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"Hi @vpj, thanks for reporting.\r\n\r\nI'm sorry, but I can't reproduce your bug: I load \"20220301.simple\"in 9 seconds:\r\n```shell\r\ntime python -c \"from datasets import load_dataset; load_dataset('wikipedia', '20220301.simple')\"\r\n\r\nDownloading and preparing dataset wikipedia/20220301.simple (download: 228.58 MiB, generated: 224.18 MiB, post-processed: Unknown size, total: 452.76 MiB) to .../.cache/huggingface/datasets/wikipedia/20220301.simple/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.66k/1.66k [00:00<00:00, 1.02MB/s]\r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 235M/235M [00:02<00:00, 82.8MB/s]\r\nDataset wikipedia downloaded and prepared to .../.cache/huggingface/datasets/wikipedia/20220301.simple/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 290.75it/s]\r\n\r\nreal\t0m9.693s\r\nuser\t0m6.002s\r\nsys\t0m3.260s\r\n```\r\n\r\nCould you please check your environment info, as requested when opening this issue?\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n```\r\nMaybe you are using an old version of `datasets`...",
"Downloading and processing `wikipedia simple` dataset completed in under 11sec on M1 Mac. Could you please check `dataset` version as mentioned by @albertvillanova? Also check system specs, if system is under load processing could take some time I guess."
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`wikipedia` pre-processed datasets
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## Describe the bug
[Wikipedia](https://huggingface.co/datasets/wikipedia) dataset readme says that certain subsets are preprocessed. However it seems like they are not available. When I try to load them it takes a really long time, and it seems like it's processing them.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("wikipedia", "20220301.en")
```
## Expected results
To load the dataset
## Actual results
Takes a very long time to load (after downloading)
After `Downloading data files: 100%`. It takes hours and gets killed.
Tried `wikipedia.simple` and it got processed after ~30mins.
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Fix type hint and documentation for `new_fingerprint`
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Currently, there are no type hints nor `Optional` for the argument `new_fingerprint` in several methods of `datasets.arrow_dataset.Dataset`.
There was some documentation missing as well.
Note that pylance is happy with the type hints, but pyright does not detect that `new_fingerprint` is set within the decorator.
The modifications in this PR are fine since here https://github.com/huggingface/datasets/blob/aa743886221d76afb409d263e1b136e7a71fe2b4/src/datasets/fingerprint.py#L446-L454
for the non-inplace case we make sure to auto-generate a new fingerprint (as indicated in the doc).
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https://github.com/huggingface/datasets/issues/4325
|
CONTRIBUTOR
|
completed
| null | null |
[
"Not sure if it's related... I was going to raise an issue for https://huggingface.co/datasets/domenicrosati/TruthfulQA which also has the same issue... https://huggingface.co/datasets/domenicrosati/TruthfulQA/viewer/domenicrosati--TruthfulQA/train \r\n\r\n",
"Yes, it's related. The backend behind the dataset viewer is currently under too much load, and these datasets are still in the jobs queue. We're actively working on this issue, and we expect to fix the issue permanently soon. Thanks for your patience 🙏 ",
"Thanks @severo and no worries! - a suggestion for a UI usability thing maybe is to indicate that the dataset processing is in the job queue (rather than no data?)",
"Thanks, these are working great now (including @domenicrosati 's, afaics!)"
] |
Dataset Viewer issue for strombergnlp/offenseval_2020, strombergnlp/polstance
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### Link
https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train
### Description
The viewer isn't running for these two datasets. I left it overnight because a wait sometimes helps things get loaded, and the error messages have all gone, but the datasets are still turning up blank in viewer. Maybe it needs a bit more time.
* https://huggingface.co/datasets/strombergnlp/polstance/viewer/PolStance/train
* https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train
While offenseval_2020 is gated w. prompt, the other gated previews I have run fine in Viewer, e.g. https://huggingface.co/datasets/strombergnlp/shaj , so I'm a bit stumped!
### Owner
Yes
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https://github.com/huggingface/datasets/issues/4324
|
CONTRIBUTOR
| null | null | null |
[
"Hi @leondz, I agree it would be nice. We'll see what we can do ;)"
] |
Support >1 PWC dataset per dataset card
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2022-05-12T10:29:07Z
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https://api.github.com/repos/huggingface/datasets/issues/4324/comments
|
**Is your feature request related to a problem? Please describe.**
Some datasets cover more than one dataset on PapersWithCode. For example, the OffensEval 2020 challenge involved five languages, and there's one dataset to cover all five datasets, [`strombergnlp/offenseval_2020`](https://huggingface.co/datasets/strombergnlp/offenseval_2020). However, the yaml `paperswithcode_id:` dataset card entry only supports one value; when multiple are added, the PWC link disappears from the dataset page.
Because the link from a PapersWithCode dataset to a Hugging Face Hub entry can't be entered manually and seems to be scraped, this means end users don't have a way of getting a dataset reader link to appear on all the PWC datasets supported by one HF Hub Dataset reader.
It's not super unusual to have papers introduce multiple parallel variants of a dataset and would be handy to reflect this, so e.g. dataset maintainers can DRY, and so dataset users can keep what they're doing simple.
**Describe the solution you'd like**
I'd like `paperswithcode_id:` to support lists and be able to connect with multiple PWC datasets.
**Describe alternatives you've considered**
De-normalising the datasets on HF Hub to create multiple readers for each variation on a task, i.e. instead of a single `offenseval_2020`, having `offenseval_2020_ar`, `offenseval_2020_da`, `offenseval_2020_gr`, ...
**Additional context**
Hope that's enough
**Priority**
Low
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https://github.com/huggingface/datasets/issues/4323
|
CONTRIBUTOR
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completed
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[
"\r\n\r\nthat is reason my bytes`s empty\r\nbut i have some confused why path prior is higher than bytes?\r\n\r\nif you can make bytes in _generate_examples , you don`t have to make bytes to path?\r\nbecause we have path and bytes already",
"> but i have some confused why path prior is higher than bytes?\r\n\r\nIf the audio file is already available locally, we don't need to store the bytes again.\r\n\r\nIf you don't specify a \"path\" to a local file, then the bytes are stored. You can set \"path\" to None for example.\r\n\r\n> if you can make bytes in _generate_examples , you don`t have to make bytes to path?\r\n> because we have path and bytes already\r\n\r\nIt's useful to pass both \"path\" and \"bytes\" in `_generate_examples`:\r\n- when the dataset has been downloaded, then the \"path\" to the audio files are stored and we can ignore \"bytes\" in order to save disk space.\r\n- when the dataset is loaded in streaming mode, the audio files are not available on your disk and therefore we use the \"bytes\" ",
"@lhoestq \r\nFirst of all, thx for reply\r\n\r\nbut, if i put in \"bytes\" and \"path\"\r\nex) {\"bytes\":\"blah blah~\", \"path\":\"blah blah~\"}\r\n\r\nthat source working that my bytes to empty first,\r\nand then, re-calculate my bytes!\r\n\r\n\r\nif you have some pcm file, pcm is can read bytes.\r\nso, i put in bytes and paths.\r\nbut bytes is been None why encode_example func make None\r\nand then, on decode_example func, we no have bytes. so, calculate bytes to path.\r\npcm is not support librosa or soundfile, error occured!\r\n\r\nthe most important thing is not announced anywhere this situation can be reproduced\r\n\r\nis that truly right process flow?",
"I don't think we support PCM files, feel free to convert your data to WAV for now.\r\n\r\nIt would be awesome to support PCM files though, let me know if you'd like to contribute this feature, I'd be happy to help",
"@lhoestq oh, how can i contribute?",
"You can clone the repository (see the guide on [how to contribute](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-create-a-pull-request)) and see how we can make the `Image.encode_example` method work with PCM data.\r\n\r\nThere might be other ways to approach this problem, but here is what I think is a reasonable one:\r\n\r\nI think `Image.encode_example` should be able to take PCM bytes as input and the sampling rate, and return the WAV bytes (built by combining the PCM bytes and the sampling rate info), so that `Image.decode_example` can read it.\r\n\r\nTo check if the input bytes are PCM data, you can just check if the extension of the `path` is \".pcm\".\r\n",
"maybe i can start to contribute on this sunday!\r\n@lhoestq ",
"@lhoestq plz check my pr #4409 \r\n\r\nam i wrong somting?",
"Thanks, I reviewed your PR :)"
] |
Audio can not find value["bytes"]
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2022-05-12T08:31:58Z
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https://api.github.com/repos/huggingface/datasets/issues/4323/comments
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## Describe the bug
I wrote down _generate_examples like:
but where is the bytes?
## Expected results
value["bytes"] is not None, so i can make datasets with bytes, not path
## bytes looks like:
blah blah~~
\xfe\x03\x00\xfb\x06\x1c\x0bo\x074\x03\xaf\x01\x13\x04\xbc\x06\x8c\x05y\x05,\t7\x08\xaf\x03\xc0\xfe\xe8\xfc\x94\xfe\xb7\xfd\xea\xfa\xd5\xf9$\xf9>\xf9\x1f\xf8\r\xf5F\xf49\xf4\xda\xf5-\xf8\n\xf8k\xf8\x07\xfb\x18\xfd\xd9\xfdv\xfd"\xfe\xcc\x01\x1c\x04\x08\x04@\x04{\x06^\tf\t\x1e\x07\x8b\x06\x02\x08\x13\t\x07\x08 \x06g\x06"\x06\xa0\x03\xc6\x002\xff \xff\x1d\xff\x19\xfd?\xfb\xdb\xfa\xfc\xfa$\xfb}\xf9\xe5\xf7\xf9\xf7\xce\xf8.\xf9b\xf9\xc5\xf9\xc0\xfb\xfa\xfcP\xfc\xba\xfbQ\xfc1\xfe\x9f\xff\x12\x00\xa2\x00\x18\x02Z\x03\x02\x04\xb1\x03\xc5\x03W\x04\x82\x04\x8f\x04U\x04\xb6\x04\x10\x05{\x04\x83\x02\x17\x01\x1d\x00\xa0\xff\xec\xfe\x03\xfe#\xfe\xc2\xfe2\xff\xe6\xfe\x9a\xfe~\x01\x91\x08\xb3\tU\x05\x10\x024\x02\xe4\x05\xa8\x07\xa7\x053\x07I\n\x91\x07v\x02\x95\xfd\xbb\xfd\x96\xff\x01\xfe\x1e\xfb\xbb\xf9S\xf8!\xf8\xf4\xf5\xd6\xf3\xf7\xf3l\xf4d\xf6l\xf7d\xf6b\xf7\xc1\xfa(\xfd\xcf\xfd*\xfdq\xfe\xe9\x01\xa8\x03t\x03\x17\x04B\x07\xce\t\t\t\xeb\x06\x0c\x07\x95\x08\x92\t\xbc\x07O\x06\xfb\x06\xd2\x06U\x04\x00\x02\x92\x00\xdc\x00\x84\x00 \xfeT\xfc\xf1\xfb\x82\xfc\x97\xfb}\xf9\x00\xf8_\xf8\x0b\xf9\xe5\xf8\xe2\xf7\xaa\xf8\xb2\xfa\x10\xfbl\xfa\xf5\xf9Y\xfb\xc0\xfd\xe8\xfe\xec\xfe1\x00\xad\x01\xec\x02E\x03\x13\x03\x9b\x03o\x04\xce\x04\xa8\x04\xb2\x04\x1b\x05\xc0\x05\xd2\x04\xe8\x02z\x01\xbe\x00\xae\x00\x07\x00$\xff|\xff\x8e\x00\x13\x00\x10\xff\x98\xff0\x05{\x0b\x05\t\xaa\x03\x82\x01n\x03
blah blah~~
that function not return None
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:2.2.1
- Platform:ubuntu 18.04
- Python version:3.6.9
- PyArrow version:6.0.1
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https://github.com/huggingface/datasets/pull/4322
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CONTRIBUTOR
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[
"> Nice thank you ! This will be super useful :)\r\n> \r\n> Could you also add some tests in test_arrow_dataset.py and add an example of usage in the `Example:` section of the `train_test_split` docstring ?\r\n\r\nI will try to do it, is there any documentation for adding test cases? I have never done it before.",
"Thanks for the changes !\r\n\r\n> I will try to do it, is there any documentation for adding test cases? I have never done it before.\r\n\r\nYou can just add a function `test_train_test_split_startify` in `test_arrow_dataset.py`.\r\n\r\nIn this function you can define a dataset and make sure that `train_test_split` with the `stratify` argument works as expected.\r\n\r\nYou can do `pytest tests/test_arrow_dataset.py::test_train_test_split_startify` to run your test.\r\n\r\nFeel free to get some inspiration from other tests like `test_interleave_datasets` for example",
"I have added tests for stratified train_test_split in `test_arrow_dataset.py` file inside `test_train_test_split_startify` function. I have also added example usage with `stratify` arg in `Example:` section of the `train_test_split` docstring.\r\nResults of tests:\r\n```\r\n(data) nandwalritik@hp:~/datasets$ pytest tests/test_arrow_dataset.py::test_train_test_split_startify -W ignore\r\n============================================================================ test session starts ============================================================================\r\nplatform linux -- Python 3.9.5, pytest-7.1.2, pluggy-1.0.0\r\nrootdir: /home/nandwalritik/datasets\r\nplugins: datadir-1.3.1, forked-1.4.0, xdist-2.5.0\r\ncollected 1 item \r\n\r\ntests/test_arrow_dataset.py . [100%]\r\n\r\n============================================================================= 1 passed in 0.12s =============================================================================\r\n\r\n```",
"Thanks a lot !\r\n\r\n`utils/stratify.py` sounds good yes :)\r\n\r\nAlso feel free to merge `master` into your branch to fix the CI ;)",
"Added all the changes as were suggested and rebased with `main`.",
"_The documentation is not available anymore as the PR was closed or merged._",
"Hi, I encounter an error when I try to specify the stratify_by_column. However, I have a columns which specific the label of the row as a string. But an error showed when I try to do it. \"ValueError: Stratifying by column is only supported for ClassLabel column, and column code is Value.\".",
"Hi @Damon03 , you can change the type of your column to ClassLabel using\r\n```python\r\nds = ds.class_encode_column(column_name)\r\n```\r\nthen you'll be free to use `stratify` :)",
"Thank you so much. It worked."
