Minimartzz/github-issues huggingface dataset:
Mark CI tests as xfail when 502 error
State: closed

To make CI more robust, we could mark as xfail when the Hub raises a 502 error (besides 500 error):

- FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_to_hub_skip_identical_files
  - https://github.com/huggingface/datasets/actions/runs/3174626525/jobs/5171672431
  ```
  >           raise HTTPError(http_error_msg, response=self)
  E           requests.exceptions.HTTPError: 502 Server Error: Bad Gateway for url: https://hub-ci.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/test-16648055339047.git/info/lfs/objects/batch
  ```

- FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_overwrite_files
  - https://github.com/huggingface/datasets/actions/runs/3145587033/jobs/5113074889
  ```
  >           raise HTTPError(http_error_msg, response=self)
  E           requests.exceptions.HTTPError: 502 Server Error: Bad Gateway for url: https://hub-ci.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/test-16643866807322.git/info/lfs/objects/verify
  ```

Currently, we mark as xfail when 500 error:
- #4845

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Fix broken URL's (GEM)
State: closed

This PR fixes the broken URL's in GEM. cc. @lhoestq, @albertvillanova 

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5056). All of your documentation changes will be reflected on that endpoint.

Thanks, @manandey. We have removed all dataset scripts from this repo. Subsequent PRs should be opened directly on the Hugging Face Hub.
Fix backward compatibility for dataset_infos.json
State: closed

While working on https://github.com/huggingface/datasets/pull/5018 I noticed a small bug introduced in #4926 regarding backward compatibility for dataset_infos.json

Indeed, when a dataset repo had both dataset_infos.json and README.md, the JSON file was ignored. This is unexpected: in practice it should be ignored only if the README.md has a dataset_info field, which has precedence over the data in the JSON file.

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Fix license/citation information of squadshifts dataset card
State: closed
Labels: description: Contribution to a dataset script
name: dataset contribution

This PR fixes the license/citation information of squadshifts dataset card, once the dataset owners have responded to our request for information:
- https://github.com/modestyachts/squadshifts-website/issues/1

Additionally, we have updated the mention in their website to our `datasets` library (they were referring old name `nlp`):
- https://github.com/modestyachts/squadshifts-website/pull/2#event-7500953009

Comments:
_The documentation is not available anymore as the PR was closed or merged._
added from_generator method to IterableDataset class.
State: closed

Hello, 

This resolves issues #4988.

I added a method `from_generator` to class `IterableDataset`.

I modified the `read` method of input stream generator to also return Iterable_dataset.


Comments:
_The documentation is not available anymore as the PR was closed or merged._

I added a test and moved the `streaming` param from `read` to `__init_`. Then, I also decided to update the `read` method of the rest of the packaged modules to account for this param. 

@hamid-vakilzadeh Are you OK with these changes? 

@mariosasko these all look great! Thanks for the updates.
Revert task removal in folder-based builders
State: closed

Reverts the removal of `task_templates` in the folder-based builders. I also added the `AudioClassifaction` task for consistency.

This is needed to fix https://github.com/huggingface/transformers/issues/19177.

I think we should soon deprecate and remove the current task API (and investigate if it's possible to integrate the `train eval index` API), but we need to update the Transformers examples before that so we don't break them.

cc @NielsRogge 

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Restore saved format state in `load_from_disk`
State: closed
Labels: description: Something isn't working
name: bug

description: Good for newcomers
name: good first issue

Even though we save the `format` state in `save_to_disk`, we don't restore it in `load_from_disk`. We should fix that.
 
Reported here: https://discuss.huggingface.co/t/save-to-disk-loses-formatting-information/23815

Comments:
Hi, can I work on this?

Hi, sure! Let us know if you need some pointers/help.
Fix bug with labels of eurlex config of lex_glue dataset
State: closed
Labels: description: Contribution to a dataset script
name: dataset contribution

Fix for a critical bug in the EURLEX dataset label list to make LexGLUE EURLEX results replicable. 

In LexGLUE (Chalkidis et al., 2022), the following is mentioned w.r.t. EUR-LEX: _"It supports four different label granularities, comprising 21, 127, 567, 7390 EuroVoc concepts, respectively. We use the 100 most frequent concepts from level 2 [...]”._ The current label list has all 127 labels, which leads to different (lower) results, as communicated by users.

Thanks!

Comments:
_The documentation is not available anymore as the PR was closed or merged._

@JamesLYC88 here is the fix! Thanks again!

