KRadim/github-issues-minimal huggingface dataset:
DatasetDict.filter and Dataset.filter crashes with any "fn_kwargs" argument
State: closed
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
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English.
State: closed

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.
Remove a reference to the open Arrow file when deleting a TF dataset created with to_tf_dataset
State: closed

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
Fix cast to Python scalar in Matthews Correlation metric
State: closed

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

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 ?
cannot shuffle dataset loaded from disk
State: open
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

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)
Fix loading compressed CSV without streaming
State: closed

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
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

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
IndexError: Invalid key: 14 is out of bounds for size 0
State: closed
Labels: name: bug

## Describe the bug
A clear and concise description of what the bug is.

Hi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq 

## Steps to reproduce the bug
```
Traceback (most recent call last):
  File "run_clm.py", line 723, in <module>
    main()
  File "run_clm.py", line 669, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/transformers/trainer.py", line 1258, in train
    for step, inputs in enumerate(epoch_iterator):
  File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
    data = self._next_data()
  File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 475, in _next_data
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
  File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1530, in __getitem__
    format_kwargs=self._format_kwargs,
  File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1517, in _getitem
    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
  File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/formatting/formatting.py", line 368, in query_table
    _check_valid_index_key(key, size)
  File "/user/dara/libs/anaconda3/envs/success/lib/python3.7/site-packages/datasets/formatting/formatting.py", line 311, in _check_valid_index_key
    raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
IndexError: Invalid key: 14 is out of bounds for size 0
```



## Expected results
not getting the index error

## Actual results
Please see the above

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets                  1.12.1 
- Platform: linux
- Python version: 3.7.11 
- PyArrow version:  5.0.0
add new dataset kan_hope
State: closed

## 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*
OpenSLR 25: ASR data for Amharic, Swahili and Wolof
State: open
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.
ValueError when computing f1 metric with average None
State: closed
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
Impossible to load compressed csv
State: closed
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
Can't load dataset
State: closed
Labels: name: bug

I'm trying to load a wikitext dataset

```
from datasets import load_dataset
raw_datasets = load_dataset("wikitext")
```

ValueError: Config name is missing.
Please pick one among the available configs: ['wikitext-103-raw-v1', 'wikitext-2-raw-v1', 'wikitext-103-v1', 'wikitext-2-v1']
Example of usage:
	`load_dataset('wikitext', 'wikitext-103-raw-v1')`.

If I try
```
from datasets import load_dataset
raw_datasets = load_dataset("wikitext-2-v1")
```

FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.12.1/datasets/wikitext-2-v1/wikitext-2-v1.py

#### Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic (colab)
- Python version: 3.7.12
- PyArrow version: 3.0.0
OSError: Not enough disk space.
State: closed
Labels: name: bug

## Describe the bug
I'm trying to download `natural_questions` dataset from the Internet, and I've specified the cache_dir which locates in a mounted disk and has enough disk space. However, even though the space is enough, the disk space checking function still reports the space of root `/` disk having no enough space. 

The file system structure is like below. The root `/` has `115G` disk space available, and the `sda1` is mounted to `/mnt`, which has `1.2T` disk space available:
```
/
/mnt/sda1/path/to/args.dataset_cache_dir 
```

## Steps to reproduce the bug
```python
dataset_config = DownloadConfig(
    cache_dir=os.path.abspath(args.dataset_cache_dir),
    resume_download=True,
)
dataset = load_dataset("natural_questions", download_config=dataset_config)
```

## Expected results

Can download the dataset without an error.

## Actual results

The following error raised:
```
OSError: Not enough disk space. Needed: 134.92 GiB (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size)
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Ubuntu 18.04
- Python version: 3.8.10
- PyArrow version:
masakhaner dataset load problem
State: closed
Labels: name: bug

## Describe the bug
Masakhaner dataset is not loading

## Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset("masakhaner",'amh')
```

## Expected results
Expected the return of a dataset

## Actual results

```

NonMatchingSplitsSizesError               Traceback (most recent call last)
<ipython-input-3-a6abc1161d4c> in <module>()
      1 from datasets import load_dataset
      2 
----> 3 dataset = load_dataset("masakhaner",'amh')



3 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py
 in verify_splits(expected_splits, recorded_splits)
     72     ]
     73     if len(bad_splits) > 0:
---> 74         raise NonMatchingSplitsSizesError(str(bad_splits))
     75     logger.info("All the splits matched successfully.")
     76 

NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=639927, num_examples=1751, dataset_name='masakhaner'), 'recorded': SplitInfo(name='train', num_bytes=639911, num_examples=1750, dataset_name='masakhaner')}, {'expected': SplitInfo(name='validation', num_bytes=92768, num_examples=251, dataset_name='masakhaner'), 'recorded': SplitInfo(name='validation', num_bytes=92753, num_examples=250, dataset_name='masakhaner')}, {'expected': SplitInfo(name='test', num_bytes=184286, num_examples=501, dataset_name='masakhaner'), 'recorded': SplitInfo(name='test', num_bytes=184271, num_examples=500, dataset_name='masakhaner')}]
```

