment conda create -n dataset_indices python=3.8 -y # activate the virtual environment conda activate dataset_indices # install huggingface datasets conda install datasets ``` <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.12.1 - Platform: Linux-5.11.0-41-generic-x86_64-with-glibc2.17 - Python version: 3.8.12 - PyArrow version: 3.0.0 ### Full Conda Environment ``` $ conda env export name: dasaset_indices channels: - defaults dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=4.5=1_gnu - abseil-cpp=20210324.2=h2531618_0 - aiohttp=3.8.1=py38h7f8727e_0 - aiosignal=1.2.0=pyhd3eb1b0_0 - arrow-cpp=3.0.0=py38h6b21186_4 - attrs=21.2.0=pyhd3eb1b0_0 - aws-c-common=0.4.57=he6710b0_1 - aws-c-event-stream=0.1.6=h2531618_5 - aws-checksums=0.1.9=he6710b0_0 - aws-sdk-cpp=1.8.185=hce553d0_0 - bcj-cffi=0.5.1=py38h295c915_0 - blas=1.0=mkl - boost-cpp=1.73.0=h27cfd23_11 - bottleneck=1.3.2=py38heb32a55_1 - brotli=1.0.9=he6710b0_2 - brotli-python=1.0.9=py38heb0550a_2 - brotlicffi=1.0.9.2=py38h295c915_0 - brotlipy=0.7.0=py38h27cfd23_1003 - bzip2=1.0.8=h7b6447c_0 - c-ares=1.17.1=h27cfd23_0 - ca-certificates=2021.10.26=h06a4308_2 - certifi=2021.10.8=py38h06a4308_0 - cffi=1.14.6=py38h400218f_0 - conllu=4.4.1=pyhd3eb1b0_0 - cryptography=36.0.0=py38h9ce1e76_0 - dataclasses=0.8=pyh6d0b6a4_7 - dill=0.3.4=pyhd3eb1b0_0 - double-conversion=3.1.5=he6710b0_1 - et_xmlfile=1.1.0=py38h06a4308_0 - filelock=3.4.0=pyhd3eb1b0_0 - frozenlist=1.2.0=py38h7f8727e_0 - gflags=2.2.2=he6710b0_0 - glog=0.5.0=h2531618_0 - gmp=6.2.1=h2531618_2 - grpc-cpp=1.39.0=hae934f6_5 - huggingface_hub=0.0.17=pyhd3eb1b0_0 - icu=58.2=he6710b0_3 - idna=3.3=pyhd3eb1b0_0 - importlib-metadata=4.8.2=py38h06a4308_0 - importlib_metadata=4.8.2=hd3eb1b0_0 - intel-openmp=2021.4.0=h06a4308_3561 - krb5=1.19.2=hac12032_0 - ld_impl_linux-64=2.35.1=h7274673_9 - libboost=1.73.0=h3ff78a5_11 - libcurl=7.80.0=h0b77cf5_0 - libedit=3.1.20210910=h7f8727e_0 - libev=4.33=h7f8727e_1 - libevent=2.1.8=h1ba5d50_1 - libffi=3.3=he6710b0_2 - libgcc-ng=9.3.0=h5101ec6_17 - libgomp=9.3.0=h5101ec6_17 - libnghttp2=1.46.0=hce63b2e_0 - libprotobuf=3.17.2=h4ff587b_1 - libssh2=1.9.0=h1ba5d50_1 - libstdcxx-ng=9.3.0=hd4cf53a_17 - libthrift=0.14.2=hcc01f38_0 - libxml2=2.9.12=h03d6c58_0 - libxslt=1.1.34=hc22bd24_0 - lxml=4.6.3=py38h9120a33_0 - lz4-c=1.9.3=h295c915_1 - mkl=2021.4.0=h06a4308_640 - mkl-service=2.4.0=py38h7f8727e_0 - mkl_fft=1.3.1=py38hd3c417c_0 - mkl_random=1.2.2=py38h51133e4_0 - multiprocess=0.70.12.2=py38h7f8727e_0 - multivolumefile=0.2.3=pyhd3eb1b0_0 - ncurses=6.3=h7f8727e_2 - numexpr=2.7.3=py38h22e1b3c_1 - numpy=1.21.2=py38h20f2e39_0 - numpy-base=1.21.2=py38h79a1101_0 - openpyxl=3.0.9=pyhd3eb1b0_0 - openssl=1.1.1l=h7f8727e_0 - orc=1.6.9=ha97a36c_3 - packaging=21.3=pyhd3eb1b0_0 - pip=21.2.4=py38h06a4308_0 - py7zr=0.16.1=pyhd3eb1b0_1 - pycparser=2.21=pyhd3eb1b0_0 - pycryptodomex=3.10.1=py38h27cfd23_1 - pyopenssl=21.0.0=pyhd3eb1b0_1 - pyparsing=3.0.4=pyhd3eb1b0_0 - pyppmd=0.16.1=py38h295c915_0 - pysocks=1.7.1=py38h06a4308_0 - python=3.8.12=h12debd9_0 - python-dateutil=2.8.2=pyhd3eb1b0_0 - python-xxhash=2.0.2=py38h7f8727e_0 - pyzstd=0.14.4=py38h7f8727e_3 - re2=2020.11.01=h2531618_1 - readline=8.1=h27cfd23_0 - requests=2.26.0=pyhd3eb1b0_0 - setuptools=58.0.4=py38h06a4308_0 - six=1.16.0=pyhd3eb1b0_0 - snappy=1.1.8=he6710b0_0 - sqlite=3.36.0=hc218d9a_0 - texttable=1.6.4=pyhd3eb1b0_0 - tk=8.6.11=h1ccaba5_0 - typing_extensions=3.10.0.2=pyh06a4308_0 - uriparser=0.9.3=he6710b0_1 - utf8proc=2.6.1=h27cfd23_0 - wheel=0.37.0=pyhd3eb1b0_1 - xxhash=0.8.0=h7f8727e_3 - xz=5.2.5=h7b6447c_0 - zipp=3.6.0=pyhd3eb1b0_0 - zlib=1.2.11=h7f8727e_4 - zstd=1.4.9=haebb681_0 - pip: - async-timeout==4.0.2 - charset-normalizer==2.0.9 - datasets==1.16.1 - fsspec==2021.11.1 - huggingface-hub==0.2.1 - multidict==5.2.0 - pandas==1.3.5 - pyarrow==6.0.1 - pytz==2021.3 - pyyaml==6.0 - tqdm==4.62.3 - typing-extensions==4.0.1 - urllib3==1.26.7 - yarl==1.7.2 ```
