mengta666/huggingface_github huggingface dataset:
Issue: ImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance'

hi :
I want to use the seqeval indicator because of direct load_ When metric ('seqeval '), it will prompt that the network connection fails. So I downloaded the seqeval Py to load locally. Loading code: metric = load_ metric(path='mymetric/seqeval/seqeval.py')

But tips:

Traceback (most recent call last):
  File "/home/ubuntu/Python3.6_project/zyf_project/transformers/examples/pytorch/token-classification/run_ner.py", line 604, in <module>
    main()
  File "/home/ubuntu/Python3.6_project/zyf_project/transformers/examples/pytorch/token-classification/run_ner.py", line 481, in main
    metric = load_metric(path='mymetric/seqeval/seqeval.py')
  File "/home/ubuntu/Python3.6_project/zyf_project/transformers_venv_0209/lib/python3.7/site-packages/datasets/load.py", line 610, in load_metric
    dataset=False,
  File "/home/ubuntu/Python3.6_project/zyf_project/transformers_venv_0209/lib/python3.7/site-packages/datasets/load.py", line 450, in prepare_module
    f"To be able to use this {module_type}, you need to install the following dependencies"
ImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance'


**What should I do? Please help me, thank you**



Comments:
![图片](https://user-images.githubusercontent.com/28425091/153547502-6bb0938d-788b-4857-b946-c3cf08fefce4.png)
My datasets version

![图片](https://user-images.githubusercontent.com/28425091/153547587-f4677166-af9b-44a0-95ad-b6dba873978a.png)


Hi! Some of our metrics require additional dependencies to work. In your case, simply installing the `seqeval` package with `pip install seqeval` should resolve the issue.

> Hi! Some of our metrics require additional dependencies to work. In your case, simply installing the `seqeval` package with `pip install seqeval` should resolve the issue.
I installed seqeval, but still reported the same error. That's too bad.


> > Hi! Some of our metrics require additional dependencies to work. In your case, simply installing the `seqeval` package with `pip install seqeval` should resolve the issue.
> > I installed seqeval, but still reported the same error. That's too bad.

Same issue here. What should I do to fix this error? Please help! Thank you.

I tried to install **seqeval** package through anaconda instead of pip:
`conda install -c conda-forge seqeval`
It worked for me!

I can run it through the following steps:
![image](https://user-images.githubusercontent.com/69563759/159264511-1e252a4e-c8c8-44ab-b7bc-b4aac609bd9e.png)
Thank you for answering for me!

just change the file name seqeval.py to myseqeval.py

Metrics are deprecated in `datasets` and `evaluate` should be used instead: https://github.com/huggingface/evaluate
Issue: Unable to load a dataset

## Describe the bug
Unable to load a dataset from Huggingface that I have just saved.


## Steps to reproduce the bug
On Google colab
`! pip install datasets `
`from datasets import load_dataset`
`my_path = "wiki_dataset"`
`dataset = load_dataset('wikipedia', "20200501.fr")`
`dataset.save_to_disk(my_path)`
`dataset = load_dataset(my_path)`


## Expected results
Loading the dataset

## Actual results
ValueError: Couldn't cast
_data_files: list<item: struct<filename: string>>
  child 0, item: struct<filename: string>
      child 0, filename: string
_fingerprint: string
_format_columns: null
_format_kwargs: struct<>
_format_type: null
_indexes: struct<>
_output_all_columns: bool
_split: string
to
{'builder_name': Value(dtype='string', id=None), 'citation': Value(dtype='string', id=None), 'config_name': Value(dtype='string', id=None), 'dataset_size': Value(dtype='int64', id=None), 'description': Value(dtype='string', id=None), 'download_checksums': {}, 'download_size': Value(dtype='int64', id=None), 'features': {'title': {'dtype': Value(dtype='string', id=None), 'id': Value(dtype='null', id=None), '_type': Value(dtype='string', id=None)}, 'text': {'dtype': Value(dtype='string', id=None), 'id': Value(dtype='null', id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'post_processed': Value(dtype='null', id=None), 'post_processing_size': Value(dtype='null', id=None), 'size_in_bytes': Value(dtype='int64', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='string', id=None)}}, 'supervised_keys': Value(dtype='null', id=None), 'task_templates': Value(dtype='null', id=None), 'version': {'version_str': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'major': Value(dtype='int64', id=None), 'minor': Value(dtype='int64', id=None), 'patch': Value(dtype='int64', id=None)}}
because column names don't match

## Environment info
- `datasets` version: 1.18.3
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 6.0.1



Comments:
Hi! `load_dataset` is intended to be used to load a canonical dataset (`wikipedia`), a packaged dataset (`csv`, `json`, ...) or a dataset hosted on the Hub. For local datasets saved with `save_to_disk("path/to/dataset")`, use `load_from_disk("path/to/dataset")`.

Maybe we should raise an informative error message in this case...

