Kokolipa/github-issues huggingface dataset:
Issue: [Audio] Allow resampling for audio datasets in streaming mode
State: closed
Labels: name: enhancement

Many audio datasets like Common Voice always need to be resampled. This can very easily be done in non-streaming mode as follows:

```python
from datasets import load_dataset

ds = load_dataset("common_voice", "ab", split="test")

ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
```

However in streaming mode it fails currently:

```python
from datasets import load_dataset

ds = load_dataset("common_voice", "ab", split="test", streaming=True)

ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
```

with the following error:

```
AttributeError: 'IterableDataset' object has no attribute 'cast_column'  
```

It would be great if we could add such a feature (I'm not 100% sure though how complex this would be)

This requires implementing `cast_column` for iterable datasets, it could be a very nice addition !


It can also be useful to be able to disable the audio/image decoding for the dataset viewer (see PR https://github.com/huggingface/datasets/pull/3430) cc @severo 

EDIT: actually following https://github.com/huggingface/datasets/issues/3145 the dataset viewer might not need it anymore

Just to clarify a bit. This feature is **always** needed when using the common voice dataset in streaming mode. So I think it's quite important
Issue: Add multimodal datasets
State: open
Labels: name: dataset request

Epic issue to track the addition of multimodal datasets:
- [ ] #2526
- [x] #1842
- [ ] #1810

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

@VictorSanh feel free to add and sort by priority any interesting dataset. I have added the multimodal dataset requests which were already present as issues.
Issue: Add task tags for multimodal datasets
State: closed
Labels: name: enhancement

## **Is your feature request related to a problem? Please describe.**

Currently, task tags are either exclusively related to text or speech processing:
- https://github.com/huggingface/datasets/blob/master/src/datasets/utils/resources/tasks.json

## **Describe the solution you'd like**

We should also add tasks related to:
- multimodality
- image
- video

CC: @VictorSanh @lewtun @lhoestq @merveenoyan @SBrandeis 

The Hub pulls these tags from [here](https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts) (allows multimodal tasks) now, so I'm closing this issue.
Issue: add new field, and get errors
State: closed

after adding new field **tokenized_examples["example_id"]**, and get errors below,
I think it is due to changing data to tensor, and **tokenized_examples["example_id"]** is string list 
**all fields**
```
***************** train_dataset 1: Dataset({
    features: ['attention_mask', 'end_positions', 'example_id', 'input_ids', 'start_positions', 'token_type_ids'],
    num_rows: 87714
})
```

**Errors**
```
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 705, in convert_to_tensors
    tensor = as_tensor(value)
ValueError: too many dimensions 'str'
```

Hi, 

could you please post this question on our [Forum](https://discuss.huggingface.co/) as we keep issues for bugs and feature requests? 

> Hi,
> 
> could you please post this question on our [Forum](https://discuss.huggingface.co/) as we keep issues for bugs and feature requests?

ok.
Issue: Mirror the canonical datasets to the Hugging Face Hub
State: closed
Labels: name: enhancement

- [ ] create a repo on https://hf.co/datasets for every canonical dataset
- [ ] on every commit related to a dataset, update the hf.co repo

See https://github.com/huggingface/moon-landing/pull/1562

@SBrandeis: I let you edit this description if needed to precise the intent.

I created a GitHub project to keep track of what needs to be done:
https://github.com/huggingface/datasets/projects/3

I also store my code in a (private for now) repository at https://github.com/huggingface/mirror_canonical_datasets_on_hub

I understand that the datasets are mirrored on the Hub now, right? Might I close @lhoestq @SBrandeis?
Issue: Typing of Dataset.__getitem__ could be improved.
State: closed
Labels: name: bug

## Describe the bug

The newly added typing for Dataset.__getitem__ is Union[Dict, List]. This makes tools like mypy a bit awkward to use as we need to check the type manually. We could use type overloading to make this easier. [Documentation](https://docs.python.org/3/library/typing.html#typing.overload)

## Steps to reproduce the bug
Let's have a file `test.py`

```python
from typing import List, Dict, Any

from datasets import Dataset

ds = Dataset.from_dict({
    'a': [1,2,3],
    'b': ["1", "2", "3"]
})

one_colum: List[str] = ds['a']
some_index: Dict[Any, Any] = ds[1]
```

## Expected results

Running `mypy test.py` should not give any error.


## Actual results

```
test.py:10: error: Incompatible types in assignment (expression has type "Union[Dict[Any, Any], List[Any]]", variable has type "List[str]")
test.py:11: error: Incompatible types in assignment (expression has type "Union[Dict[Any, Any], List[Any]]", variable has type "Dict[Any, Any]")
Found 2 errors in 1 file (checked 1 source file)
```


## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.13.3
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 6.0.1


Hi ! Thanks for the suggestion, I didn't know about this decorator.

If you are interesting in contributing, feel free to open a pull request to add the overload methods for each typing combination :) To assign you to this issue, you can comment `#self-assign` in this thread.