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Fix duplicated tag in wikicorpus dataset card
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[ "CI is failing just because of empty sections - merging" ]
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Add CMU Graphics Lab Motion Capture dataset
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## Adding a Dataset - **Name:** CMU Graphics Lab Motion Capture database - **Description:** The database contains free motions which you can download and use. - **Data:** http://mocap.cs.cmu.edu/ - **Motivation:** Nice motion capture 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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[WER] Better error message for wer
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[ "Hi ! I don't think this would solve this issue.\r\nCurrently it looks like there's a bug that converts the list `[\"hello it's nice\"]` to a string `'[\"hello it's nice\"]'` since this is what the metric expects as input. The conversion is done before the data are passed to `_compute()`.\r\n\r\nThis is `Value(\"string\").encode_example` that is called to do the conversion. Since `str()` encoding is too permissive we should consider raising an error if the example is not a string (even though it can be converted to string). ", "> called\r\n\r\nAh yeah you're right", "I just opened https://github.com/huggingface/datasets/pull/3460 to fix that. It now raises an error instead of computing the wrong WER", "Thank you - that should be good enough!" ]
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Currently we have the following problem when using the WER. When the input format to the WER metric is wrong, instead of throwing an error message a word-error-rate is computed which is incorrect. E.g. when doing the following: ```python from datasets import load_metric wer = load_metric("wer") target_str = ["hello this is nice", "hello the weather is bloomy"] pred_str = [["hello it's nice"], ["hello it's the weather"]] print("Wrong:", wer.compute(predictions=pred_str, references=target_str)) print("Correct", wer.compute(predictions=[x[0] for x in pred_str], references=target_str)) ``` We get: ``` Wrong: 1.0 Correct 0.5555555555555556 ``` meaning that we get a word-error rate for incorrectly passed input formats. We should raise an error here instead so that people don't spent hours fixing a model while it's their incorrect evaluation metric is the problem for a low WER.
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[ "Hi ! I guess you are talking about the dataset cards that are in this repository on github ?\r\n\r\nI think github allows to submit a PR even for 1 line though the `Edit file` button on the page of the dataset card.\r\n\r\nMaybe let's mention this in `CONTRIBUTING.md` ?" ]
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**Is your feature request related to a problem? Please describe.** It requires a lot of effort to improve a datasheet. **Describe the solution you'd like** UI or at least a link to the place where the code that needs to be edited is (and an easy way to edit this code directly from the site, without cloning, branching, makefile etc.) **Describe alternatives you've considered** The current Ux is to have the 8 steps for contribution while One just wishes to change a line a type etc.
