NyssaW/github-issues huggingface dataset:
`wikipedia` pre-processed datasets

## Describe the bug
[Wikipedia](https://huggingface.co/datasets/wikipedia) dataset readme says that certain subsets are preprocessed. However it seems like they are not available. When I try to load them it takes a really long time, and it seems like it's processing them.

## Steps to reproduce the bug
```python
from datasets import load_dataset

load_dataset("wikipedia", "20220301.en")
```

## Expected results
To load the dataset

## Actual results
Takes a very long time to load (after downloading)

After `Downloading data files: 100%`. It takes hours and gets killed.
Tried `wikipedia.simple` and it got processed after ~30mins.

Comments:
Hi @vpj, thanks for reporting.

I'm sorry, but I can't reproduce your bug: I load "20220301.simple"in 9 seconds:
```shell
time python -c "from datasets import load_dataset; load_dataset('wikipedia', '20220301.simple')"

Downloading and preparing dataset wikipedia/20220301.simple (download: 228.58 MiB, generated: 224.18 MiB, post-processed: Unknown size, total: 452.76 MiB) to .../.cache/huggingface/datasets/wikipedia/20220301.simple/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.66k/1.66k [00:00<00:00, 1.02MB/s]
Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 235M/235M [00:02<00:00, 82.8MB/s]
Dataset wikipedia downloaded and prepared to .../.cache/huggingface/datasets/wikipedia/20220301.simple/2.0.0/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559. Subsequent calls will reuse this data.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 290.75it/s]

real	0m9.693s
user	0m6.002s
sys	0m3.260s
```