`Dataset.__getitem__` is defined right here: https://github.com/huggingface/datasets/blob/e6f1352fe19679de897f3d962e616936a17094f5/src/datasets/arrow_dataset.py#L1840

#self-assign
Issue: Integrate Polars library
State: closed
Labels: name: enhancement

Check potential integration of the Polars library: https://github.com/pola-rs/polars
- Benchmark: https://h2oai.github.io/db-benchmark/

CC: @thomwolf @lewtun 


If possible, a neat API could be something like `Dataset.to_polars()`, as well as `Dataset.set_format("polars")`

Note they use a "custom" implementation of Arrow: [Arrow2](https://github.com/jorgecarleitao/arrow2).

Polars has grown rapidly in popularity over the last year - could you consider integrating the Polars functionality again?

I don't think the "custom" implementation should be a barrier, it still conforms to the Arrow specification 

Is there some direction regarding this from the HF team @lewtun ? Can conversion from polars to HF dataset be implemented with limited/zero copy? So, something like ``Dataset.from_polars()`` and ``Dataset.to_polars()`` like you mentioned. Happy to contribute if I can get some pointers on how this may be implemented.

Hi, is there any updates? Thanks!

> Hi, is there any updates? Thanks!

The feature has been there for a bit 😊 You can call `dataset.to_polars()` (on a `Dataset`, not a `DatasetDict`). The issue can be closed, I guess! @lhoestq 

Looks great and thanks!

Thank you.
Issue: AttributeError: 'CommunityDatasetModuleFactoryWithoutScript' object has no attribute 'path'
State: closed
Labels: name: bug

## Describe the bug
I add a new question answering dataset to huggingface datasets manually. Here is the link: [luozhouyang/question-answering-datasets](https://huggingface.co/datasets/luozhouyang/question-answering-datasets)

But when I load the dataset, an error raised: 

```bash
AttributeError: 'CommunityDatasetModuleFactoryWithoutScript' object has no attribute 'path'
```

## Steps to reproduce the bug
```python
from datasets import load_dataset

dataset = load_dataset("luozhouyang/question-answering-datasets", data_files=["dureader_robust.train.json"])
```

## Expected results
Load dataset successfully without any error.

## Actual results
```bash
Traceback (most recent call last):
  File "/mnt/home/zhouyang.lzy/github/naivenlp/naivenlp/tests/question_answering_tests/dataset_test.py", line 89, in test_load_dataset_with_hf
    data_files=["dureader_robust.train.json"],
  File "/mnt/home/zhouyang.lzy/.conda/envs/naivenlp/lib/python3.6/site-packages/datasets/load.py", line 1616, in load_dataset
    **config_kwargs,
  File "/mnt/home/zhouyang.lzy/.conda/envs/naivenlp/lib/python3.6/site-packages/datasets/load.py", line 1443, in load_dataset_builder
    path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files
  File "/mnt/home/zhouyang.lzy/.conda/envs/naivenlp/lib/python3.6/site-packages/datasets/load.py", line 1157, in dataset_module_factory
    raise e1 from None
  File "/mnt/home/zhouyang.lzy/.conda/envs/naivenlp/lib/python3.6/site-packages/datasets/load.py", line 1144, in dataset_module_factory
    download_mode=download_mode,
  File "/mnt/home/zhouyang.lzy/.conda/envs/naivenlp/lib/python3.6/site-packages/datasets/load.py", line 798, in get_module
    raise FileNotFoundError(f"No data files or dataset script found in {self.path}")
AttributeError: 'CommunityDatasetModuleFactoryWithoutScript' object has no attribute 'path'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: linux
- Python version: 3.6.13
- PyArrow version: 6.0.1


Hi,

the fix was merged and will be available in the next release of `datasets`.
In the meantime, you can use it by installing `datasets` directly from master as follows:
```
pip install git+https://github.com/huggingface/datasets.git
```
Issue: Map function: Type error on iter #999
State: closed
Labels: name: bug

## Describe the bug
Using the map function, it throws a type error on iter #999

Here is the code I am calling:
```
dataset = datasets.load_dataset('squad')
dataset['validation'].map(text_numbers_to_int, input_columns=['context'], fn_kwargs={'column': 'context'})
``` 
text_numbers_to_int returns the input text with numbers replaced in the format {'context': text}

It happens at 
`
File "C:\Users\lonek\anaconda3\envs\ai\Lib\site-packages\datasets\arrow_writer.py", line 289, in <listcomp>
    [row[0][col] for row in self.current_examples], type=col_type, try_type=col_try_type, col=col
`

The issue is that the list comprehension expects self.current_examples to be type tuple(dict, str), but for some reason 26 out of 1000 of the sefl.current_examples are type tuple(str, str)

Here is an example of what self.current_examples should be
({'context': 'Super Bowl 50 was an...merals 50.'}, '')

Here is an example of what self.current_examples are when it throws the error:
('The Panthers used th... Marriott.', '')


Hi, thanks for reporting.

It would be really helpful if you could provide the actual code of the `text_numbers_to_int` function so we can reproduce the error.

```
def text_numbers_to_int(text, column=""):
    """
    Convert text numbers to int.