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Fix iter_archive generator
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This PR: - Adds tests to DownloadManager and StreamingDownloadManager `iter_archive` for both path and file inputs - Fixes bugs in `iter_archive` introduced in: - #3443 Fix #3453.
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ValueError while iter_archive
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## Describe the bug After the merge of: - #3443 the method `iter_archive` throws a ValueError: ``` ValueError: read of closed file ``` ## Steps to reproduce the bug ```python for path, file in dl_manager.iter_archive(archive_path): pass ```
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why the stratify option is omitted from test_train_split function?
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[ "Hi ! It's simply not added yet :)\r\n\r\nIf someone wants to contribute to add the `stratify` parameter I'd be happy to give some pointers.\r\n\r\nIn the meantime, I guess you can use `sklearn` or other tools to do a stratified train/test split over the **indices** of your dataset and then do\r\n```\r\ntrain_dataset = dataset.select(train_indices)\r\ntest_dataset = dataset.select(test_indices)\r\n```" ]
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why the stratify option is omitted from test_train_split function? is there any other way implement the stratify option while splitting the dataset? as it is important point to be considered while splitting the dataset.
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[Staging] Update dataset repos automatically on the Hub
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[ "do keep us updated on how it's going in staging! cc @SBrandeis ", "Sure ! For now it works smoothly. We'll also do a new release today.\r\n\r\nI can send you some repos to explore on staging, in case you want to see how they look like after being updated.\r\nFor example [swahili_news](https://moon-staging.huggingface.co/datasets/swahili_news/tree/main)" ]
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Let's have a script that updates the dataset repositories on staging for now. This way we can make sure it works fine before going in prod. Related to https://github.com/huggingface/datasets/issues/3341 The script runs on each commit on `master`. It checks the datasets that were changed, and it pushes the changes to the corresponding repositories on the Hub. If there's a new dataset, then a new repository is created. If the commit is a new release of `datasets`, it also pushes the tag to all the repositories.
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Unexpected behavior doing Split + Filter
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[ "Hi ! This is an issue with `datasets` 1.12. Sorry for the inconvenience. Can you update to `>=1.13` ?\r\nsee https://github.com/huggingface/datasets/issues/3190\r\n\r\nMaybe we should also backport the bug fix to `1.12` (in a new version `1.12.2`)" ]
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## Describe the bug I observed unexpected behavior when applying 'train_test_split' followed by 'filter' on dataset. Elements of the training dataset eventually end up in the test dataset (after applying the 'filter') ## Steps to reproduce the bug ``` from datasets import Dataset import pandas as pd dic = {'x': [1,2,3,4,5,6,7,8,9], 'y':['q','w','e','r','t','y','u','i','o']} df = pd.DataFrame.from_dict(dic) dataset = Dataset.from_pandas(df) split_dataset = dataset.train_test_split(test_size=0.5, shuffle=False, seed=42) train_dataset = split_dataset["train"] eval_dataset = split_dataset["test"] eval_dataset_2 = eval_dataset.filter(lambda example: example['x'] % 2 == 0) print( eval_dataset['x']) print(eval_dataset_2['x']) ``` One observes that elements in eval_dataset2 are actually coming from the training dataset... ## Expected results The expected results would be that the filtered eval dataset would only contain elements from the original eval dataset. ## 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: 1.12.1 - Platform: Windows 10 - Python version: 3.7 - PyArrow version: 5.0.0
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Add `__add__()`, `__iadd__()` and similar to `Dataset` class
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[ "I was going through the codebase, and I believe the implementation of __add__() and __iadd__() will be similar to concatenate_datasets() after the elimination of code for arguments other than the list of datasets (info, split, axis). \r\n(Assuming elimination of axis means concatenating over axis 1.)" ]
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**Is your feature request related to a problem? Please describe.** No. **Describe the solution you'd like** I would like to be able to concatenate datasets as follows: ```python >>> dataset["train"] += dataset["validation"] ``` ... instead of using `concatenate_datasets()`: ```python >>> raw_datasets["train"] = concatenate_datasets([raw_datasets["train"], raw_datasets["validation"]]) >>> del raw_datasets["validation"] ``` **Describe alternatives you've considered** Well, I have considered `concatenate_datasets()` 😀 **Additional context** N.a.