How should we load a locally self-gathered dataset then?
Issue: Pyarrow version error

## Describe the bug
I installed datasets(version 1.17.0, 1.18.0, 1.18.3) but i'm right now nor able to import it because of pyarrow. when i try to import it, i get the following error:
`To use datasets, the module pyarrow>=3.0.0 is required, and the current version of pyarrow doesn't match this condition`.
i tryed with all version of pyarrow execpt `4.0.0` but still get the same error.

## Steps to reproduce the bug
```python
import datasets
```

## Expected results
A clear and concise description of the expected results.

## Actual results
AttributeError                            Traceback (most recent call last)
<ipython-input-19-652e886d387f> in <module>
----> 1 import datasets

~\AppData\Local\Continuum\anaconda3\lib\site-packages\datasets\__init__.py in <module>
     26 
     27 
---> 28 if _version.parse(pyarrow.__version__).major < 3:
     29     raise ImportWarning(
     30         "To use `datasets`, the module `pyarrow>=3.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"

AttributeError: 'Version' object has no attribute 'major'

## Environment info
Traceback (most recent call last):
  File "c:\users\alex\appdata\local\continuum\anaconda3\lib\runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "c:\users\alex\appdata\local\continuum\anaconda3\lib\runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "C:\Users\Alex\AppData\Local\Continuum\anaconda3\Scripts\datasets-cli.exe\__main__.py", line 5, in <module>
  File "c:\users\alex\appdata\local\continuum\anaconda3\lib\site-packages\datasets\__init__.py", line 28, in <module>
    if _version.parse(pyarrow.__version__).major < 3:
AttributeError: 'Version' object has no attribute 'major'

- `datasets` version:
- Platform: Linux(Ubuntu) and Windows: conda on the both
- Python version: 3.7
- PyArrow version: 7.0.0


Comments:
Hi @Zaker237, thanks for reporting.

This is weird: the error you get is only thrown if the installed pyarrow version is less than 3.0.0.

Could you please check that you install pyarrow in the same Python virtual environment where you installed datasets?

From the Python command line (or terminal) where you get the error, please type:
```
import pyarrow
print(pyarrow.__version__)
import datasets
print(datasets.__version__)
``` 

hi @albertvillanova  i try yesterday to create a new python environement with python 7 and try it on the environement and it worked. so i think that the error was not the package but may be jupyter notebook on conda. still yet i'm not yet sure but it worked in an environment created with venv

OK, thanks @Zaker237 for your feedback.

I close this issue then. Please, feel free to reopen it if the problem arises again.
Issue: Download datasets from a private hub

In the context of a private hub deployment, customers would like to use load_dataset() to load datasets from their hub, not from the public hub. This doesn't seem to be configurable at the moment and it would be nice to add this feature.

The obvious workaround is to clone the repo first and then load it from local storage, but this adds an extra step. It'd be great to have the same experience regardless of where the hub is hosted.

The same issue exists with the transformers library and the CLI. I'm going to create issues there as well, and I'll reference them below.

Comments:
For reference:
https://github.com/huggingface/transformers/issues/15514
https://github.com/huggingface/huggingface_hub/issues/650

Hi ! For information one can set the environment variable `HF_ENDPOINT` (default is `https://huggingface.co`) if they want to use a private hub.

We may need to coordinate with the other libraries to have a consistent way of changing the hub endpoint

Yes, I tested it successfully this morning. Thanks.
Issue: Add CodeContests dataset

## Adding a Dataset
- **Name:** CodeContests
- **Description:** CodeContests is a competitive programming dataset for machine-learning.
- **Paper:**
- **Data:** https://github.com/deepmind/code_contests
- **Motivation:** This dataset was used when training [AlphaCode](https://deepmind.com/blog/article/Competitive-programming-with-AlphaCode).

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


Comments:
@mariosasko Can I take this up?

This dataset is now available here: https://huggingface.co/datasets/deepmind/code_contests.
Issue: `load_dataset("snli")` is different from dataset viewer

## Describe the bug
The dataset that is downloaded from the Hub via `load_dataset("snli")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., "neutral", "entailment", "contradiction"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2).

Is this expected? 


## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Ubuntu 20.4
- Python version: 3.7


Comments:
Yes, we decided to replace the encoded label with the corresponding label when possible in the dataset viewer. But
1. maybe it's the wrong default
2. we could find a way to show both (with a switch, or showing both ie. `0 (neutral)`).


Hi @severo,

Thanks for clarifying. 

I think this default is a bit counterintuitive for the user. However, this is a personal opinion that might not be general. I think it is nice to have the actual (non-encoded) labels in the viewer. On the other hand, it would be nice to match what the user sees with what they get when they download a dataset. I don't know - I can see the difficulty of choosing a default :)
Maybe having non-encoded labels as a default can be useful?

Anyway, I think the issue has been addressed. Thanks a lot for your super-quick answer!

 

Thanks for the 👍  in https://github.com/huggingface/datasets/issues/3673#issuecomment-1029008349 @mariosasko @gary149 @pietrolesci, but as I proposed various solutions, it's not clear to me which you prefer. Could you write your preferences as a comment?

_(note for myself: one idea per comment in the future)_

As I am working with seq2seq, I prefer having the label in string form rather than numeric. So the viewer is fine and the underlying dataset should be "decoded" (from int to str). In this way, the user does not have to search for a mapping `int -> original name` (even though is trivial to find, I reckon). Also, encoding labels is rather easy.