] |
Added stratify option to train_test_split function.
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https://api.github.com/repos/huggingface/datasets/issues/4322/comments
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This PR adds `stratify` option to `train_test_split` method. I took reference from scikit-learn's `StratifiedShuffleSplit` class for implementing stratified split and integrated the changes as were suggested by @lhoestq.
It fixes #3452.
@lhoestq Please review and let me know, if any changes are required.
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CONTRIBUTOR
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[
"@lhoestq Thank you for the great feedback! Looks like all tests are passing now :)",
"_The documentation is not available anymore as the PR was closed or merged._"
] |
Adding dataset enwik8
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Because I regularly work with enwik8, I would like to contribute the dataset loader 🤗
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CONTRIBUTOR
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completed
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"Hi ! Thanks for reporting :)\r\n\r\nThis dataset was simply converted from [tensorflow datasets](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/summarization/multi_news.py)\r\n\r\nI think we can just remove the `.strip(\"- \")` and keep this character",
"Cool! I made a PR."
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Multi-news dataset loader attempts to strip wrong character from beginning of summaries
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## Describe the bug
The `multi_news.py` data loader has [a line which attempts to strip `"- "` from the beginning of summaries](https://github.com/huggingface/datasets/blob/aa743886221d76afb409d263e1b136e7a71fe2b4/datasets/multi_news/multi_news.py#L97). The actual character in the multi-news dataset, however, is `"– "`, which is different, e.g. `"– " != "- "`.
I would have just opened a PR to fix the mistake, but I am wondering what the motivation for stripping this character is? AFAICT most approaches just leave it in, e.g. the current SOTA on this dataset, [PRIMERA](https://huggingface.co/allenai/PRIMERA-multinews) (you can see its in the generated summaries of the model in their [example notebook](https://github.com/allenai/PRIMER/blob/main/Evaluation_Example.ipynb)).
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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Adding eval metadata for ade v2
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Adding metadata to allow evaluation
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Don't check f.loc in _get_extraction_protocol_with_magic_number
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`f.loc` doesn't always exist for file-like objects in python. I removed it since it was not necessary anyway (we always seek the file to 0 after reading the magic number)
Fix https://github.com/huggingface/datasets/issues/4310
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Fix cnn_dailymail (dm stories were ignored)
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https://github.com/huggingface/datasets/pull/4188 introduced a bug in `datasets` 2.2.0: DailyMail stories are ignored when generating the dataset.
I fixed that, and removed the google drive link (it has annoying quota limitations issues)
We can do a patch release after this is merged
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Support passing config_kwargs to CLI run_beam
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This PR supports passing `config_kwargs` to CLI run_beam, so that for example for "wikipedia" dataset, we can pass:
```
--date 20220501 --language ca
```
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Fix CLI run_beam namespace
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Currently, it raises TypeError:
```
TypeError: __init__() got an unexpected keyword argument 'namespace'
```
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Catch pull error when mirroring
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Catch pull errors when mirroring so that the script continues to update the other datasets.
The error will still be printed at the end of the job. In this case the job also fails, and asks to manually update the datasets that failed.
|
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Add API code examples for Builder classes
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This PR adds API code examples for the Builder classes.
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"Thanks for your contribution, @batubayk.\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nI would suggest you create this dataset there. Please, feel free to tell us if you need some help."
] |
added TR-News dataset
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"Merging this one since mario is off, I took care of adding some tests to make sure everything is fine. Will do the release after it"
] |
[Imagefolder] Docs + Don't infer labels from file names when there are metadata + Error messages when metadata and images aren't linked correctly
|
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2022-05-10T15:52:15Z
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https://api.github.com/repos/huggingface/datasets/issues/4311/comments
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I updated the `docs/source/image_process.mdx` documentation and added an example for image captioning and object detection using `ImageFolder`.
While doing so I also improved a few aspects:
- we don't need to infer labels from file names when there are metadata - they can just be in the metadata if necessary
- raise informative error messages when metadata and images aren't linked correctly:
- when an image is missing a metadata file
- when a metadata file is missing an image
I added some tests for these changes as well
cc @mariosasko
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Loading dataset with streaming: '_io.BufferedReader' object has no attribute 'loc'
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2022-05-10T15:12:53Z
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https://api.github.com/repos/huggingface/datasets/issues/4310/comments
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## Describe the bug
Loading a datasets with `load_dataset` and `streaming=True` returns `AttributeError: '_io.BufferedReader' object has no attribute 'loc'`. Notice that loading with `streaming=False` works fine.
In the following steps we load parquet files but the same happens with pickle files. The problem seems to come from `fsspec` lib, I put in the environment info also `s3fs` and `fsspec` versions since I'm loading from an s3 bucket.
## Steps to reproduce the bug
```python
from datasets import load_dataset
# path is the path to parquet files
data_files = {"train": path + "meta_train.parquet.gzip", "test": path + "meta_test.parquet.gzip"}
dataset = load_dataset("parquet", data_files=data_files, streaming=True)
```
## Expected results
A dataset object `datasets.dataset_dict.DatasetDict`
## Actual results
```
AttributeError Traceback (most recent call last)
<command-562086> in <module>
11
12 data_files = {"train": path + "meta_train.parquet.gzip", "test": path + "meta_test.parquet.gzip"}
---> 13 dataset = load_dataset("parquet", data_files=data_files, streaming=True)
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/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, **config_kwargs)
1679 if streaming:
1680 extend_dataset_builder_for_streaming(builder_instance, use_auth_token=use_auth_token)
-> 1681 return builder_instance.as_streaming_dataset(
1682 split=split,
1683 use_auth_token=use_auth_token,
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/builder.py in as_streaming_dataset(self, split, base_path, use_auth_token)
904 )
905 self._check_manual_download(dl_manager)
--> 906 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
907 # By default, return all splits
908 if split is None:
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/packaged_modules/parquet/parquet.py in _split_generators(self, dl_manager)
30 if not self.config.data_files:
31 raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}")
---> 32 data_files = dl_manager.download_and_extract(self.config.data_files)
33 if isinstance(data_files, (str, list, tuple)):
34 files = data_files
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in download_and_extract(self, url_or_urls)
798
799 def download_and_extract(self, url_or_urls):
--> 800 return self.extract(self.download(url_or_urls))
801
802 def iter_archive(self, urlpath_or_buf: Union[str, io.BufferedReader]) -> Iterable[Tuple]:
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in extract(self, path_or_paths)
776
777 def extract(self, path_or_paths):
--> 778 urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)
779 return urlpaths
780
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc)
312 num_proc = 1
313 if num_proc <= 1 or len(iterable) <= num_proc:
--> 314 mapped = [
315 _single_map_nested((function, obj, types, None, True, None))
316 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in <listcomp>(.0)
313 if num_proc <= 1 or len(iterable) <= num_proc:
314 mapped = [
--> 315 _single_map_nested((function, obj, types, None, True, None))
316 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
317 ]
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in _single_map_nested(args)
267 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
268 else:
--> 269 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
270 if isinstance(data_struct, list):
271 return mapped
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in <listcomp>(.0)
267 return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
268 else:
--> 269 mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]
270 if isinstance(data_struct, list):
271 return mapped
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/py_utils.py in _single_map_nested(args)
249 # Singleton first to spare some computation
250 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 251 return function(data_struct)
252
253 # Reduce logging to keep things readable in multiprocessing with tqdm
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _extract(self, urlpath)
781 def _extract(self, urlpath: str) -> str:
782 urlpath = str(urlpath)
--> 783 protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)
784 if protocol is None:
785 # no extraction
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _get_extraction_protocol(urlpath, use_auth_token)
371 urlpath, kwargs = urlpath, {}
372 with fsspec.open(urlpath, **kwargs) as f:
--> 373 return _get_extraction_protocol_with_magic_number(f)
374
375
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _get_extraction_protocol_with_magic_number(f)
335 def _get_extraction_protocol_with_magic_number(f) -> Optional[str]:
336 """read the magic number from a file-like object and return the compression protocol"""
--> 337 prev_loc = f.loc
338 magic_number = f.read(MAGIC_NUMBER_MAX_LENGTH)
339 f.seek(prev_loc)
/local_disk0/.ephemeral_nfs/envs/pythonEnv-a7e72260-221c-472b-85f4-bec801aee66d/lib/python3.8/site-packages/fsspec/implementations/local.py in __getattr__(self, item)
337
338 def __getattr__(self, item):
--> 339 return getattr(self.f, item)
340
341 def __enter__(self):
AttributeError: '_io.BufferedReader' object has no attribute 'loc'
```
## Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.4.0-1071-aws-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
- `fsspec` version: 2021.08.1
- `s3fs` version: 2021.08.1
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"_The documentation is not available anymore as the PR was closed or merged._",
"```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('./datasets/tedlium', 'release1', cache_dir='/home/sanchitgandhi/cache')\r\n```\r\n\r\n```\r\nDownloading and preparing dataset tedlium/release1 to /home/sanchitgandhi/cache/tedlium/release1/1.0.1/5a9fcb97b4b52d5a1c9dc7bde4b1d5994cd89c4a3425ea36c789bf6096fee4f0...\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\n File \"/home/sanchit_huggingface_co/datasets/src/datasets/load.py\", line 1703, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/sanchit_huggingface_co/datasets/src/datasets/builder.py\", line 605, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/sanchit_huggingface_co/datasets/src/datasets/builder.py\", line 1240, in _download_and_prepare\r\n raise MissingBeamOptions(\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n `load_dataset('tedlium', 'release1', beam_runner='DirectRunner')`\r\n```\r\nSpecifying the `beam_runner='DirectRunner'` works:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('./datasets/tedlium', 'release1', cache_dir='/home/sanchitgandhi/cache', beam_runner='DirectRunner')\r\n```",
"Extra Python imports/Linux packages:\r\n```\r\npip install pydub\r\nsudo apt install ffmpeg\r\n```",
"Script heavily inspired by the TF datasets script at: https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/audio/tedlium.py\r\n\r\nThe TF datasets script uses the module AudioSegment from the package `pydub` (https://github.com/jiaaro/pydub), which is used to to open the audio files (stored in .sph format):\r\nhttps://github.com/huggingface/datasets/blob/61bf6123634bf6e7c7287cd6097909eb26118c58/datasets/tedlium/tedlium.py#L167-L170\r\nThis package requires the pip install of `pydub` and the system installation of `ffmpeg`: https://github.com/jiaaro/pydub#installation\r\nIs it ok to use these packages? Or do we tend to avoid introducing additional dependencies?\r\n\r\nThe TF datasets script also uses `_build_pcollection`:\r\nhttps://github.com/huggingface/datasets/blob/8afbbb6fe66b40d05574e2e72e65e974c72ae769/datasets/tedlium/tedlium.py#L200-L206\r\nHowever, I was advised against using `beam` logic. Thus, I have reverted to generating the examples file-by-file: https://github.com/huggingface/datasets/blob/61bf6123634bf6e7c7287cd6097909eb26118c58/datasets/tedlium/tedlium.py#L112-L138\r\n\r\nI am now able to generate examples by running the `load_dataset` command:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('./datasets/tedlium', 'release1', cache_dir='/home/sanchitgandhi/cache')\r\n```\r\n\r\nHere, generating examples is **extremely** slow: it takes ~1 second per example, so ~60k seconds for the train set (~16 hours). Is there a way of paralleling this to make it faster?",