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[ "Hi ! I think the issue comes from the dataset_infos.json file: it has the \"flat\" field twice.\r\n\r\nCan you try deleting this file and regenerating it please ?", "Thanks! That fixed that, but now I am getting:\r\nServer Error\r\nStatus code: 400\r\nException: KeyError\r\nMessage: 'feature'\r\n\r\nI checked the dataset_infos.json and pubmed_neg.py script, I don't use 'feature' anywhere as a key. Is the dataset viewer expecting that I do?", "It seems that the `feature` key is missing from some feature type definition in your dataset_infos.json:\r\n```json\r\n\t\t\t\"tokens\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n\t\t\t},\r\n\t\t\t\"tags\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n\t\t\t}\r\n```\r\nThey should be\r\n```json\r\n\t\t\t\"tokens\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n \"feature\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"}\r\n\t\t\t},\r\n\t\t\t\"tags\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\",\r\n \"feature\": {\"num_classes\": 5, \"names\": [\"-\", \"S\", \"H\", \"N\", \"C\"], \"names_file\": null, \"id\": null, \"_type\": \"ClassLabel\"}\r\n\t\t\t}\r\n```\r\n\r\nNote that you can generate the dataset_infos.json automatically to avoid mistakes:\r\n```bash\r\ndatasets-cli test ./path/to/dataset --save_infos\r\n```" ]
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## Dataset viewer issue for 'pubmed_neg' **Link:** https://huggingface.co/datasets/IGESML/pubmed_neg I am getting the error: Status code: 400 Exception: JSONDecodeError Message: Expecting property name enclosed in double quotes: line 61 column 2 (char 1202) I have checked all files - I am not using single quotes anywhere. Not sure what is causing this issue. Am I the one who added this dataset ? Yes
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HF_DATASETS_OFFLINE=1 didn't stop datasets.builder from downloading
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[ "Hi ! Indeed it says \"downloading and preparing\" but in your case it didn't need to download anything since you used local files (it would have thrown an error otherwise). I think we can improve the logging to make it clearer in this case", "@lhoestq Thank you for explaining. I am sorry but I was not clear about my intention. I didn't want to kill internet traffic; I wanted to kill all write activity. In other words, you can imagine that my storage has only read access but crashes on write.\r\n\r\nWhen run_clm.py is invoked with the same parameters, the hash in the cache directory \"datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/...\" doesn't change, and my job can load cached data properly. This is great.\r\n\r\nUnfortunately, when params change (which happens sometimes), the hash changes and the old cache is invalid. datasets builder would create a new cache directory with the new hash and create JSON builder there, even though every JSON builder is the same. I didn't find a way to avoid such behavior.\r\n\r\nThis problem can be resolved when using datasets.map() for tokenizing and grouping text. This function allows me to specify output filenames with --cache_file_names, so that the cached files are always valid.\r\n\r\nThis is the code that I used to freeze cache filenames for tokenization. I wish I could do the same to datasets.load_dataset()\r\n```\r\n tokenized_datasets = raw_datasets.map(\r\n tokenize_function,\r\n batched=True,\r\n num_proc=data_args.preprocessing_num_workers,\r\n remove_columns=column_names,\r\n load_from_cache_file=not data_args.overwrite_cache,\r\n desc=\"Running tokenizer on dataset\",\r\n cache_file_names={k: os.path.join(model_args.cache_dir, f'{k}-tokenized') for k in raw_datasets},\r\n )\r\n```", "Hi ! `load_dataset` may re-generate your dataset if some parameters changed indeed. If you want to freeze a dataset loaded with `load_dataset`, I think the best solution is just to save it somewhere on your disk with `.save_to_disk(my_dataset_dir)` and reload it with `load_from_disk(my_dataset_dir)`. This way you will be able to reload the dataset without having to run `load_dataset`" ]
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## Describe the bug According to https://huggingface.co/docs/datasets/loading_datasets.html#loading-a-dataset-builder, setting HF_DATASETS_OFFLINE to 1 should make datasets to "run in full offline mode". It didn't work for me. At the very beginning, datasets still tried to download "custom data configuration" for JSON, despite I have run the program once and cached all data into the same --cache_dir. "Downloading" is not an issue when running with local disk, but crashes often with cloud storage because (1) multiply GPU processes try to access the same file, AND (2) FileLocker fails to synchronize all processes, due to storage throttling. 