    :param text: text numbers
    :return: int
    """
    try:
        numbers = find_numbers(text)
        if not numbers:
            return text
        result = ""
        i, j = 0, 0
        while i < len(text):
            if j < len(numbers) and i == numbers[j][1]:
                n = int(numbers[j][0]) if numbers[j][0] % 1 == 0 else float(numbers[j][0])
                result += str(n)
                i = numbers[j][2] #end
                j += 1
            else:
                result += text[i]
                i += 1
        if column:
            return{column: result}
        else:
            return {column: result}
    except Exception as e:
        print(e)
        return {column: result}
```

Maybe this is because of the `return text` line ? I think it should return a dictionary rather than a string

Yes that was it, good catch! Thanks
Issue: "Shape of query is incorrect, it has to be either a 1D array or 2D (1, N)"
State: closed
Labels: name: bug

## Describe the bug
Passing a correctly shaped Numpy-Array to get_nearest_examples leads to the Exception

"Shape of query is incorrect, it has to be either a 1D array or 2D (1, N)"

Probably the reason for this is a wrongly converted assertion.

1.15.1:

`assert len(query.shape) == 1 or (len(query.shape) == 2 and query.shape[0] == 1)`

1.16.1:

```
 if len(query.shape) != 1 or (len(query.shape) == 2 and query.shape[0] != 1):
            raise ValueError("Shape of query is incorrect, it has to be either a 1D array or 2D (1, N)")
```

## Steps to reproduce the bug

follow the steps described here: https://huggingface.co/course/chapter5/6?fw=tf

```python
 question_embedding.shape # (1, 768)

 scores, samples = embeddings_dataset.get_nearest_examples(
    "embeddings", question_embedding, k=5 # Error
)