"> This package requires the pip install of pydub and the system installation of ffmpeg: https://github.com/jiaaro/pydub#installation\r\nIs it ok to use these packages? Or do we tend to avoid introducing additional dependencies?\r\n\r\nIt's ok, windows users will have have a bad time but I'm not sure we can do much about it.\r\n\r\n> Here, generating examples is extremely slow: it takes ~1 second per example, so ~60k seconds for the train set (~16 hours). Is there a way of paralleling this to make it faster?\r\n\r\nNot at the moment. For such cases we advise hosting the dataset ourselves in a processed format. The license doesn't allow this since the license is \"NoDerivatives\". Currently the only way to parallelize it is by keeping is as a beam dataset and let users pay Google Dataflow to process it (or use spark or whatever).",
"Thanks for your super speedy reply @lhoestq!\r\n\r\nI’ve uploaded the script and README.md to the org here: https://huggingface.co/datasets/LIUM/tedlium\r\nIs any modification of the script required to be able to use it from the Hub? When I run:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ntedlium = load_dataset(\"LIUM/tedlium\", \"release1\") # for Release 1\r\n```\r\nI get the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\nInput In [2], in <cell line: 1>()\r\n----> 1 load_dataset(\"LIUM/tedlium\", \"release1\")\r\n\r\nFile ~/datasets/src/datasets/load.py:1676, 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, **config_kwargs)\r\n 1673 ignore_verifications = ignore_verifications or save_infos\r\n 1675 # Create a dataset builder\r\n-> 1676 builder_instance = load_dataset_builder(\r\n 1677 path=path,\r\n 1678 name=name,\r\n 1679 data_dir=data_dir,\r\n 1680 data_files=data_files,\r\n 1681 cache_dir=cache_dir,\r\n 1682 features=features,\r\n 1683 download_config=download_config,\r\n 1684 download_mode=download_mode,\r\n 1685 revision=revision,\r\n 1686 use_auth_token=use_auth_token,\r\n 1687 **config_kwargs,\r\n 1688 )\r\n 1690 # Return iterable dataset in case of streaming\r\n 1691 if streaming:\r\n\r\nFile ~/datasets/src/datasets/load.py:1502, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)\r\n 1500 download_config = download_config.copy() if download_config else DownloadConfig()\r\n 1501 download_config.use_auth_token = use_auth_token\r\n-> 1502 dataset_module = dataset_module_factory(\r\n 1503 path,\r\n 1504 revision=revision,\r\n 1505 download_config=download_config,\r\n 1506 download_mode=download_mode,\r\n 1507 data_dir=data_dir,\r\n 1508 data_files=data_files,\r\n 1509 )\r\n 1511 # Get dataset builder class from the processing script\r\n 1512 builder_cls = import_main_class(dataset_module.module_path)\r\n\r\nFile ~/datasets/src/datasets/load.py:1254, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)\r\n 1249 if isinstance(e1, FileNotFoundError):\r\n 1250 raise FileNotFoundError(\r\n 1251 f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n 1252 f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n 1253 ) from None\r\n-> 1254 raise e1 from None\r\n 1255 else:\r\n 1256 raise FileNotFoundError(\r\n 1257 f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory.\"\r\n 1258 )\r\n\r\nFile ~/datasets/src/datasets/load.py:1227, in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)\r\n 1225 raise e\r\n 1226 if filename in [sibling.rfilename for sibling in dataset_info.siblings]:\r\n-> 1227 return HubDatasetModuleFactoryWithScript(\r\n 1228 path,\r\n 1229 revision=revision,\r\n 1230 download_config=download_config,\r\n 1231 download_mode=download_mode,\r\n 1232 dynamic_modules_path=dynamic_modules_path,\r\n 1233 ).get_module()\r\n 1234 else:\r\n 1235 return HubDatasetModuleFactoryWithoutScript(\r\n 1236 path,\r\n 1237 revision=revision,\r\n (...)\r\n 1241 download_mode=download_mode,\r\n 1242 ).get_module()\r\n\r\nFile ~/datasets/src/datasets/load.py:940, in HubDatasetModuleFactoryWithScript.get_module(self)\r\n 938 def get_module(self) -> DatasetModule:\r\n 939 # get script and other files\r\n--> 940 local_path = self.download_loading_script()\r\n 941 dataset_infos_path = self.download_dataset_infos_file()\r\n 942 imports = get_imports(local_path)\r\n\r\nFile ~/datasets/src/datasets/load.py:918, in HubDatasetModuleFactoryWithScript.download_loading_script(self)\r\n 917 def download_loading_script(self) -> str:\r\n--> 918 file_path = hf_hub_url(path=self.name, name=self.name.split(\"/\")[1] + \".py\", revision=self.revision)\r\n 919 download_config = self.download_config.copy()\r\n 920 if download_config.download_desc is None:\r\n\r\nTypeError: hf_hub_url() got an unexpected keyword argument 'name'\r\n```\r\n\r\nNote that I am able to load the dataset from the `datasets` repo with the following lines of code:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('./datasets/tedlium', 'release1', cache_dir='/home/sanchitgandhi/cache')\r\n```",
"What version of `datasets` do you have ?\r\nUpdating `datasets` should fix the error ;)\r\n",
"> This package requires the pip install of pydub and the system installation of ffmpeg: https://github.com/jiaaro/pydub#installation\r\nIs it ok to use these packages? Or do we tend to avoid introducing additional dependencies?\r\n\r\n`soundfile`, which is a required audio dependency, should also work with `.sph` files, no?",
"> `soundfile`, which is a required audio dependency, should also work with `.sph` files, no?\r\n\r\nAwesome, thanks for the pointer @mariosasko! Switched `pydub` to `soundfile`, and having specifying the `dtype` argument in `soundfile.read` as `np.int16`, the arrays match with those from `pydub` ✅\r\n\r\nI also did some heavy optimising of the script with the processing of the `.stm` and `.sph` files - it now runs 2000x faster than before, so there probably isn't a need to upload the data to the Hub @lhoestq. The total processing time is just ~2mins now 🚀\r\n",
"TEDLIUM completed and uploaded to the HF Hub: https://huggingface.co/datasets/LIUM/tedlium",
"Awesome !"
] |
[WIP] Add TEDLIUM dataset
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Adds the TED-LIUM dataset https://www.tensorflow.org/datasets/catalog/tedlium#tedliumrelease3
TODO:
- [x] Port `tedium.py` from TF datasets using `convert_dataset.sh` script
- [x] Make `load_dataset` work
- [ ] ~~Run `datasets-cli` command to generate `dataset_infos.json`~~
- [ ] ~~Create dummy data for continuous testing~~
- [ ] ~~Dummy data tests~~
- [ ] ~~Real data tests~~
- [ ] Create the metadata JSON
- [ ] Close PR and add directly to the Hub under LIUM org
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Remove unused multiprocessing args from test CLI
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Multiprocessing is not used in the test CLI.
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Add the packaged builders configurations to the docs reference is useful to show the list of all parameters one can use when loading data in many formats: CSV, JSON, etc.
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`load_dataset` does not work with certain filename.
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## Describe the bug
This is a weird bug that took me some time to find out.
I have a JSON dataset that I want to load with `load_dataset` like this:
```
data_files = dict(train="train.json.zip", val="val.json.zip")
dataset = load_dataset("json", data_files=data_files, field="data")
```
## Expected results
No error.
## Actual results
The val file is loaded as expected, but the train file throws JSON decoding error:
```
╭──────────────────────────── Traceback (most recent call last) ────────────────────────────╮
│ <ipython-input-74-97947e92c100>:5 in <module> │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/load.py:1687 in │
│ load_dataset │
│ │
│ 1684 │ try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES │
│ 1685 │ │
│ 1686 │ # Download and prepare data │
│ ❱ 1687 │ builder_instance.download_and_prepare( │
│ 1688 │ │ download_config=download_config, │
│ 1689 │ │ download_mode=download_mode, │
│ 1690 │ │ ignore_verifications=ignore_verifications, │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:605 in │
│ download_and_prepare │
│ │
│ 602 │ │ │ │ │ │ except ConnectionError: │
│ 603 │ │ │ │ │ │ │ logger.warning("HF google storage unreachable. Downloa │
│ 604 │ │ │ │ │ if not downloaded_from_gcs: │
│ ❱ 605 │ │ │ │ │ │ self._download_and_prepare( │
│ 606 │ │ │ │ │ │ │ dl_manager=dl_manager, verify_infos=verify_infos, **do │
│ 607 │ │ │ │ │ │ ) │
│ 608 │ │ │ │ │ # Sync info │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:694 in │
│ _download_and_prepare │
│ │
│ 691 │ │ │ │
│ 692 │ │ │ try: │
│ 693 │ │ │ │ # Prepare split will record examples associated to the split │
│ ❱ 694 │ │ │ │ self._prepare_split(split_generator, **prepare_split_kwargs) │
│ 695 │ │ │ except OSError as e: │
│ 696 │ │ │ │ raise OSError( │
│ 697 │ │ │ │ │ "Cannot find data file. " │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:1151 in │
│ _prepare_split │
│ │
│ 1148 │ │ │
│ 1149 │ │ generator = self._generate_tables(**split_generator.gen_kwargs) │
│ 1150 │ │ with ArrowWriter(features=self.info.features, path=fpath) as writer: │
│ ❱ 1151 │ │ │ for key, table in logging.tqdm( │
│ 1152 │ │ │ │ generator, unit=" tables", leave=False, disable=True # not loggin │
│ 1153 │ │ │ ): │
│ 1154 │ │ │ │ writer.write_table(table) │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/tqdm/notebook.py:257 in │
│ __iter__ │
│ │
│ 254 │ │
│ 255 │ def __iter__(self): │
│ 256 │ │ try: │
│ ❱ 257 │ │ │ for obj in super(tqdm_notebook, self).__iter__(): │
│ 258 │ │ │ │ # return super(tqdm...) will not catch exception │
│ 259 │ │ │ │ yield obj │
│ 260 │ │ # NB: except ... [ as ...] breaks IPython async KeyboardInterrupt │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/tqdm/std.py:1183 in │
│ __iter__ │
│ │
│ 1180 │ │ # If the bar is disabled, then just walk the iterable │
│ 1181 │ │ # (note: keep this check outside the loop for performance) │
│ 1182 │ │ if self.disable: │
│ ❱ 1183 │ │ │ for obj in iterable: │
│ 1184 │ │ │ │ yield obj │
│ 1185 │ │ │ return │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/packaged_modules/j │
│ son/json.py:90 in _generate_tables │
│ │
│ 87 │ │ │ # If the file is one json object and if we need to look at the list of │
│ 88 │ │ │ if self.config.field is not None: │
│ 89 │ │ │ │ with open(file, encoding="utf-8") as f: │
│ ❱ 90 │ │ │ │ │ dataset = json.load(f) │
│ 91 │ │ │ │ │
│ 92 │ │ │ │ # We keep only the field we are interested in │
│ 93 │ │ │ │ dataset = dataset[self.config.field] │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/__init__.py:293 in load │
│ │
│ 290 │ To use a custom ``JSONDecoder`` subclass, specify it with the ``cls`` │
│ 291 │ kwarg; otherwise ``JSONDecoder`` is used. │
│ 292 │ """ │
│ ❱ 293 │ return loads(fp.read(), │
│ 294 │ │ cls=cls, object_hook=object_hook, │
│ 295 │ │ parse_float=parse_float, parse_int=parse_int, │
│ 296 │ │ parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw) │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/__init__.py:357 in loads │
│ │
│ 354 │ if (cls is None and object_hook is None and │
│ 355 │ │ │ parse_int is None and parse_float is None and │
│ 356 │ │ │ parse_constant is None and object_pairs_hook is None and not kw): │
│ ❱ 357 │ │ return _default_decoder.decode(s) │
│ 358 │ if cls is None: │
│ 359 │ │ cls = JSONDecoder │
│ 360 │ if object_hook is not None: │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/decoder.py:337 in decode │
│ │
│ 334 │ │ containing a JSON document). │
│ 335 │ │ │
│ 336 │ │ """ │
│ ❱ 337 │ │ obj, end = self.raw_decode(s, idx=_w(s, 0).end()) │
│ 338 │ │ end = _w(s, end).end() │
│ 339 │ │ if end != len(s): │
│ 340 │ │ │ raise JSONDecodeError("Extra data", s, end) │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/decoder.py:353 in raw_decode │
│ │
│ 350 │ │ │
│ 351 │ │ """ │
│ 352 │ │ try: │
│ ❱ 353 │ │ │ obj, end = self.scan_once(s, idx) │
│ 354 │ │ except StopIteration as err: │
│ 355 │ │ │ raise JSONDecodeError("Expecting value", s, err.value) from None │
│ 356 │ │ return obj, end │
╰───────────────────────────────────────────────────────────────────────────────────────────╯
JSONDecodeError: Unterminated string starting at: line 85 column 20 (char 60051)
```
However, when I rename the `train.json.zip` to other names (like `training.json.zip`, or even to `train.json`), everything works fine; when I unzip the file to `train.json`, it works as well.
## Environment info
```
- `datasets` version: 2.1.0
- Platform: Linux-4.4.0-131-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
```
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[] |
https://github.com/huggingface/datasets/pull/4305
|
CONTRIBUTOR
| null | false | null |
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4305). All of your documentation changes will be reflected on that endpoint.",
"> predictions and references are swapped. Basically Frugalscore is commutative, however some tiny differences can occur if we swap the references and the predictions. I decided to swap them just to obtain the exact results as reported in the paper.\r\n\r\nWhat is the order of magnitude of the difference ? Do you know what causes this ?\r\n\r\n> I switched to dynamic padding that was was used in the training, forcing the padding to max_length introduces errors for some reason that I ignore.\r\n\r\nWhat error ?"
