helmo/github-issues huggingface dataset:
load_dataset_builder should error if "name" does not exist?
State: open | Author: severo (COLLABORATOR)
Labels: name: bug

name: dataset-viewer

```
import datasets as ds
builder = ds.load_dataset_builder('sent_comp', name="doesnotexist")
builder.info.config_name
```

returns

```
'doesnotexist'
```

Shouldn't it raise an error instead?

For this dataset, the only valid values for `name` should be: `"default"` or `None` (ie. argument not passed)

Comments: Yes I think it should raise an error. Currently it looks like it instantiates a custom configuration with the name given by the user:
https://github.com/huggingface/datasets/blob/ba27ce33bf568374cf23a07669fdd875b5718bc2/src/datasets/builder.py#L391-L397
DatasetDict.filter and Dataset.filter crashes with any "fn_kwargs" argument
State: closed | Author: DrMatters (NONE)
Labels: name: bug

## Describe the bug
The ".filter" method of DatasetDict or Dataset objects fails when passing any "fn_kwargs" argument

## Steps to reproduce the bug
```python
import datasets

example_dataset = datasets.Dataset.from_dict({"a": {1, 2, 3, 4}})

def filter_value(example, value):
    return example['a'] == value

filtered = example_dataset.filter(filter_value, fn_kwargs={'value': 3})
```

## Expected results
`filtered` is a dataset containing {"a": {3}}

## Actual results

> Traceback (most recent call last):
>   File "C:\Users\qsemi\Documents\git\nlp_experiments\gpt_celebrity\src\test_faulty_filter.py", line 8, in <module>
>     filtered = example_dataset.filter(filter_value, fn_kwargs={'value': 3})
>   File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 185, in wrapper
>     out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
>   File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\fingerprint.py", line 398, in wrapper
>     out = func(self, *args, **kwargs)
>   File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 2169, in filter
>     indices = self.map(
>   File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 1686, in map
>     return self._map_single(
>   File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 185, in wrapper
>     out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
>   File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\fingerprint.py", line 398, in wrapper
>     out = func(self, *args, **kwargs)
>   File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 2048, in _map_single
>     batch = apply_function_on_filtered_inputs(
>   File "C:\Users\qsemi\miniconda3\envs\main\lib\site-packages\datasets\arrow_dataset.py", line 1939, in apply_function_on_filtered_inputs
>     function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
> TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'value'

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Windows-10-10.0.19042-SP0
- Python version: 3.9.7
- PyArrow version: 5.0.0


Comments: Hi @DrMatters, thanks for reporting.

This issue was fixed 14 days ago: #2950.

Currently, the fix is only in the master branch and will be made available in our next library release.

In the meantime, you can incorporate the fix by installing datasets from the master branch:
```shell
pip install -U git+ssh://git@github.com/huggingface/datasets.git@master#egg=datasest
```
or
```shell
pip install -U git+https://github.com/huggingface/datasets.git@master#egg=datasets
```

Thanks, sorry for bothering
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English.
State: closed | Author: iliaschalkidis (CONTRIBUTOR)

Inspired by the recent widespread use of the GLUE multi-task benchmark NLP dataset (Wang et al., 2018), the subsequent more difficult SuperGLUE (Wang et al., 2019), other previous multi-task NLP benchmarks (Conneau and Kiela, 2018; McCann et al., 2018), and similar initiatives in other domains (Peng et al., 2019), we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a benchmark dataset to evaluate the performance of NLP methods in legal tasks. LexGLUE is based on seven existing legal NLP datasets, selected using criteria largely from SuperGLUE.

As in GLUE and SuperGLUE (Wang et al., 2019b,a), one of our goals is to push towards generic (or ‘foundation’) models that can cope with multiple NLP tasks, in our case legal NLP tasks possibly with limited task-specific fine-tuning. Another goal is to provide a convenient and informative entry point for NLP researchers and practitioners wishing to explore or develop methods for legalNLP. Having these goals in mind, the datasets we include in LexGLUE and the tasks they address have been simplified in several ways to make it easier for newcomers and generic models to address all tasks.