# "Shape of query is incorrect, it has to be either a 1D array or 2D (1, N)"
```

## Expected results
Should work without exception

## Actual results
Throws exception

## Environment info
- `datasets` version: 1.15.1
- Platform: Darwin-20.6.0-x86_64-i386-64bit
- Python version: 3.7.12
- PyArrow version: 6.0.


#3323
Issue: Can't import `datasets` in python 3.10
State: closed

When importing `datasets` I'm getting this error in python 3.10:
```python
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/__init__.py", line 34, in <module>
    from .arrow_dataset import Dataset, concatenate_datasets
  File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/arrow_dataset.py", line 47, in <module>
    from .arrow_reader import ArrowReader
  File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/arrow_reader.py", line 33, in <module>
    from .table import InMemoryTable, MemoryMappedTable, Table, concat_tables
  File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/table.py", line 334, in <module>
    class InMemoryTable(TableBlock):
  File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/table.py", line 361, in InMemoryTable
    def from_pandas(cls, *args, **kwargs):
  File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/table.py", line 24, in wrapper
    out = wraps(arrow_table_method)(method)
  File "/Users/quentinlhoest/.pyenv/versions/3.10.0/lib/python3.10/functools.py", line 61, in update_wrapper
    wrapper.__wrapped__ = wrapped
AttributeError: readonly attribute
```

This makes the conda build fail.
I'm opening a PR to fix this and do a patch release 1.16.1
Issue: Can't get tatoeba.rus dataset
State: closed
Labels: name: bug

## Describe the bug
It gives an error.

> FileNotFoundError: Couldn't find file at https://github.com/facebookresearch/LASER/raw/master/data/tatoeba/v1/tatoeba.rus-eng.rus

## Steps to reproduce the bug
```python
data=load_dataset("xtreme","tatoeba.rus", split="validation")
```

## Solution
The library tries to access the **master** branch. In the github repo of facebookresearch, it is in the **main** branch.
Issue: Add desc parameter to Dataset filter method
State: closed
Labels: name: enhancement

**Is your feature request related to a problem? Please describe.**
As I was filtering very large datasets I noticed the filter method doesn't have the desc parameter which is available in the map method. Why don't we add a desc parameter to the filter method both for consistency and it's nice to give some feedback to users during long operations on Datasets?

**Describe the solution you'd like**
Add desc parameter to Dataset filter method

**Describe alternatives you've considered**
N/A

**Additional context**
N/A


Hi,

`Dataset.map` allows more generic transforms compared to `Dataset.filter`, which purpose is very specific (to filter examples based on a condition). That's why I don't think we need the `desc` parameter there for consistency. #3196 has added descriptions to the  `Dataset` methods that call `.map` internally, but not for the `filter` method, so we should do that.

Do you have a description in mind? Maybe `"Filtering the dataset"` or `"Filtering the indices"`? If yes, feel free to open a PR.

I'm personally ok with adding the `desc` parameter actually. Let's say you have different filters, it can be nice to differentiate between the different filters when they're running no ?

@mariosasko the use case is filtering of a dataset prior to tokenization and subsequent training. As the dataset is huge it's just a matter of giving a user (model trainer) some feedback on what's going on. Otherwise, feedback is given for all steps in training preparation and not for filtering and the filtering in my use case lasts about 4-5 minutes. And yes, if there are more filtering stages, as @lhoestq pointed out, it would be nice to give some feedback. I thought desc is there already and got confused when I got the script error. 

I don't have a strong opinion on that, so having `desc` as a parameter is also OK.
Issue: Add RedCaps dataset
State: closed
Labels: name: dataset request

name: vision

## Adding a Dataset
- **Name:** RedCaps
- **Description:** Web-curated image-text data created by the people, for the people
- **Paper:** https://arxiv.org/abs/2111.11431
- **Data:** https://redcaps.xyz/
- **Motivation:** Multimodal image-text dataset:  12M+ Image-text pairs 

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

Proposed by @patil-suraj
Issue: TriviaQA License Mismatch
State: closed
Labels: name: bug

## Describe the bug

TriviaQA Webpage at http://nlp.cs.washington.edu/triviaqa/ says they do not own the copyright to the data. However, Huggingface datasets at https://huggingface.co/datasets/trivia_qa mentions that the dataset is released under Apache License

Is the License Information on HuggingFace correct?

Hi ! You're completely right, this must be mentioned in the dataset card.
If you're interesting in contributing, feel free to open a pull request to mention this in the `trivia_qa` dataset card in the "Licensing Information" section at https://github.com/huggingface/datasets/blob/master/datasets/trivia_qa/README.md
Issue: Add WebSRC
State: open
Labels: name: dataset request

## Adding a Dataset
- **Name:** WebSRC
- **Description:** WebSRC is a novel Web-based Structural Reading Comprehension dataset. It consists of 0.44M question-answer pairs, which are collected from 6.5K web pages with corresponding HTML source code, screenshots and metadata. 
- **Paper:** https://arxiv.org/abs/2101.09465
- **Data:** https://x-lance.github.io/WebSRC/dashboard.html#
- **Motivation:** Currently adding MarkupLM to HuggingFace Transformers, which achieves SOTA on this dataset.

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Issue: "dataset_infos.json" missing for chr_en and mc4
State: open
Labels: name: bug

name: dataset bug

## Describe the bug
In the repository, every dataset has its metadata in a file called`dataset_infos.json`. But, this file is missing for two datasets: `chr_en` and `mc4`.

## Steps to reproduce the bug
Check [chr_en](https://github.com/huggingface/datasets/tree/master/datasets/chr_en) and [mc4](https://github.com/huggingface/datasets/tree/master/datasets/mc4)

Hi ! Thanks for reporting :) 
We can easily add the metadata for `chr_en` IMO, but for mC4 it will take more time, since it requires to count the number of examples in each language

No problem. I am trying to do some analysis on the metadata of all available datasets. Is reading `metadata_infos.json` for each dataset the correct way to go? 

I noticed that the same information is also available as special variables inside .py file of each dataset. So, I was wondering if `metadata_infos.json` has been deprecated?

![image](https://user-images.githubusercontent.com/8587189/142914413-a95a1abf-6f3e-4fbe-96e5-16d3ca39c831.png)


The `dataset_infos.json` files have more information and are made to be used to analyze the datasets without having to run/parse the python scripts. Moreover some datasets on the Hugging face don't even have a python script, and for those ones we'll make tools to generate the JSON file automatically :)
Issue: nested sequence feature won't encode example if the first item of the outside sequence is an empty list
State: closed
Labels: name: bug

## Describe the bug
As the title, nested sequence feature won't encode example if the first item of the outside sequence is an empty list.

## Steps to reproduce the bug
```python
from datasets import Features, Sequence, ClassLabel
features = Features({
    'x': Sequence(Sequence(ClassLabel(names=['a', 'b']))),
})
print(features.encode_batch({
    'x': [
        [['a'], ['b']],
        [[], ['b']],
    ]
}))
```

## Expected results
print `{'x': [[[0], [1]], [[], ['1']]]}`

## Actual results
print `{'x': [[[0], [1]], [[], ['b']]]}`

## Environment info
- `datasets` version: 1.15.1
- Platform: Linux-5.13.0-21-generic-x86_64-with-glibc2.34
- Python version: 3.9.7
- PyArrow version: 6.0.0

## Additional information
I think the issue stems from [here](https://github.com/huggingface/datasets/blob/8555197a3fe826e98bd0206c2d031c4488c53c5c/src/datasets/features/features.py#L847-L848).



knock knock

Hi, thanks for reporting! I've linked a PR that should fix the issue.

I've checked the PR and it looks great, thanks a lot!
Issue: Dataset object has no attribute `to_tf_dataset`
State: closed
Labels: name: bug

I am following HuggingFace Course. I am at Fine-tuning a model. 
Link: https://huggingface.co/course/chapter3/2?fw=tf

I use tokenize_function and `map` as mentioned in the course to process data.

`# define a tokenize function`
`def Tokenize_function(example):`
`    return tokenizer(example['sentence'], truncation=True)`

`# tokenize entire data`
`tokenized_data = raw_data.map(Tokenize_function, batched=True)`

I get Dataset object at this point. When I try converting this to a TF dataset object as mentioned in the course, it throws the following error.

`# convert to TF dataset`
`train_data = tokenized_data["train"].to_tf_dataset( `
`   columns = ['attention_mask', 'input_ids', 'token_type_ids'], `
`    label_cols = ['label'], `
`    shuffle = True, `
`    collate_fn = data_collator, `
`    batch_size = 8 `
`)`

Output:

`---------------------------------------------------------------------------`
`AttributeError                            Traceback (most recent call last)`
`/tmp/ipykernel_42/103099799.py in <module>`
`      1 # convert to TF dataset`
`----> 2 train_data = tokenized_data["train"].to_tf_dataset( \`
`      3     columns = ['attention_mask', 'input_ids', 'token_type_ids'], \`
`      4     label_cols = ['label'], \`
`      5     shuffle = True, \`
`AttributeError: 'Dataset' object has no attribute 'to_tf_dataset'`

When I look for `dir(tokenized_data["train"])`, there is no method or attribute in the name of `to_tf_dataset`.

Why do I get this error? And how to clear this?

Please help me.

The issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.