99% of times, when the main process releases FileLocker, the file is not actually ready for access in cloud storage and thus triggers "FileNotFound" errors for all other processes. Well, another way to resolve the problem is to investigate super reliable cloud storage, but that's out of scope here. ## Steps to reproduce the bug ``` export HF_DATASETS_OFFLINE=1 python run_clm.py --model_name_or_path=models/gpt-j-6B --train_file=trainpy.v2.train.json --validation_file=trainpy.v2.eval.json --cache_dir=datacache/trainpy.v2 ``` ## Expected results datasets should stop all "downloading" behavior but reuse the cached JSON configuration. I think the problem here is part of the cache directory path, "default-471372bed4b51b53", is randomly generated, and it could change if some parameters changed. And I didn't find a way to use a fixed path to ensure datasets to reuse cached data every time. ## Actual results The logging shows datasets are still downloading into "datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426". ``` 12/16/2021 10:25:59 - WARNING - datasets.builder - Using custom data configuration default-471372bed4b51b53 12/16/2021 10:25:59 - INFO - datasets.builder - Generating dataset json (datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426) Downloading and preparing dataset json/default to datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426... 100%|██████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 17623.13it/s] 12/16/2021 10:25:59 - INFO - datasets.utils.download_manager - Downloading took 0.0 min 12/16/2021 10:26:00 - INFO - datasets.utils.download_manager - Checksum Computation took 0.0 min 100%|███████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1206.99it/s] 12/16/2021 10:26:00 - INFO - datasets.utils.info_utils - Unable to verify checksums. 12/16/2021 10:26:00 - INFO - datasets.builder - Generating split train 12/16/2021 10:26:01 - INFO - datasets.builder - Generating split validation 12/16/2021 10:26:02 - INFO - datasets.utils.info_utils - Unable to verify splits sizes. Dataset json downloaded and prepared to datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426. Subsequent calls will reuse this data. 100%|█████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 53.54it/s] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.16.1 - Platform: Linux - Python version: 3.8.10 - PyArrow version: 6.0.1
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Remove redundant local path information in audio/image datasets
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[ "Cool, I'm in favor of this PR. Our official examples in speech already make use of `\"audio\"` so no need to change anything there. It would be great if we could prominently feature how one can get the audio path without decoding in the docs.", "@patrickvonplaten Yes, I agree.\r\n\r\ncc @stevhliu we should add an example where decoding is disabled (to read paths) to [this section](https://github.com/huggingface/datasets/blob/master/docs/source/audio_process.rst#audio-datasets) in the docs and remove the mentions of `path`/`file` (if we merge this PR)." ]
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CONTRIBUTOR
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Remove the redundant path information in the audio/image dataset as discussed in https://github.com/huggingface/datasets/pull/3430#issuecomment-994734828 TODOs: * [ ] merge https://github.com/huggingface/datasets/pull/3430 * [ ] merge https://github.com/huggingface/datasets/pull/3364 * [ ] re-generate the info files of the updated audio datasets cc: @patrickvonplaten @anton-l @nateraw (I expect this to break the audio/vision examples in Transformers; after this change you'll be able to access underlying paths as follows `dset = dset.cast_column("audio", Audio(..., decode=False)); path = dset[0]["audio"]`)
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[ "Hi ! What's your question ?" ]
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## Dataset viewer issue for '*name of the dataset*' **Link:** *link to the dataset viewer page* *short description of the issue* Am I the one who added this dataset ? Yes-No
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Align the Dataset and IterableDataset processing API
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[ "Yes I agree, these should be as aligned as possible. Maybe we can also check the feedback in the survey at http://hf.co/oss-survey and see if people mentioned related things on the API (in particular if we go the breaking change way, it would be good to be sure we are taking the right direction for the community).", "I like this proposal.\r\n\r\n> There is also an important difference in terms of behavior:\r\nDataset.map adds new columns (with dict.update)\r\nBUT\r\nIterableDataset discards previous columns (it overwrites the dict)\r\nIMO the two methods should have the same behavior. This would be an important breaking change though.\r\n\r\n> The main breaking change would be the change of behavior of IterableDataset.map, because currently it discards all the previous columns instead of keeping them.\r\n\r\nYes, this behavior of `IterableDataset.map` was surprising to me the first time I used it because I was expecting the same behavior as `Dataset.map`, so I'm OK with the breaking change here.\r\n\r\n> IterableDataset only supports \"torch\" (it misses tf, jax, pandas, arrow) and is missing the parameters: columns, output_all_columns and format_kwargs\r\n\r\n\\+ it's also missing the actual formatting code (we return unformatted tensors)\r\n> We could have a completely aligned map method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that.\r\n\r\n> For information, TFDS does lazy map by default, and has an additional .cache() method.