NyssaW/github-issues huggingface dataset:
Frugal score deprecations

## Describe the bug
The frugal score returns a really verbose output with warnings that can be easily changed. 

## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets.load import load_metric

frugal = load_metric("frugalscore")
frugal.compute(predictions=["Do you like spinachis"], references=["Do you like spinach"])
```

## Expected results
A clear and concise description of the expected results.
```
{'scores': [0.9946]}
```

## Actual results
Specify the actual results or traceback.
```
PyTorch: setting up devices
The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 864.09ba/s]
Using amp half precision backend
The following columns in the test set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, sentence1. If sentence2, sentence1 are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
***** Running Prediction *****
  Num examples = 1
  Batch size = 64
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 4644.85it/s]
 {'scores': [0.9946]}
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: Linux-5.13.0-30-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0


Comments:
Hi @Ierezell, thanks for reporting.

I'm making a PR to suppress those logs from the terminal.
Fix main_classes docs index

Currently the `main_classes` documentation has a wrong index. I believe this comes from issues in the examples of the Translation feature types

![image](https://user-images.githubusercontent.com/42851186/158426345-2ee1ceef-ddf3-4a6f-a93e-d1a8f38a44f5.png)


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

Hmm it's still not good 
![image](https://user-images.githubusercontent.com/42851186/158429361-e19ce25b-c259-4ded-8473-075deafdbb96.png)

any idea what could cause this ?

Ok fixed :)
Document cases for github datasets

In general we recommend adding the new dataset under a username or organization in the Hugging Face Hub at [hf.co/datasets](hf.co/datasets), but users can still add a dataset on github in some cases.

I added a paragraph in the documentation to explain in which cases it can make more sense to open a PR on github:

- when you need the dataset to be reviewed
- when you need long-term maintenance from the HF team
- when there’s no clear org name / namespace that you can put the dataset under

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3924). All of your documentation changes will be reflected on that endpoint.

Yay!
Add methods to IterableDatasetDict

Following the new methods added in #3826 and https://github.com/huggingface/datasets/pull/3862 I added several methods to IterableDatasetDict:
    - map
    - filter
    - shuffle
    - with_format
    - cast
    - cast_column
    - remove_columns
    - rename_column
    - rename_columns


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3923). All of your documentation changes will be reflected on that endpoint.

Is this feature stale or needs any help to it ? If so I can quickly send a PR. Thanks

CC : @lhoestq, @albertvillanova 

These features have been merged and are already available, thanks :)

Hello @lhoestq, I see that `IterableDataset` doesn't allow features like `take`, `len`, `slice`  which can enable a lot of stuffs. Is it worth an addition ? Or is it intended that they didn't have those features ?

IterableDataset objects don't have `len` or `slice` because they can be possibly unbounded (you don't know in advance how many items they contain). Though IterableDataset.take and IterableDataset.skip do exist.
'datasets.features' is not a package

@albertvillanova 
python 3.9
os: ubuntu 20.04

In conda environment

torch installed by


```/env/bin/pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html```
datasets package is installed by 
```
/env/bin/pip  install datasets==1.8.0

```
During runing the code I have this error 


```
[6]<stderr>:  File "/home/arij/Memory-transformer-with-hierarchical-attention_MLM/env/lib/python3.9/site-packages/torch/serialization.py", line 875, in find_class
[6]<stderr>:    return super().find_class(mod_name, name)
[6]<stderr>:ModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package

```
precisely this error appears when 

torch.load('data_file.pt')
```

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/arij/Memory-transformer-with-hierarchical-attention_MLM/env/lib/python3.9/site-packages/torch/serialization.py", line 607, in load
    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
  File "/home/arij/Memory-transformer-with-hierarchical-attention_MLM/env/lib/python3.9/site-packages/torch/serialization.py", line 882, in _load
    result = unpickler.load()
  File "/home/arij/Memory-transformer-with-hierarchical-attention_MLM/env/lib/python3.9/site-packages/torch/serialization.py", line 875, in find_class
    return super().find_class(mod_name, name)
ModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package
```

Why I am getting this error?


Comments:
Hi @Arij-Aladel,

You are using a very old version of our library `datasets`: 1.8.0
Current version is 2.0.0 (and the previous one was 1.18.4)

Please, try to update `datasets` library and check if the problem persists:
```shell
/env/bin/pip install -U datasets