Thanks, @albertvillanova. When do you expect that this change will take effect when someone downloads the dataset?

The change is immediately available now, since this change we made to our library:
- #4059
Audiofolder creates empty Dataset if files same level as metadata
State: closed
Labels: description: Something isn't working
name: bug

description: Good for newcomers
name: good first issue

name: hacktoberfest

## Describe the bug
When audio files are at the same level as the metadata (`metadata.csv` or `metadata.jsonl` ), the `load_dataset` returns a `DatasetDict` with no rows but the correct columns.

https://github.com/huggingface/datasets/blob/1ea4d091b7a4b83a85b2eeb8df65115d39af3766/docs/source/audio_dataset.mdx?plain=1#L88

## Steps to reproduce the bug
`metadata.csv`:
```csv
file_name,duration,transcription
./2063_fe9936e7-62b2-4e62-a276-acbd344480ce_1.wav,10.768,hello
```


```python
>>> audio_dataset = load_dataset("audiofolder", data_dir="/audio-data/")
>>> audio_dataset
DatasetDict({
    train: Dataset({
        features: ['audio', 'duration', 'transcription'],
        num_rows: 0
    })
    validation: Dataset({
        features: ['audio', 'duration', 'transcription'],
        num_rows: 0
    })
})
```

I've tried, with no success,:
- setting `split` to something else so I don't get a `DatasetDict`,
- removing the `./`,
- using `.jsonl`.


## Expected results
```
 Dataset({
        features: ['audio', 'duration', 'transcription'],
        num_rows: 1
})
```

## Actual results
```
DatasetDict({
    train: Dataset({
        features: ['audio', 'duration', 'transcription'],
        num_rows: 0
    })
    validation: Dataset({
        features: ['audio', 'duration', 'transcription'],
        num_rows: 0
    })
})
```

## Environment info
- `datasets` version: 2.5.1
- Platform: Linux-5.13.0-1025-aws-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.5.0



Comments:
Hi! Unfortunately, I can't reproduce this behavior. Instead, I get `ValueError: audio at 2063_fe9936e7-62b2-4e62-a276-acbd344480ce_1.wav doesn't have metadata in /audio-data/metadata.csv`, which can be fixed by removing the `./` from the file name.

(Link to a Colab that tries to reproduce this behavior: https://colab.research.google.com/drive/1IhQzULYi0Van1xLrN_SddBX1JF7mLZZK?usp=sharing)

I think we can make the file name matching part more robust by replacing `file_name` with `os.path.normpath(file_name)`, to ignore "./" among other things, in these two places:
* https://github.com/huggingface/datasets/blob/85cd129bde605cd9acacdff0d065fc02e39e09b1/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L319
* https://github.com/huggingface/datasets/blob/85cd129bde605cd9acacdff0d065fc02e39e09b1/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L388

@mariosasko Some tests failed (see my PR). Any thoughts on that?

Yes, I mentioned the solution in my review.

I realized what I was doing wrong.

The documentation puts the files in a subfolder.
Once I have done that, it worked.

But l agree that this should be handled better if possible.
Hendrycks Checksum
State: closed
Labels: description: A bug in a dataset script provided in the library
name: dataset bug

Hi, 
The checksum for [hendrycks_test](https://huggingface.co/datasets/hendrycks_test) does not compare correctly, I guess it has been updated on the remote.

```
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://people.eecs.berkeley.edu/~hendrycks/data.tar']
```


Comments:
Thanks for reporting, @DanielHesslow. We are fixing it. 

@albertvillanova thanks for taking care of this so quickly!

The dataset metadata is fixed. You can download it normally.
`Dataset.unique` showing wrong output after filtering
State: closed
Labels: description: Something isn't working
name: bug

## Describe the bug
After filtering a dataset, and if no samples remain, `Dataset.unique` will return the unique values of the unfiltered dataset.

## Steps to reproduce the bug
```python
from datasets import Dataset

dataset = Dataset.from_dict({'id': [0]})
dataset = dataset.filter(lambda _: False)
print(dataset.unique('id'))
```

## Expected results
The above code should return an empty list since the dataset is empty.

## Actual results
```bash
[0]
```

## Environment info
- `datasets` version: 2.5.1
- Platform: Linux-5.18.19-100.fc35.x86_64-x86_64-with-glibc2.34
- Python version: 3.9.14
- PyArrow version: 7.0.0
- Pandas version: 1.3.5

Comments:
Hi! It seems like `flatten_indices` (called in `unique`) doesn't know how to handle empty indices mappings. I'm working on the fix.