```
# upgrade transformers and datasets to latest versions
!pip install --upgrade transformers
!pip install --upgrade datasets
```

Regards!
Issue: DataCollatorWithPadding: TypeError
State: closed
Labels: name: bug

Hi,
I am following the HuggingFace course. I am now at Fine-tuning [https://huggingface.co/course/chapter3/3?fw=tf](https://huggingface.co/course/chapter3/3?fw=tf).  When I set up `DataCollatorWithPadding` as following I got an error while trying to reproduce the course code in Kaggle. This error occurs with either a CPU-only-device or a GPU-device.

Input:
```checkpoint = 'bert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
```

Output:
```---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/tmp/ipykernel_42/1563280798.py in <module>
      1 checkpoint = 'bert-base-uncased'
      2 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="pt")
TypeError: __init__() got an unexpected keyword argument 'return_tensors'
```

When I call `help` method, it too confirms that there is no argument `return_tensors`.
Input:
```
help(DataCollatorWithPadding.__init__)
```
Output:
```
Help on function __init__ in module transformers.data.data_collator:

__init__(self, tokenizer: transformers.tokenization_utils_base.PreTrainedTokenizerBase, padding: Union[bool, str, transformers.file_utils.PaddingStrategy] = True, max_length: Union[int, NoneType] = None, pad_to_multiple_of: Union[int, NoneType] = None) -> None
```

But, the source file *[Data Collator - docs](https://huggingface.co/transformers/main_classes/data_collator.html#datacollatorwithpadding)* says that there is such an argument. By default, it returns Pytorch tensors while I need TF tensors.

Where do I miss?
Please help me.


> 
> Input:
> 
> ```
> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
> data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
> ```
> 
> Output:
> 
> ```
> TypeError                                 Traceback (most recent call last)
> /tmp/ipykernel_42/1563280798.py in <module>
>       1 checkpoint = 'bert-base-uncased'
>       2 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
> ----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="pt")
> TypeError: __init__() got an unexpected keyword argument 'return_tensors'
> ```
> 

The issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.

`# upgrade transformers and datasets to latest versions`
`!pip install --upgrade transformers`
`!pip install --upgrade datasets`

Cheers!
Issue: Add option to find unique elements in nested sequences when calling `Dataset.unique`
State: open
Labels: name: enhancement

It would be nice to have an option to flatten nested sequences to find unique elements stored in them when calling `Dataset.unique`. ~~Currently, `Dataset.unique` only supports finding unique sequences and not unique elements in that situation.~~

Hi @mariosasko!

Has this been patched into any of the releases?

Hi! Not yet, would you be interested in contributing a PR? I can give you some pointers if needed. 

@mariosasko did this ever get implemented? Willing to help if you are still up for it.

@dcruiz01 No, but here is an example of how to do this with the existing API:


```python
ds = Dataset.from_dict({"tokens": [["a", "b"], ["c", "a"], ["c", "e"]]})

def flatten_tokens(pa_table):
    return pa.table([pc.list_flatten(pa_table["tokens"])], ["flat_tokens"])

ds = ds.with_format("arrow")
ds = ds.map(flatten_tokens, batched=True)
ds = ds.with_format(None)