The problem I can no I have build my project on this version and old version on transformers. I have preprocessed the data again to use it. Thank for your reply
AttributeError: 'DatasetDict' object has no attribute 'features'

## Describe the bug
Receiving the error when trying to check for Dataset features

## Steps to reproduce the bug
from datasets import Dataset
dataset = Dataset.from_pandas(df[['id', 'words', 'bboxes', 'ner_tags', 'image_path']])

dataset.features

## Expected results
A clear and concise description of the expected results.

## Actual results
Getting the following errror

AttributeError: 'DatasetDict' object has no attribute 'features'

## Environment info
Copy-and-paste the text below in your GitHub issue.

- `datasets` version: 1.18.4
- Platform: Linux-4.14.252-131.483.amzn1.x86_64-x86_64-with-glibc2.9
- Python version: 3.6.13
- PyArrow version: 6.0.1


Comments:
You are likely trying to get the `features` from a `DatasetDict`, a dictionary containing `Datasets`. You probably first want to index into a particular split from your `DatasetDict` i.e. `dataset['train'].features`. 

For example 

```python 
ds = load_dataset('mnist')
ds.features
```
Returns 
```python
---------------------------------------------------------------------------

AttributeError                            Traceback (most recent call last)

[<ipython-input-39-791c1f9df6c2>](https://localhost:8080/#) in <module>()
----> 1 ds.features

AttributeError: 'DatasetDict' object has no attribute 'features'
```

If we look at the dataset variable, we see it is a `DatasetDict`:

```python 
print(ds)
DatasetDict({
    train: Dataset({
        features: ['image', 'label'],
        num_rows: 60000
    })
    test: Dataset({
        features: ['image', 'label'],
        num_rows: 10000
    })
})
```

We can grab the features from a split by indexing into `train`:
```python
ds['train'].features
{'image': Image(decode=True, id=None),
 'label': ClassLabel(num_classes=10, names=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], id=None)}
```

Hope that helps 

Yes, Thanks for that clarification,
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files

## Describe the bug
Can't load the dataset

## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
from datasets import load_dataset

dataset = load_dataset('multi_news')
dataset_2=load_dataset("reddit_tifu", "long")


## Actual results
 raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']

## Environment info
- `datasets` version: 1.18.4
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.0
- PyArrow version: 6.0.1


Comments:
Hi @willowdong! These issues were fixed on master. We will have a new release of `datasets` later today. In the meantime, you can avoid these issues by installing `datasets` from master as follows:
```bash
pip install git+https://github.com/huggingface/datasets.git
```