Could you please check your environment info, as requested when opening this issue?
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
```
Maybe you are using an old version of `datasets`...

Downloading and processing `wikipedia simple` dataset completed in under 11sec on M1 Mac. Could you please check `dataset` version as mentioned by @albertvillanova? Also check system specs, if system is under load processing could take some time I guess.
Fix type hint and documentation for `new_fingerprint`

Currently, there are no type hints nor `Optional` for the argument `new_fingerprint` in several methods of `datasets.arrow_dataset.Dataset`.

There was some documentation missing as well.

Note that pylance is happy with the type hints, but pyright does not detect that `new_fingerprint` is set within the decorator.

The modifications in this PR are fine since here https://github.com/huggingface/datasets/blob/aa743886221d76afb409d263e1b136e7a71fe2b4/src/datasets/fingerprint.py#L446-L454

for the non-inplace case we make sure to auto-generate a new fingerprint (as indicated in the doc).

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Dataset Viewer issue for strombergnlp/offenseval_2020, strombergnlp/polstance

### Link

https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train

### Description

The viewer isn't running for these two datasets. I left it overnight because a wait sometimes helps things get loaded, and the error messages have all gone, but the datasets are still turning up blank in viewer. Maybe it needs a bit more time.

* https://huggingface.co/datasets/strombergnlp/polstance/viewer/PolStance/train

* https://huggingface.co/datasets/strombergnlp/offenseval_2020/viewer/ar/train

While offenseval_2020 is gated w. prompt, the other gated previews I have run fine in Viewer, e.g. https://huggingface.co/datasets/strombergnlp/shaj , so I'm a bit stumped!

### Owner

Yes

Comments:
Not sure if it's related... I was going to raise an issue for https://huggingface.co/datasets/domenicrosati/TruthfulQA which also has the same issue... https://huggingface.co/datasets/domenicrosati/TruthfulQA/viewer/domenicrosati--TruthfulQA/train 



Yes, it's related. The backend behind the dataset viewer is currently under too much load, and these datasets are still in the jobs queue. We're actively working on this issue, and we expect to fix the issue permanently soon. Thanks for your patience 🙏  

Thanks @severo and no worries! - a suggestion for a UI usability thing maybe is to indicate that the dataset processing is in the job queue (rather than no data?)

Thanks, these are working great now (including @domenicrosati 's, afaics!)
Support >1 PWC dataset per dataset card

**Is your feature request related to a problem? Please describe.**
Some datasets cover more than one dataset on PapersWithCode. For example, the OffensEval 2020 challenge involved five languages, and there's one dataset to cover all five datasets, [`strombergnlp/offenseval_2020`](https://huggingface.co/datasets/strombergnlp/offenseval_2020). However, the yaml `paperswithcode_id:` dataset card entry only supports one value; when multiple are added, the PWC link disappears from the dataset page.

Because the link from a PapersWithCode dataset to a Hugging Face Hub entry can't be entered manually and seems to be scraped, this means end users don't have a way of getting a dataset reader link to appear on all the PWC datasets supported by one HF Hub Dataset reader.

It's not super unusual to have papers introduce multiple parallel variants of a dataset and would be handy to reflect this, so e.g. dataset maintainers can DRY, and so dataset users can keep what they're doing simple.

**Describe the solution you'd like**
I'd like `paperswithcode_id:` to support lists and be able to connect with multiple PWC datasets.

**Describe alternatives you've considered**
De-normalising the datasets on HF Hub to create multiple readers for each variation on a task, i.e. instead of a single `offenseval_2020`, having `offenseval_2020_ar`, `offenseval_2020_da`, `offenseval_2020_gr`, ...

**Additional context**
Hope that's enough

**Priority**
Low

Comments:
Hi @leondz, I agree it would be nice. We'll see what we can do ;)
Multi-news dataset loader attempts to strip wrong character from beginning of summaries

## Describe the bug

The `multi_news.py` data loader has [a line which attempts to strip `"- "` from the beginning of summaries](https://github.com/huggingface/datasets/blob/aa743886221d76afb409d263e1b136e7a71fe2b4/datasets/multi_news/multi_news.py#L97). The actual character in the multi-news dataset, however, is `"– "`, which is different, e.g. `"– " != "- "`.

I would have just opened a PR to fix the mistake, but I am wondering what the motivation for stripping this character is? AFAICT most approaches just leave it in, e.g. the current SOTA on this dataset, [PRIMERA](https://huggingface.co/allenai/PRIMERA-multinews) (you can see its in the generated summaries of the model in their [example notebook](https://github.com/allenai/PRIMER/blob/main/Evaluation_Example.ipynb)).

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5


Comments:
Hi ! Thanks for reporting :)

This dataset was simply converted from [tensorflow datasets](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/summarization/multi_news.py)

I think we can just remove the `.strip("- ")` and keep this character

Cool! I made a PR.
Fix cnn_dailymail (dm stories were ignored)

https://github.com/huggingface/datasets/pull/4188 introduced a bug in `datasets` 2.2.0: DailyMail stories are ignored when generating the dataset.

I fixed that, and removed the google drive link (it has annoying quota limitations issues)

We can do a patch release after this is merged

Comments:
_The documentation is not available anymore as the PR was closed or merged._
[Imagefolder] Docs + Don't infer labels from file names when there are metadata + Error messages when metadata and images aren't linked correctly

I updated the `docs/source/image_process.mdx` documentation and added an example for image captioning and object detection using `ImageFolder`.

While doing so I also improved a few aspects:
- we don't need to infer labels from file names when there are metadata - they can just be in the metadata if necessary
- raise informative error messages when metadata and images aren't linked correctly:
  - when an image is missing a metadata file
  - when a metadata file is missing an image

I added some tests for these changes as well

cc @mariosasko 

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

Merging this one since mario is off, I took care of adding some tests to make sure everything is fine. Will do the release after it
Fixes FrugalScore

There are two minor modifications in this PR:
1) `predictions` and `references` are swapped. Basically Frugalscore is commutative, however some tiny differences can occur if we swap the references and the predictions. I decided to swap them just to obtain the exact results as reported in the paper.
2) I switched to dynamic padding that was was used in the training, forcing the padding to `max_length`  introduces errors for some reason that I ignore.

@lhoestq 

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4305). All of your documentation changes will be reflected on that endpoint.

> predictions and references are swapped. Basically Frugalscore is commutative, however some tiny differences can occur if we swap the references and the predictions. I decided to swap them just to obtain the exact results as reported in the paper.

What is the order of magnitude of the difference ? Do you know what causes this ?

> I switched to dynamic padding that was was used in the training, forcing the padding to max_length introduces errors for some reason that I ignore.

What error ?
Language code search does direct matches

## Describe the bug

Hi. Searching for bcp47 tags that are just the language prefix (e.g. `sq` or `da`) excludes datasets that have added extra information in their language metadata (e.g. `sq-AL` or `da-bornholm`). The example codes given in the [tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging) encourages addition of the additional codes ("_expected format is BCP47 tags separated for ';' e.g. 'en-US;fr-FR'_") but this would lead to those datasets being hidden in datasets search.

## Steps to reproduce the bug
1. Add a dataset using a variant tag (e.g. [`sq-AL`](https://huggingface.co/datasets?languages=languages:sq-AL))
2. Look for datasets using the full code 
3. Note that they're missing when just the language is searched for (e.g. [`sq`](https://huggingface.co/datasets?languages=languages:sq))

Some datasets are already affected by this - e.g. `AmazonScience/massive` is listed under `sq-AL` but not `sq`.

One workaround is for dataset creators to add an additional root language tag to dataset YAML metadata, but it's unclear how to communicate this. It might be possible to index the search on `languagecode.split('-')[0]` but I wanted to float this issue before trying to write any code :)

## Expected results
Datasets using longer bcp47 tags also appear under searches for just the language code; e.g. Quebecois datasets (`fr-CA`) would come up when looking for French datasets with no region specification (`fr`), or US English (`en-US`) datasets would come up when searching for English datasets (`en`).

## Actual results
The language codes seem to be directly string matched, excluding datasets with specific language tags from non-specific searches.

## Environment info
(web app)

Comments:
Thanks for reporting ! I forwarded the issue to the front-end team :)

Will keep you posted !

I also changed the tagging app to suggest two letters code for now.
Remove hacking license tags when mirroring datasets on the Hub

Currently, when mirroring datasets on the Hub, the license tags are hacked: removed of characters "." and "$". On the contrary, this hacking is not applied to community datasets on the Hub. This generates multiple variants of the same tag on the Hub. 

I guess this hacking is no longer necessary:
- it is not applied to community datasets
- all canonical datasets are validated by maintainers before being merged: CI + maintainers make sure license tags are the right ones

Fix #4298.

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

The Hub doesn't allow these characters in the YAML tags, and git push fails if you want to push a dataset card containing these characters.

Ok, let me rename the bad config names :) I think I can also keep backward compatibility with a warning

Almost done with it btw, will submit a PR that shows all the configuration name changes (from a bit more than 20 datasets)

Please, let me know when the renaming of configs is done. If not enough bandwidth, I can take care of it...

Will focus on this this afternoon ;)

I realized when renaming all the configurations with dots in https://github.com/huggingface/datasets/pull/4365 that it's not ideal for certain cases. For example:
- many configurations have a version like "1.0.0" in their names
- to avoid breaking changes we need to replace dots with underscores in the user input and show a warning, which hurts the experience
- our second most downloaded dataset at the moment is affected: `newsgroup`
- if we disallow dots, then we'll never be able to make the [allenai/c4](https://huggingface.co/datasets/allenai/c4) work with its different configurations since they contain dots, and we can't rename them because they are the official download links

I was thinking of other alternatives:
1. just stop separating tags per config name completely, and have a single flat YAML for all configurations. Dataset search doesn't use this info anyway
2. use another YAML structure to avoid having config names as keys, such as
```yaml
languages:
- config: 20220301_en
  values:
  - en
```

I'm down for 1, to keep things simple

@lhoestq I agree:
- better not changing config names (so that we do not introduce any braking change)
- therefore, we should not use them as keys

In relation with the proposed solutions, I have no strong opinion:
- option 1 is simpler and aligns better with current usage on the Hub (configs are ignored)
- however:
  - we will lose all the information per config we already have (for those datasets containing config keys; contributors made an effort to put that information per config)
  - and this information might be useful on the Hub in the future, in case we would like to enrich the search feature with more granularity; this is only applicable if this feature could eventually make sense

So, no strong opinion...

Closing in favor of https://github.com/huggingface/datasets/pull/4367
Add ImageNet-Sketch dataset

This PR adds the ImageNet-Sketch dataset and resolves #3953 .

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

I think you can go ahead with uploading the data, and also ping the author in parallel. I think the images may subject to copyright anyway (scrapped from google image) so the dataset author is not allowed to set a license to the data.

I think it's fine to upload the dataset as soon as we mention explicitly that the images may be subject to copyright.
Add API code examples for loading methods

This PR adds API code examples for loading methods, let me know if I've missed any important parameters we should showcase :)

I was a bit confused about `inspect_dataset` and `inspect_metric`. The `path` parameter says it will accept a dataset identifier from the Hub. But when I try the identifier `rotten_tomatoes`, it gives me:

```py
from datasets import inspect_dataset
inspect_dataset('rotten_tomatoes', local_path='/content/rotten_tomatoes')

FileNotFoundError: Couldn't find a dataset script at /content/rotten_tomatoes/rotten_tomatoes.py or any data file in the same directory.
```

Does the user need to have an existing copy of `rotten_tomatoes.py` on their local drive (in which case, it seems like the same option as the first option in `path`)?

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Remove manual download from imagenet-1k

Remove the manual download code from `imagenet-1k` to make it a regular dataset.

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

Thanks for the reviews @apsdehal and @lhoestq! As suggested by @lhoestq, I'll separate the train/val/test splits, apply the validation split fixes and shuffle the images files to simplify the script and make streaming faster.

@apsdehal I dismissed your review as it's no longer relevant after the data files changes suggested by @lhoestq.
Normalise license names

**Is your feature request related to a problem? Please describe.**
When browsing datasets, the Licenses tag cloud (bottom left of e.g. https://huggingface.co/datasets) has multiple variants of the same license. This means the options exclude datasets arbitrarily, giving users artificially low recall. The cause of the dupes is probably due to a bit of variation in metadata.

**Describe the solution you'd like**
I'd like the licenses in metadata to follow the same standard as much as possible, to remove this problem. I'd like to go ahead and normalise the dataset metadata to follow the format & values given in [src/datasets/utils/resources/licenses.json](https://github.com/huggingface/datasets/blob/master/src/datasets/utils/resources/licenses.json) .

**Describe alternatives you've considered**
None

**Additional context**
None

**Priority** 
Low


Comments:
we'll add the same server-side metadata validation system as for hf.co/models soon-ish

(you can check on hf.co/models that licenses are "clean")

Fixed by #4367.
Datasets YAML tagging space is down

## Describe the bug
The neat hf spaces app for generating YAML tags for dataset `README.md`s is down

## Steps to reproduce the bug
1. Visit https://huggingface.co/spaces/huggingface/datasets-tagging

## Expected results
There'll be a HF spaces web app for generating dataset metadata YAML

## Actual results
There's an error message; here's the step where it breaks:

```
Step 18/29 : RUN pip install -r requirements.txt
 ---> Running in e88bfe7e7e0c
Defaulting to user installation because normal site-packages is not writeable
Collecting git+https://github.com/huggingface/datasets.git@update-task-list (from -r requirements.txt (line 4))
  Cloning https://github.com/huggingface/datasets.git (to revision update-task-list) to /tmp/pip-req-build-bm8t0r0k
  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/datasets.git /tmp/pip-req-build-bm8t0r0k
  WARNING: Did not find branch or tag 'update-task-list', assuming revision or ref.
  Running command git checkout -q update-task-list
  error: pathspec 'update-task-list' did not match any file(s) known to git
  error: subprocess-exited-with-error
  
  × git checkout -q update-task-list did not run successfully.
  │ exit code: 1
  ╰─> See above for output.
  
  note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error

× git checkout -q update-task-list did not run successfully.
│ exit code: 1
╰─> See above for output.
```

## Environment info

- Platform: Linux / Brave


Comments:
@lhoestq @albertvillanova `update-task-list` branch does not exist anymore, should point to `main` now i guess

Thanks for reporting, fixing it now

It's up again :)
Dataset Viewer issue for strombergnlp/ipm_nel : preview is empty, no error message

### Link

https://huggingface.co/datasets/strombergnlp/ipm_nel/viewer/ipm_nel/train

### Description

The viewer is blank. I tried my best to emulate a dataset with a working viewer, but this one just doesn't seem to want to come up. What did I miss?

### Owner

Yes

Comments:
Hi @leondz, thanks for reporting.

Indeed, the dataset viewer relies on the dataset being streamable (passing `streaming=True` to `load_dataset`). Whereas most of the datastes are streamable out of the box (thanks to our implementation of streaming), there are still some exceptions.

In particular, in your case, that is due to the data file being TAR. This format is not streamable out of the box (it does not allow random access to the archived files), but we use a trick to allow streaming: using `dl_manager.iter_archive`.

Let me know if you need some help: I could push a commit to your repo with the fix.

Ah, right! The preview is working now, but this explanation is good to know, thank you. I'll prefer formats with random file access supported in datasets.utils.extract in future, and try out this fix for the tarfiles :)
Don't do unnecessary list type casting to avoid replacing None values by empty lists

In certain cases, `None` values are replaced by empty lists when casting feature types.

It happens every time you cast an array of nested lists like [None, [0, 1, 2, 3]] to a different type (to change the integer precision for example). In this case you'd get [[], [0, 1, 2, 3]] for example. This issue comes from PyArrow, see the discussion in https://github.com/huggingface/datasets/issues/3676

This issue also happens when no type casting is needed, because casting is supposed to be a no-op in this case. But as https://github.com/huggingface/datasets/issues/3676 shown, it's not the case and `None` are replaced by empty lists even if we cast to the exact same type.

In this PR I just workaround this bug in the case where no type casting is needed. In particular, I only call `pa.ListArray.from_arrays` only when necessary.

I also added a warning when some `None` are effectively replaced by empty lists. I wanted to raise an error in this case, but maybe we should wait a major update to do so

This PR fixes this particular case, that is occurring in `run_qa.py` in `transformers`:
```python
from datasets import Dataset

ds = Dataset.from_dict({"a": range(4)})
ds = ds.map(lambda x: {"b": [[None, [0]]]}, batched=True, batch_size=1, remove_columns=["a"])
print(ds.to_pandas())
# before:
#              b
# 0  [None, [0]]
# 1    [[], [0]]
# 2    [[], [0]]
# 3    [[], [0]]
#
# now:
#              b
# 0  [None, [0]]
# 1  [None, [0]]
# 2  [None, [0]]
# 3  [None, [0]]
```

cc @sgugger 

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

Quick question about the message in the warning. You say "will be fixed in a future major version" but don't you mean "will raise an error in a future major version"?

Right ! Good catch, thanks, I updated the message to say "will raise an error in a future major version"
Enable label alignment for token classification datasets

This PR extends the `Dataset.align_labels_with_mapping()` method to support alignment of label mappings between datasets and models for token classification (e.g. NER).

Example of usage:

```python
from datasets import load_dataset

ner_ds = load_dataset("conll2003", split="train")
# returns [3, 0, 7, 0, 0, 0, 7, 0, 0]
ner_ds[0]["ner_tags"]
# hypothetical model mapping with O <--> B-LOC
label2id = {
    "B-LOC": "0",
    "B-MISC": "7",
    "B-ORG": "3",
    "B-PER": "1",
    "I-LOC": "6",
    "I-MISC": "8",
    "I-ORG": "4",
    "I-PER": "2",
    "O": "5"
  }
ner_aligned_ds = ner_ds.align_labels_with_mapping(label2id, "ner_tags")
# returns [3, 5, 7, 5, 5, 5, 7, 5, 5]
ner_aligned_ds[0]["ner_tags"]
```

Context: we need this in AutoTrain to automatically align datasets / models during evaluation. cc @abhishekkrthakur 

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

Hmm, not sure why the Windows tests are failing with:

```
Did not find path entry C:\tools\miniconda3\bin
C:\tools\miniconda3\envs\py37\python.exe: No module named pytest
```

Edit: running the CI again fixed the problem 🙃 

> One last nit and we can merge then

Thanks, done!
CommonSenseQA has missing and inconsistent field names

## Describe the bug
In short, CommonSenseQA implementation is inconsistent with the original dataset.

More precisely, we need to:

1. Add the dataset matching "id" field. The current dataset, instead, regenerates monotonically increasing id.  
2. The [“question”][“stem”] field is flattened into "question". We should match the original dataset and unflatten it
3. Add the missing "question_concept" field in the question tree node
4. Anything else? Go over the data structure of the newly repaired CommonSenseQA and make sure it matches the original

## Expected results
Every data item of the CommonSenseQA should structurally and data-wise match the original CommonSenseQA dataset.

## Actual results
TBD

## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2

Comments:
Thanks for reporting, @vblagoje.

I'm opening a PR to address this.
Fix typo in logging docs

This PR fixes #4271.

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

> This PR fixes #4271.

Things have not changed when searching "tqdm" in the Dataset document. The second result still performs as "Enable".

Hi @jiangwy99, the fix will appear on the `main` version of the docs:

![Screen Shot 2022-05-04 at 8 38 29 AM](https://user-images.githubusercontent.com/59462357/166718225-6848ab91-87d1-4572-9912-40a909af6cb9.png)


Fixed now, thanks.
Add HF Speech Bench to Librispeech Dataset Card

Adds the HF Speech Bench to Librispeech Dataset Card in place of the Papers With Code Leaderboard. Should improve usage and visibility of this leaderboard! Wondering whether this can also be done for [Common Voice 7](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) and [8](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) through someone with permissions? 

cc @patrickvonplaten: more leaderboard promotion!

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Rename imagenet2012 -> imagenet-1k

On the Hugging Face Hub, users refer to imagenet2012 (from #4178 ) as imagenet-1k in their model tags.

To correctly link models to imagenet, we should rename this dataset `imagenet-1k`.

Later we can add `imagenet-21k` as a new dataset if we want.

Once this one is merged we can delete the `imagenet2012` dataset repository on the Hub.

EDIT: to complete the rationale on why we should name it `imagenet-1k`:
If users specifically added the tag `imagenet-1k` , then it could be for two reasons (not sure which one is predominant), either they
- wanted to make it explicit that it’s not 21k -> the distinction is important for the community
- or they have been following this convention from other models -> the convention implicitly exists already

Comments:
> Later we can add imagenet-21k as a new dataset if we want.

isn't it what models refer to as `imagenet` already?

> isn't it what models refer to as imagenet already?

I wasn't sure, but it looks like it indeed. Therefore having a dataset `imagenet` for ImageNet 21k makes sense actually.

EDIT: actually not all `imagenet` tag refer to ImageNet 21k - we will need to correct some of them

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

should we remove the repo mirror on the hub side or will you do it?
Add mr_polarity movie review sentiment classification

Add the MR (Movie Review) dataset. The original dataset contains sentences from Rotten Tomatoes labeled as either "positive" or  "negative". 

Homepage: [https://www.cs.cornell.edu/people/pabo/movie-review-data/](https://www.cs.cornell.edu/people/pabo/movie-review-data/)
paperswithcode: [https://paperswithcode.com/dataset/mr](https://paperswithcode.com/dataset/mr)

- [ ] I was not able to generate dummy data, the original dataset files have ".pos" and ".neg" as file extensions so the auto-generator does not work. Is it fine like this or should dummy data be added?


Comments:
whoops just found https://huggingface.co/datasets/rotten_tomatoes
Fix/start token mask issue and update documentation

This pr fixes a couple bugs:

1) the perplexity was calculated with a 0 in the attention mask for the start token, which was causing high perplexity scores that were not correct
2) the documentation was not updated

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

> Good catch ! Thanks :)
> 
> Next time can you describe your fix in the Pull Request description please ?

Thanks. Also whoops, sorry about not being very descriptive. I updated the pull request description, and will keep this in mind for future PRs.
Replace data URL in SAMSum dataset and support streaming

This PR replaces data URL in SAMSum dataset:
- original host (arxiv.org) does not allow HTTP Range requests
- we have hosted the data on the Hub (license: CC BY-NC-ND 4.0)

Moreover, it implements support for streaming.

Fix #4146.
Related to: #4236.

CC: @severo 

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Bump PyArrow Version to 6

Fixes #4152 

This PR updates the PyArrow version to 6 in setup.py, CI job files .circleci/config.yaml and .github/workflows/benchmarks.yaml files.
This will fix ArrayND error which exists in pyarrow 5.

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

Updated meta.yaml as well. Thanks.

I'm OK with bumping PyArrow to version 6 to match the version in Colab, but maybe a better solution would be to stop using extension types in our codebase to avoid similar issues.

> but maybe a better solution would be to stop using extension types in our codebase to avoid similar issues.

I agree, not much attention has been payed to extension arrays in the latest developments of Arrow anyway.

Let's not use them more that what we do right now, and try to remove them at one point
The data preview of XGLUE

It seems that something wrong with the data previvew of XGLUE

Comments:
![image](https://user-images.githubusercontent.com/49108847/165700611-915b4343-766f-4b81-bdaa-b31950250f06.png)


Thanks for reporting @czq1999.

Note that the dataset viewer uses the dataset in streaming mode and that not all datasets support streaming yet.

That is the case for XGLUE dataset (as the error message points out): this must be refactored to support streaming. 

Fixed, thanks @albertvillanova !

https://huggingface.co/datasets/xglue

<img width="824" alt="Capture d’écran 2022-04-29 à 10 23 14" src="https://user-images.githubusercontent.com/1676121/165909391-9f98d98a-665a-4e57-822d-8baa2dc9b7c9.png">
Support to load dataset with TSV files by passing only dataset name

This PR implements support to load a dataset (w/o script) containing TSV files by passing only the dataset name (no need to pass `sep='\t'`):
```python
ds = load_dataset("dataset/name")
```

The refactoring allows for future builder kwargs customizations based on file extension.

Related to #4238.

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Fix yield for crd3

Modified the `_generate_examples` function to consider all the turns for a chunk id as a single example
Modified the features accordingly