I hope this is useful

I like the idea of "0 (neutral)". The label name can even be greyed to make it clear that it's not part of the actual item in the dataset, it's just the meaning.

I like @lhoestq's idea of having grayed-out labels.

Proposals by @gary149. Which one do you prefer? Please vote with the thumbs

- 👍 

    ![image](https://user-images.githubusercontent.com/1676121/152387949-883c7d7e-a9f3-48aa-bff9-11a691555e6e.png)

- 👎 

    ![image (1)](https://user-images.githubusercontent.com/1676121/152388061-32d95e42-cade-4ae4-9a77-7365e7b72b8f.png)



I like Option 1 better as it shows clearly what the user is downloading

Thanks! 

It's [live](https://huggingface.co/datasets/glue/viewer/cola/train):

<img width="1126" alt="Capture d’écran 2022-02-14 à 10 26 03" src="https://user-images.githubusercontent.com/1676121/153836716-25f6205b-96af-42d8-880a-7c09cb24c420.png">

Thanks all for the help to improve the UI!

Love it ! thanks :)
Issue: push_to_hub but preview not working

## Dataset viewer issue for '*happifyhealth/twitter_pnn*'

**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/happifyhealth/twitter_pnn)*

I used 
```
dataset.push_to_hub("happifyhealth/twitter_pnn")
```
but the preview is not working.

Am I the one who added this dataset ? Yes




Comments:
Hi @thomas-happify, please note that the preview may take some time before rendering the data.

I've seen it is already working.

I close this issue. Please feel free to reopen it if the problem arises again.
Issue: Pubmed dataset not reachable

## Describe the bug
Trying to use the `pubmed` dataset fails to reach / download the source files.

## Steps to reproduce the bug
```python
pubmed_train = datasets.load_dataset('pubmed', split='train')
```

## Expected results
Should begin downloading the pubmed dataset.

## Actual results
```
ConnectionError: Couldn't reach ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed21n0865.xml.gz (InvalidSchema("No connection adapters were found for 'ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed21n0865.xml.gz'"))
```

## Environment info
- `datasets` version: 1.18.2
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.2
- PyArrow version: 6.0.0


Comments:
Hi @abhi-mosaic, thanks for reporting.

I'm looking at it... 

also hitting this issue

Hey @albertvillanova, sorry to reopen this... I can confirm that on `master` branch the dataset is downloadable now but it is still broken in streaming mode:

```python
 >>> import datasets
 >>> pubmed_train = datasets.load_dataset('pubmed', split='train', streaming=True)
 >>> next(iter(pubmed_train))
```
```
 No such file or directory: 'gzip://pubmed22n0001.xml::ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed22n0001.xml.gz'
```


Hi @abhi-mosaic, would you mind opening another issue for this new problem?

First issue (already solved) was a ConnectionError due to the yearly update release of PubMed: we fixed it by updating the URLs from year 2021 to year 2022.

However this is another problem: to make pubmed streamable. Please note that NOT all our datastes are streamable: we are making streamable more and more of them... but this is an on-going process...

Thanks.

@albertvillanova 
When I tried below codes, I got the similar error

```

dataset=load_dataset("pubmed",split="train")

Couldn't reach ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed21n0601.xml.gz
```

@y-rok you need to update `datasets`:
```shell
pip install -U datasets
```
Issue: Issues with custom dataset in Wav2Vec2

We are training Vav2Vec using the run_speech_recognition_ctc_bnb.py-script.

This is working fine with Common Voice, however using our custom dataset and data loader at [NbAiLab/NPSC]( https://huggingface.co/datasets/NbAiLab/NPSC) it crashes after roughly 1 epoch with the following stack trace:

![image](https://user-images.githubusercontent.com/9079808/151355893-6d5887cc-ca19-4b12-948a-124eb6dac372.png)


We are able to work around the issue, for instance by adding this check in line#222 in transformers/models/wav2vec2/modeling_wav2vec2.py:
```python
if input_length - (mask_length - 1) < num_masked_span:
           num_masked_span = input_length - (mask_length - 1)
```
Interestingly, these are the variable values before the adjustment:
```
input_length=10
mask_length=10
num_masked_span=2
````
After adjusting num_masked_spin to 1, the training script runs. The issue is also fixed by setting “replace=True” in the same function.

Do you have any  idea what is causing this, and how to fix this error permanently? If you do not think this is an Datasets issue, feel free to move the issue.


Comments:
Closed and moved to transformers.
Issue: Adding CC-100: Monolingual Datasets from Web Crawl Data (Datasets links are invalid)

## Describe the bug
The dataset links are no longer valid for CC-100. It seems that the website which was keeping these files are no longer accessible and therefore this dataset became unusable. 
Check out the dataset [homepage](http://data.statmt.org/cc-100/)  which isn't accessible.
Also the URLs for dataset file per language isn't accessible: http://data.statmt.org/cc-100/<language code here>.txt.xz (language codes: am, sr, ka, etc.)

## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("cc100", "ka")
```
It throws 503 error.

## Expected results
It should successfully download and load dataset but it throws an exception because the dataset files are no longer accessible.


## Environment info
Run from google colab. Just installed the library using pip:
```!pip install -U datasets```


Comments:
Hi @AnzorGozalishvili,

Maybe their site was temporarily down, but it seems to work fine now.

Could you please try again and confirm if the problem persists? 

Hi @albertvillanova 
I checked and it works. 
It seems that it was really temporarily down.
Thanks!
Issue: DuplicatedKeysError of NewsQA dataset

After processing the dataset following official [NewsQA](https://github.com/Maluuba/newsqa), I used datasets to load it:
```
a = load_dataset('newsqa', data_dir='news')
```
and the following error occurred: 
```
Using custom data configuration default-data_dir=news
Downloading and preparing dataset newsqa/default to /root/.cache/huggingface/datasets/newsqa/default-data_dir=news/1.0.0/b0b23e22d94a3d352ad9d75aff2b71375264a122fae301463079ee8595e05ab9...
Traceback (most recent call last):        
  File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1084, in _prepare_split
    writer.write(example, key)
  File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 442, in write
    self.check_duplicate_keys()
  File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 453, in check_duplicate_keys
    raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: ./cnn/stories/6a0f9c8a5d0c6e8949b37924163c92923fe5770d.story
Keys should be unique and deterministic in nature

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 1694, in load_dataset
    builder_instance.download_and_prepare(
  File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 595, in download_and_prepare
    self._download_and_prepare(
  File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 684, in _download_and_prepare
    self._prepare_split(split_generator, **prepare_split_kwargs)
  File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1086, in _prepare_split
    num_examples, num_bytes = writer.finalize()
  File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 524, in finalize
    self.check_duplicate_keys()
  File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 453, in check_duplicate_keys
    raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: ./cnn/stories/6a0f9c8a5d0c6e8949b37924163c92923fe5770d.story
Keys should be unique and deterministic in nature
```

Comments:
Thanks for reporting, @StevenTang1998.

I'm fixing it.
Issue: Consider adding `ipywidgets` as a dependency.

When I install `datasets` in a fresh virtualenv with jupyterlab I always see this error. 

```
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
```

It's a bit of a nuisance, because I need to run shut down the jupyterlab server in order to install the required dependency. Might it be an option to just include it as a dependency here? 

Comments:
Hi! We use `tqdm` to display progress bars, so I suggest you open this issue in their repo.

It depends on how you use `tqdm`, no? 

Doesn't this library import via; 

```
from tqdm.notebook import tqdm
```

Hi! Sorry for the late reply. We import `tqdm` as `from tqdm.auto import tqdm`, which should be equal to `from tqdm.notebook import tqdm` in Jupyter.

Any objection if I make a PR that checks if the widgets library is installed beforehand?
Issue: Dataset BnL Historical Newspapers does not work in streaming mode

## Describe the bug
When trying to load in streaming mode, it "hangs"...

## Steps to reproduce the bug
```python
ds = load_dataset("bnl_newspapers", split="train", streaming=True)
```

## Expected results
The code should be optimized, so that it works fast in streaming mode.

CC: @davanstrien 


Comments:
@albertvillanova let me know if there is anything I can do to help with this. I had a quick look at the code again and though I could try the following changes:
- use `download` instead of `download_and_extract`
https://github.com/huggingface/datasets/blob/d3d339fb86d378f4cb3c5d1de423315c07a466c6/datasets/bnl_newspapers/bnl_newspapers.py#L136
- swith to using `iter_archive` to loop through downloaded data to replace
https://github.com/huggingface/datasets/blob/d3d339fb86d378f4cb3c5d1de423315c07a466c6/datasets/bnl_newspapers/bnl_newspapers.py#L159

Let me know if it's useful for me to try and make those changes. 

Thanks @davanstrien.

I have already been working on it so that it can be used in the BigScience workshop.

I agree that the `rglob()` is not efficient in this case.

I tried different solutions without success:
- `iter_archive` cannot be used in this case because it does not support ZIP files yet

Finally I have used `iter_files()`.

I see this is fixed now 🙂.  I also picked up a few other tips from your redactors so hopefully my next attempts will support streaming from the start.
Issue: Files not updating in dataset viewer

## Dataset viewer issue for '*name of the dataset*'

**Link:**
Some examples:
* https://huggingface.co/datasets/abidlabs/crowdsourced-speech4
* https://huggingface.co/datasets/abidlabs/test-audio-13

*short description of the issue*
It seems that the dataset viewer is reading a cached version of the dataset and it is not updating to reflect new files that are added to the dataset. I get this error:

![image](https://user-images.githubusercontent.com/1778297/150566660-30dc0dcd-18fd-4471-b70c-7c4bdc6a23c6.png)


Am I the one who added this dataset? Yes

Comments:
Yes. The jobs queue is full right now, following an upgrade... Back to normality in the next hours hopefully. I'll look at your datasets to be sure the dataset viewer works as expected on them.