Thanks, that was fast!
Add oversampling strategy iterable datasets interleave
State: closed

Hello everyone,
Following the issue #4893 and the PR #4831, I propose here an oversampling strategy for a `IterableDataset` list.  
The `all_exhausted` strategy stops building the new dataset as soon as all samples in each dataset have been added at least once. 
It follows roughly the same logic behind #4831, namely:
- if ``probabilities`` is `None` and the strategy is `all_exhausted`, it simply performs a round robin interleaving that stops when the longest dataset is out of samples. Here the new dataset length will be $maxLengthDataset*nbDataset$.
- if ``probabilities`` is not `None` and the strategy is `all_exhausted`, it keeps trace of the datasets which were out of samples but continues to add them to the new dataset, and stops as soons as every dataset runs out of samples at least once.

In order to be consistent and also to align with the `Dataset` behavior, please note that the behavior of the default strategy (`first_exhausted`) has been changed. Namely, it really stops when a dataset is out of samples whereas it used to stop when receiving the `StopIteration` error.  
To give an example of the last note, with the following snippet:
```
>>> from tests.test_iterable_dataset import *
>>> d1 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {"a": i}) for i in [0, 1, 2]])), {}))
>>> d2 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {"a": i}) for i in [10, 11, 12, 13]])), {}))
>>> d3 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {"a": i}) for i in [20, 21, 22, 23, 24]])), {}))
>>> dataset = interleave_datasets([d1, d2, d3])
>>> [x["a"] for x in dataset]
```
The result here will then be `[10, 0, 11, 1, 2]` instead of `[10, 0, 11, 1, 2, 20, 12, 13]`.

I modified the behavior because I found it to be consistent with the under/oversampling approach and because it unified the undersampling and oversampling code, but I stay open to any suggestions.


Comments:
_The documentation is not available anymore as the PR was closed or merged._
Update README.md of yahoo_answers_topics dataset
State: closed


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5034). All of your documentation changes will be reflected on that endpoint.

Thanks, @borgr. We have removed all dataset scripts from this repo. Subsequent PRs should be opened directly on the Hugging Face Hub.

Do you mean to edit through "edit dataset card" button? because it just leads to a broken page...
https://huggingface.co/datasets/yahoo_answers_topics
![image](https://user-images.githubusercontent.com/6416600/193852796-009ba537-1e8f-4c8b-898a-8c4f817b86ee.png)
https://github.com/huggingface/datasets/tree/main/datasets/yahoo_answers_topics

Hi @borgr, good catch! I'm going to report the button leading to a broken link.

In the meantime, you can propose a PR to the `README.md` file using this link: https://huggingface.co/datasets/yahoo_answers_topics/blob/main/README.md
new dataset type: single-label and multi-label video classification
State: open
Labels: description: New feature or request
name: enhancement

**Is your feature request related to a problem? Please describe.**
In my research, I am dealing with multi-modal (audio+text+frame sequence) video classification. It would be great if the datasets library supported generating multi-modal batches from a video dataset.

**Describe the solution you'd like**
Assume I have video files having single/multiple labels. I want to train a single/multi-label video classification model. I want datasets to support generating multi-modal batches (audio+frame sequence) from video files. Audio waveform and frame sequence can be extracted from each video clip then I can use any audio, image and video model from transformers library to extract features which will be fed into my model.

**Describe alternatives you've considered**
Currently, I am using https://github.com/facebookresearch/pytorchvideo dataloaders. There seems to be not much alternative.

**Additional context**
I am wiling to open a PR but don't know where to start.


Comments:
Hi ! You can in the `features` folder how we implemented the audio and image feature types.

We can have something similar to videos. What we need to decide:
- the video loading library to use
- the output format when a user accesses a video type object
- what parameters a `Video()` feature type needs

also cc @nateraw who also took a look at what we can do for video

@lhoestq @nateraw is there any progress on adding video classification datasets? 