] |
Fixes FrugalScore
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2022-05-10T12:44:06Z
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https://api.github.com/repos/huggingface/datasets/issues/4305/comments
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There are two minor modifications in this PR:
1) `predictions` and `references` are swapped. Basically Frugalscore is commutative, however some tiny differences can occur if we swap the references and the predictions. I decided to swap them just to obtain the exact results as reported in the paper.
2) I switched to dynamic padding that was was used in the training, forcing the padding to `max_length` introduces errors for some reason that I ignore.
@lhoestq
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2022-05-10T12:38:42Z
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[] |
https://github.com/huggingface/datasets/issues/4304
|
CONTRIBUTOR
| null | null | null |
[
"Thanks for reporting ! I forwarded the issue to the front-end team :)\r\n\r\nWill keep you posted !\r\n\r\nI also changed the tagging app to suggest two letters code for now."
] |
Language code search does direct matches
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2022-05-10T11:59:16Z
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https://api.github.com/repos/huggingface/datasets/issues/4304/comments
|
## Describe the bug
Hi. Searching for bcp47 tags that are just the language prefix (e.g. `sq` or `da`) excludes datasets that have added extra information in their language metadata (e.g. `sq-AL` or `da-bornholm`). The example codes given in the [tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging) encourages addition of the additional codes ("_expected format is BCP47 tags separated for ';' e.g. 'en-US;fr-FR'_") but this would lead to those datasets being hidden in datasets search.
## Steps to reproduce the bug
1. Add a dataset using a variant tag (e.g. [`sq-AL`](https://huggingface.co/datasets?languages=languages:sq-AL))
2. Look for datasets using the full code
3. Note that they're missing when just the language is searched for (e.g. [`sq`](https://huggingface.co/datasets?languages=languages:sq))
Some datasets are already affected by this - e.g. `AmazonScience/massive` is listed under `sq-AL` but not `sq`.
One workaround is for dataset creators to add an additional root language tag to dataset YAML metadata, but it's unclear how to communicate this. It might be possible to index the search on `languagecode.split('-')[0]` but I wanted to float this issue before trying to write any code :)
## Expected results
Datasets using longer bcp47 tags also appear under searches for just the language code; e.g. Quebecois datasets (`fr-CA`) would come up when looking for French datasets with no region specification (`fr`), or US English (`en-US`) datasets would come up when searching for English datasets (`en`).
## Actual results
The language codes seem to be directly string matched, excluding datasets with specific language tags from non-specific searches.
## Environment info
(web app)
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https://github.com/huggingface/datasets/pull/4303
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"The CI failure is unrelated to this PR and fixed on master, merging :)"
] |
Fix: Add missing comma
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"The Hub doesn't allow these characters in the YAML tags, and git push fails if you want to push a dataset card containing these characters.",
"Ok, let me rename the bad config names :) I think I can also keep backward compatibility with a warning",
"Almost done with it btw, will submit a PR that shows all the configuration name changes (from a bit more than 20 datasets)",
"Please, let me know when the renaming of configs is done. If not enough bandwidth, I can take care of it...",
"Will focus on this this afternoon ;)",
"I realized when renaming all the configurations with dots in https://github.com/huggingface/datasets/pull/4365 that it's not ideal for certain cases. For example:\r\n- many configurations have a version like \"1.0.0\" in their names\r\n- to avoid breaking changes we need to replace dots with underscores in the user input and show a warning, which hurts the experience\r\n- our second most downloaded dataset at the moment is affected: `newsgroup`\r\n- if we disallow dots, then we'll never be able to make the [allenai/c4](https://huggingface.co/datasets/allenai/c4) work with its different configurations since they contain dots, and we can't rename them because they are the official download links\r\n\r\nI was thinking of other alternatives:\r\n1. just stop separating tags per config name completely, and have a single flat YAML for all configurations. Dataset search doesn't use this info anyway\r\n2. use another YAML structure to avoid having config names as keys, such as\r\n```yaml\r\nlanguages:\r\n- config: 20220301_en\r\n values:\r\n - en\r\n```\r\n\r\nI'm down for 1, to keep things simple",
"@lhoestq I agree:\r\n- better not changing config names (so that we do not introduce any braking change)\r\n- therefore, we should not use them as keys\r\n\r\nIn relation with the proposed solutions, I have no strong opinion:\r\n- option 1 is simpler and aligns better with current usage on the Hub (configs are ignored)\r\n- however:\r\n - we will lose all the information per config we already have (for those datasets containing config keys; contributors made an effort to put that information per config)\r\n - and this information might be useful on the Hub in the future, in case we would like to enrich the search feature with more granularity; this is only applicable if this feature could eventually make sense\r\n\r\nSo, no strong opinion...",
"Closing in favor of https://github.com/huggingface/datasets/pull/4367"
] |
Remove hacking license tags when mirroring datasets on the Hub
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2022-05-10T05:52:46Z
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https://api.github.com/repos/huggingface/datasets/issues/4302/comments
|
Currently, when mirroring datasets on the Hub, the license tags are hacked: removed of characters "." and "$". On the contrary, this hacking is not applied to community datasets on the Hub. This generates multiple variants of the same tag on the Hub.
I guess this hacking is no longer necessary:
- it is not applied to community datasets
- all canonical datasets are validated by maintainers before being merged: CI + maintainers make sure license tags are the right ones
Fix #4298.
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"_The documentation is not available anymore as the PR was closed or merged._",
"I think you can go ahead with uploading the data, and also ping the author in parallel. I think the images may subject to copyright anyway (scrapped from google image) so the dataset author is not allowed to set a license to the data.\r\n\r\nI think it's fine to upload the dataset as soon as we mention explicitly that the images may be subject to copyright."
] |
Add ImageNet-Sketch dataset
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This PR adds the ImageNet-Sketch dataset and resolves #3953 .
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Add API code examples for loading methods
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This PR adds API code examples for loading methods, let me know if I've missed any important parameters we should showcase :)
I was a bit confused about `inspect_dataset` and `inspect_metric`. The `path` parameter says it will accept a dataset identifier from the Hub. But when I try the identifier `rotten_tomatoes`, it gives me:
```py
from datasets import inspect_dataset
inspect_dataset('rotten_tomatoes', local_path='/content/rotten_tomatoes')
FileNotFoundError: Couldn't find a dataset script at /content/rotten_tomatoes/rotten_tomatoes.py or any data file in the same directory.
```
Does the user need to have an existing copy of `rotten_tomatoes.py` on their local drive (in which case, it seems like the same option as the first option in `path`)?
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COLLABORATOR
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for the reviews @apsdehal and @lhoestq! As suggested by @lhoestq, I'll separate the train/val/test splits, apply the validation split fixes and shuffle the images files to simplify the script and make streaming faster.",
"@apsdehal I dismissed your review as it's no longer relevant after the data files changes suggested by @lhoestq. "
] |
Remove manual download from imagenet-1k
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2022-05-09T20:49:18Z
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Remove the manual download code from `imagenet-1k` to make it a regular dataset.
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completed
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"we'll add the same server-side metadata validation system as for hf.co/models soon-ish\r\n\r\n(you can check on hf.co/models that licenses are \"clean\")",
"Fixed by #4367."
] |
Normalise license names
|
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**Is your feature request related to a problem? Please describe.**
When browsing datasets, the Licenses tag cloud (bottom left of e.g. https://huggingface.co/datasets) has multiple variants of the same license. This means the options exclude datasets arbitrarily, giving users artificially low recall. The cause of the dupes is probably due to a bit of variation in metadata.
**Describe the solution you'd like**
I'd like the licenses in metadata to follow the same standard as much as possible, to remove this problem. I'd like to go ahead and normalise the dataset metadata to follow the format & values given in [src/datasets/utils/resources/licenses.json](https://github.com/huggingface/datasets/blob/master/src/datasets/utils/resources/licenses.json) .
**Describe alternatives you've considered**
None
**Additional context**
None
**Priority**
Low
|
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"Thanks for reporting, fixing it now",
"It's up again :)"
] |
Datasets YAML tagging space is down
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## Describe the bug
The neat hf spaces app for generating YAML tags for dataset `README.md`s is down
## Steps to reproduce the bug
1. Visit https://huggingface.co/spaces/huggingface/datasets-tagging
## Expected results
There'll be a HF spaces web app for generating dataset metadata YAML
## Actual results
There's an error message; here's the step where it breaks:
```
Step 18/29 : RUN pip install -r requirements.txt
---> Running in e88bfe7e7e0c
Defaulting to user installation because normal site-packages is not writeable
Collecting git+https://github.com/huggingface/datasets.git@update-task-list (from -r requirements.txt (line 4))
Cloning https://github.com/huggingface/datasets.git (to revision update-task-list) to /tmp/pip-req-build-bm8t0r0k
Running command git clone --filter=blob:none --quiet https://github.com/huggingface/datasets.git /tmp/pip-req-build-bm8t0r0k
WARNING: Did not find branch or tag 'update-task-list', assuming revision or ref.
Running command git checkout -q update-task-list
error: pathspec 'update-task-list' did not match any file(s) known to git
error: subprocess-exited-with-error
× git checkout -q update-task-list did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× git checkout -q update-task-list did not run successfully.
│ exit code: 1
╰─> See above for output.
```
## Environment info
- Platform: Linux / Brave
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Fix URL query parameters in compression hop path when streaming
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Fix #3488.
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Fix missing lz4 dependency for tests
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Currently, `lz4` is not defined as a dependency for tests. Therefore, all tests marked with `@require_lz4` are skipped.
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Fix CLI run_beam save_infos
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Currently, it raises TypeError:
```
TypeError: _download_and_prepare() got an unexpected keyword argument 'save_infos'
```
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Fix wrong map parameter name in cache docs
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The `load_from_cache` parameter of `map` should be `load_from_cache_file`.
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This PR adds API code examples for the remaining functions in the Main classes. I wasn't too familiar with some of the functions (`decode_batch`, `decode_column`, `decode_example`, etc.) so please feel free to add an example of usage and I can fill in the rest :)
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"Hi @leondz, thanks for reporting.\r\n\r\nIndeed, the dataset viewer relies on the dataset being streamable (passing `streaming=True` to `load_dataset`). Whereas most of the datastes are streamable out of the box (thanks to our implementation of streaming), there are still some exceptions.\r\n\r\nIn particular, in your case, that is due to the data file being TAR. This format is not streamable out of the box (it does not allow random access to the archived files), but we use a trick to allow streaming: using `dl_manager.iter_archive`.\r\n\r\nLet me know if you need some help: I could push a commit to your repo with the fix.",
"Ah, right! The preview is working now, but this explanation is good to know, thank you. I'll prefer formats with random file access supported in datasets.utils.extract in future, and try out this fix for the tarfiles :)"
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Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message
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### Link
https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train
### Description
The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss?
### Owner
Yes
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Update paper link in medmcqa dataset card
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Updating readme in medmcqa dataset.
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Add missing `faiss` import to fix https://github.com/huggingface/datasets/issues/4287
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2022-05-05T15:21:49Z
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https://api.github.com/repos/huggingface/datasets/issues/4288/comments
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This PR fixes the issue recently mentioned in https://github.com/huggingface/datasets/issues/4287 🤗
|
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https://github.com/huggingface/datasets/issues/4287
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MEMBER
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completed
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[
"So I managed to solve this by adding a missing `import faiss` in the `@staticmethod` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L305, triggered from https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L249 when trying to `ds_with_embeddings.add_faiss_index(column='embeddings', device=0)` with the code above.\r\n\r\nAs it seems that the `@staticmethod` doesn't recognize the `import faiss` defined in https://github.com/huggingface/datasets/blob/f51b6994db27ea69261ef919fb7775928f9ec10b/src/datasets/search.py#L261, so whenever the value of `device` is not None in https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L438, that exception is triggered.\r\n\r\nSo on, adding `import faiss` inside https://github.com/huggingface/datasets/blob/71f76e0bdeaddadedc4f9c8d15cfff5a36d62f66/src/datasets/search.py#L305 right after the check of `device`'s value, solves the issue and lets you calculate the indices in GPU.\r\n\r\nI'll add the code in a PR linked to this issue in case you want to merge it!",
"Adding here the complete error traceback!\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/alvarobartt/lol.py\", line 12, in <module>\r\n ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None`\r\n File \"/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 3656, in add_faiss_index\r\n super().add_faiss_index(\r\n File \"/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py\", line 478, in add_faiss_index\r\n faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=True)\r\n File \"/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py\", line 281, in add_vectors\r\n self.faiss_index = self._faiss_index_to_device(index, self.device)\r\n File \"/home/alvarobartt/.local/lib/python3.9/site-packages/datasets/search.py\", line 327, in _faiss_index_to_device\r\n faiss_res = faiss.StandardGpuResources()\r\nNameError: name 'faiss' is not defined\r\n```",
"Closed as https://github.com/huggingface/datasets/pull/4288 already merged! :hugs:"
] |
"NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None
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I_kwDODunzps5JH5V8
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2022-05-05T15:09:45Z
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https://api.github.com/repos/huggingface/datasets/issues/4287/comments
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## Describe the bug
When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception.