LexGLUE benchmark is accompanied by experimental infrastructure that relies on Hugging Face Transformers library and resides at: https://github.com/coastalcph/lex-glue.

Comments: Please wait until Tuesday. Arxiv pre-print is pending. 🤗 

Hi @lhoestq, I updated the README with the Arxiv publication info and now the tests are not passing.

It seems that the error is completely irrelevant to my code:

```
 Attempting uninstall: ruamel.yaml
    Found existing installation: ruamel-yaml 0.15.87
ERROR: Cannot uninstall 'ruamel-yaml'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.
```

Hi ! Thanks for adding this one. Amazing work :o 

Yea you can ignore the `ruamel-yaml` error, it's unrelated to your PR and fixed on `master`

Hi @lhoestq, 

- I fetched and merged the upstream master to get the `ruamel-yaml` fix.
- I updated the README.md file including more information for the inputs and labels, while I also turned all tables in HTML format hoping that they will render nicely in the dataset card in the HF website.
- I also simplified the CaseHOLD input, excl. the unused `question` field and the `context` replicas, as suggested.
Remove a reference to the open Arrow file when deleting a TF dataset created with to_tf_dataset
State: closed | Author: mariosasko (COLLABORATOR)

This [comment](https://github.com/huggingface/datasets/issues/2934#issuecomment-922970919) explains the issue. This PR fixes that with a `weakref` callback, and additionally:
* renames `TensorflowDatasetMixIn` to `TensorflowDatasetMixin` for consistency
* correctly indents `TensorflowDatasetMixin`'s docstring
* replaces `tf.data.AUTOTUNE` with `tf.data.experimental.AUTOTUNE` (we support TF>=2.2 according to the [setup.py](https://github.com/huggingface/datasets/blob/fc46bba66ba4f432cc10501c16a677112e13984c/setup.py#L188) and `AUTOTUNE` has been moved to the experimental part of `tf.data` in 1.X if I'm not mistaken)

Fixes #2934

Comments: @lhoestq The test passes even without the try/except block!

Hey, I'm a little late because I was caught up in the course work, but I double-checked this and it looks great. Thanks for fixing!
Fix cast to Python scalar in Matthews Correlation metric
State: closed | Author: mariosasko (COLLABORATOR)

This PR is motivated by issue #2964.

The Matthews Correlation metric relies on sklearn's `matthews_corrcoef` function to compute the result. This function returns either `float` or `np.float64` (see the [source](https://github.com/scikit-learn/scikit-learn/blob/844b4be24d20fc42cc13b957374c718956a0db39/sklearn/metrics/_classification.py#L906-L909)). Obviously, calling `.item()` on the float value will fail, so I'm fixing this with the built-in `float()` function, which covers both cases. Surprisingly, on my machine, casting `np.float64` to a Python scalar with `float()` is even faster than with the `.item()` method.
Fix json loader when conversion not implemented
State: closed | Author: lhoestq (MEMBER)

Sometimes the arrow json parser fails if the `block_size` is too small and returns an `ArrowNotImplementedError: JSON conversion to struct...` error.

By increasing the block size it makes it work again.

Hopefully it should help with https://github.com/huggingface/datasets/issues/2799

I tried with the file mentioned in the issue and it worked for me
cc @lewtun can you try again from this branch ?