Hi ! I think we just missing which lib we're going to use to decode the videos + which parameters must go in the `Video` type

Hmm. `decord` could be nice but it's no longer maintained [it seems](https://github.com/dmlc/decord/issues/214). 

pytorchvideo uses [pyav](https://github.com/PyAV-Org/PyAV) as the default decoder: https://github.com/facebookresearch/pytorchvideo/blob/c8d23d8b7e597586a9e2d18f6ed31ad8aa379a7a/pytorchvideo/data/labeled_video_dataset.py#L37

Also it would be great if `optionally` audio can also be decoded from the video as in pytorchvideo: https://github.com/facebookresearch/pytorchvideo/blob/c8d23d8b7e597586a9e2d18f6ed31ad8aa379a7a/pytorchvideo/data/labeled_video_dataset.py#L35

Here are the other decoders supported in pytorchvideo: https://github.com/facebookresearch/pytorchvideo/blob/c8d23d8b7e597586a9e2d18f6ed31ad8aa379a7a/pytorchvideo/data/encoded_video.py#L17


@sayakpaul I did do quite a bit of work on [this PR](https://github.com/huggingface/datasets/pull/4532) a while back to add a video feature. It's outdated, but uses my `encoded_video` [package](https://github.com/nateraw/encoded-video) under the hood, which is basically a wrapper around PyAV stolen from [pytorchvideo](https://github.com/facebookresearch/pytorchvideo/) that gets rid of the `torch` dependency. 

would be really great to get something like this in...it's just a really tricky and time consuming feature to add.
Support hfh 0.10 implicit auth
State: closed

In huggingface-hub 0.10 the `token` parameter is deprecated for dataset_info and list_repo_files in favor of use_auth_token.
Moreover if use_auth_token=None then the user's token is used implicitly.

I took those two changes into account

Close https://github.com/huggingface/datasets/issues/4990

TODO:
- [x] fix tests

We should wait hfh 0.10 to be relased first to make sure it works correctly before merging

Comments:
_The documentation is not available anymore as the PR was closed or merged._

@lhoestq it is now released so you can move forward with it :) 

I took your comments into account @Wauplin :)
I also bumped the requirement to 0.2.0 because we're using `set_access_token`

cc @albertvillanova WDYT ? I edited the CI job to also check for our minimum supported version of hfh at the same time as the minimum pyarrow version

@lhoestq great, thanks ! :)
Fast dataset iter
State: closed

Use `pa.Table.to_reader` to make iteration over examples/batches faster in `Dataset.{__iter__, map}`

TODO:
* [x] benchmarking (the only benchmark for now - iterating over (single) examples of `bookcorpus` (75 mil examples) in Colab is approx. 2.3x faster)
* [x] check if iterating over bigger chunks + slicing to fetch individual examples in `_iter` yields better performance




Comments:
_The documentation is not available anymore as the PR was closed or merged._

I ran some benchmarks (focused on the data fetching part of `__iter__`) and it seems like the combination `table.to_reader(batch_size)` + `RecordBatch.slice` performs the best ([script](https://gist.github.com/mariosasko/0248288a2e3a7556873969717c1fe52b) with the results). I think we can choose (implicit) `batch_size=10` in the final implementation to avoid having problems with fetching large examples.
passing parameters to the method passed to Dataset.from_generator()
State: closed
Labels: description: New feature or request
name: enhancement

Big thanks for providing dataset creation via a generator.
I want to ask whether there is any way that parameters can be passed to the method Dataset.from_generator() method, like as follows. 

```
from datasets import Dataset

def gen(param1):
    for idx in len(custom_dataset):
        yield custom_dataset[idx] + param1

ds = Dataset.from_generator(gen(param1))
```


Comments:
Hi! Yes, you can either use the `gen_kwargs` param in `Dataset.from_generator` (`ds = Dataset.from_generator(gen, gen_kwargs={"param1": val})`) or wrap the generator function with `functools.partial`
(`ds = Dataset.from_generator(functools.partial(gen, param1="val"))`) to pass custom parameters to it.
patch CI_HUB_TOKEN_PATH with Path instead of str
State: closed

Should fix the tests for `huggingface_hub==0.10.0rc0` prerelease (see [failed CI](https://github.com/huggingface/datasets/actions/runs/3127805250/jobs/5074879144)). 
Related to [this thread](https://huggingface.slack.com/archives/C02V5EA0A95/p1664195165294559) (internal link).