\r\n\r\nIf I understand this part correctly, the idea would be for `Dataset.map` to behave similarly to `Dataset.with_transform` (lazy processing) and to have an option to cache processed data (with `.cache()`). This idea is really nice because it can also be applied to `IterableDataset` to fix https://github.com/huggingface/datasets/issues/3142 (again we get the aligned APIs). However, this change would break a lot of things, so I'm still not sure if this is a step in the right direction (maybe it's OK for Datasets 2.0?) \r\n> If the two APIs are more aligned it would be awesome for the examples in transformers, and it would create a satisfactory experience for users that want to switch from one mode to the other.\r\n\r\nYes, it would be amazing to have an option to easily switch between these two modes.\r\n\r\nI agree with the rest.\r\n", "> If I understand this part correctly, the idea would be for Dataset.map to behave similarly to Dataset.with_transform (lazy processing) and to have an option to cache processed data (with .cache()). This idea is really nice because it can also be applied to IterableDataset to fix #3142 (again we get the aligned APIs). However, this change would break a lot of things, so I'm still not sure if this is a step in the right direction (maybe it's OK for Datasets 2.0?)\r\n\r\nYea this is too big of a change in my opinion. Anyway it's fine as it is right now with streaming=lazy and regular=eager." ]
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MEMBER
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## Intro Currently the two classes have two distinct API for processing: ### The `.map()` method Both have those parameters in common: function, batched, batch_size - IterableDataset is missing those parameters: with_indices, with_rank, input_columns, drop_last_batch, remove_columns, features, disable_nullable, fn_kwargs, num_proc - Dataset also has additional parameters that are exclusive, due to caching: keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, suffix_template, new_fingerprint - There is also an important difference in terms of behavior: **Dataset.map adds new columns** (with dict.update) BUT **IterableDataset discards previous columns** (it overwrites the dict) IMO the two methods should have the same behavior. This would be an important breaking change though. - Dataset.map is eager while IterableDataset.map is lazy ### The `.shuffle()` method - Both have an optional seed parameter, but IterableDataset requires a mandatory parameter buffer_size to control the size of the local buffer used for approximate shuffling. - IterableDataset is missing the parameter generator - Also Dataset has exclusive parameters due to caching: keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint ### The `.with_format()` method - IterableDataset only supports "torch" (it misses tf, jax, pandas, arrow) and is missing the parameters: columns, output_all_columns and format_kwargs ### Other methods - Both have the same `remove_columns` method - IterableDataset is missing: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard - Some other methods are missing but we can discuss them: set_transform, formatted_as, with_transform - And others don't really make sense for an iterable dataset: select, sort, add_column, add_item - Dataset is missing skip and take, that IterableDataset implements. ## Questions I think it would be nice to be able to switch between streaming and regular dataset easily, without changing the processing code significantly. 1. What should be aligned and what shouldn't between those two APIs ? IMO the minimum is to align the main processing methods. It would mean aligning breaking the current `Iterable.map` to have the same behavior as `Dataset.map` (add columns with dict.update), and add multiprocessing as well as the missing parameters. It would also mean implementing the missing methods: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard 2. What are the breaking changes for IterableDataset ? The main breaking change would be the change of behavior of `IterableDataset.map`, because currently it discards all the previous columns instead of keeping them. 3. Shall we also do some changes for regular datasets ? I agree the simplest would be to have the exact same methods for both Dataset and IterableDataset. However this is probably not a good idea because it would prevent users from using the best benefits of them. That's why we can keep some aspects of regular datasets as they are: - keep the eager Dataset.map with caching - keep the with_transform method for lazy processing - keep Dataset.select (it could also be added to IterableDataset even though it's not recommended) We could have a completely aligned `map` method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that. For information, TFDS does lazy map by default, and has an additional `.cache()` method. ## Opinions ? I'd love to gather some opinions about this here. If the two APIs are more aligned it would be awesome for the examples in `transformers`, and it would create a satisfactory experience for users that want to switch from one mode to the other. cc @mariosasko @albertvillanova @thomwolf @patrickvonplaten @sgugger
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Extend iter_archive to support file object input
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This PR adds support to passing a file object to `[Streaming]DownloadManager.iter_archive`. With this feature, we can iterate over a tar file inside another tar file.
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