```
"turns": [
                        {
                            "names": datasets.features.Sequence(datasets.Value("string")),
                            "utterances": datasets.features.Sequence(datasets.Value("string")),
                            "number": datasets.Value("int32"),
                        }
                    ],
                }
```

I wasn't able to run `datasets-cli dummy_data datasets` command. Is there a workaround for this? 

Comments:
I don't think you need to generate new dummy data, since they're in the same format as the original data.

The CI is failing because of this error:
```python
>                       turn["names"] = turn["NAMES"]
E                       TypeError: tuple indices must be integers or slices, not str
```

Do you know what could cause this ? If I understand correctly, `turn` is supposed to be a list of dictionaries right ?

> ```  
>   
> Do you know what could cause this ? If I understand correctly, turn is supposed to be a list of dictionaries right ?
> ```

This is strange. Let me look into this. As per https://github.com/RevanthRameshkumar/CRD3/blob/master/data/aligned%20data/c%3D2/C1E001_2_0.json TURNS is a list of dictionaries. So when we iterate over `row["TURNS]"` each `turn` is essentially a dictionary. Not sure why it's being considered a tuple here.
Replace data URL in big_patent dataset and support streaming

This PR replaces the Google Drive URL with our Hub one, once the data owners have approved to host their data on the Hub.

Moreover, this PR makes the dataset streamable.

Fix #4217.

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

I first uploaded the data files to the Hub: I think it is a good option because we have git lfs to track versions and changes. Moreover people will be able to make PRs to propose updates on the data files.
- I would have preferred to upload it it to the "data" org namespace, but it is already taken (although not used): might be possible to take it?

As an alternative (and to be consistent with previous datasets), I also uploaded the data files to our AWS bucket.

We should decide which to use (now and for future datasets) and set it here before merging. We should remove the data files for the non-chosen option.

CC: @lhoestq @mariosasko @polinaeterna 

Would it make sense to make the dataset a community one (so, create an organization for it) and store the script and the data in a single repository? Just as it is for most of the datasets. That way we can also access the data using a relative path inside the repo (that's not the point though). The point is that to me it seems a bit more straightforward to store everything in one place. 

I guess the strong argument against this logic is that in this case the canonical version won't work... But maybe there is some redirecting mechanism I don't know about? :)

Anyway, I'm in favor of hosting data on the Hub instead of AWS :) 

I also think storing everything in one place/single repository is the best option.

@polinaeterna Canonical datasets also support data files (see the [`red_caps` repo](https://huggingface.co/datasets/red_caps/tree/main) for instance) 

Thanks @polinaeterna and @mariosasko for your comments.

Yes, definitely it is much better to have everything in the same repo. 

I'm transferring their data files to the Hub under "big_patent" and deleting them from the other repo and AWS.
How to load VERY LARGE dataset?

### System Info