unique_tokens = ds.unique("flat_tokens")
```

When I think about it, `.unique` on `Sequence(Value(...))` should return unique sequences/arrays, not unique elements of these sequences...
Issue: Agnews dataset viewer is not working
State: closed
Labels: name: dataset-viewer

## Dataset viewer issue for '*name of the dataset*'

**Link:** https://huggingface.co/datasets/ag_news

Hi there, the `ag_news` dataset viewer is not working.

Am I the one who added this dataset? No


Hi ! Thanks for reporting
We've already fixed the code that generates the preview for this dataset, we'll release the fix soon :)

Hi @lhoestq, thanks for your feedback!

Fixed in the viewer.

https://huggingface.co/datasets/ag_news
Issue: Temporary dataset_path for remote fs URIs not built properly in arrow_dataset.py::load_from_disk
State: closed
Labels: name: bug

## Describe the bug
When trying to build a temporary dataset path from a remote URI in this block of code:

https://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1038-L1042

the result is not the expected when passing an absolute path in an URI like `hdfs:///absolute/path`.

## Steps to reproduce the bug
```python
dataset_path = "hdfs:///absolute/path"
src_dataset_path = extract_path_from_uri(dataset_path)
tmp_dir = get_temporary_cache_files_directory()
dataset_path = Path(tmp_dir, src_dataset_path)
print(dataset_path)
```

## Expected results
With the code above, we would expect a value in `dataset_path` similar to:
`/tmp/tmpnwxyvao5/absolute/path`

## Actual results
However, we get a `dataset_path` value like:
`/absolute/path`

This is because this line here: https://github.com/huggingface/datasets/blob/42f6b1d18a4a1b6009b6e62d115491be16dfca22/src/datasets/arrow_dataset.py#L1041
returns the last absolute path when two absolute paths (the one in `tmp_dir` and the one extracted from the URI in `src_dataset_path`) are passed as arguments.

## Environment info
- `datasets` version: 1.13.3
- Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33
- Python version: 3.9.7
- PyArrow version: 5.0.0


Hi ! Good catch and thanks for opening a PR :)

I just responded in your PR
Issue: Add Natural Adversarial Objects dataset
State: open
Labels: name: dataset request

name: vision

## Adding a Dataset
- **Name:** Natural Adversarial Objects (NAO)
- **Description:** Natural Adversarial Objects (NAO) is a new dataset to evaluate the robustness of object detection models. NAO contains 7,934 images and 9,943 objects that are unmodified and representative of real-world scenarios, but cause state-of-the-art detection models to misclassify with high confidence.
- **Paper:** https://arxiv.org/abs/2111.04204v1
- **Data:** https://drive.google.com/drive/folders/15P8sOWoJku6SSEiHLEts86ORfytGezi8
- **Motivation:** interesting object detection dataset useful for miscclassifications

cc @NielsRogge 

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Issue: Add IEMOCAP dataset
State: open
Labels: name: dataset request

name: speech

name: vision

## Adding a Dataset
- **Name:** IEMOCAP
- **Description:** acted, multimodal and multispeaker database
- **Paper:** https://sail.usc.edu/iemocap/Busso_2008_iemocap.pdf
- **Data:** https://sail.usc.edu/iemocap/index.html
- **Motivation:** Useful multimodal dataset

cc @anton-l 

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


The IEMOCAP dataset is private and available only on request.
```
To obtain the IEMOCAP data you just need to fill out an electronic release form below.
```

- [Request form](https://sail.usc.edu/iemocap/release_form.php)
- [License ](https://sail.usc.edu/iemocap/Data_Release_Form_IEMOCAP.pdf)


> We do not share the dataset for commercial purposes due to privacy concerns surrounding the participants of the research. The login details will only be emailed to the given academic email address.

I think it won't be possible to add this dataset to 🤗 datasets.

Hi @dnaveenr ! We can contact the authors to see if they are interested in hosting the dataset on the Hub. In the meantime, feel free to work on a script with manual download.

Hi @mariosasko . Thanks for your response. Sure, I will mail them and find out if they're open to this.

Work on a script with manual download ? This is new to me, any guidelines would be helpful here.


> Thanks for your response. Sure, I will mail them and find out if they're open to this.

It's best to leave this part to us because we have to explain how login would work and (potentially) set up a custom verification for the dataset.

> Work on a script with manual download ? This is new to me, any guidelines would be helpful here.

For instance, this is one of the scripts with manual download: https://huggingface.co/datasets/arxiv_dataset. Compared to the standard dataset, it has the `manual_download_instructions` attribute and uses `dl_manager.manual_dir` (derived from `load_dataset(..., data_dir="path/to/data")`) to access the dataset's data files.

> It's best to leave this part to us because we have to explain how login would work and (potentially) set up a custom verification for the dataset.

Yes. That would be perfect. Thanks.

----
Okay. Thanks for giving a reference. This is helpful. I will go through it.



@mariosasko  has this been solved? I would like to use login and custom verification for training on my private dataset.

@flckv I think the [gating mechanism](https://huggingface.co/docs/hub/datasets-gated) is what you are looking for. 

@mariosasko Thanks, but no. I would like to keep my HuggingFace Dataset private and train a model on it. Is this possible?
Issue: Add VoxLingua107 dataset
State: open
Labels: name: dataset request

name: speech

## Adding a Dataset
- **Name:** VoxLingua107
- **Description:** VoxLingua107 is a speech dataset for training spoken language identification models. The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives.
- **Paper:** https://arxiv.org/abs/2011.12998
- **Data:** http://bark.phon.ioc.ee/voxlingua107/
- **Motivation:** Nice audio classification dataset

cc @anton-l 

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


#self-assign
Issue: Add Speech Commands dataset
State: closed
Labels: name: dataset request

name: speech

## Adding a Dataset
- **Name:** Speech commands
- **Description:** A Dataset for Limited-Vocabulary Speech Recognition
- **Paper:** https://arxiv.org/abs/1804.03209
- **Data:** https://www.tensorflow.org/datasets/catalog/speech_commands,  Available:
http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz
- **Motivation:** Nice dataset for audio classification training

cc @anton-l 

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


#self-assign
Issue: ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
State: closed
Labels: name: dataset-viewer

## Dataset viewer issue for '*oscar-corpus/OSCAR-2109*'

**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)*

*The dataset library cannot download any language from the oscar-corpus/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*

```
    raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```

Am I the one who added this dataset ? No

Using the older version of [OSCAR](https://huggingface.co/datasets/oscar) I don't have any issues downloading languages with the dataset library.