## Environment info
Google Colab
Magnet’s
State: closed
Labels: name: dataset request

## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
medical-dialog error
State: closed
Labels: name: bug

## Describe the bug
A clear and concise description of what the bug is.
When I attempt to download the huggingface datatset medical_dialog it errors out midway through
## Steps to reproduce the bug
```python
raw_datasets = load_dataset("medical_dialog", "en", split="train", download_mode="force_redownload", data_dir="./Medical-Dialogue-Dataset-English")
```

## Expected results
A clear and concise description of the expected results.
No error
## Actual results

```
3 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_splits(expected_splits, recorded_splits)
     72     ]
     73     if len(bad_splits) > 0:
---> 74         raise NonMatchingSplitsSizesError(str(bad_splits))
     75     logger.info("All the splits matched successfully.")
     76 

NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='medical_dialog'), 'recorded': SplitInfo(name='train', num_bytes=295097913, num_examples=229674, dataset_name='medical_dialog')}]
```
Specify the actual results or traceback.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.21.1
- Platform: colab
- Python version: colab 3.7
- PyArrow version: N/A
Adding vision-and-language datasets (e.g., VQA, VCR) to Datasets
State: closed
Labels: name: enhancement

**Is your feature request related to a problem? Please describe.**
Would you like to add any vision-and-language datasets (e.g., VQA, VCR) to Huggingface Datasets?

**Describe the solution you'd like**
N/A

**Describe alternatives you've considered**
N/A

**Additional context**
This is Da Yin at UCLA. Recently, we have published an EMNLP 2021 paper about geo-diverse visual commonsense reasoning (https://arxiv.org/abs/2109.06860). We propose a new dataset called GD-VCR, a vision-and-language dataset to evaluate how well V&L models perform on scenarios involving geo-location-specific commonsense. We hope to have our V&L dataset incorporated into Huggingface to further promote our project, but I haven't seen much V&L datasets in the current package. Is it  possible to add V&L datasets, and if so, how should we prepare for the loading? Thank you very much!
Invalid download URL of WMT17 `zh-en` data 
State: closed
Labels: name: bug, name: dataset bug

## Describe the bug
Partial data (wmt17 zh-en) cannot be downloaded due to an invalid URL.

## Steps to reproduce the bug
```python
from datasets import load_dataset
 dataset = load_dataset('wmt17','zh-en')
```

## Expected results
ConnectionError: Couldn't reach ftp://cwmt-wmt:cwmt-wmt@datasets.nju.edu.cn/parallel/casia2015.zip
raise TypeError( TypeError: Provided `function` which is applied to all elements of table returns a variable of type <class 'list'>. Make sure provided `function` returns a variable of type `dict` to update the dataset or `None` if you are only interested in side effects.
State: closed
Labels: name: bug

## Describe the bug
A clear and concise description of what the bug is.
I am trying to use Dataset to load my file in order to use Bert embeddings model baut when I finished loading using dataset and I want to pass to the tokenizer using the function map; I get the following error : raise TypeError(
TypeError: Provided `function` which is applied to all elements of table returns a variable of type <class 'list'>. Make sure provided `function` returns a variable of type `dict` to update the dataset or `None` if you are only interested in side effects.

I was able to load my file using dataset before but since this morning , I keep getting this erreor.
## Steps to reproduce the bug
```python
# Xtrain, ytrain, filename, len_labels = read_file_2(fic)
	# Xtrain, lge_size = get_flaubert_layer(Xtrain, path_to_model_lge)

	data_preprocessed = make_new_traindata(Xtrain)
	
	my_dict = {"verbatim": data_preprocessed[1], "label": ytrain} # lemme avec conjonction
	dataset = Dataset.from_dict(my_dict)
```

## Expected results
A clear and concise description of the expected results.

## Actual results
Specify the actual results or traceback.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
Enable splits during streaming the dataset
State: open
Labels: name: enhancement

## Describe the Problem

I'd like to stream only a specific percentage or part of the dataset. 
I want to do splitting when I'm streaming dataset as well.