All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
import torch
torch.set_grad_enabled(False)
ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
from datasets import load_dataset
ds = load_dataset('crime_and_punish', split='train[:100]')
ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()})
ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None`
```
## Expected results
A new column named `embeddings` in the dataset that we're adding the index to.
## Actual results
An exception is triggered with the following message `NameError: name 'faiss' is not defined`.
## Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
Add Lahnda language tag
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2022-05-05T14:34:20Z
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https://api.github.com/repos/huggingface/datasets/issues/4286/comments
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This language is present in [Wikimedia's WIT](https://huggingface.co/datasets/wikimedia/wit_base) dataset.
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Update LexGLUE README.md
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2022-05-05T08:36:50Z
|
https://api.github.com/repos/huggingface/datasets/issues/4285/comments
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Update the leaderboard based on the latest results presented in the ACL 2022 version of the article.
|
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[] |
https://github.com/huggingface/datasets/issues/4284
|
NONE
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completed
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[
"Hi ! `datasets` doesn't load the dataset in memory. Instead it uses memory mapping to load your dataset from your disk (it is stored as arrow files). Do you know at what point you have RAM issues exactly ?\r\n\r\nHow big are your graph_data_train dictionaries btw ?",
"Closing this issue due to inactivity."
] |
Issues in processing very large datasets
|
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I_kwDODunzps5JFlaX
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2022-05-05T05:01:09Z
|
https://api.github.com/repos/huggingface/datasets/issues/4284/comments
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## Describe the bug
I'm trying to add a feature called "subgraph" to CNN/DM dataset (modifications on run_summarization.py of Huggingface Transformers script) --- I'm not quite sure if I'm doing it the right way, though--- but the main problem appears when the training starts where the error ` [OSError: [Errno 12] Cannot allocate memory]` appears. I suppose this problem roots in RAM issues and how the dataset is loaded during training, but I have no clue of what I can do to fix it. Observing the dataset's cache directory, I see that it takes ~600GB of memory and that's why I believe special care is needed when loading it into the memory.
Here are my modifications to `run_summarization.py` code.
```
# loading pre-computed dictionary where keys are 'id' of article and values are corresponding subgraph
graph_data_train = get_graph_data('train')
graph_data_validation = get_graph_data('val')
...
...
with training_args.main_process_first(desc="train dataset map pre-processing"):
train_dataset = train_dataset.map(
preprocess_function_train,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
```
And here is the modified preprocessed function:
```
def preprocess_function_train(examples):
inputs, targets, sub_graphs, ids = [], [], [], []
for i in range(len(examples[text_column])):
if examples[text_column][i] is not None and examples[summary_column][i] is not None:
# if examples['doc_id'][i] in graph_data.keys():
inputs.append(examples[text_column][i])
targets.append(examples[summary_column][i])
sub_graphs.append(graph_data_train[examples['id'][i]])
ids.append(examples['id'][i])
inputs = [prefix + inp for inp in inputs]
model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True,
sub_graphs=sub_graphs, ids=ids)
# Setup the tokenizer for targets
with tokenizer.as_target_tokenizer():
labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True)
# If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
# padding in the loss.
if padding == "max_length" and data_args.ignore_pad_token_for_loss:
labels["input_ids"] = [
[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
]
model_inputs["labels"] = labels["input_ids"]
return model_inputs
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux Ubuntu
- Python version: 3.6
- PyArrow version: 6.0.1
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
Fix filesystem docstring
|
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2022-05-04T17:42:42Z
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This PR untangles the `S3FileSystem` docstring so the [parameters](https://huggingface.co/docs/datasets/master/en/package_reference/main_classes#parameters) are properly displayed.
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"Right ! Good catch, thanks, I updated the message to say \"will raise an error in a future major version\""
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Don't do unnecessary list type casting to avoid replacing None values by empty lists
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2022-05-04T16:37:01Z
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In certain cases, `None` values are replaced by empty lists when casting feature types.
It happens every time you cast an array of nested lists like [None, [0, 1, 2, 3]] to a different type (to change the integer precision for example). In this case you'd get [[], [0, 1, 2, 3]] for example. This issue comes from PyArrow, see the discussion in https://github.com/huggingface/datasets/issues/3676
This issue also happens when no type casting is needed, because casting is supposed to be a no-op in this case. But as https://github.com/huggingface/datasets/issues/3676 shown, it's not the case and `None` are replaced by empty lists even if we cast to the exact same type.
In this PR I just workaround this bug in the case where no type casting is needed. In particular, I only call `pa.ListArray.from_arrays` only when necessary.
I also added a warning when some `None` are effectively replaced by empty lists. I wanted to raise an error in this case, but maybe we should wait a major update to do so
This PR fixes this particular case, that is occurring in `run_qa.py` in `transformers`:
```python
from datasets import Dataset
ds = Dataset.from_dict({"a": range(4)})
ds = ds.map(lambda x: {"b": [[None, [0]]]}, batched=True, batch_size=1, remove_columns=["a"])
print(ds.to_pandas())
# before:
# b
# 0 [None, [0]]
# 1 [[], [0]]
# 2 [[], [0]]
# 3 [[], [0]]
#
# now:
# b
# 0 [None, [0]]
# 1 [None, [0]]
# 2 [None, [0]]
# 3 [None, [0]]
```
cc @sgugger
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Remove a copy-paste sentence in dataset cards
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Remove the following copy-paste sentence from dataset cards:
```
We show detailed information for up to 5 configurations of the dataset.
```
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Add missing features to commonsense_qa dataset
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Fix partially #4275.
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Update minimal PyArrow version warning
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Update the minimal PyArrow version warning (should've been part of #4250).
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Add missing features to openbookqa dataset for additional config
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Fix partially #4276.
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Enable label alignment for token classification datasets
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2022-05-04T07:15:16Z
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This PR extends the `Dataset.align_labels_with_mapping()` method to support alignment of label mappings between datasets and models for token classification (e.g. NER).
Example of usage:
```python
from datasets import load_dataset
ner_ds = load_dataset("conll2003", split="train")
# returns [3, 0, 7, 0, 0, 0, 7, 0, 0]
ner_ds[0]["ner_tags"]
# hypothetical model mapping with O <--> B-LOC
label2id = {
"B-LOC": "0",
"B-MISC": "7",
"B-ORG": "3",
"B-PER": "1",
"I-LOC": "6",
"I-MISC": "8",
"I-ORG": "4",
"I-PER": "2",
"O": "5"
}
ner_aligned_ds = ner_ds.align_labels_with_mapping(label2id, "ner_tags")
# returns [3, 5, 7, 5, 5, 5, 7, 5, 5]
ner_aligned_ds[0]["ner_tags"]
```
Context: we need this in AutoTrain to automatically align datasets / models during evaluation. cc @abhishekkrthakur
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CONTRIBUTOR
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"Thanks for reporting, @vblagoje.\r\n\r\nIndeed, I noticed some of these issues while reviewing this PR:\r\n- #4259 \r\n\r\nThis is in my TODO list. ",
"Ok, awesome @albertvillanova How about #4275 ?",
"On the other hand, I am not sure if we should always preserve the original nested structure. I think we should also consider other factors as convenience or consistency.\r\n\r\nFor example, other datasets also flatten \"question.stem\" into \"question\":\r\n- ai2_arc:\r\n ```python\r\n question = data[\"question\"][\"stem\"]\r\n choices = data[\"question\"][\"choices\"]\r\n text_choices = [choice[\"text\"] for choice in choices]\r\n label_choices = [choice[\"label\"] for choice in choices]\r\n yield id_, {\r\n \"id\": id_,\r\n \"answerKey\": answerkey,\r\n \"question\": question,\r\n \"choices\": {\"text\": text_choices, \"label\": label_choices},\r\n }\r\n ```\r\n- commonsense_qa:\r\n ```python\r\n question = data[\"question\"]\r\n stem = question[\"stem\"]\r\n yield id_, {\r\n \"answerKey\": answerkey,\r\n \"question\": stem,\r\n \"choices\": {\"label\": labels, \"text\": texts},\r\n }\r\n ```\r\n- cos_e:\r\n ```python\r\n \"question\": cqa[\"question\"][\"stem\"],\r\n ```\r\n- qasc\r\n- quartz\r\n- wiqa\r\n\r\nExceptions:\r\n- exams\r\n\r\nI think we should agree on a CONVENIENT format for QA and use always CONSISTENTLY the same.",
"@albertvillanova I agree that we should be consistent. In the last month, I have come across tons of code that deals with OpenBookQA and CommonSenseQA and all of that code relies on the original data format structure. We can't expect users to adopt HF Datasets if we arbitrarily change the structure of the format just because we think something makes more sense. I am in that position now (downloading original data rather than using HF Datasets) and undoubtedly it hinders HF Datasets' widespread use and adoption. Missing fields like in the case of #4275 is definitely bad and not even up for a discussion IMHO! cc @lhoestq ",
"I'm opening a PR that adds the missing fields.\r\n\r\nLet's agree on the feature structure: @lhoestq @mariosasko @polinaeterna ",
"IMO we should always try to preserve the original structure unless there is a good reason not to (and I don't see one in this case).",
"I agree with @mariosasko . The transition to the original format could be done in one PR for the next minor release, clearly documenting all dataset changes just as @albertvillanova outlined them above and perhaps even providing a per dataset util method to convert the new valid format to the old for backward compatibility. Users who relied on the old format will update their code with either the util method for a quick fix or slightly more elaborate for the new. ",
"I don't have a strong opinion on this, besides the fact that whatever decision we agree on, should be applied to all datasets.\r\n\r\nThere is always the tension between:\r\n- preserving each dataset original structure (which has the advantage of not forcing users to learn other structure for the same dataset),\r\n- and on the other hand performing some kind of standardization/harmonization depending on the task (this has the advantage that once learnt, the same structure applies to all datasets; this has been done for e.g. POS tagging: all datasets have been adapted to a certain \"standard\" structure).\r\n - Another advantage: datasets can easily be interchanged (or joined) to be used by the same model\r\n\r\nRecently, in the BigScience BioMedical hackathon, they adopted a different approach:\r\n- they implement a \"source\" config, respecting the original structure as much as possible\r\n- they implement additional config for each task, with a \"standard\" nested structure per task, which is most useful for users.",
"@albertvillanova, thanks for the detailed answer and the new perspectives. I understand the friction for the best design approach much better now. Ultimately, it is essential to include all the missing fields and the correct data first. Whatever approach is determined to be optimal is important but not as crucial once all the data is there, and users can create lambda functions to create whatever structure serves them best. ",
"Datasets are not tracked in this repository anymore. I think we must move this thread to the [discussions tab of the dataset](https://huggingface.co/datasets/openbookqa/discussions)",
"Indeed @osbm thanks. I'm closing this issue if it's fine for you all then"
] |
OpenBookQA has missing and inconsistent field names
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https://api.github.com/repos/huggingface/datasets/issues/4276/comments
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## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fact1': row['fact1'],
- 'humanScore': row['humanScore'],
- 'clarity': row['clarity'],
- 'turkIdAnonymized': row['turkIdAnonymized']
3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version.
## Expected results
The structure and every data item in the original OpenBookQA matches our OpenBookQA version.
## Actual results
TBD
## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
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CommonSenseQA has missing and inconsistent field names
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2022-05-04T05:38:59Z
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https://api.github.com/repos/huggingface/datasets/issues/4275/comments
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## Describe the bug
In short, CommonSenseQA implementation is inconsistent with the original dataset.
More precisely, we need to:
1. Add the dataset matching "id" field. The current dataset, instead, regenerates monotonically increasing id.
2. The [“question”][“stem”] field is flattened into "question". We should match the original dataset and unflatten it
3. Add the missing "question_concept" field in the question tree node
4. Anything else? Go over the data structure of the newly repaired CommonSenseQA and make sure it matches the original
## Expected results
Every data item of the CommonSenseQA should structurally and data-wise match the original CommonSenseQA dataset.
## Actual results
TBD
## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
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This PR adds API code examples for `IterableDataset` and `IterableDatasetDicts`.
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This PR fixes #4271.
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"Hi! Thanks for catching and reporting the typo, a PR has been opened to fix it :)"
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A typo in docs of datasets.disable_progress_bar
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## Describe the bug
in the docs of V2.1.0 datasets.disable_progress_bar, we should replace "enable" with "disable".
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Fix style in openbookqa dataset
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2022-05-03T15:21:34Z
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https://api.github.com/repos/huggingface/datasets/issues/4270/comments
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CI in PR:
- #4259
was green, but after merging it to master, a code quality error appeared.