You should force redownload:
```python
dataset = load_dataset("multi_news", download_mode="force_redownload")
dataset_2 = load_dataset("reddit_tifu", "long", download_mode="force_redownload")

Fixed by:
- #3787 
- #3843
Create README.md for GLUE

I still have a hesitation regarding the format of inputs -- whether it's a list or a list of lists? -- hopefully @lhoestq will be able to clarify.

Also tagging @yjernite for the Limitations section. Happy to hear your thoughts!

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3916). All of your documentation changes will be reflected on that endpoint.
Use templates for doc-builidng jobs

This PR updates the jobs for all doc-building related things by using the templates introduced on `doc-builder`. By putting those once there, we make sure every repo gets the latest fixes on the doc-building github actions :-)

Note: all libraries must share the same docker image for those doc-building jobs. For now, all the one used (`huggingface/transformers-doc-builder`) contains all extra steps of the datasets install for docbuling (mainly libsndfile) but if in the future some additional steps are necessary on top of `pip install -e .[dev]`, this docker image will need to be updated with the extra deps.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3914). All of your documentation changes will be reflected on that endpoint.

You can ignore the CI failures btw, they're unrelated to this PR
Deterministic split order in DatasetDict.map

The order in which the splits are processed by `map` is not deterministic in `DatasetDict.map`. This can cause caching issues when the processing function is stateful and sensible to the order in which examples are processed

Close https://github.com/huggingface/datasets/issues/3847

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3913). All of your documentation changes will be reflected on that endpoint.

I'm surprised this is needed because the order of the `dict` keys is deterministic as of Python 3.6 (documented in 3.7). Is there a reproducer for this behavior? I wouldn't make this change unless it's absolutely needed because `sorted` modifies the initial order of the keys.

Indeed this doesn't fix the issue apparently. Actually this is probably because the tokenizer used to process the second split is in a state that has been modified by the first split.

Therefore after reloading the first split from the cache, then the second split can't be reloaded since the tokenizer hasn't seen the first split (and therefore is considered a different tokenizer).
Fix text loader to split only on universal newlines

Currently, `text` loader breaks on a superset of universal newlines, which also contains Unicode line boundaries. See: https://docs.python.org/3/library/stdtypes.html#str.splitlines

However, the expected behavior is to get the lines splitted only on universal newlines: "\n", "\r\n" and "\r".

See: oscar-corpus/corpus#18

Fix #3729.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3910). All of your documentation changes will be reflected on that endpoint.

Looks like the test needs to be updated for windows ^^'

I don't think this is the same issue as in https://github.com/oscar-corpus/corpus/issues/18, where the OSCAR metadata has line offsets that use only `\n` as the newline marker to count lines, not `\r\n` or `\r`.

It looks like the OSCAR data loader is opening the data files with `gzip.open` directly and I don't think this text loader is used, but I'm not familiar with a lot of `datasets` internals so I could be mistaken?

You are right @adrianeboyd.

This PR fixes #3729.

Additionally, this PR is somehow related to the OSCAR issue. However, the OSCAR issue have multiple root causes: one is the offset initialization (as you pointed out); other is similar to this case: Unicode newlines are not properly handled.

I will make a change proposal for OSCAR this afternoon.

@lhoestq I'm working on fixing the Windows tests on my Windows machine...

I finally changed the approach in order to avoid having "\r\n" and "\r" line breaks in Python `str` read from files on Windows/old Macintosh machines.
NonMatchingChecksumError on Spider dataset 

## Describe the bug
Failure to generate dataset ```spider``` because of checksums error for dataset source files.

## Steps to reproduce the bug
```
from datasets import load_dataset
spider = load_dataset("spider")
```


## Expected results
Checksums should match for files from url ['https://drive.google.com/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0']

## Actual results
```
>>> load_dataset("spider")
load_dataset("spider")
Downloading and preparing dataset spider/spider (download: 95.12 MiB, generated: 5.17 MiB, post-processed: Unknown size, total: 100.29 MiB) to /home/user/.cache/huggingface/datasets/spider/spider/1.0.0/79778ebea87c59b19411f1eb3eda317e9dd5f7788a556d837ef25c3ae6e5e8b7...
Traceback (most recent call last):
  File "/home/user/py3_env/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3441, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-5-d4cb54197348>", line 1, in <module>
    load_dataset("spider")
  File "/home/user/py3_env/lib/python3.8/site-packages/datasets/load.py", line 1702, in load_dataset
    builder_instance.download_and_prepare(
  File "/home/user/py3_env/lib/python3.8/site-packages/datasets/builder.py", line 594, in download_and_prepare
    self._download_and_prepare(
  File "/home/user/py3_env/lib/python3.8/site-packages/datasets/builder.py", line 665, in _download_and_prepare
    verify_checksums(
  File "/home/user/py3_env/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
    raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0']
```


## Environment info
datasets version: 1.18.3
Platform: Ubuntu 20 LTS
Python version: 3.8.10
PyArrow version: 6.0.1 





Comments:
Hi @kolk, thanks for reporting.

Indeed, Google Drive service recently changed their service and we had to add a fix to our library to cope with that change:
- #3787 

We just made patch release last week: 1.18.4 https://github.com/huggingface/datasets/releases/tag/1.18.4

Please, feel free to update your local `datasets` version, so that you get the fix:
```shell
pip install -U datasets
```
Perplexity Metric Card

Add Perplexity metric card

Note that it is currently still missing the citation, but I plan to add it later today.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3905). All of your documentation changes will be reflected on that endpoint.

I'm wondering if we should add that perplexity can be used for analyzing datasets as well

Otherwise, looks good! Good job, @emibaylor !
Dataset viewer issue for IndicParaphrase- the preview doesn't show

## Dataset viewer issue for '*IndicParaphrase*'

**Link:** *[IndicParaphrase](https://huggingface.co/datasets/ai4bharat/IndicParaphrase/viewer/hi/validation)*

*The preview of the dataset doesn't come up. 
The error on the console is:
Status code:   400
Exception:     FileNotFoundError
Message:       [Errno 2] No such file or directory: '/home/hf/datasets-preview-backend/hi_IndicParaphrase_v1.0.tar'*

Am I the one who added this dataset ? Yes


Comments:
It seems to have been fixed:

<img width="1534" alt="Capture d’écran 2022-04-12 à 14 10 07" src="https://user-images.githubusercontent.com/1676121/162959599-6b7fef7c-8411-4e03-8f00-90040a658079.png">
Create README.md for WER metric

Proposing a draft WER metric card, @lhoestq I'm not very certain about "Values from popular papers" -- I don't know ASR very well, what do you think of the examples I found?

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3898). All of your documentation changes will be reflected on that endpoint.

For ASR you can probably ping @patrickvonplaten 

Ah only noticed now that ` # Values from popular papers` is from a template. @lhoestq @sashavor - not really sure if this section is useful in general really. 

IMO, it's more confusing/misleading than it helps. E.g. a value of 0.03 WER on a fake read-out audio dataset is not better than a WER of 0.3 on a real-world noisy, conversational audio dataset. I think the same holds true for other metrics no? I can think of very little metrics where a metric value is not dataset dependent. E.g. perplexity is super dataset dependent, summarization metrics like ROUGE as well, ...

Also, I don't really see what this section tries to achieve - is the idea here to give the reader some papers that use this metric to better understand in which context it is used? Should we maybe rename the section to `Popular papers making use of this metric` or something? 



I put "Values from popular papers" as a subsection of "Output values" -- I hope that's a compromise that works for everyone :hugs:
Align tqdm control/cache control with Transformers

This PR:
* aligns the `tqdm` logic with Transformers (follows https://github.com/huggingface/transformers/pull/15167) by moving the code to `utils/logging.py`, adding `enable_progres_bar`/`disable_progres_bar` and removing `set_progress_bar_enabled` (a note for @lhoestq: I'm not adding `logging.tqdm` to the public namespace in this PR to avoid the situation where `from datasets import *; tqdm` would overshadow the standard `tqdm` 
* aligns the cache control with the new `tqdm` logic by adding `enable_caching`/`disable_caching` to the  public namespace and deprecating `set_caching_enabled` (not fully removing it because it's used more often than `set_progress_bar_enabled` and has a dedicated example in the old docs)

Fix #3586 

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3897). All of your documentation changes will be reflected on that endpoint.
Missing google file for `multi_news` dataset

## Dataset viewer issue for '*multi_news*'

**Link:** https://huggingface.co/datasets/multi_news