Should have been fixed now.
Issue: Indexing bug after dataset.select()

## Describe the bug
A clear and concise description of what the bug is.
 
Dataset indexing is not working as expected after `dataset.select(range(100))`


## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
import datasets

task_to_keys = {
    "cola": ("sentence", None),
    "mnli": ("premise", "hypothesis"),
    "mrpc": ("sentence1", "sentence2"),
    "qnli": ("question", "sentence"),
    "qqp": ("question1", "question2"),
    "rte": ("sentence1", "sentence2"),
    "sst2": ("sentence", None),
    "stsb": ("sentence1", "sentence2"),
    "wnli": ("sentence1", "sentence2"),
}

task_name = "sst2"
raw_datasets = datasets.load_dataset("glue", task_name)


train_dataset = raw_datasets["train"]

print("before select: ",train_dataset[-2:])
# before select:  {'sentence': ['a patient viewer ', 'this new jangle of noise , mayhem and stupidity must be a serious contender for the title . '], 'label': [1, 0], 'idx': [67347, 67348]}

train_dataset = train_dataset.select(range(100))

print("after select: ",train_dataset[-2:])
# after select:  {'sentence': [], 'label': [], 'idx': []}

```

link to colab: https://colab.research.google.com/drive/1LngeRC9f0jE7eSQ4Kh1cIeb411lRXQD-?usp=sharing

## Expected results
A clear and concise description of the expected results.
showing 98, 99 index data

## Actual results
Specify the actual results or traceback.
empty

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0


Comments:
Hi! Thanks for reporting! I've opened a PR with the fix.
Issue: Checksum error when trying to load amazon_review dataset

## Describe the bug
A clear and concise description of what the bug is.

## Steps to reproduce the bug
I am getting the issue when trying to load dataset using
```
dataset = load_dataset("amazon_polarity")
```

## Expected results
dataset loaded

## Actual results
```
---------------------------------------------------------------------------
NonMatchingChecksumError                  Traceback (most recent call last)
<ipython-input-3-b4758ba980ae> in <module>()
----> 1 dataset = load_dataset("amazon_polarity")
      2 dataset.set_format(type='pandas')
      3 content_series = dataset['train']['content']
      4 label_series = dataset['train']['label']
      5 df = pd.concat([content_series, label_series], axis=1)

3 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
     38     if len(bad_urls) > 0:
     39         error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))
     41     logger.info("All the checksums matched successfully" + for_verification_name)
     42 

NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: Google colab
- Python version: 3.7.12

Comments:
It is solved now
Issue: Add support for continuous metrics (RMSE, MAE)

**Is your feature request related to a problem? Please describe.**

I am uploading our dataset and models for the "Constructing interval measures" method we've developed, which uses item response theory to convert multiple discrete labels into a continuous spectrum for hate speech. Once we have this outcome our NLP models conduct regression rather than classification, so binary metrics are not relevant. The only continuous metrics available at https://huggingface.co/metrics are pearson & spearman correlation, which don't ensure that the prediction is on the same scale as the outcome.

**Describe the solution you'd like**
I would like to be able to tag our models on the Hub with the following metrics:
- RMSE
- MAE

**Describe alternatives you've considered**

I don't know if there are any alternatives.

**Additional context**
Our preprint is available here: https://arxiv.org/abs/2009.10277 . We are making it available for use in Jigsaw's Toxic Severity Rating Kaggle competition: https://www.kaggle.com/c/jigsaw-toxic-severity-rating/overview . I have our first model uploaded to the Hub at https://huggingface.co/ucberkeley-dlab/hate-measure-roberta-large

Thanks,
Chris


Comments:
Hey @ck37 

You can always use a custom metric as explained [in this guide from HF](https://huggingface.co/docs/datasets/master/loading_metrics.html#using-a-custom-metric-script).

If this issue needs to be contributed to (for enhancing the metric API) I think [this link](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html) would be helpful for the `MAE` metric.

You can use a local metric script just by providing its path instead of the usual shortcut name 

#self-assign I have starting working on this issue to enhance the metric API.
Issue: Dataset Viewer not showing Previews for Private Datasets

## Dataset viewer issue for 'abidlabs/test-audio-13'

It seems that the dataset viewer does not show previews for `private` datasets, even for the user who's private dataset it is. See [1] for example. If I change the visibility to public, then it does show, but it would be useful to have the viewer even for private datasets.

![image](https://user-images.githubusercontent.com/1778297/150200515-93ff1545-11fd-4793-be64-6bed3cd895e2.png)

**Link:** 

[1] https://huggingface.co/datasets/abidlabs/test-audio-13

**Am I the one who added this dataset?** 

Yes


Comments:
Sure, it's on the roadmap.

Closing in favor of https://github.com/huggingface/datasets-server/issues/39.
Issue: Readme info not being parsed to show on Dataset card page

## Describe the bug
The info contained in the README.md file is not being shown in the dataset main page. Basic info and table of contents are properly formatted in the README.