Hi ! Thanks for reporting :)
I think this is because the dataset is behind an access page. We can fix the dataset viewer

If you also have this error when you use the `datasets` library in python, you should probably pass `use_auth_token=True` to the `load_dataset()` function to use your account to access the dataset.

Ah ok, I didn't realise about the login page. I'll try `use_auth_token=True` and see if that solves it.

Regards!

Hi, 

Using `use_auth_token=True` and downloading the credentials with `huggingface-cli login` (stored in .huggingface/token) solved the issue.

Should I leave the issue open until you fix the Dataset viewer issue?

Cool ! Yes let's keep this issue open until the viewer is fixed - I'll close it when this is fixed. Thanks

The error I get when trying to load OSCAR 21.09 is this
```
ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
```

The URL I get in the browser is this
```
https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py
```

Maybe URL is the issue? (resolve vs blob)

> The error I get when trying to load OSCAR 21.09 is this
> 
> ```
> ConnectionError: Couldn't reach https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/resolve/main/OSCAR-2109.py
> ```
> 
> The URL I get in the browser is this
> 
> ```
> https://huggingface.co/datasets/oscar-corpus/OSCAR-2109/blob/main/OSCAR-2109.py
> ```
> 
> Maybe URL is the issue? (resolve vs blob)

You need to download your login credentials. See `huggingface-cli login` documentation and when loading the dataset use `use_auth_token=True`:
`
load_dataset(corpus, language, split=None, use_auth_token=True, cache_dir=cache_folder)`

Fixed.

<img width="1542" alt="Capture d’écran 2022-04-12 à 13 57 24" src="https://user-images.githubusercontent.com/1676121/162957585-af96d19c-f86c-47fe-80c4-2b071083cee4.png">
Issue: Respect row ordering when concatenating datasets along axis=1
State: closed
Labels: name: bug

Currently, there is a bug when concatenating datasets along `axis=1` if more than one dataset has the `_indices` attribute defined. In that scenario, all indices mappings except the first one get ignored.

A minimal reproducible example:
```python
>>> from datasets import Dataset, concatenate_datasets
>>> a = Dataset.from_dict({"a": [30, 20, 10]})
>>> b = Dataset.from_dict({"b": [2, 1, 3]})
>>> d = concatenate_datasets([a.sort("a"), b.sort("b")], axis=1)
>>> print(d[:3])              # expected: {'a': [10, 20, 30], 'b': [1, 2, 3]}
{'a': [10, 20, 30], 'b': [3, 1, 2]}
```

I've noticed the bug while working on #3195.
Issue: Make iter_archive work with ZIP files
State: open
Labels: name: enhancement

Currently users can use `dl_manager.iter_archive` in their dataset script to iterate over all the files of a TAR archive.
It would be nice if it could work with ZIP files too !

Hello, is this issue open for any contributor ? can I work on it ?



Hi ! Sure this is open for any contributor. If you're interested feel free to self-assign this issue to you by commenting `#self-assign`. Then if you have any question or if I can help, feel free to ping me.

To begin with, feel free to take a look at both implementations of `iter_archive` for local downloads and for data streaming:

In the `DownloadManager` for local dowloads:
https://github.com/huggingface/datasets/blob/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f/src/datasets/utils/download_manager.py#L218-L242

In the `StreamingDownloadManager` to stream the content of the archive directly from the remote file:
https://github.com/huggingface/datasets/blob/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f/src/datasets/utils/streaming_download_manager.py#L502-L526

Notice the call to `xopen` that opens and streams a file given either an URL or a local path :)

Okay thank you for the information. I will work on this :) 

#self-assign
Issue: Dataset viewer issue for 'liweili/c4_200m'
State: closed
Labels: name: dataset-viewer

## Dataset viewer issue for '*liweili/c4_200m*'

**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/liweili/c4_200m)*

*Server Error*
```
Status code:   404
Exception:     Status404Error
Message:       Not found. Maybe the cache is missing, or maybe the ressource does not exist.
```

Am I the one who added this dataset ? Yes


Hi ! I think the issue comes from this [line](https://huggingface.co/datasets/liweili/c4_200m/blob/main/c4_200m.py#L87):
```python
path = filepath + "/*.tsv*"
```

You can fix this by doing this instead:
```python
path = os.path.join(filepath, "/*.tsv*")
```

Here is why:

Locally you can append `"/*.tsv*"` to your local path, however it doesn't work in streaming mode, and the dataset viewer does use the streaming mode.
In streaming mode, the download and extract part is done lazily. It means that instead of using local paths, it's still passing around URLs and [chained URLs](https://filesystem-spec.readthedocs.io/en/latest/features.html#url-chaining)

Therefore in streaming mode, `filepath` is not a local path, but instead is equal to
```python
zip://::https://huggingface.co/datasets/liweili/c4_200m/resolve/main/data.zip
```
The `zip://` part means that we navigate inside the remote ZIP file.