```
Server error

Status code:   400
Exception:     FileNotFoundError
Message:       https://drive.google.com/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C/multi-news-original/train.src
```

Am I the one who added this dataset ? No


Comments:
reported by @abidlabs 

related to https://github.com/huggingface/datasets/pull/3843?

`datasets` 1.18.4 fixes the issue when you load the dataset with `load_dataset`.

When loading in streaming mode, the fix is indeed on https://github.com/huggingface/datasets/pull/3843 which will be merged soon :)

That is. The PR #3843 was just opened a bit later we had made our 1.18.4 patch release...
Once merged, that will fix this issue. 

OK. Should fix the viewer for 50 datasets

<img width="148" alt="Capture d’écran 2022-03-14 à 11 51 02" src="https://user-images.githubusercontent.com/1676121/158157853-6c544a47-2d6d-4ac4-964a-6f10951ec36b.png">
Fix code examples indentation

Some code examples are currently not rendered correctly. I think this is because they are over-indented

cc @mariosasko 

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3895). All of your documentation changes will be reflected on that endpoint.

Still not rendered properly: https://moon-ci-docs.huggingface.co/docs/datasets/pr_3895/en/package_reference/main_classes#datasets.Dataset.align_labels_with_mapping

My last commit should have fixed it, I don't know why the dev doc build is not showing my last changes

Let me merge this and we can see on `master` how it renders, until the dev doc build is fixed
[docs] make dummy data creation optional

Related to #3507 : dummy data for datasets created on the Hugging Face Hub are optional.

We can discuss later to make them optional for datasets in this repository as well

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3894). All of your documentation changes will be reflected on that endpoint.

The dev doc build rendering doesn't seem to be updated with my last commit for some reason

Merging it anyway since I'd like to share this page with users 🙃
Add default branch for doc building

Since other libraries use `main` as their default branch and it's now the standard default, you have to specify a different name in the doc config if you're using `master` like datasets (`doc-builder` tries to guess it, but in the job, we have weird checkout of merge commits so it doesn't always manage to get it right).

This PR makes sure it will always use master for the dev doc (until you decide to switchto main)

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3893). All of your documentation changes will be reflected on that endpoint.

Yes! And when we discovered on the Transformers side that this check fails on the GitHub actions, we added a config attribute to have a default. Setting in Transformers fixed the issue of the doc being deployed to main, so porting the fix here too :-)
Fix CLI test checksums

Previous PR:
- #3796 

introduced a side effect: `datasets-cli test` generates `dataset_infos.json` with `None` checksum values.

See:
- #3805 

This PR introduces a way for `datasets-cli test` to force to record infos, even if `verify_infos=False`

Close #3848.

CC: @craffel 

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3892). All of your documentation changes will be reflected on that endpoint.

Feel free to merge if it's good for you :)

I've added a test @lhoestq. Once all green, I'll merge. 

Last failing tests do not have nothing to do with this PR.
Fix race condition in doc build

Following https://github.com/huggingface/datasets/runs/5499386744 it seems that race conditions that create issues when updating the doc. I took the same approach as in `transformers` to fix race conditions

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3891). All of your documentation changes will be reflected on that endpoint.
Update beans download urls

Replace the old URLs with the Hub [URLs](https://huggingface.co/datasets/beans/tree/main/data).

Also reported by @stevhliu. 

Fix #3889 

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3890). All of your documentation changes will be reflected on that endpoint.

@albertvillanova Thanks for investigating and fixing that issue. I regenerated the `dataset_infos.json` file.
Cannot load beans dataset (Couldn't reach the dataset)

## Describe the bug
The beans dataset is unavailable to download.

## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset('beans')
```