Comments: And we're already at PR number 3,000 ! ^^

Thank you so much for fixing this @lhoestq 😍 ! I just tested the branch out and it works like a charm!
cannot shuffle dataset loaded from disk
State: open | Author: pya25 (NONE)
Labels: name: bug

## Describe the bug
dataset loaded from disk cannot be shuffled.  

## Steps to reproduce the bug
```
my_dataset = load_from_disk('s3://my_file/validate', fs=s3)
sample = my_dataset.select(range(100)).shuffle(seed=1234)
```

## Actual results
```
sample = my_dataset .select(range(100)).shuffle(seed=1234)
  File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 185, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/fingerprint.py", line 398, in wrapper
    out = func(self, *args, **kwargs)
  File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2494, in shuffle
    new_fingerprint=new_fingerprint,
  File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 185, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/fingerprint.py", line 398, in wrapper
    out = func(self, *args, **kwargs)
  File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2303, in select
    tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(indices_cache_file_name), delete=False)
  File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/tempfile.py", line 547, in NamedTemporaryFile
    (fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)
  File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/tempfile.py", line 258, in _mkstemp_inner
    fd = _os.open(file, flags, 0o600)
FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmpnnu5uhnx/my_file/validate/tmpy76d70g4'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1

- Python version: 3.7
- PyArrow version: 5.0.0
Dataset has incorrect labels
State: closed | Author: heiko-hotz (NONE)

The dataset https://huggingface.co/datasets/turkish_product_reviews has incorrect labels - all reviews are labelled with "1" (positive sentiment). None of the reviews is labelled with "0". See screenshot attached:

![Capture](https://user-images.githubusercontent.com/63367770/135617428-14ce0b27-5208-4e66-a3ee-71542e3257b4.PNG)


Comments: Hi @marshmellow77, thanks for reporting.

That issue is fixed since `datasets` version 1.9.0 (see 16bc665f2753677c765011ef79c84e55486d4347).

Please, update `datasets` with: `pip install -U datasets`

Thanks. Please note that the dataset explorer (https://huggingface.co/datasets/viewer/?dataset=turkish_product_reviews) still shows the incorrect state. The sentiment for the first few customer reviews is actually negative and should be labelled with "0", see screenshot:

![Capture1](https://user-images.githubusercontent.com/63367770/135637150-93d9b09b-f1dd-4701-97a5-5cb2672ec0c7.PNG)




Thanks @marshmellow77, good catch! I'm transferring this issue to https://github.com/huggingface/datasets-viewer.
Fix loading compressed CSV without streaming
State: closed | Author: albertvillanova (MEMBER)

When implementing support to stream CSV files (https://github.com/huggingface/datasets/commit/ad489d4597381fc2d12c77841642cbeaecf7a2e0#diff-6f60f8d0552b75be8b3bfd09994480fd60dcd4e7eb08d02f721218c3acdd2782), a regression was introduced preventing loading compressed CSV files in non-streaming mode.

This PR fixes it, allowing loading compressed/uncompressed CSV files in streaming/non-streaming mode.

Fix #2977.
add docmentation for the `Unix style pattern` matching feature that can be leverage for `data_files` into `load_dataset`
State: open | Author: SaulLu (CONTRIBUTOR)
Labels: name: enhancement

Unless I'm mistaken, it seems that in the new documentation it is no longer mentioned that you can use Unix style pattern matching in the  `data_files` argument of the `load_dataset` method.

This feature was mentioned [here](https://huggingface.co/docs/datasets/loading_datasets.html#from-a-community-dataset-on-the-hugging-face-hub) in the previous documentation.

I'd love to hear your opinion @lhoestq , @albertvillanova and @stevhliu
Add CommonLanguage
State: closed | Author: anton-l (MEMBER)

This PR adds the Common Language dataset (https://zenodo.org/record/5036977)
The dataset is intended for language-identification speech classifiers and is already used by models on the Hub:
* https://huggingface.co/speechbrain/lang-id-commonlanguage_ecapa
* https://huggingface.co/anton-l/wav2vec2-base-langid

cc @patrickvonplaten
add new dataset kan_hope