Note: this should be a backward compatible fix (e.g. works also with previous versions of `huggingface_hub`)

I am not sure where to put the changes so feel free to cherry-pick the commit and close this one without merging.

cc @lhoestq 

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Text strings are split into lists of characters in xcsr dataset
State: closed
Labels: description: A bug in a dataset script provided in the library
name: dataset bug

## Describe the bug
Text strings are split into lists of characters.

Example for "X-CSQA-en":
```
{'id': 'd3845adc08414fda',
 'lang': 'en',
 'question': {'stem': ['T',
   'h',
   'e',
   ' ',
   'd',
   'e',
   'n',
   't',
   'a',
   'l',
   ' ',
   'o',
   'f',
   'f',
   'i',
   'c',
   'e',
   ' ',
   'h',
   'a',
   'n',
   'd',
   'l',
   'e',
   'd',
   ' ',
   'a',
   ' ',
   'l',
   'o',
   't',
   ' ',
   'o',
   'f',
   ' ',
   'p',
   'a',
   't',
   'i',
   'e',
   'n',
   't',
   's',
   ' ',
   'w',
   'h',
   'o',
   ' ',
   'e',
   'x',
   'p',
   'e',
   'r',
   'i',
   'e',
   'n',
   'c',
   'e',
   'd',
   ' ',
   't',
   'r',
   'a',
   'u',
   'm',
   'a',
   't',
   'i',
   'c',
   ' ',
   'm',
   'o',
   'u',
   't',
   'h',
   ' ',
   'i',
   'n',
   'j',
   'u',
   'r',
   'y',
   ',',
   ' ',
   'w',
   'h',
   'e',
   'r',
   'e',
   ' ',
   'w',
   'e',
   'r',
   'e',
   ' ',
   't',
   'h',
   'e',
   's',
   'e',
   ' ',
   'p',
   'a',
   't',
   'i',
   'e',
   'n',
   't',
   's',
   ' ',
   'c',
   'o',
   'm',
   'i',
   'n',
   'g',
   ' ',
   'f',
   'r',
   'o',
   'm',
   '?'],
  'choices': [{'label': ['A'], 'text': ['t', 'o', 'w', 'n']},
   {'label': ['B'], 'text': ['m', 'i', 'c', 'h', 'i', 'g', 'a', 'n']},
   {'label': ['C'], 'text': ['h', 'o', 's', 'p', 'i', 't', 'a', 'l']},
   {'label': ['D'], 'text': ['s', 'c', 'h', 'o', 'o', 'l', 's']},
   {'label': ['E'],
    'text': ['o',
     'f',
     'f',
     'i',
     'c',
     'e',
     ' ',
     'b',
     'u',
     'i',
     'l',
     'd',
     'i',
     'n',
     'g']}]},
 'answerKey': 'C'}

## Steps to reproduce the bug
```python
ds = load_dataset("datasets/xcsr", "X-CSQA-en", split="validation", streaming=True)
item = next(iter(ds))
item
```

## Expected results
```
{'id': 'd3845adc08414fda',
 'lang': 'en',
 'question': {'stem': 'The dental office handled a lot of patients who experienced traumatic mouth injury, where were these patients coming from?',
  'choices': {'label': ['A', 'B', 'C', 'D', 'E'],
   'text': ['town', 'michigan', 'hospital', 'schools', 'office building']}},
 'answerKey': 'C'}
```


Comments:
Fix languages of X-CSQA configs in xcsr dataset
State: closed
Labels: description: Contribution to a dataset script
name: dataset contribution

Fix #5017.

CC: @yangxqiao, @yuchenlin

Comments:
_The documentation is not available anymore as the PR was closed or merged._

Thanks @lhoestq, I had missed that... 

thx for the super fast work @albertvillanova ! any estimate for when the relevant release will happen?

Thanks again 

@thesofakillers after a recent change in our library (see #4059), now fixes in all datasets are immediately accessible. You can try it:
```python
french = datasets.load_dataset("xcsr", "X-CSQA-fr")
```

Please note there is an additional fix to that dataset in progress (to be merged today):
- #5024
Fix URLs of sbu_captions dataset
State: closed
Labels: description: Contribution to a dataset script
name: dataset contribution

Forbidden
You don't have permission to access /~vicente/sbucaptions/sbu-captions-all.tar.gz on this server.

Additionally, a 403 Forbidden error was encountered while trying to use an ErrorDocument to handle the request.

Apache/2.4.6 (Red Hat Enterprise Linux) OpenSSL/1.0.2k-fips PHP/5.4.16 mod_fcgid/2.3.9 mod_wsgi/3.4 Python/2.7.5 mod_perl/2.0.11 Perl/v5.16.3 Server at [www.cs.virginia.edu](mailto:csroot@virginia.edu) Port 443

Comments:
_The documentation is not available anymore as the PR was closed or merged._