## Steps to reproduce the bug

# Sample code to reproduce the bug
The README file is this one: https://huggingface.co/datasets/softcatala/Tilde-MODEL-Catalan/blob/main/README.md

## Expected results
README info should appear in the Dataset card page.

## Actual results
Nothing is shown. However, labels are parsed and shown successfully.


Comments:
i suspect a markdown parsing error, @severo do you want to take a quick look at it when you have some time?

# Problem
The issue seems to coming from the front matter of the README
```---
annotations_creators:
- no-annotation
language_creators:
- machine-generated
languages:
- 'ca'
- 'de'
licenses:
- cc-by-4.0
multilinguality:
- translation
pretty_name: Catalan-German aligned corpora to train NMT systems.
size_categories:
- "1M<n<10M"            
source_datasets:
- extended|tilde_model
task_categories:
- machine-translation
task_ids:
- machine-translation
---
``` 
# Solution
The fix is to correctly style the README as explained [here](https://huggingface.co/docs/datasets/v1.12.0/dataset_card.html). I have also correctly parsed the font matter as shown below:
```
---
annotations_creators: []
language_creators: [machine-generated]
languages: ['ca', 'de']
licenses: []
multilinguality:
- multilingual
pretty_name: 'Catalan-German aligned corpora to train NMT systems.'
size_categories: 
- 1M<n<10M
source_datasets: ['extended|tilde_model']
task_categories: ['machine-translation']
task_ids: ['machine-translation']
---
```
You can find the README for a sample dataset [here](https://huggingface.co/datasets/ritwikraha/Test)

Thank you. It finally worked implementing your changes and leaving a white line between title and text in the description.

Thanks, if this solves your issue, can you please close it?
Issue: ERROR: File "setup.py" or "setup.cfg" not found. Directory cannot be installed in editable mode: /content

## Bug
The install of streaming dataset is giving following error.

## Steps to reproduce the bug
```python
! git clone https://github.com/huggingface/datasets.git
! cd datasets
! pip install -e ".[streaming]"
```

## Actual results
Cloning into 'datasets'...
remote: Enumerating objects: 50816, done.
remote: Counting objects: 100% (2356/2356), done.
remote: Compressing objects: 100% (1606/1606), done.
remote: Total 50816 (delta 834), reused 1741 (delta 525), pack-reused 48460
Receiving objects: 100% (50816/50816), 72.47 MiB | 27.68 MiB/s, done.
Resolving deltas: 100% (22541/22541), done.
Checking out files: 100% (6722/6722), done.
ERROR: File "setup.py" or "setup.cfg" not found. Directory cannot be installed in editable mode: /content



Comments:
Hi! The `cd` command in Jupyer/Colab needs to start with `%`, so this should work:
```
!git clone https://github.com/huggingface/datasets.git
%cd datasets
!pip install -e ".[streaming]"
```

thanks @mariosasko i had the same mistake and your solution is what was needed
Issue: Datasets streaming + map doesn't work for `Audio`

## Describe the bug
When using audio datasets in streaming mode, applying a `map(...)` before iterating leads to an error as the key `array` does not exist anymore.

## Steps to reproduce the bug

```python
from datasets import load_dataset

ds = load_dataset("common_voice", "en", streaming=True, split="train")


def map_fn(batch):
    print("audio keys", batch["audio"].keys())
    batch["audio"] = batch["audio"]["array"][:100]
    return batch


ds = ds.map(map_fn) 

sample = next(iter(ds))
```

I think the audio is somehow decoded before `.map(...)` is actually called.

## Expected results

IMO, the above code snippet should work.

## Actual results

```bash
audio keys dict_keys(['path', 'bytes'])
Traceback (most recent call last):
  File "./run_audio.py", line 15, in <module>
    sample = next(iter(ds))
  File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 341, in __iter__
    for key, example in self._iter():
  File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 338, in _iter
    yield from ex_iterable
  File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 192, in __iter__
    yield key, self.function(example)
  File "./run_audio.py", line 9, in map_fn
    batch["input"] = batch["audio"]["array"][:100]
KeyError: 'array'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.1.dev0
- Platform: Linux-5.3.0-64-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.1


Comments:
This seems related to https://github.com/huggingface/datasets/issues/3505.
Issue: Add The Medical Segmentation Decathlon Dataset

## Adding a Dataset
- **Name:** *The Medical Segmentation Decathlon Dataset*
- **Description:** The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data, and small objects. 
- **Paper:** [link to the dataset paper if available](https://arxiv.org/abs/2106.05735)
- **Data:** http://medicaldecathlon.com/
- **Motivation:** Hugging Face seeks to democratize ML for society. One of the growing niches within ML is the ML + Medicine community. Key data sets will help increase the supply of HF resources for starting an initial community.

(cc @osanseviero @abidlabs )

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


Comments:
Hello! I have recently been involved with a medical image segmentation project myself and was going through the `The Medical Segmentation Decathlon Dataset` as well. 
I haven't yet had experience adding datasets to this repository yet but would love to get started. Should I take this issue?
If yes, I've got two questions -
1. There are 10 different datasets available, so are all datasets to be added in a single PR, or one at a time? 
2. Since it's a competition, masks for the test-set are not available. How is that to be tackled? Sorry if it's a silly question, I have recently started exploring `datasets`.

Hi! Sure, feel free to take this issue. You can self-assign the issue by commenting `#self-assign`.

To answer your questions:
1. It makes the most sense to add each one as a separate config, so one dataset script with 10 configs in a single PR.
2. Just set masks in the test set to `None`.

Note that the images/masks in this dataset are in NIfTI format, which our `Image` feature currently doesn't support, so I think it's best to yield the paths to the images/masks in the script and add a preprocessing section to the card where we explain how to load/process the images/masks with `nibabel` (I can help with that).   



> Note that the images/masks in this dataset are in NIfTI format, which our `Image` feature currently doesn't support, so I think it's best to yield the paths to the images/masks in the script and add a preprocessing section to the card where we explain how to load/process the images/masks with `nibabel` (I can help with that).

Gotcha, thanks. Will start working on the issue and let you know in case of any doubt.

#self-assign

This is great! There is a first model on the HUb that uses this dataset! https://huggingface.co/MONAI/example_spleen_segmentation
Issue: label information get lost after parquet serialization

## Describe the bug
In *dataset_info.json* file, information about the label get lost after the dataset serialization.

## Steps to reproduce the bug
```python
from datasets import load_dataset