State: closed | Author: adeepH (CONTRIBUTOR)

## Adding a Dataset
- **Name:** *KanHope*
- **Description:** *A code-mixed English-Kannada dataset for Hope speech detection*
- **Task:** *Binary Text Classification*
- **Paper:** *https://arxiv.org/abs/2108.04616* 
- **Data:** *https://github.com/adeepH/kan_hope/tree/main/dataset*
- **Motivation:** *The dataset is amongst the very few resources available for code-mixed low-resourced Dravidian languages of India*
add wit dataset
State: closed | Author: nateraw (CONTRIBUTOR)

Resolves #2902 based on conversation there - would also close #2810. Open to suggestions/help 😀

CC @hassiahk @lhoestq @yjernite 

Comments: Opening this up - Here's whats left to make it really shine:
- [ ] Update dataset card (this is being worked on by the author)
- [ ] Add `dataset_info.json`. Requires downloading the entire dataset. I believe @lhoestq mentioned he may have a machine he's using for this sort of thing.

I think both of these _could_ be done in separate PRs, but I'm not picky either way.

Also, we may find that users (and/or the authors) would like additional configurations for languages, image only, embeddings only, etc. I think we can wait and see what the community says on this and address in different PR.

Finally got around to this PR, sorry for the delay!

I added some minimal info to the dataset card so we can at least have it on the hub and work more on it later.

Unfortunately, this re-triggered the tests and it looks like we need a rebase? cc @nateraw 

Yea we need to rebase. Also I think it would be best to update the features to all be strings to match the original datasets format. I ran into a lot of issues with incorrect data types deep into the Dataset info Json process because it couldn't cast correctly. 

I mentioned this to @mariosasko - he might pick up this PR if I don't have bandwidth to finish soon. 

> I think this is a clear occasion to start supporting "canonical" datasets:
> - without dataset_infos.json file
> - without dummy data

I agree we can support datasets without them. However the dataset_infos.json file can at least be **highly encouraged** since it has very nice info about the size of the dataset in MB and in number of examples, and it also helps guaranteeing the integrity of the dataset when it's downloaded (verification of the file checksums and the split sizes).

This dataset now lives here: https://huggingface.co/datasets/wikimedia/wit_base. @nateraw @yjernite Thanks for the initial work.
OpenSLR 25: ASR data for Amharic, Swahili and Wolof
State: open | Author: cdleong (CONTRIBUTOR)
Labels: name: dataset request

## Adding a Dataset
- **Name:** *SLR25*
- **Description:** *Subset 25 from OpenSLR. Other subsets have been added to https://huggingface.co/datasets/openslr, 25 covers Amharic, Swahili and Wolof data*
- **Paper:** *https://www.openslr.org/25/ has citations for each of the three subsubsets. *
- **Data:** *Currently the three links to the .tar.bz2 files can be found a thttps://www.openslr.org/25/*
- **Motivation:** *Increase ASR data for underrepresented African languages. Also, other subsets of OpenSLR speech recognition have been uploaded, so this would be easy.*


https://github.com/huggingface/datasets/blob/master/datasets/openslr/openslr.py already has been created for various other OpenSLR subsets, this should be relatively straightforward to do. 



Comments: Whoever handles this just needs to: 

- [ ] fork the HuggingFace Datasets repo
- [ ] update the [existing dataset script](https://github.com/huggingface/datasets/blob/master/datasets/openslr/openslr.py) to add SLR25. Lots of copypasting from other sections of the script should make that easy. 
Amharic URL: https://www.openslr.org/resources/25/data_readspeech_am.tar.bz2. 
Swahili URL: https://www.openslr.org/resources/25/data_broadcastnews_sw.tar.bz2, 
Wolof URL: https://www.openslr.org/resources/25/data_readspeech_wo.tar.bz2
- [ ] update the [data card](https://github.com/huggingface/datasets/blob/master/datasets/openslr/README.md) to include information about SLR25. There's lots of other examples to draw from. 
- [ ] add the appropriate language tags to the data card as well. https://www.w3.org/International/questions/qa-choosing-language-tags, or just use `sw`, `am`, and `wo` for consistency. 
- [ ] make a pull request to merge your changes back into HuggingFace's repo