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Add license and point of contact to big_patent dataset
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2022-05-03T09:24:07Z
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https://api.github.com/repos/huggingface/datasets/issues/4269/comments
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Update metadata of big_patent dataset with:
- license
- point of contact
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NONE
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[
"It would help a lot to be able to preview the dataset - I'd like to see if the pronunciations are in the dataset, eg. for [\"word\"](https://en.wiktionary.org/wiki/word),\r\n\r\nPronunciation\r\n([Received Pronunciation](https://en.wikipedia.org/wiki/Received_Pronunciation)) [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɜːd/\r\n([General American](https://en.wikipedia.org/wiki/General_American)) [enPR](https://en.wiktionary.org/wiki/Appendix:English_pronunciation): wûrd, [IPA](https://en.wiktionary.org/wiki/Wiktionary:International_Phonetic_Alphabet)([key](https://en.wiktionary.org/wiki/Appendix:English_pronunciation)): /wɝd/",
"Hi @i-am-neo, thanks for reporting.\r\n\r\nNormally this dataset should be private and not accessible for public use. @cakiki, @lvwerra, any reason why is it public? I see many other Wikimedia datasets are also public.\r\n\r\nAlso note that last commit \"Add metadata\" (https://huggingface.co/datasets/bigscience-catalogue-lm-data/lm_en_wiktionary_filtered/commit/dc2f458dab50e00f35c94efb3cd4009996858609) introduced buggy data files (`data/file-01.jsonl.gz.lock`, `data/file-01.jsonl.gz.lock.lock`). The same bug appears in other datasets as well.\r\n\r\n@i-am-neo, please note that in the near future we are planning to make public all datasets used for the BigScience project (at least all of them whose license allows to do that). Once public, they will be accessible for all the NLP community.",
"Ah this must be a bug introduced at creation time since the repos were created programmatically; I'll go ahead and make them private; sorry about that!",
"All datasets are private now. \r\n\r\nRe:that bug I think we're currently avoiding it by avoiding verifications. (i.e. `ignore_verifications=True`)",
"Thanks a lot, @cakiki.\r\n\r\n@i-am-neo, I'm closing this issue for now because the dataset is not publicly available yet. Just stay tuned, as we will soon release all the BigScience open-license datasets. ",
"Thanks for letting me know, @albertvillanova @cakiki.\r\nAny chance of having a subset alpha version in the meantime? \r\nI only need two dicts out of wiktionary: 1) phoneme(as key): word, and 2) word(as key): its phonemes.\r\n\r\nWould like to use it for a mini-poc [Robust ASR](https://github.com/huggingface/transformers/issues/13162#issuecomment-1096881290) decoding, cc @patrickvonplaten. \r\n\r\n(Patrick, possible to email you so as not to litter github with comments? I have some observations after experiments training hubert on some YT AMI-like data (11.44% wer). Also wonder if a robust ASR is on your/HG's roadmap). Thanks!",
"Hey @i-am-neo,\r\n\r\nCool to hear that you're working on Robust ASR! Feel free to drop me a mail :-)",
"@i-am-neo This particular subset of the dataset was taken from the [CirrusSearch dumps](https://dumps.wikimedia.org/other/cirrussearch/current/)\r\nYou're specifically after the [enwiktionary-20220425-cirrussearch-content.json.gz](https://dumps.wikimedia.org/other/cirrussearch/current/enwiktionary-20220425-cirrussearch-content.json.gz) file",
"thanks @cakiki ! <del>I could access the gz file yesterday (but neglected to tuck it away somewhere safe), and today the link is throwing a 404. Can you help? </del> Never mind, got it!",
"thanks @patrickvonplaten. will do - getting my observations together."
] |
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
|
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2022-05-02T20:34:25Z
|
https://api.github.com/repos/huggingface/datasets/issues/4268/comments
|
## Describe the bug
Error generated when attempting to download dataset
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered")
```
## Expected results
A clear and concise description of the expected results.
## Actual results
```
ExpectedMoreDownloadedFiles Traceback (most recent call last)
[<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered")
3 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
31 return
32 if len(set(expected_checksums) - set(recorded_checksums)) > 0:
---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
34 if len(set(recorded_checksums) - set(expected_checksums)) > 0:
35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))
ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
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Replace data URL in SAMSum dataset within the same repository
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Replace data URL with one in the same repository.
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Add HF Speech Bench to Librispeech Dataset Card
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2022-05-02T16:59:31Z
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Adds the HF Speech Bench to Librispeech Dataset Card in place of the Papers With Code Leaderboard. Should improve usage and visibility of this leaderboard! Wondering whether this can also be done for [Common Voice 7](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) and [8](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) through someone with permissions?
cc @patrickvonplaten: more leaderboard promotion!
|
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"> Later we can add imagenet-21k as a new dataset if we want.\r\n\r\nisn't it what models refer to as `imagenet` already?",
"> isn't it what models refer to as imagenet already?\r\n\r\nI wasn't sure, but it looks like it indeed. Therefore having a dataset `imagenet` for ImageNet 21k makes sense actually.\r\n\r\nEDIT: actually not all `imagenet` tag refer to ImageNet 21k - we will need to correct some of them",
"_The documentation is not available anymore as the PR was closed or merged._",
"should we remove the repo mirror on the hub side or will you do it?"
] |
Rename imagenet2012 -> imagenet-1k
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2022-05-02T10:26:21Z
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https://api.github.com/repos/huggingface/datasets/issues/4263/comments
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On the Hugging Face Hub, users refer to imagenet2012 (from #4178 ) as imagenet-1k in their model tags.
To correctly link models to imagenet, we should rename this dataset `imagenet-1k`.
Later we can add `imagenet-21k` as a new dataset if we want.
Once this one is merged we can delete the `imagenet2012` dataset repository on the Hub.
EDIT: to complete the rationale on why we should name it `imagenet-1k`:
If users specifically added the tag `imagenet-1k` , then it could be for two reasons (not sure which one is predominant), either they
- wanted to make it explicit that it’s not 21k -> the distinction is important for the community
- or they have been following this convention from other models -> the convention implicitly exists already
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
Add YAML tags to Dataset Card rotten tomatoes
|
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2022-05-01T11:59:08Z
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https://api.github.com/repos/huggingface/datasets/issues/4262/comments
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The dataset card for the rotten tomatoes / MR movie review dataset had some missing YAML tags. Hopefully, this also improves the visibility of this dataset now that paperswithcode and huggingface link to eachother.
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NONE
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completed
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[
"Hi @xflashxx, thanks for reporting.\r\n\r\nPlease note that this dataset was generated and shared by Webis Group: https://huggingface.co/webis\r\n\r\nWe are contacting the dataset owners to inform them about the issue you found. We'll keep you updated of their reply.",
"i'd suggest just pinging the authors here in the issue if possible?",
"Thanks for reporting this @xflashxx. I'll have a look and get back to you on this.",
"Hi @xflashxx and @albertvillanova,\r\n\r\nI have updated the files with de-duplicated splits. Apparently the debate portals from which part of the examples were sourced had unique timestamps for some examples (up to 6%; updated counts in the README) without any actual content updated that lead to \"new\" items. The length of `ids_validation` and `ids_testing` is zero.\r\n\r\nRegarding impact on scores:\r\n1. We employed automatic evaluation (on a separate set of 1000 examples) only to justify the exclusion of the smaller models for manual evaluation (due to budget constraints). I am confident the ranking still stands (unsurprisingly, the bigger models doing better than those trained on the smaller splits). We also highlight this in the paper. \r\n\r\n2. The examples used for manual evaluation have no overlap with any splits (also because they do not have any ground truth as we applied the trained models on an unlabeled sample to test its practical usage). I've added these two files to the dataset repository.\r\n\r\nHope this helps!",
"Thanks @shahbazsyed for your fast fix.\r\n\r\nAs a side note:\r\n- Your email appearing as Point of Contact in the dataset README has a typo: @uni.leipzig.de instead of @uni-leipzig.de\r\n- Your commits on the Hub are not linked to your profile on the Hub: this is because we use the email address to make this link; the email address used in your commit author and the email address set on your Hub account settings."
] |
data leakage in `webis/conclugen` dataset
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2022-04-30T17:43:37Z
|
https://api.github.com/repos/huggingface/datasets/issues/4261/comments
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## Describe the bug
Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results.
Furthermore, all splits contain duplicate samples.
## Steps to reproduce the bug
```python
from datasets import load_dataset
training = load_dataset("webis/conclugen", "base", split="train")
validation = load_dataset("webis/conclugen", "base", split="validation")
testing = load_dataset("webis/conclugen", "base", split="test")
# collect which sample id's are present in the training split
ids_validation = list()
ids_testing = list()
for train_sample in training:
train_argument = train_sample["argument"]
train_conclusion = train_sample["conclusion"]
train_id = train_sample["id"]
# test if current sample is in validation split
if train_argument in validation["argument"]:
for validation_sample in validation:
validation_argument = validation_sample["argument"]
validation_conclusion = validation_sample["conclusion"]
validation_id = validation_sample["id"]
if train_argument == validation_argument and train_conclusion == validation_conclusion:
ids_validation.append(validation_id)
# test if current sample is in test split
if train_argument in testing["argument"]:
for testing_sample in testing:
testing_argument = testing_sample["argument"]
testing_conclusion = testing_sample["conclusion"]
testing_id = testing_sample["id"]
if train_argument == testing_argument and train_conclusion == testing_conclusion:
ids_testing.append(testing_id)
```
## Expected results
Length of both lists `ids_validation` and `ids_testing` should be zero.
## Actual results
Length of `ids_validation` = `2556`
Length of `ids_testing` = `287`
Furthermore, there seems to be duplicate samples in (at least) the *training* split, since:
`print(len(set(ids_validation)))` = `950`
`print(len(set(ids_testing)))` = `101`
All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: macOS-12.3.1-arm64-arm-64bit
- Python version: 3.9.10
- PyArrow version: 7.0.0
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CONTRIBUTOR
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[
"whoops just found https://huggingface.co/datasets/rotten_tomatoes"
] |
Add mr_polarity movie review sentiment classification
|
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2022-04-30T13:19:33Z
|
https://api.github.com/repos/huggingface/datasets/issues/4260/comments
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Add the MR (Movie Review) dataset. The original dataset contains sentences from Rotten Tomatoes labeled as either "positive" or "negative".
Homepage: [https://www.cs.cornell.edu/people/pabo/movie-review-data/](https://www.cs.cornell.edu/people/pabo/movie-review-data/)
paperswithcode: [https://paperswithcode.com/dataset/mr](https://paperswithcode.com/dataset/mr)
- [ ] I was not able to generate dummy data, the original dataset files have ".pos" and ".neg" as file extensions so the auto-generator does not work. Is it fine like this or should dummy data be added?
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] |
Fix bug in choices labels in openbookqa dataset
|
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2022-04-30T07:41:39Z
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This PR fixes the Bug in the openbookqa dataset as mentioned in this issue #3550.
Fix #3550.
cc. @lhoestq @mariosasko
|
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"> Good catch ! Thanks :)\r\n> \r\n> Next time can you describe your fix in the Pull Request description please ?\r\n\r\nThanks. Also whoops, sorry about not being very descriptive. I updated the pull request description, and will keep this in mind for future PRs."
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Fix/start token mask issue and update documentation
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2022-04-29T22:42:44Z
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This pr fixes a couple bugs:
1) the perplexity was calculated with a 0 in the attention mask for the start token, which was causing high perplexity scores that were not correct
2) the documentation was not updated
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proposing a metric card to better explain how Mahalanobis distance works (last one for now :sweat_smile:
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2022-04-29T18:21:22Z
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Proposing a metric card for Mean Squared Error
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No google drive URL for pubmed_qa
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2022-04-29T15:55:46Z
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I hosted the data files in https://huggingface.co/datasets/pubmed_qa. This is allowed because the data is under the MIT license.
cc @stas00
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Replace data URL in SAMSum dataset and support streaming
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2022-04-29T08:21:43Z
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This PR replaces data URL in SAMSum dataset:
- original host (arxiv.org) does not allow HTTP Range requests
- we have hosted the data on the Hub (license: CC BY-NC-ND 4.0)
Moreover, it implements support for streaming.
Fix #4146.
Related to: #4236.
CC: @severo
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Create metric cards for mean IOU
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2022-04-28T20:58:27Z
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Proposing a metric card for mIoU :rocket:
sorry for spamming you with review requests, @albertvillanova ! :hugs:
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Creating metric card for MAE
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Initial proposal for MAE metric card
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Metric card for the XTREME-S dataset
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Proposing a metric card for the XTREME-S dataset :hugs:
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CONTRIBUTOR
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"_The documentation is not available anymore as the PR was closed or merged._",
"Updated meta.yaml as well. Thanks.",
"I'm OK with bumping PyArrow to version 6 to match the version in Colab, but maybe a better solution would be to stop using extension types in our codebase to avoid similar issues.",
"> but maybe a better solution would be to stop using extension types in our codebase to avoid similar issues.\r\n\r\nI agree, not much attention has been payed to extension arrays in the latest developments of Arrow anyway.\r\n\r\nLet's not use them more that what we do right now, and try to remove them at one point"
] |
Bump PyArrow Version to 6
|
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2022-04-28T18:10:50Z
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Fixes #4152
This PR updates the PyArrow version to 6 in setup.py, CI job files .circleci/config.yaml and .github/workflows/benchmarks.yaml files.
This will fix ArrayND error which exists in pyarrow 5.
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Support streaming XGLUE dataset
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Support streaming XGLUE dataset.
Fix #4247.