```shell
I am using transformer trainer while meeting the issue.
The trainer requests torch.utils.data.Dataset as input, which loads the whole dataset into the memory at once. Therefore, when the dataset is too large to load, there's nothing I can do except using IterDataset, which loads samples of data seperately, and results in low efficiency. 
I wonder if there are any tricks like Sharding in huggingface trainer.
Looking forward to your reply.
```


### Who can help?

Trainer: @sgugger

### Information

- [ ] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

None

### Expected behavior

```shell
I wonder if there are any tricks like fairseq Sharding very large datasets https://fairseq.readthedocs.io/en/latest/getting_started.html.
Thanks a lot!
```


Comments:
The `Trainer` support `IterableDataset`, not just datasets.
Autoeval config

Added autoeval config to imdb as pilot

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

Related to: https://github.com/huggingface/autonlp-backend/issues/414 and https://github.com/huggingface/autonlp-backend/issues/424

The tests are failing due to the changed metadata:

```
got an unexpected keyword argument 'train-eval-index'
```

I think you can fix this by updating the `DatasetMetadata` class and implementing an appropriate `validate_train_eval_index()` function

@lhoestq we are working with an arbitrary set of tags for `autoeval config`. See https://github.com/huggingface/autonlp-backend/issues/414
I need to add a validator function though for the tests to pass. Our set is not well-defined as in the rest https://github.com/huggingface/datasets/tree/master/src/datasets/utils/resources. What's a workaround for this?

On the question of validating the `train-eval-index` metadata, I think the simplest approach would be to validate that the required fields exist and not worry about their values (which are open-ended).

For me, the required fields include:

* `config`
* `task`
* `task_id`
* `splits` (train / validation / eval)
* `col_mapping`
* `metrics` (checking that each one has `type`, `name`) 

Here I'm using the spec defined in https://github.com/huggingface/autonlp-backend/issues/414 as a guide.

WDYT @lhoestq ?

Makes sense ! Currently the metadata type validator doesn't support subfields - let me open a PR to add it

I ended up improving the metadata validation in this PR x)

In particular:
- I added support YAML keys with dashes instead of underscores for `train-eval-index`
- I added `train-eval-index` validation  with `validate_train_eval_index`. It does nothing fancy, it just checks that it is a list if it exists in the YAML, but feel free to improve it if you want

Let me know if it sounds good to you ! I think we can improve `validate_train_eval_index` in another PR

Come on windows... I didn't do anything advanced...

Anyway, will try to fix this when I get back home x)

> Come on windows... I didn't do anything advanced...
> 
> Anyway, will try to fix this when I get back home x)

Hehe, thanks!

Thanks, @lhoestq this is great! 

Did I just fix it for windows and now it fails on linux ? xD

> Did I just fix it for windows and now it fails on linux ? xD

Looks like the Heisenberg uncertainty principle is at play here - you cannot simultaneously have unit tests passing in both Linux and Windows 😅 

The worst is that the tests pass locally both on my windows and my linux x)

Ok fixed it, the issue came from python 3.6 that doesn't return the right `__origin__` for Dict and List types

> Alright thanks for adding the first Autoeval config ! :D

Woohoo! Thank you so much 🤗 

This is cool!
Why the `conll2003` dataset on huggingface only contains the `en` subset? Where is the  German data?

![image](https://user-images.githubusercontent.com/37113676/165416606-96b5db18-b16c-4b6b-928c-de8620fd943e.png)

But on huggingface datasets:
![image](https://user-images.githubusercontent.com/37113676/165416649-8fd77980-ca0d-43f0-935e-f398ba8323a4.png)

Where is the  German data?

Comments:
Thanks for reporting @beyondguo.

Indeed, we generate this dataset from this raw data file URL: https://data.deepai.org/conll2003.zip
And that URL only contains the English version.

The German data requires payment

The [original task page](https://www.clips.uantwerpen.be/conll2003/ner/) states "The German data is a collection of articles from the Frankfurter Rundschau. The named entities have been annotated by people of the University of Antwerp. Only the annotations are available here. In order to build these data sets you need access to the ECI Multilingual Text Corpus. It can be ordered from the Linguistic Data Consortium (2003 non-member price: US$ 35.00)."

Inflation since 2003 has also affected LDC's prices, and today the dataset [LDC94T5](https://catalog.ldc.upenn.edu/LDC94T5) is available under license for $75 a copy. The [license](https://catalog.ldc.upenn.edu/license/eci-slash-mci-user-agreement.pdf) includes a non-distribution condition, which is probably why the data has not turned up openly.

The ACL hold copyright of this data; I'll mail them and anyone I can find at ECI to see if they'll open this up now. After all, it worked with Microsoft 3DMM, why not here too, after 28 years? :)


Closing this issue as we are not allowed to share publicly the German subset.
Add pearsonr mc, update functionality to match the original docs

- adds pearsonr metric card
- adds ability to return p-value
    - p-value was mentioned in the original docs as a return value, but there was no option to return it. I updated the _compute function slightly to have an option to return the p-value.

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

thank you @lhoestq!! :hugs:
Dictionary Feature

Hi, I'm trying to create the loading script for a dataset in which one feature is a list of dictionaries, which afaik doesn't fit very well the values and structures supported by Value and Sequence. Is there any suggested workaround, am I missing something?

Thank you in advance.

Comments:
Hi @jordiae,

Instead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`:
```python
"list_of_dict_feature": [
    {
        "key1_in_dict": datasets.Value("string"),
        "key2_in_dict": datasets.Value("int32"),
        ...
    }
],
```

Feel free to re-open this issue if that does not work for your use case.

> Hi @jordiae,
> 
> Instead of the `Sequence` feature, you can use just a regular list: put the dict between `[` and `]`:
> 
> ```python
> "list_of_dict_feature": [
>     {
>         "key1_in_dict": datasets.Value("string"),
>         "key2_in_dict": datasets.Value("int32"),
>         ...
>     }
> ],
> ```
> 
> Feel free to re-open this issue if that does not work for your use case.

Thank you
Altered faiss installation comment


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

Hi ! Can you explain why this change is needed ?

Facebook recommends installing FAISS using conda (https://github.com/facebookresearch/faiss/blob/main/INSTALL.md). pip does not seem to have the latest version of FAISS. The latest version of faiss is 1.7.2 (https://anaconda.org/conda-forge/faiss), but the latest one available through pip is 1.5.3 (https://pypi.org/project/faiss/).
Big_Patent dataset broken

## Dataset viewer issue for '*big_patent*'

**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train)*

*Unable to view because it says FileNotFound, also cannot download it through the python API*

Am I the one who added this dataset ? No


Comments:
Thanks for reporting. The issue seems not to be directly related to the dataset viewer or the `datasets` library, but instead to it being hosted on Google Drive.

See related issues: https://github.com/huggingface/datasets/issues?q=is%3Aissue+is%3Aopen+drive.google.com

To quote [@lhoestq](https://github.com/huggingface/datasets/issues/4075#issuecomment-1087362551):

> PS: if possible, please try to not use Google Drive links in your dataset script, since Google Drive has download quotas and is not always reliable.



We should find out if the dataset license allows redistribution and contact the data owners to propose them to host their data on our Hub.

The data owners have agreed on hosting their data on the Hub.
Avoid recursion error in map if example is returned as dict value

I noticed this bug while answering [this question](https://discuss.huggingface.co/t/correct-way-to-create-a-dataset-from-a-csv-file/15686/11?u=mariosasko). 

This code replicates the bug:
```python
from datasets import Dataset
dset = Dataset.from_dict({"en": ["aa", "bb"], "fr": ["cc", "dd"]})
dset.map(lambda ex: {"translation": ex})
```
and this is the fix for it (before this PR):
```python
from datasets import Dataset
dset = Dataset.from_dict({"en": ["aa", "bb"], "fr": ["cc", "dd"]})
dset.map(lambda ex: {"translation": dict(ex)})
```

Internally, this can be fixed by merging two dicts via dict unpacking (instead of `dict.update) `in `Dataset.map`, which avoids creating recursive dictionaries.

P.S. `{**a, **b}` is slightly more performant than `a.update(b)` in my bencmarks.

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Add Recall Metric Card

What this PR mainly does:
- add metric card for recall metric
- update docs in recall python file

Note: I've also included a .json file with all of the metric card information. I've started compiling the relevant information in this type of .json files, and then using a script I wrote to generate the formatted metric card, as well as the docs to go in the .py file. I figured I'd upload the .json because it could be useful, especially if I also make a PR with the script I'm using (let me know if that's something you think would be beneficial!)

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

This looks good to me!
Add Precision Metric Card

What this PR mainly does:
- add metric card for precision metric
- update docs in precision python file

Note: I've also included a .json file with all of the metric card information. I've started compiling the relevant information in this type of .json files, and then using a script I wrote to generate the formatted metric card, as well as the docs to go in the .py file. I figured I'd upload the .json because it could be useful, especially if I also make a PR with the script I'm using (let me know if that's something you think would be beneficial!)

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Update GH template for dataset viewer issues

Update template to use new issue forms instead.

With this PR we can check if this new feature is useful for us.

Once validated, we can update the other templates.

CC: @severo 

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

You can see rendering at: https://github.com/huggingface/datasets/blob/6b48fedbdafe12a42c7b6edcecc32820af1a4822/.github/ISSUE_TEMPLATE/dataset-viewer.yml