... also the example in "use in datasets library" should be updated. It currently says 
![image](https://user-images.githubusercontent.com/4109253/135115980-8583a44a-cae6-4121-b699-00667020849f.png)
But you actually have to specify a subset, e.g. 
```python
dataset = load_dataset("openslr", "SLR32")
```

![image](https://user-images.githubusercontent.com/4109253/135116070-82d4e732-b7b3-4c5b-bd4e-a40d8ccabb0e.png)
ValueError when computing f1 metric with average None
State: closed | Author: asofiaoliveira (CONTRIBUTOR)
Labels: name: bug

## Describe the bug
When I try to compute the f1 score for each class in a multiclass classification problem, I get a ValueError. The same happens with recall and precision. I traced the error to the `.item()` in these scripts, which is probably there for the other averages. E.g. from f1.py:
```python 
return {
            "f1": f1_score(
                references,
                predictions,
                labels=labels,
                pos_label=pos_label,
                average=average,
                sample_weight=sample_weight,
            ).item(),
        }
```
Since the result is an array with more than one item, the `.item()` throws the error. I didn't submit a PR because this might be needed for the other averages, I'm not very familiar with the library

## Steps to reproduce the bug
```python
from datasets import load_metric
metric = load_metric("f1")
metric.add_batch(predictions=[2,34,1,34,1,2,3], references=[23,52,1,3,523,5,8])
metric.compute(average=None)
```

## Expected results
`array([0.66666667, 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        ])`

## Actual results
ValueError: can only convert an array of size 1 to a Python scalar

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.5
- PyArrow version: 5.0.0


Comments: Hi @asofiaoliveira, thanks for reporting.

I'm fixing it.
Run CI tests against non-production server
State: open | Author: albertvillanova (MEMBER)

Currently, the CI test suite performs requests to the HF production server.

As discussed with @elishowk, we should refactor our tests to use the HF staging server instead, like `huggingface_hub` and `transformers`.

Comments: Hey @albertvillanova could you provide more context, including extracts from the discussion we had ?

Let's ping @Pierrci @julien-c and @n1t0 for their opinion about that

@julien-c increased the huggingface.co production workers in order to see if it solve [the 502 you had this morning](https://app.circleci.com/pipelines/github/huggingface/datasets/7843/workflows/fc83fa32-18f5-4dc3-9e2f-ba277ae1af74)

For the decision process: be aware that moon-staging does not have persistent repos (they are deleted regularly). as a consequence, **if the moon-staging solution is validated**, you should consider a way to keep the repository that are loaded in tests. These are the ones I found : https://github.com/huggingface/datasets/blob/d488db2f64f312f88f72bbc57a09b7eddb329182/tests/test_load.py and https://github.com/huggingface/datasets/blob/40773111c3e7db8a992fa1c48af32d900a1018d6/tests/test_streaming_download_manager.
Impossible to load compressed csv
State: closed | Author: Valahaar (CONTRIBUTOR)
Labels: name: bug

## Describe the bug
It is not possible to load from a compressed csv anymore.

## Steps to reproduce the bug
```python
load_dataset('csv', data_files=['/path/to/csv.bz2'])
```

## Problem and possible solution
This used to work, but the commit that broke it is [this one](https://github.com/huggingface/datasets/commit/ad489d4597381fc2d12c77841642cbeaecf7a2e0#diff-6f60f8d0552b75be8b3bfd09994480fd60dcd4e7eb08d02f721218c3acdd2782).

`pandas` usually gets the compression information from the filename itself (which was previously directly passed). Now, since it gets a file descriptor, it might be good to auto-infer the compression or let the user pass the `compression` kwarg to `load_dataset` (or maybe warn the user if the file ends with a commonly known compression scheme?).

## Environment info
- `datasets` version: 1.10.0 (and over)
- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 3.0.0


Comments: Hi @Valahaar, thanks for reporting and for your investigation about the source cause.

You are right and that commit prevents `pandas` from inferring the compression. On the other hand, @lhoestq did that change to support loading that dataset in streaming mode. 

I'm fixing it.