You must use `os.path.join` to navigate inside it and get your TSV files:
```python
>>> os.path.join(filepath, "/*.tsv*")
zip://*.tsv*::https://huggingface.co/datasets/liweili/c4_200m/resolve/main/data.zip
```

`datasets` extends `os.path.join`, `glob.glob`, etc. in your dataset scripts to work with remote files.

hi @lhoestq ! thanks for the tip! i've updated the line of code but it's still not working. am i doing something else wrong? thank you!

Hi ! Your dataset code is all good now :)
```python
In [1]: from datasets import load_dataset

In [2]: d = load_dataset("liweili/c4_200m", streaming=True)
Downloading: 100%|█████████████████████████████████████████████| 2.79k/2.79k [00:00<00:00, 4.83MB/s]
Using custom data configuration default

In [3]: next(iter(d["train"]))
Out[3]: 
{'input': 'Bitcoin is for $7,094 this morning, which CoinDesk says.',
 'output': 'Bitcoin goes for $7,094 this morning, according to CoinDesk.'}
```
Though the viewer doesn't seem to be updated, I'll take a look at what's wrong

thank you @lhoestq! 😄 

It's working

<img width="1424" alt="Capture d’écran 2021-12-21 à 11 24 29" src="https://user-images.githubusercontent.com/1676121/146914238-24bf87c0-c68d-4699-8d6c-fa3065656d1d.png">
Issue: Checksum error for kilt_task_wow
State: closed
Labels: name: bug

## Describe the bug
Checksum failed when downloads kilt_tasks_wow. See error output for details.

## Steps to reproduce the bug
```python
import datasets
datasets.load_datasets('kilt_tasks','wow')
```

## Expected results
Download successful

## Actual results
```
Downloading and preparing dataset kilt_tasks/wow (download: 72.07 MiB, generated: 61.82 MiB, post-processed: Unknown size, total: 133.89 MiB) to /root/.cache/huggingface/datasets/kilt_tasks/wow/1.0.0/57dc8b2431e76637e0c6ef79689ca4af61ed3a330e2e0cd62c8971465a35db3a...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 5121.25it/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1527.42it/s]
Traceback (most recent call last):
  File "kilt_wow.py", line 30, in <module>
    main()
  File "kilt_wow.py", line 27, in main
    train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
  File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
    dataset = self.load_dataset()
  File "kilt_wow.py", line 21, in load_dataset
    return datasets.load_dataset('kilt_tasks','wow')
  File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
    builder_instance.download_and_prepare(
  File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
    self._download_and_prepare(
  File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 679, in _download_and_prepare
    verify_checksums(
  File "/opt/conda/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:
['http://dl.fbaipublicfiles.com/KILT/wow-train-kilt.jsonl', 'http://dl.fbaipublicfiles.com/KILT/wow-dev-kilt.jsonl']
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 4.0.1


Using `dataset = load_dataset("kilt_tasks", "wow", ignore_verifications=True)` may fix it, but I do not think it is a elegant solution.

Hi @slyviacassell, thanks for reporting.

Yes, there is an issue with the checksum verification. I'm fixing it.

And as you pointed out, in the meantime, you can circumvent the problem by passing `ignore_verifications=True`.
Issue: FET DATA
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).
Issue: Scifi_TV_Shows: Having trouble getting viewer to find appropriate files
State: closed
Labels: name: dataset-viewer

## Dataset viewer issue for '*Science Fiction TV Show Plots Corpus  (Scifi_TV_Shows)*'

**Link:** [link](https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows)

I tried adding both a script (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/blob/main/Scifi_TV_Shows.py) and some dummy examples (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/tree/main/dummy), but the viewer still has a 404 error ("Not found. Maybe the cache is missing, or maybe the ressource does not exist."). I'm not sure what to try next. Thanks in advance!

Am I the one who added this dataset? Yes


Hi ! I think this is because `iter_archive` doesn't support ZIP files yet. See https://github.com/huggingface/datasets/issues/3272

You can navigate into the archive this way instead:
```python
# in split_generators
data_dir = dl_manager.download_and_extract(url)
train_filepath = os.path.join(data_dir, "all-sci-fi-data-train.txt")
return [
        datasets.SplitGenerator(
            name=datasets.Split.TRAIN,
            gen_kwargs={
                "filepath": train_filepath,
            },
        ),
...
])

# in generate_examples
with open(filepath, encoding="utf-8") as f:
    ...
```

It's working: https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/viewer/Scifi_TV_Shows/test

<img width="1494" alt="Capture d’écran 2021-12-21 à 11 23 51" src="https://user-images.githubusercontent.com/1676121/146914068-f4b7225f-42c5-471d-9c73-2adac722162f.png">
Issue: Use f-strings for string formatting 
State: closed
Labels: name: good first issue

f-strings offer better readability/performance than `str.format` and `%`, so we should use them in all places in our codebase unless there is good reason to keep the older syntax.

> **NOTE FOR CONTRIBUTORS**: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under `datasets/*`.

Hi, I would be glad to help with this. Is there anyone else working on it?

Hi, I would be glad to work on this too.

#self-assign

Hi @Carlosbogo,

would you be interested in replacing the `.format` and `%` syntax with f-strings in the modules in the `datasets` directory since @Mehdi2402 has opened a PR that does that for all the other directories?

Oh I see. I will be glad to help with the `datasets` directory then.