CC: @severo
|
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"Thanks for reporting @sue99.\r\n\r\nUnfortunately. I'm not able to reproduce your problem:\r\n```python\r\nIn [1]: import datasets\r\n ...: from datasets import load_dataset\r\n ...: dataset = load_dataset(\"conll2003\")\r\n\r\nIn [2]: dataset\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 14042\r\n })\r\n validation: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 3251\r\n })\r\n test: Dataset({\r\n features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 3454\r\n })\r\n})\r\n\r\nIn [3]: dataset[\"train\"][0]\r\nOut[3]: \r\n{'id': '0',\r\n 'tokens': ['EU',\r\n 'rejects',\r\n 'German',\r\n 'call',\r\n 'to',\r\n 'boycott',\r\n 'British',\r\n 'lamb',\r\n '.'],\r\n 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7],\r\n 'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0],\r\n 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0]}\r\n```\r\n\r\nJust guessing: might be the case that you are calling `load_dataset` from a working directory that contains a local folder named `conll2003` (containing the raw data files)? If that is the case, `datasets` library gives precedence to the local folder over the dataset on the Hub. "
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conll2003 dataset loads original data.
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2022-04-28T09:33:31Z
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## Describe the bug
I load `conll2003` dataset to use refined data like [this](https://huggingface.co/datasets/conll2003/viewer/conll2003/train) preview, but it is original data that contains `'-DOCSTART- -X- -X- O'` text.
Is this a bug or should I use another dataset_name like `lhoestq/conll2003` ?
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
dataset = load_dataset("conll2003")
```
## Expected results
{
"chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0],
"id": "0",
"ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7],
"tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."]
}
## Actual results
```python
print(dataset)
DatasetDict({
train: Dataset({
features: ['text'],
num_rows: 219554
})
test: Dataset({
features: ['text'],
num_rows: 50350
})
validation: Dataset({
features: ['text'],
num_rows: 55044
})
})
```
```python
for i in range(20):
print(dataset['train'][i])
{'text': '-DOCSTART- -X- -X- O'}
{'text': ''}
{'text': 'EU NNP B-NP B-ORG'}
{'text': 'rejects VBZ B-VP O'}
{'text': 'German JJ B-NP B-MISC'}
{'text': 'call NN I-NP O'}
{'text': 'to TO B-VP O'}
{'text': 'boycott VB I-VP O'}
{'text': 'British JJ B-NP B-MISC'}
{'text': 'lamb NN I-NP O'}
{'text': '. . O O'}
{'text': ''}
{'text': 'Peter NNP B-NP B-PER'}
{'text': 'Blackburn NNP I-NP I-PER'}
{'text': ''}
{'text': 'BRUSSELS NNP B-NP B-LOC'}
{'text': '1996-08-22 CD I-NP O'}
{'text': ''}
{'text': 'The DT B-NP O'}
{'text': 'European NNP I-NP B-ORG'}
```
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"\r\n",
"Thanks for reporting @czq1999.\r\n\r\nNote that the dataset viewer uses the dataset in streaming mode and that not all datasets support streaming yet.\r\n\r\nThat is the case for XGLUE dataset (as the error message points out): this must be refactored to support streaming. ",
"Fixed, thanks @albertvillanova !\r\n\r\nhttps://huggingface.co/datasets/xglue\r\n\r\n<img width=\"824\" alt=\"Capture d’écran 2022-04-29 à 10 23 14\" src=\"https://user-images.githubusercontent.com/1676121/165909391-9f98d98a-665a-4e57-822d-8baa2dc9b7c9.png\">\r\n"
] |
The data preview of XGLUE
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It seems that something wrong with the data previvew of XGLUE
|
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Support to load dataset with TSV files by passing only dataset name
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2022-04-28T07:30:15Z
|
https://api.github.com/repos/huggingface/datasets/issues/4246/comments
|
This PR implements support to load a dataset (w/o script) containing TSV files by passing only the dataset name (no need to pass `sep='\t'`):
```python
ds = load_dataset("dataset/name")
```
The refactoring allows for future builder kwargs customizations based on file extension.
Related to #4238.
|
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Add code examples for DatasetDict
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2022-04-27T22:52:22Z
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https://api.github.com/repos/huggingface/datasets/issues/4245/comments
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This PR adds code examples for `DatasetDict` in the API reference :)
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"Reverted the multi-input-text-classification tag from task_categories and added it as task_ids @lhoestq ",
"_The documentation is not available anymore as the PR was closed or merged._"
] |
task id update
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2022-04-27T18:28:14Z
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https://api.github.com/repos/huggingface/datasets/issues/4244/comments
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changed multi input text classification as task id instead of category
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"Thanks for your contribution, @shayne-longpre.\r\n\r\nAre you still interested in adding this dataset? As we are transferring the dataset scripts from this GitHub repo, we would recommend you to add this to the Hugging Face Hub: https://huggingface.co/datasets"
] |
WIP: Initial shades loading script and readme
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Update auth when mirroring datasets on the hub
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2022-04-27T17:22:31Z
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https://api.github.com/repos/huggingface/datasets/issues/4242/comments
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We don't need to use extraHeaders anymore for rate limits anymore. Anyway extraHeaders was not working with git LFS because it was passing the wrong auth to S3.
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[
"Hi :)\r\n\r\nI think your issue may be related to the older `nlp` library. I was able to download `glue` with the latest version of `datasets`. Can you try updating with:\r\n\r\n```py\r\npip install -U datasets\r\n```\r\n\r\nThen you can download:\r\n\r\n```py\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"glue\", \"rte\")\r\n```",
"This appears to work. Thank you!\n\nOn Wed, Apr 27, 2022, 1:18 PM Steven Liu ***@***.***> wrote:\n\n> Hi :)\n>\n> I think your issue may be related to the older nlp library. I was able to\n> download glue with the latest version of datasets. Can you try updating\n> with:\n>\n> pip install -U datasets\n>\n> Then you can download:\n>\n> from datasets import load_datasetds = load_dataset(\"glue\", \"rte\")\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/4241#issuecomment-1111267650>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ACJUEKLUP2EL7ES3RRWJRPTVHFZHBANCNFSM5UPJBYXA>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n"
] |
NonMatchingChecksumError when attempting to download GLUE
|
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2022-04-27T14:14:21Z
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## Describe the bug
I am trying to download the GLUE dataset from the NLP module but get an error (see below).
## Steps to reproduce the bug
```python
import nlp
nlp.__version__ # '0.2.0'
nlp.load_dataset('glue', name="rte", download_mode="force_redownload")
```
## Expected results
I expect the dataset to download without an error.
## Actual results
```
INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports.
INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue
INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4
INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py
INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json
INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json
INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4
INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0)
INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source
INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb
INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805
Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0...
Downloading: 100%|██████████| 73.0/73.0 [00:00<00:00, 73.9kB/s]
INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64
INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-7-669a8343dcc1> in <module>
----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload")
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
518 download_mode=download_mode,
519 ignore_verifications=ignore_verifications,
--> 520 save_infos=save_infos,
521 )
522
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
418 verify_infos = not save_infos and not ignore_verifications
419 self._download_and_prepare(
--> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
421 )
422 # Sync info
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
458 # Checksums verification
459 if verify_infos:
--> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())
461 for split_generator in split_generators:
462 if str(split_generator.split_info.name).lower() == "all":
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums)
34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
35 if len(bad_urls) > 0:
---> 36 raise NonMatchingChecksumError(str(bad_urls))
37 logger.info("All the checksums matched successfully.")
38
NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa
- Python version: 3.6.13
- PyArrow version: 6.0.1
- Pandas version: 1.1.5
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2022-04-29T12:41:41Z
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[] |
https://github.com/huggingface/datasets/pull/4240
|
CONTRIBUTOR
| null | false | null |
[
"I don't think you need to generate new dummy data, since they're in the same format as the original data.\r\n\r\nThe CI is failing because of this error:\r\n```python\r\n> turn[\"names\"] = turn[\"NAMES\"]\r\nE TypeError: tuple indices must be integers or slices, not str\r\n```\r\n\r\nDo you know what could cause this ? If I understand correctly, `turn` is supposed to be a list of dictionaries right ?",
"> ``` \r\n> \r\n> Do you know what could cause this ? If I understand correctly, turn is supposed to be a list of dictionaries right ?\r\n> ```\r\n\r\nThis is strange. Let me look into this. As per https://github.com/RevanthRameshkumar/CRD3/blob/master/data/aligned%20data/c%3D2/C1E001_2_0.json TURNS is a list of dictionaries. So when we iterate over `row[\"TURNS]\"` each `turn` is essentially a dictionary. Not sure why it's being considered a tuple here."
] |
Fix yield for crd3
|
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2022-04-27T12:31:36Z
|
https://api.github.com/repos/huggingface/datasets/issues/4240/comments
|
Modified the `_generate_examples` function to consider all the turns for a chunk id as a single example
Modified the features accordingly
```
"turns": [
{
"names": datasets.features.Sequence(datasets.Value("string")),
"utterances": datasets.features.Sequence(datasets.Value("string")),
"number": datasets.Value("int32"),
}
],
}
```
I wasn't able to run `datasets-cli dummy_data datasets` command. Is there a workaround for this?
|
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CONTRIBUTOR
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[
"_The documentation is not available anymore as the PR was closed or merged._"
] |
Small fixes in ROC AUC docs
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2022-04-27T12:15:50Z
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https://api.github.com/repos/huggingface/datasets/issues/4239/comments
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The list of use cases did not render on GitHub with the prepended spacing.
Additionally, some typo's we're fixed.
|
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[
"Hi @loretoparisi, thanks for reporting.\r\n\r\nThere is an option to force the redownload of the data files (and thus not using previously download and cached data files): `load_dataset(..., download_mode=\"force_redownload\")`.\r\n\r\nPlease, let me know if this fixes your problem.\r\n\r\nI can confirm you that your dataset loads without any problem for me:\r\n```python\r\nIn [2]: ds = load_dataset(\"loretoparisi/tatoeba-sentences\", data_files={\"train\": \"train.csv\", \"test\": \"test.csv\"}, delimiter=\"\\t\", column_names=['label', 'text'])\r\n\r\nIn [3]: ds\r\nOut[3]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['label', 'text'],\r\n num_rows: 8256449\r\n })\r\n test: Dataset({\r\n features: ['label', 'text'],\r\n num_rows: 2061204\r\n })\r\n})\r\n``` ",
"@albertvillanova thank you, it seems it still does not work using:\r\n\r\n```python\r\nsentences = load_dataset(\r\n \"loretoparisi/tatoeba-sentences\",\r\n data_files=data_files,\r\n delimiter='\\t', \r\n column_names=['label', 'text'],\r\n download_mode=\"force_redownload\"\r\n)\r\n```\r\n[This](https://colab.research.google.com/drive/1EA6FWo5pHxU8rPHHRn24NlHqRPiOlPTr?usp=sharing) is my notebook!\r\n\r\nThe problem is that the download file's revision for `test.csv` is not correctly parsed\r\n\r\n\r\n\r\nIf you download that file `test.csv` from the repo, the line `\\\\N` is not there anymore (it was there at the first file upload).\r\n\r\nMy impression is that the Apache Arrow file is still cached - so server side, despite of enabling a forced download. For what I can see I get those two arrow files, but I cannot grep the bad line (`\\\\N`) since are binary files:\r\n\r\n```\r\n!ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519\r\n!ls -l /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/csv-test.arrow\r\n!head /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519/dataset_info.json\r\n```\r\n",
"SOLVED! The problem was the with the file itself, using caching parameter helped indeed.\r\nThanks for helping!"
] |
Dataset caching policy
|
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| null |
2022-04-27T10:42:11Z
|
https://api.github.com/repos/huggingface/datasets/issues/4238/comments
|
## Describe the bug
I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error
```
[/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values)
852 if value not in self._str2int:
853 value = str(value).strip()
--> 854 output.append(self._str2int[str(value)])
855 else:
856 # No names provided, try to integerize
KeyError: '\\N'
```
The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally:
```python
from datasets import load_dataset_builder
dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences")
print(dataset_builder.cache_dir)
print(dataset_builder.info.features)
print(dataset_builder.info.splits)
```
```
Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd
/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519
None
None
```
and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`.
Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it?
Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last.
Thank you.
## Steps to reproduce the bug
```python
from datasets import load_dataset,Features,Value,ClassLabel
class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"]
features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')})
num_labels = features['label'].num_classes
data_files = { "train": "train.csv", "test": "test.csv" }
sentences = load_dataset(
"loretoparisi/tatoeba-sentences",
data_files=data_files,
delimiter='\t',
column_names=['label', 'text'],
)
# You can make this part faster with num_proc=<some int>
sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features)
sentences = sentences.shuffle()
```
## Expected results
Properly tokenize dataset file `test.csv` without issues.
## Actual results
Specify the actual results or traceback.
```
Downloading data files: 100%
2/2 [00:16<00:00, 7.34s/it]
Downloading data: 100%
391M/391M [00:12<00:00, 36.6MB/s]
Downloading data: 100%
92.4M/92.4M [00:02<00:00, 40.0MB/s]
Extracting data files: 100%
2/2 [00:00<00:00, 47.66it/s]
Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data.