## Solution
Enabling splits when `streaming = True` as well.
`e.g. dataset = load_dataset('dataset', split='train[:100]', streaming = True)`

## Alternatives
Below is the alternative of doing it.
`dataset = load_dataset("dataset", split='train', streaming = True).take(100)`
MultiWOZ Dataset NonMatchingChecksumError
State: closed
Labels: name: bug

## Describe the bug
The checksums for the downloaded MultiWOZ dataset and source MultiWOZ dataset aren't matching.

## Steps to reproduce the bug
Both of the below dataset versions yield the checksum error:
```python
from datasets import load_dataset
dataset = load_dataset('multi_woz_v22', 'v2.2')
dataset = load_dataset('multi_woz_v22', 'v2.2_active_only')
```

## Expected results
For the above calls to `load_dataset` to work.

## Actual results
NonMatchingChecksumError. Traceback:
> Traceback (most recent call last):
  File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3441, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-15-4e91280e112e>", line 1, in <module>
    dataset = load_dataset('multi_woz_v22', 'v2.2')
  File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/datasets/load.py", line 847, in load_dataset
    builder_instance.download_and_prepare(
  File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/datasets/builder.py", line 615, in download_and_prepare
    self._download_and_prepare(
  File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
    verify_checksums(
  File "/Users/brady/anaconda3/envs/elysium/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
    raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_001.json']

## Environment info
- `datasets` version: 1.11.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.10
- PyArrow version: 5.0.0
Cache problem in the `load_dataset` method for local compressed file(s)
State: open
Labels: name: bug

## Describe the bug

Cache problem in the `load_dataset` method: when modifying a compressed file in a local folder `load_dataset` doesn't detect the change and load the previous version.

## Steps to reproduce the bug

To test it directly, I have prepared a [Google Colaboratory notebook](https://colab.research.google.com/drive/11Em_Amoc-aPGhSBIkSHU2AvEh24nVayy?usp=sharing) that shows this behavior. 

For this example, I have created a toy dataset at: https://huggingface.co/datasets/SaulLu/toy_struc_dataset

This dataset is composed of two versions: 
- v1 on commit `a6beb46` which has a single example `{'id': 1, 'value': {'tag': 'a', 'value': 1}}` in file `train.jsonl.gz`
- v2 on commit `e7935f4` (`main` head) which has a single example `{'attr': 1, 'id': 1, 'value': 'a'}` in file `train.jsonl.gz`

With a terminal, we can start to get the v1 version of the dataset
```bash
git lfs install
git clone https://huggingface.co/datasets/SaulLu/toy_struc_dataset
cd toy_struc_dataset
git checkout a6beb46
```
Then we can load it with python and look at the content:
```python
from datasets import load_dataset

path = "/content/toy_struc_dataset"
dataset = load_dataset(path, data_files={"train": "*.jsonl.gz"})
print(dataset["train"][0])
```
Output
```
{'id': 1, 'value': {'tag': 'a', 'value': 1}} # This is the example in v1
```

With a terminal, we can now start to get the v1 version of the dataset
```bash
git checkout main
```
Then we can load it with python and look at the content:
```python
from datasets import load_dataset

path = "/content/toy_struc_dataset"
dataset = load_dataset(path, data_files={"train": "*.jsonl.gz"})
print(dataset["train"][0])
```
Output
```
{'id': 1, 'value': {'tag': 'a', 'value': 1}} # This is the example in v1 (not v2)
```
## Expected results
The last output should have been 
```
{"id":1, "value": "a", "attr": 1} # This is the example in v2
```

## Ideas

As discussed offline with Quentin, if the cache hash was ever sensitive to changes in a compressed file we would probably not have the problem anymore.

This situation leads me to suggest 2 other features:
- to also have an `load_from_cache_file` argument in the "load_dataset" method
- to reorganize the cache so that we can delete the caches related to a dataset (cf issue #ToBeFilledSoon)

And thanks again for this great library :hugs: 

## Environment info
- `datasets` version: 1.12.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
Update legacy Python image for CI tests in Linux
State: closed

Instead of legacy, use next-generation convenience images, built from the ground up with CI, efficiency, and determinism in mind. Here are some of the highlights:

- Faster spin-up time - In Docker terminology, these next-gen images will generally have fewer and smaller layers. Using these new images will lead to faster image downloads when a build starts, and a higher likelihood that the image is already cached on the host.