## Expected results
The dataset would be downloaded with no issue.

## Actual results

```
ConnectionError: Couldn't reach https://storage.googleapis.com/ibeans/train.zip (error 403)
```

[It looks like the billing of this project has been disabled because it is associated with a delinquent account.](https://storage.googleapis.com/ibeans/train.zip )

## Environment info
Google Colab


Comments:
Hi ! A pull request is open to fix the dataset, we'll release a patch soon with a new release of `datasets` :)
IterableDataset columns and feature types

Right now, an IterableDataset (e.g. when streaming a dataset) doesn't require to know the list of columns it contains, nor their types: `my_iterable_dataset.features` may be `None`

However it's often interesting to know the column types and types. This helps knowing what's inside your dataset without having to manually check a few examples, and this is useful to prepare a processing pipeline or to train models.

Here are a few cases that lead to `features` being `None`:

1. when loading a dataset with `load_dataset` on CSV, JSON Lines, etc. files: type inference is only done when iterating over the dataset
2. when calling `map`, because we don't know in advance what's the output of the user's function passed to `map`
3. when calling `rename_columns`, `remove_columns`, etc. because they rely on `map`

Things we can consider, for each point above:

1.a infer the type automatically from the first samples on the dataset using prefetching, when the dataset builder doesn't provide the `features`
2.a allow the user to specify the `features` as an argument to `map` (this would be consistent with the non-streaming API)
2.b prefetch the first output value to infer the type
3.a don't rely on `map` directly and reuse the previous `features` and rename/remove the corresponding ones

The thing is that prefetching can take a few seconds, while the operations above are instantaneous since no data are downloaded. Therefore I'm not sure whether this solution may be worth it. Maybe prefetching could also be done when explicitly asked by the user

cc @mariosasko @albertvillanova 

Comments:
#self-assign

@alvarobartt I've assigned you the issue since I'm not actively working on it.

Cool thanks @mariosasko I'll try to fix it in the upcoming days, thanks!

@lhoestq so in order to address what’s not completed in this issue, do you think it makes sense to add a param `features` to `IterableDataset.map` so that the output features right after the `map` are defined there? 

Yes that would be ideal IMO, thanks again for the help :)

@lhoestq cool then if you agree I can work on that! I’ll also update the docs accordingly once done, thanks!

I've already started with a PR as a draft @lhoestq, should we also try to look for a way to explicitly request pre-fetching right after a map operation is applied, so that the features are inferred if the user says explicitly so? Thanks!

> should we also try to look for a way to explicitly request pre-fetching right after a map operation is applied, so that the features are inferred if the user says explicitly so?