100%
2/2 [00:00<00:00, 25.94it/s]
11%
942339/8256449 [01:55<13:11, 9245.85ex/s]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
[<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>()
12 )
13 # You can make this part faster with num_proc=<some int>
---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features)
15 sentences = sentences.shuffle()
10 frames
[/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values)
852 if value not in self._str2int:
853 value = str(value).strip()
--> 854 output.append(self._str2int[str(value)])
855 else:
856 # No names provided, try to integerize
KeyError: '\\N'
```
## Environment info
```
- `datasets` version: 2.1.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
- ```
```
- `transformers` version: 4.18.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- Huggingface_hub version: 0.5.1
- PyTorch version (GPU?): 1.11.0+cu113 (True)
- Tensorflow version (GPU?): 2.8.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
- ```
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2022-05-10T12:17:05Z
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[] |
https://github.com/huggingface/datasets/issues/4237
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CONTRIBUTOR
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"Thanks for reporting. I understand it's an error in the dataset script. To reproduce:\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> split_names = ds.get_dataset_split_names(\"mozilla-foundation/common_voice_8_0\", use_auth_token=\"**********\")\r\nDownloading builder script: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10.9k/10.9k [00:00<00:00, 10.9MB/s]\r\nDownloading extra modules: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.98k/2.98k [00:00<00:00, 3.36MB/s]\r\nDownloading extra modules: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 53.1k/53.1k [00:00<00:00, 650kB/s]\r\nNo config specified, defaulting to: common_voice/en\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 280, in get_dataset_config_info\r\n for split_generator in builder._split_generators(\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py\", line 153, in _split_generators\r\n self._log_download(self.config.name, bundle_version, hf_auth_token)\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_8_0/720589e6e5ad674019008b719053303a71716db1b27e63c9846df02fdf93f2f3/common_voice_8_0.py\", line 139, in _log_download\r\n email = HfApi().whoami(auth_token)[\"email\"]\r\nKeyError: 'email'\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 323, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-preview-backend/libs/libmodels/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 285, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```",
"Thanks for reporting @patrickvonplaten and thanks for the investigation @severo.\r\n\r\nUnfortunately I'm not able to reproduce the error.\r\n\r\nI think the error has to do with authentication with `huggingface_hub`, because the exception is thrown from these code lines: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/blob/main/common_voice_8_0.py#L137-L139\r\n```python\r\nfrom huggingface_hub import HfApi, HfFolder\r\n\r\nif isinstance(auth_token, bool):\r\n email = HfApi().whoami(auth_token)\r\nemail = HfApi().whoami(auth_token)[\"email\"]\r\n```\r\n\r\nCould you please verify the previous code with the `auth_token` you pass to `load_dataset(..., use_auth_token=auth_token,...`?",
"OK, thanks for digging a bit into it. Indeed, the error occurs with the dataset-viewer, but not with a normal user token, because we use an app token, and it does not have a related email!\r\n\r\n```python\r\n>>> from huggingface_hub import HfApi, HfFolder\r\n>>> auth_token = \"hf_app_******\"\r\n>>> t = HfApi().whoami(auth_token)\r\n>>> t\r\n{'type': 'app', 'name': 'dataset-preview-backend'}\r\n>>> t[\"email\"]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\nKeyError: 'email'\r\n```\r\n\r\nNote also that the doc (https://huggingface.co/docs/huggingface_hub/package_reference/hf_api#huggingface_hub.HfApi.whoami) does not state that `whoami` should return an `email` key.\r\n\r\n@SBrandeis @julien-c: do you think the app token should have an email associated, like the users?",
"We can workaround this with\r\n```python\r\nemail = HfApi().whoami(auth_token).get(\"email\", \"system@huggingface.co\")\r\n```\r\nin the common voice scripts",
"Hmmm, does this mean that any person who downloads the common voice dataset will be logged as \"system@huggingface.co\"? If so, it would defeat the purpose of sending the user's email to the commonvoice API, right?",
"I agree with @severo: we cannot set our system email as default, allowing anybody not authenticated to by-pass the Common Voice usage policy.\r\n\r\nAdditionally, looking at the code, I think we should implement a more robust way to send user email to Common Voice: currently anybody can tweak the script and send somebody else email instead.\r\n\r\nCC: @patrickvonplaten @lhoestq @SBrandeis @julien-c ",
"Hmm I don't agree here. \r\n\r\nAnybody can always just bypass the system by setting whatever email. As soon as someone has access to the downloading script it's trivial to tweak the code to not send the \"correct\" email but to just whatever and it would work.\r\n\r\nNote that someone only has visibility on the code after having \"signed\" the access-mechanism so I think we can expect the users to have agreed to not do anything malicious. \r\n\r\nI'm fine with both @lhoestq's solution or we find a way that forces the user to be logged in + being able to load the data for the datasets viewer. Wdyt @lhoestq @severo @albertvillanova ?",
"> Additionally, looking at the code, I think we should implement a more robust way to send user email to Common Voice: currently anybody can tweak the script and send somebody else email instead.\r\n\r\nYes, I agree we can forget about this @patrickvonplaten. After having had a look at Common Voice website, I've seen they only require sending an email (no auth is inplace on their side, contrary to what I had previously thought). Therefore, currently we impose stronger requirements than them: we require the user having logged in and accepted the access mechanism.\r\n\r\nCurrently the script as it is already requires the user being logged in:\r\n```python\r\nHfApi().whoami(auth_token)\r\n```\r\nthrows an exception if None/invalid auth_token is passed.\r\n\r\nOn the other hand, we should agree on the way to allow the viewer to stream the data.",
"The preview is back now, thanks !"
] |
Common Voice 8 doesn't show datasets viewer
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2022-06-10T08:10:55Z
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https://github.com/huggingface/datasets/pull/4236
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MEMBER
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"_The documentation is not available anymore as the PR was closed or merged._",
"I first uploaded the data files to the Hub: I think it is a good option because we have git lfs to track versions and changes. Moreover people will be able to make PRs to propose updates on the data files.\r\n- I would have preferred to upload it it to the \"data\" org namespace, but it is already taken (although not used): might be possible to take it?\r\n\r\nAs an alternative (and to be consistent with previous datasets), I also uploaded the data files to our AWS bucket.\r\n\r\nWe should decide which to use (now and for future datasets) and set it here before merging. We should remove the data files for the non-chosen option.\r\n\r\nCC: @lhoestq @mariosasko @polinaeterna ",
"Would it make sense to make the dataset a community one (so, create an organization for it) and store the script and the data in a single repository? Just as it is for most of the datasets. That way we can also access the data using a relative path inside the repo (that's not the point though). The point is that to me it seems a bit more straightforward to store everything in one place. \r\n\r\nI guess the strong argument against this logic is that in this case the canonical version won't work... But maybe there is some redirecting mechanism I don't know about? :)\r\n\r\nAnyway, I'm in favor of hosting data on the Hub instead of AWS :) ",
"I also think storing everything in one place/single repository is the best option.\r\n\r\n@polinaeterna Canonical datasets also support data files (see the [`red_caps` repo](https://huggingface.co/datasets/red_caps/tree/main) for instance) ",
"Thanks @polinaeterna and @mariosasko for your comments.\r\n\r\nYes, definitely it is much better to have everything in the same repo. \r\n\r\nI'm transferring their data files to the Hub under \"big_patent\" and deleting them from the other repo and AWS."
] |
Replace data URL in big_patent dataset and support streaming
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2022-04-27T10:01:13Z
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This PR replaces the Google Drive URL with our Hub one, once the data owners have approved to host their data on the Hub.
Moreover, this PR makes the dataset streamable.
Fix #4217.
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https://github.com/huggingface/datasets/issues/4235
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NONE
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"The `Trainer` support `IterableDataset`, not just datasets."
] |
How to load VERY LARGE dataset?
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2022-04-27T07:50:13Z
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### System Info
```shell
I am using transformer trainer while meeting the issue.
The trainer requests torch.utils.data.Dataset as input, which loads the whole dataset into the memory at once. Therefore, when the dataset is too large to load, there's nothing I can do except using IterDataset, which loads samples of data seperately, and results in low efficiency.
I wonder if there are any tricks like Sharding in huggingface trainer.
Looking forward to your reply.
```
### Who can help?
Trainer: @sgugger
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
None
### Expected behavior
```shell
I wonder if there are any tricks like fairseq Sharding very large datasets https://fairseq.readthedocs.io/en/latest/getting_started.html.
Thanks a lot!
```
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https://github.com/huggingface/datasets/pull/4234
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"_The documentation is not available anymore as the PR was closed or merged._",
"Related to: https://github.com/huggingface/autonlp-backend/issues/414 and https://github.com/huggingface/autonlp-backend/issues/424",
"The tests are failing due to the changed metadata:\r\n\r\n```\r\ngot an unexpected keyword argument 'train-eval-index'\r\n```\r\n\r\nI think you can fix this by updating the `DatasetMetadata` class and implementing an appropriate `validate_train_eval_index()` function\r\n\r\n@lhoestq we are working with an arbitrary set of tags for `autoeval config`. See https://github.com/huggingface/autonlp-backend/issues/414\r\nI need to add a validator function though for the tests to pass. Our set is not well-defined as in the rest https://github.com/huggingface/datasets/tree/master/src/datasets/utils/resources. What's a workaround for this?",
"On the question of validating the `train-eval-index` metadata, I think the simplest approach would be to validate that the required fields exist and not worry about their values (which are open-ended).\r\n\r\nFor me, the required fields include:\r\n\r\n* `config`\r\n* `task`\r\n* `task_id`\r\n* `splits` (train / validation / eval)\r\n* `col_mapping`\r\n* `metrics` (checking that each one has `type`, `name`) \r\n\r\nHere I'm using the spec defined in https://github.com/huggingface/autonlp-backend/issues/414 as a guide.\r\n\r\nWDYT @lhoestq ?",
"Makes sense ! Currently the metadata type validator doesn't support subfields - let me open a PR to add it",
"I ended up improving the metadata validation in this PR x)\r\n\r\nIn particular:\r\n- I added support YAML keys with dashes instead of underscores for `train-eval-index`\r\n- I added `train-eval-index` validation with `validate_train_eval_index`. It does nothing fancy, it just checks that it is a list if it exists in the YAML, but feel free to improve it if you want\r\n\r\nLet me know if it sounds good to you ! I think we can improve `validate_train_eval_index` in another PR",
"Come on windows... I didn't do anything advanced...\r\n\r\nAnyway, will try to fix this when I get back home x)",
"> Come on windows... I didn't do anything advanced...\r\n> \r\n> Anyway, will try to fix this when I get back home x)\r\n\r\nHehe, thanks!",
"Thanks, @lhoestq this is great! ",
"Did I just fix it for windows and now it fails on linux ? xD",
"> Did I just fix it for windows and now it fails on linux ? xD\r\n\r\nLooks like the Heisenberg uncertainty principle is at play here - you cannot simultaneously have unit tests passing in both Linux and Windows 😅 ",
"The worst is that the tests pass locally both on my windows and my linux x)",
"Ok fixed it, the issue came from python 3.6 that doesn't return the right `__origin__` for Dict and List types",
"> Alright thanks for adding the first Autoeval config ! :D\r\n\r\nWoohoo! Thank you so much 🤗 ",
"This is cool!"
] |
Autoeval config
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Added autoeval config to imdb as pilot
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4233). All of your documentation changes will be reflected on that endpoint."
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Autoeval
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adding new tag to tasks.json and modified for existing datasets
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Fix invalid url to CC-Aligned dataset
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The CC-Aligned dataset url has changed to https://data.statmt.org/cc-aligned/, the old address http://www.statmt.org/cc-aligned/ is no longer valid
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[
"Thanks for reporting @beyondguo.\r\n\r\nIndeed, we generate this dataset from this raw data file URL: https://data.deepai.org/conll2003.zip\r\nAnd that URL only contains the English version.",
"The German data requires payment\r\n\r\nThe [original task page](https://www.clips.uantwerpen.be/conll2003/ner/) states \"The German data is a collection of articles from the Frankfurter Rundschau. The named entities have been annotated by people of the University of Antwerp. Only the annotations are available here. In order to build these data sets you need access to the ECI Multilingual Text Corpus. It can be ordered from the Linguistic Data Consortium (2003 non-member price: US$ 35.00).\"\r\n\r\nInflation since 2003 has also affected LDC's prices, and today the dataset [LDC94T5](https://catalog.ldc.upenn.edu/LDC94T5) is available under license for $75 a copy. The [license](https://catalog.ldc.upenn.edu/license/eci-slash-mci-user-agreement.pdf) includes a non-distribution condition, which is probably why the data has not turned up openly.\r\n\r\nThe ACL hold copyright of this data; I'll mail them and anyone I can find at ECI to see if they'll open this up now. After all, it worked with Microsoft 3DMM, why not here too, after 28 years? :)\r\n",
"Closing this issue as we are not allowed to share publicly the German subset."
] |
Why the `conll2003` dataset on huggingface only contains the `en` subset? Where is the German data?
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But on huggingface datasets:
Where is the German data?
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new task tag
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multi-input-text-classification tag for classification datasets that take more than one input
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