- Improved reliability and stability - The existing legacy convenience images are rebuilt practically every day with potential changes from upstream that we cannot always test fast enough. This leads to frequent breaking changes, which is not the best environment for stable, deterministic builds. Next-gen images will only be rebuilt for security and critical-bugs, leading to more stable and deterministic images.

More info: https://circleci.com/docs/2.0/circleci-images
Trying to get in touch regarding a security issue
State: closed

Hey there!

I'd like to report a security issue but cannot find contact instructions on your repository.

If not a hassle, might you kindly add a `SECURITY.md` file with an email, or another contact method? GitHub [recommends](https://docs.github.com/en/code-security/getting-started/adding-a-security-policy-to-your-repository) this best practice to ensure security issues are responsibly disclosed, and it would serve as a simple instruction for security researchers in the future.

Thank you for your consideration, and I look forward to hearing from you!

(cc @huntr-helper)
Don't use old, incompatible cache for the new `filter`
State: closed

#2836 changed `Dataset.filter` and the resulting data that are stored in the cache are different and incompatible with the ones of the previous `filter` implementation.

However the caching mechanism wasn't able to differentiate between the old and the new implementation of filter (only the method name was taken into account). 

This is an issue because anyone that update `datasets` and re-runs some code that uses `filter` would see an error, because the cache would try to load an incompatible `filter` result.

To fix this I added the notion of versioning for dataset transform in the caching mechanism, and bumped the version of the `filter` implementation to 2.0.0

This way the new `filter` outputs are now considered different from the old ones from the caching point of view.

This should fix #2943

cc @anton-l
Protect master branch
State: closed
Labels: name: enhancement

After accidental merge commit (91c55355b634d0dc73350a7ddee1a6776dbbdd69) into `datasets` master branch, all commits present in the feature branch were permanently added to `datasets` master branch history, as e.g.:
- 00cc036fea7c7745cfe722360036ed306796a3f2
- 13ae8c98602bbad8197de3b9b425f4c78f582af1
- ...

I propose to protect our master branch, so that we avoid we can accidentally make this kind of mistakes in the future:
- [x] For Pull Requests using GitHub, allow only squash merging, so that only a single commit per Pull Request is merged into the master branch
  - Currently, simple merge commits are already disabled
  - I propose to disable rebase merging as well
- ~~Protect the master branch from direct pushes (to avoid accidentally pushing of merge commits)~~
  - ~~This protection would reject direct pushes to master branch~~
  - ~~If so, for each release (when we need to commit directly to the master branch), we should previously disable the protection and re-enable it again after the release~~
- [x] Protect the master branch only from direct pushing of **merge commits**
  - GitHub offers the possibility to protect the master branch only from merge commits (which are the ones that introduce all the commits from the feature branch into the master branch).
  - No need to disable/re-enable this protection on each release 

This purpose of this Issue is to open a discussion about this problem and to agree in a solution.
Add  `remove_columns` to `IterableDataset ` 
State: closed
Labels: name: enhancement, name: good first issue

**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.

```python
from datasets import load_dataset
dataset = load_dataset("c4", 'realnewslike', streaming =True, split='train')
dataset = dataset.remove_columns('url')
```
```
AttributeError: 'IterableDataset' object has no attribute 'remove_columns'
```

**Describe the solution you'd like**

It would be nice to have `.remove_columns()` to match the `Datasets` api. 


**Describe alternatives you've considered**

This can be done with a single call to `.map()`, 

I can try to help add this. 🤗
OSCAR unshuffled_original_ko: NonMatchingSplitsSizesError
State: open
Labels: name: bug, name: dataset bug

## Describe the bug

Cannot download OSCAR `unshuffled_original_ko` due to `NonMatchingSplitsSizesError`.

## Steps to reproduce the bug

```python
>>> dataset = datasets.load_dataset('oscar', 'unshuffled_original_ko')
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=25292102197, num_examples=7345075, dataset_name='oscar'), 'recorded': SplitInfo(name='train', num_bytes=25284578514, num_examples=7344907, dataset_name='oscar')}]
```

## Expected results

Loading is successful.

## Actual results

Loading throws above error.

## Environment info

- `datasets` version: 1.12.1
- Platform: Linux-5.4.0-81-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 5.0.0
Take namespace into account in caching
State: closed

Loading a dataset "username/dataset_name" hosted by a user on the hub used to cache the dataset only taking into account the dataset name, and ignorign the username. Because of this, if a user later loads "dataset_name" without specifying the username, it would reload the dataset from the cache instead of failing.

