helmo/github-issues huggingface dataset:
`Dataset.unique` showing wrong output after filtering
State: closed | Author: mxschmdt (CONTRIBUTOR)
Labels: 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 | Author: ylacombe (CONTRIBUTOR)

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 | Author: borgr (CONTRIBUTOR)


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 | Author: fcakyon (NONE)
Labels: 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 | Author: lhoestq (MEMBER)

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 | Author: mariosasko (COLLABORATOR)

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 | Author: Basir-mahmood (NONE)
Labels: 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 | Author: Wauplin (CONTRIBUTOR)

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 | Author: albertvillanova (MEMBER)
Labels: 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'}
```
Fix languages of X-CSQA configs in xcsr dataset
State: closed | Author: albertvillanova (MEMBER)
Labels: 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 | Author: donglixp (CONTRIBUTOR)
Labels: 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._
Update swiss judgment prediction
State: closed | Author: JoelNiklaus (CONTRIBUTOR)
Labels: name: dataset contribution

Hi,

I updated the dataset to include additional data made available recently. When I test it locally, it seems to work. However, I get the following error with the dummy data creation: 
`Dummy data generation done but dummy data test failed since splits ['train', 'validation', 'test'] have 0 examples for config 'fr'`. Do you know why this could be the case?

Cheers,
Joel

Comments: Thank you very much for the detailed review @albertvillanova!

I updated the PR with the requested changes. 

At the end, I had to manually fix the conflict, so that CI tests are launched.

PLEASE NOTE: you should first pull to incorporate the previous commit
```shell
git pull
```

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

Thank you very much for the detailed feedback and your time @albertvillanova! 
Yes, thanks. My other datasets are already on the hub: https://huggingface.co/joelito
Create all YAML dataset_info
State: closed | Author: lhoestq (MEMBER)
Labels: name: dataset contribution

Following https://github.com/huggingface/datasets/pull/4926

Creates all the `dataset_info` YAML fields in the dataset cards
The JSON are also updated using the simplified backward compatible format added in https://github.com/huggingface/datasets/pull/4926

Needs https://github.com/huggingface/datasets/pull/4926 to be merged first

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5018). All of your documentation changes will be reflected on that endpoint.

Closing since https://github.com/huggingface/datasets/pull/4974 removed all the datasets scripts.

Individual PRs must be opened on the Hugging face Hub to add the YAML metadata
xcsr: X-CSQA simply uses english for all alleged non-english data
State: closed | Author: thesofakillers (NONE)
Labels: name: dataset bug

## Describe the bug
All the alleged non-english subcollections for the X-CSQA task in the [xcsr benchmark dataset ](https://huggingface.co/datasets/xcsr) seem to be copies of the english subcollection, rather than translations. This is in contrast to the data description:

> we automatically translate the original CSQA and CODAH datasets, which only have English versions, to 15 other languages, forming development and test sets for studying X-CSR
 
## Steps to reproduce the bug
```python
# let's say you want to load the french X-CSQA subcollection
french = datasets.load_dataset("xcsr", "X-CSQA-fr")
# for good measure, let's load english too
english = datasets.load_dataset("xcsr", "X-CSQA-en")

# let's inspect
"".join(english['test'][0]['question']['stem'])
# output: 'The people wanted to stop the parade, so what did they set up to thwart it?'
"".join(french['test'][0]['question']['stem'])
# output: 'The people wanted to stop the parade, so what did they set up to thwart it?'
# what? Why are they both in english?

# I've checked this for validation and train splits too, across many datapoints. It's all the same english dataset
# maybe i need to look better?
french['test'].unique('lang')
# output: ['en']
# no, it's all english

```

## Expected results
Accessing a subcollection in language X should return a subcollection containg samples in language X

## Actual results
Accessing a subcollection in language X returns a subcollection containing samples in English.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->

- `datasets` version: 2.5.1
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.3




Comments: Thanks for reporting, @thesofakillers. Good catch. We are fixing this.
Fix tar extraction vuln
State: closed | Author: lhoestq (MEMBER)

Fix for CVE-2007-4559

Description:
Directory traversal vulnerability in the (1) extract and (2) extractall functions in the tarfile
module in Python allows user-assisted remote attackers to overwrite arbitrary files via a .. (dot dot)
sequence in filenames in a TAR archive, a related issue to CVE-2001-1267.

I fixed it by using the solution proposed in https://stackoverflow.com/questions/10060069/safely-extract-zip-or-tar-using-python

It blocks extraction of files with an absolute path or double dots and symlinks.

Comments: _The documentation is not available anymore as the PR was closed or merged._
Transfer dataset scripts to Hub
State: closed | Author: albertvillanova (MEMBER)

Before merging:
- #4974 

TODO:
- [x] Create label: ["dataset contribution"](https://github.com/huggingface/datasets/pulls?q=label%3A%22dataset+contribution%22)
- [x] Create project: [Datasets: Transfer datasets to Hub](https://github.com/orgs/huggingface/projects/22/)
- [x] PRs:
  - [x] Add dataset: we should recommend transfer all additions of datasets to the Hub, under the appropriate namespace; no more additions of datasets on GitHub
  - [x] Update dataset: in general, we should merge bug fixes; enhancements should be considered on a case-by-case basis, depending on whether there is a more suitable namespace on the Hub
- [ ] Issues

Finally:
- [x] #4974

Let me know what you think! :hugs: 

Comments: Sounds good ! Can I help with anything ?
I need to read the custom dataset in conll format
State: open | Author: shell-nlp (NONE)
Labels: name: enhancement

I need to read the custom dataset in conll format


Comments: Hi! We don't currently have a builder for parsing custom `conll` datasets, but I guess we could add one as a packaged module (similarly to what [TFDS](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/core/dataset_builders/conll/conll_dataset_builder.py) did). @lhoestq @albertvillanova WDYT?

In the meantime, you can use `Dataset.from_generator` to create a dataset as follows:
```python
from datasets import Dataset