# normal save
dataset = load_dataset('glue', 'sst2', split='train')
dataset.save_to_disk("normal_save")


# save after parquet serialization
dataset.to_parquet("glue-sst2-train.parquet")
dataset = load_dataset("parquet", data_files='glue-sst2-train.parquet')
dataset.save_to_disk("save_after_parquet")
```

## Expected results
I expected to keep label information in *dataset_info.json* file even after parquet serialization

## Actual results
In the normal serialization i got
```json
"label": {
      "num_classes": 2,
      "names": [
        "negative",
        "positive"
      ],
      "names_file": null,
      "id": null,
      "_type": "ClassLabel"
    },
```
And after parquet serialization i got
```json
"label": {
      "dtype": "int64",
      "id": null,
      "_type": "Value"
    },
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.0
- Platform: ubuntu 20.04
- Python version: 3.8.10
- PyArrow version: 6.0.1


Comments:
Hi ! We did a release of `datasets` today that may fix this issue. Can you try updating `datasets` and trying again ?

EDIT: the issue is still there actually

I think we can fix that by storing the Features in the parquet schema metadata, and then reload them when loading the parquet file

This info is stored in the Parquet schema metadata as of https://github.com/huggingface/datasets/pull/5516
Issue: ConnectionError in IndicGLUE dataset

While I am trying to load IndicGLUE dataset (https://huggingface.co/datasets/indic_glue) it is giving me with the error:
```
ConnectionError: Couldn't reach https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/evaluations/wikiann-ner.tar.gz (error 403)

Comments:
@sahoodib, thanks for reporting.

Indeed, none of the data links appearing in the IndicGLUE website are working, e.g.: https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/evaluations/soham-articles.tar.gz
```
<Error>
<Code>UserProjectAccountProblem</Code>
<Message>User project billing account not in good standing.</Message>
<Details>
The billing account for the owning project is disabled in state delinquent
</Details>
</Error>
```

We have contacted the data owners to inform them about their issue and ask them if they plan to fix it.

Yesterday I resent a reminder email with more AI4Bharat-related people in the loop.

I also opened an issue in their repos:
- https://github.com/AI4Bharat/indicnlp_corpus/issues/14
- https://github.com/AI4Bharat/ai4bharat.org/issues/71

We have received a reply from the authors reporting they have updated the URLs of their data files and opened a PR. See:
- #4978
Issue: Dataset.from_pandas preserves useless index

## Describe the bug
Let's say that you want to create a Dataset object from pandas dataframe. Most likely you will write something like this:

```
import pandas as pd
from datasets import Dataset


df = pd.read_csv('some_dataset.csv')
# Some DataFrame preprocessing code...
dataset = Dataset.from_pandas(df)
```
If your preprocessing code contain indexing operations like this:
```
df = df[df.col1 == some_value]
```
then your df.index can be changed from (default) ```RangeIndex(start=0, stop=16590, step=1)``` to something like this ```Int64Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,
                9,
            ...
            83979, 83980, 83981, 83982, 83983, 83984, 83985, 83986, 83987,
            83988],
           dtype='int64', length=16590)```

In this case, PyArrow (by default) will preserve this non-standard index. In the result, your dataset object will have the extra field that you likely don't want to have: '__index_level_0__'.

You can easily fix this by just adding extra argument ```preserve_index=False``` to call of ```InMemoryTable.from_pandas``` in ```arrow_dataset.py```.

If you approve that this isn't desirable behavior, I can make a PR fixing that.

## Environment info
- `datasets` version: 1.16.1
- Platform: Linux-5.11.0-44-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 6.0.1



Comments:
Hi! That makes sense. Sure, feel free to open a PR! Just a small suggestion: let's make `preserve_index` a parameter of `Dataset.from_pandas` (which we then pass to `InMemoryTable.from_pandas`) with `None` as a default value to not have this as a breaking change.
Issue: Cannot load ‘bookcorpusopen’

## Describe the bug
Cannot load 'bookcorpusopen'


## Steps to reproduce the bug
```python
dataset = load_dataset('bookcorpusopen')
```
or
```python
dataset = load_dataset('bookcorpusopen',script_version='master')
```

## Actual results
ConnectionError: Couldn't reach https://the-eye.eu/public/AI/pile_preliminary_components/books1.tar.gz

## Environment info
- `datasets` version: 1.9.0
- Platform: Linux version 3.10.0-1160.45.1.el7.x86_64
- Python version:  3.6.13
- PyArrow version: 6.0.1


Comments:
The host of this copy of the dataset (https://the-eye.eu) is down and has been down for a good amount of time ([potentially months](https://www.reddit.com/r/Roms/comments/q82s15/theeye_downdied/))

Finding this dataset is a little esoteric, as the original authors took down the official BookCorpus dataset some time ago.

There are community-created versions of BookCorpus, such as the files hosted in the link below.
https://battle.shawwn.com/sdb/bookcorpus/

And more discussion here:
https://github.com/soskek/bookcorpus

Do we want to remove this dataset entirely?  There's a fair argument for this, given that the official BookCorpus dataset was taken down by the authors.   If not, perhaps can open a PR with the link to the community-created tar above and updated dataset description.

Hi! The `bookcorpusopen` dataset is not working for the same reason as explained in this comment: https://github.com/huggingface/datasets/issues/3504#issuecomment-1004564980

Hi @HUIYINXUE, it should work now that the data owners created a mirror server with all data, and we updated the URL in our library.
Issue: Integrate Milvus (pymilvus) library

Milvus is a popular open-source vector database. We should add a new vector index to support this project.

Comments:
Hi @mariosasko，Just search randomly and I found this issue~ I'm the tech lead of Milvus and we are looking forward to integrate milvus together with huggingface datasets.

Any suggestion on how we could start?


Feel free to assign to me and we probably need some guide on it

@mariosasko any updates my man?


Hi! For starters, I suggest you take a look at this file: https://github.com/huggingface/datasets/blob/master/src/datasets/search.py, which contains all the code for Faiss/ElasticSearch support. We could set up a Slack channel for additional guidance. Let me know what you prefer.

> Hi! For starters, I suggest you take a look at this file: https://github.com/huggingface/datasets/blob/master/src/datasets/search.py, which contains all the code for Faiss/ElasticSearch support. We could set up a Slack channel for additional guidance. Let me know what you prefer.

Sure, we take a look and do some research
Issue: DuplicatedKeysError when loading tweet_qa dataset

When loading the tweet_qa dataset with `load_dataset('tweet_qa')`, the following error occurs: 

`DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 2a167f9e016ba338e1813fed275a6a1e
Keys should be unique and deterministic in nature
`
Might be related to issues #2433 and #2333

- `datasets` version: 1.17.0
- Python version: 3.8.5


Comments:
Hi, we've just merged the PR with the fix. The fixed version of the dataset can be downloaded as follows:
```python
import datasets
dset = datasets.load_dataset("tweet_qa", revision="master")
```
Issue: set_format("np") no longer works for Image data

## Describe the bug
`dataset.set_format("np")` no longer works for image data, previously you could load the MNIST like this:

```python
dataset = load_dataset("mnist")
dataset.set_format("np")
X_train = dataset["train"]["image"][..., None] # <== No longer a numpy array
```
but now it doesn't work, `set_format("np")` seems to have no effect and the dataset just returns a list/array of PIL images instead of numpy arrays as requested.


Comments:
A quick fix for now is doing this:

```python
X_train = np.stack(dataset["train"]["image"])[..., None]

This error also propagates to jax and is even trickier to fix, since `.with_format(type='jax')` will use numpy conversion internally (and fail). For a three line failure:

```python
dataset = datasets.load_dataset("mnist")
dataset.set_format("jax")
X_train = dataset["train"]["image"]
```

Hi! We've recently introduced a new Image feature that yields PIL Images (and caches transforms on them) instead of arrays.

However, this feature requires a custom transform to yield np arrays directly:
```python
ddict = datasets.load_dataset("mnist")

def pil_image_to_array(batch):
     return {"image": [np.array(img) for img in batch["image"]]}  # or jnp.array(img) for Jax

ddict.set_transform(pil_image_to_array, columns="image", output_all_columns=True)
```

[Docs](https://huggingface.co/docs/datasets/master/process.html#format-transform) on `set_transform`.

Also, the approach proposed by @cgarciae is not the best because it loads the entire column in memory.

@albertvillanova @lhoestq WDYT? The Audio and the Image feature currently don't support the TF/Jax/PT Formatters, but for the Numpy Formatter maybe it makes more sense to return np arrays (and not a dict in the case of the Audio feature or a PIL Image object in the case of the Image feature).

Yes I agree it should return arrays and not a PIL image (and possible an array instead of a dict for audio data).
I'm currently finishing some code refactoring of the image and audio and opening a PR today. Maybe we can look into that after the refactoring

This has been fixed in https://github.com/huggingface/datasets/pull/5072, which is included in the latest release of `datasets`.