I changed the dataset cache and module cache mechanism to include the username in the name of the cache directory that is used:


`~/.cache/huggingface/datasets/username/dataset_name`  for the data
`~/.cache/huggingface/modules/datasets_modules/datasets/username/dataset_name` for the python files


EDIT: actually using three underscores:
`~/.cache/huggingface/datasets/username___dataset_name` for the data
`~/.cache/huggingface/modules/datasets_modules/datasets/username___dataset_name` for the python files

This PR should fix the issue https://github.com/huggingface/datasets/issues/2842

cc @stas00
load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied
State: closed
Labels: name: bug

## Describe the bug
Standard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11.

## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset('wiki_bio')
```

## Expected results
It is expected that the dataset downloads without any errors.

## Actual results
PermissionError see trace below:
```
Using custom data configuration default
Downloading and preparing dataset wiki_bio/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\Users\username\.cache\huggingface\datasets\wiki_bio\default\1.1.0\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\username\.conda\envs\hf\lib\site-packages\datasets\load.py", line 1112, in load_dataset
    builder_instance.download_and_prepare(
  File "C:\Users\username\.conda\envs\hf\lib\site-packages\datasets\builder.py", line 644, in download_and_prepare
    self._save_info()
  File "C:\Users\username\.conda\envs\hf\lib\contextlib.py", line 120, in __exit__
    next(self.gen)
  File "C:\Users\username\.conda\envs\hf\lib\site-packages\datasets\builder.py", line 598, in incomplete_dir
    os.rename(tmp_dir, dirname)
PermissionError: [WinError 5] Access is denied: 'C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9'
```
By commenting out the os.rename() [L604](https://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L604) and the shutil.rmtree() [L607](https://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed.

It seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https://github.com/conan-io/conan/issues/6560) with similar issue with os.rename() if it helps debug this issue.

## Environment info
- `datasets` version: 1.12.1
- Platform: Windows-10-10.0.22449-SP0
- Python version: 3.8.12
- PyArrow version: 5.0.0
to_tf_dataset keeps a reference to the open data somewhere, causing issues on windows
State: closed
Labels: name: bug

To reproduce:
```python
import datasets as ds
import weakref
import gc

d = ds.load_dataset("mnist", split="train")
ref = weakref.ref(d._data.table)
tfd = d.to_tf_dataset("image", batch_size=1, shuffle=False, label_cols="label")
del tfd, d
gc.collect()
assert ref() is None, "Error: there is at least one reference left"
```

This causes issues because the table holds a reference to an open arrow file that should be closed. So on windows it's not possible to delete or move the arrow file afterwards.

Moreover the CI test of the `to_tf_dataset` method isn't able to clean up the temporary arrow files because of this.

cc @Rocketknight1
Replace script_version with revision
State: closed

As discussed in https://github.com/huggingface/datasets/pull/2718#discussion_r707013278, the parameter name `script_version` is no longer applicable to datasets without loading script (i.e., datasets only with raw data files).

This PR replaces the parameter name `script_version` with `revision`.

This way, we are also aligned with:
- Transformers: `AutoTokenizer.from_pretrained(..., revision=...)`
- Hub: `HfApi.dataset_info(..., revision=...)`, `HfApi.upload_file(..., revision=...)`
Mutable columns argument breaks set_format
State: closed
Labels: name: bug

## Describe the bug
If you pass a mutable list to the `columns` argument of `set_format` and then change the list afterwards, the returned columns also change.

## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("glue", "cola")

column_list = ["idx", "label"]
dataset.set_format("python", columns=column_list)
column_list[1] = "foo"  # Change the list after we call `set_format`
dataset['train'][:4].keys()
```

## Expected results
```python
dict_keys(['idx', 'label'])
```

## Actual results
```python
dict_keys(['idx'])
```
Error when downloading datasets to non-traditional cache directories
State: open
Labels: name: bug

## Describe the bug
When the cache directory is linked (soft link) to a directory on a NetApp device, the download fails. 