# 2009 version
INPUT_COLUMNS = "ID FORM LEMMA PLEMMA POS PPOS FEAT PFEAT HEAD PHEAD DEPREL PDEPREL".split()

def read_conll(file):
    example = {col: [] for col in INPUT_COLUMNS}
    idx = 0
    with open(file) as f:
        for line in f:
            if line.startswith("-DOCSTART-") or line == "\n" or not line:
                if example[next(iter(example))]:
                    yield idx, example
                idx += 1
                example = {col: [] for col in INPUT_COLUMNS}
            else:
                row_cols = line.split()
                for i, col in enumerate(example):
                    example[col] = row_cols[i].rstrip()

# (optional) pass custom features with `features=Features(...)`
dset = Dataset.from_generator(read_conll, gen_kwargs={"file": "path/to/conll/file"})   
``` 

I think we could add a dedicated builder if you think this format is general enough.




> I think we could add a dedicated builder if you think this format is general enough.

I think its functions are incomplete. It should have to_ Conll and from_ There are two methods of conll.
would huggingface like publish cpp binding for datasets package ?
State: closed | Author: mullerhai (NONE)
Labels: name: wontfix

HI:
I use cpp env libtorch, I like use hugggingface ,but huggingface not cpp binding, would you like publish cpp binding for it.
thanks

Comments: Hi ! Can you share more information about your use case ? How could it help you to have cpp bindings versus using the python libraries ?

> Hi ! Can you share more information about your use case ? How could it help you to have cpp bindings versus using the python libraries ?

for example ,the huggingface  load_model() and load_dataset()  can  execute in cpp env

If it's a viable option for you, you can check [tch-rs](https://github.com/LaurentMazare/tch-rs) to load models in Rust. Regarding datasets, you can first download them in python and then use Arrow C++ or Rust to load them

If you are more adventurous, another option is to embed python calls inside c++ e.g. with `pybind11`.

> pybind11

I think it is not the best solution
Force JSON format regardless of file naming on S3
State: closed | Author: junwang-wish (NONE)
Labels: name: enhancement

I have a file on S3 created by Data Version Control, it looks like `s3://dvc/ac/badff5b134382a0f25248f1b45d7b2` but contains a json file. If I run 
```python
dataset = load_dataset(
    "json",
    data_files='s3://dvc/ac/badff5b134382a0f25248f1b45d7b2' 
)
```
It gives me 
```
InvalidSchema: No connection adapters were found for 's3://dvc/ac/badff5b134382a0f25248f1b45d7b2'
```
However, I cannot go ahead and change the names of the s3 file. Is there a way to "force" load a S3 url with certain decoder (JSON, CSV, etc.) regardless of s3 URL naming?

Comments: Hi ! Support for URIs like `s3://...` is not implemented yet in `data_files=`. You can use the HTTP URL instead if your data is public in the meantime

Hi,
I want to make sure I understand this response. I have a set of files on S3 that are private for security reasons. Because they are not public files  I cannot read those files (many are parquet) into my hf notebooks in Kaggle? That can't be correct, can it? 

Hi ! There is a discussion at https://github.com/huggingface/datasets/issues/5281

Using the latest `datasets` 2.11 you can try passing fsspec URLs to private buckets to `data_files` in `load_dataset()`. Though this is still experimental and undocumented, so feedback is welcome. You may not have the best experience though, since anything related to performance and caching hasn't been tested properly yet.

closing this one since data_files supports fsspec (still experimental/untested/undocumented for s3 though)