## Steps to reproduce the bug
```bash
ln -s /path/to/netapp/.cache ~/.cache
```

```python
load_dataset("imdb")
```

## Expected results
Successfully loading IMDB dataset

## Actual results
```
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=33432835, 
num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0,
 dataset_name='imdb')}, {'expected': SplitInfo(name='test', num_bytes=32650697, num_examples=25000, dataset_name='imdb'),
 'recorded': SplitInfo(name='test', num_bytes=659932, num_examples=503, dataset_name='imdb')}, {'expected':
 SplitInfo(name='unsupervised', num_bytes=67106814, num_examples=50000, dataset_name='imdb'), 'recorded':
 SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.1.2
- Platform: Ubuntu 
- Python version: 3.8

## Extra notes
Stranger yet, trying to debug the phenomenon, I found the range of results to vary a lot without clear direction:
 - With `cache_dir="/path/to/netapp/.cache"` the same thing happens.
 - However, when linking `~/netapp/` to `/path/to/netapp` *and* setting `cache_dir="~/netapp/.cache/huggingface/datasets"` - it does work
 - On the other hand, when linking `~/.cache` to `~/netapp/.cache` without using `cache_dir`, it does work anymore.

While I could test it only for a NetApp device, it might have to do with any other mounted FS.

Thanks :)
"File name too long" error for file locks
State: closed
Labels: name: bug

## Describe the bug

Getting the following error when calling `load_dataset("gar1t/test")`:

```
OSError: [Errno 36] File name too long: '<user>/.cache/huggingface/datasets/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'
```

## Steps to reproduce the bug

Where the user cache dir (e.g. `~/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):

```python
from datasets import load_dataset
load_dataset("gar1t/test")
```

## Expected results

Expect the function to return without an error.

## Actual results

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<python_venv>/lib/python3.9/site-packages/datasets/load.py", line 1112, in load_dataset
    builder_instance.download_and_prepare(
  File "<python_venv>/lib/python3.9/site-packages/datasets/builder.py", line 644, in download_and_prepare
    self._save_info()
  File "<python_venv>/lib/python3.9/site-packages/datasets/builder.py", line 765, in _save_info
    with FileLock(lock_path):
  File "<python_venv>/lib/python3.9/site-packages/datasets/utils/filelock.py", line 323, in __enter__
    self.acquire()
  File "<python_venv>/lib/python3.9/site-packages/datasets/utils/filelock.py", line 272, in acquire
    self._acquire()
  File "<python_venv>/lib/python3.9/site-packages/datasets/utils/filelock.py", line 403, in _acquire
    fd = os.open(self._lock_file, open_mode)
OSError: [Errno 36] File name too long: '<user>/.cache/huggingface/datasets/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'
```

## Environment info

- `datasets` version: 1.12.1
- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 5.0.0
Fix conversion of multidim arrays in list to arrow
State: closed

Arrow only supports 1-dim arrays. Previously we were converting all the numpy arrays to python list before instantiating arrow arrays to workaround this limitation.
However in #2361 we started to keep numpy arrays in order to keep their dtypes.
It works when we pass any multi-dim numpy array (the conversion to arrow has been added on our side), but not for lists of multi-dim numpy arrays.

In this PR I added two strategies:
- one that takes a list of multi-dim numpy arrays on returns an arrow array in an optimized way (more common case)
- one that takes a list of possibly very nested data (lists, dicts, tuples) containing multi-dim arrays. This one is less optimized since it converts all the multi-dim numpy arrays into lists of 1-d arrays for compatibility with arrow. This strategy is simpler that just trying to create the arrow array from a possibly very nested data structure, but in the future we can improve it if needed.

Fix https://github.com/huggingface/datasets/issues/2921
Using a list of multi-dim numpy arrays raises an error "can only convert 1-dimensional array values"
State: closed

This error has been introduced in https://github.com/huggingface/datasets/pull/2361

To reproduce:
```python
import numpy as np
from datasets import Dataset

d = Dataset.from_dict({"a": [np.zeros((2, 2))]})
```
raises
```python
Traceback (most recent call last):
  File "playground/ttest.py", line 5, in <module>
    d = Dataset.from_dict({"a": [np.zeros((2, 2))]}).with_format("torch")
  File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/arrow_dataset.py", line 458, in from_dict
    pa_table = InMemoryTable.from_pydict(mapping=mapping)
  File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/table.py", line 365, in from_pydict
    return cls(pa.Table.from_pydict(*args, **kwargs))
  File "pyarrow/table.pxi", line 1639, in pyarrow.lib.Table.from_pydict
  File "pyarrow/array.pxi", line 332, in pyarrow.lib.asarray
  File "pyarrow/array.pxi", line 223, in pyarrow.lib.array
  File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
  File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/arrow_writer.py", line 107, in __arrow_array__
    out = pa.array(self.data, type=type)
  File "pyarrow/array.pxi", line 306, in pyarrow.lib.array
  File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array
  File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
  File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Can only convert 1-dimensional array values