Right now one can use `ds = ds._resolve_features()` do to so. It can be used after `map` or `load_dataset` if the features are not known. Maybe we can make this method public ?
ImageFolder improvements

This PR adds the following improvements to the `imagefolder` dataset:
* skip the extract step for image files (as discussed in https://github.com/huggingface/datasets/pull/2830#discussion_r816817919)
* option to drop labels by setting `drop_labels=True` (useful for image pretraining cc @NielsRogge). This is faster than loading a dataset and removing the `label` column because we don't need to iterate over the files to infer class labels.  

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3887). All of your documentation changes will be reflected on that endpoint.
Retry HfApi call inside push_to_hub when 504 error

Ass suggested by @lhoestq in #3872, this PR:
- Implements a retry function
- Retries HfApi call inside `push_to_hub` when 504 error. To be agreed:
  - max_retries = 2 (at 0.5 and 1 seconds)

Fix #3872.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3886). All of your documentation changes will be reflected on that endpoint.

I made it more robust by increasing the wait time, and I also added some logs when a request is retried. Let me know if it's ok for you

At the end you did not set the agreed max value of 60s. 

Moreover, with the new numbers, there is a slight contradiction: although you set max_retries=5, we will only make 4 retries at most because of the combined values of `base_wait_time` and `max_wait_time`.

Yea I thought that in total we could wait 1min, but if we have a max_wait_time of 20sec between each request it's fine IMO

> Moreover, with the new numbers, there is a slight contradiction: although you set max_retries=5, we will only make 4 retries at most because of the combined values of base_wait_time and max_wait_time.

What makes you think this ? If the exponential wait time becomes bigger than `max_wait_time` then it still does the retry, but after a wait time of `max_wait_time`

Sorry, I meant 4 retries **with exponential backoff**; the fifth one is with constant backoff.

OK, and one question: do you think that the retries do not affect the time the server needs to be operational again and able to process the request? I guess that if does not affect, then the cause are other users' requests, or others; not our specific request.

Just to be sure: 
- Then 20s at most between consecutive requests do not impact the server.
- And we expect after a total of 5 retries (within a total 50s of wait time + request processing/uploading time), the server should be able to come back to normality.

> do you think that the retries do not affect the time the server needs for being able to process the request (I guess in this case the cause are other users' requests, or other causes; not our specific request).

Yes I don't think the retries would affect the server, I think the cause of the 504 errors is elsewhere

> Just to be sure:
>
> Then 20s at most between consecutive requests do not impact the server.
> And we expect after a total of 5 retries (within a total 50s of wait time + request processing/uploading time), the server should be able to come back to normality.

Yes I think it's fine for now, we can still adapt this later if needed

Will be curious to see the impact of this in terms of upload reliability! Don't forget to let us know when you have more data. cc @huggingface/moon-landing-back
The metric Meteor doesn't work for nltk ==3.6.4

## Describe the bug
Using the metric Meteor with nltk == 3.6.4 gives a TypeError:
TypeError: descriptor 'lower' for 'str' objects doesn't apply to a 'list' object

## Steps to reproduce the bug
```python
import datasets
metric = datasets.load_metric("meteor")
predictions = ["hello world"]
references = ["hello world"]
metric.compute(predictions=predictions, references=references)
```

## Expected results
TypeError: descriptor 'lower' for 'str' objects doesn't apply to a 'list' object

I think this TypeError exists because input sentences are tokenized into lists of tokens and the str.lower() is applied to this list of tokens.

## Actual results
No error but a meteor score

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: linux
- Python version: 3.8.12
- PyArrow version: 7.0.0


Comments:
Hi @zhaowei-wang98, thanks for reporting.

We are fixing it...
Image process doc

This PR is a first draft of how to process image data. It adds:

- Load an image dataset with `image` and `path` (adds tip about `decode=False` param to access the path and bytes, thanks to @mariosasko).
- Load an image using the `ImageFolder` builder. I know there is an [example](https://huggingface.co/docs/datasets/master/en/loading#image-folders) of this already, but I also wanted to add it here so users don't miss it. This doc seems important for centralizing all of the image-related things so far. Datasets has grown so quickly 🚀  now that I think maybe splitting up the How-to guides by modality may be better since working with vision/audio data is slightly different from what users have seen up until now. This way we can continue to scale the docs to better accommodate vision/audio things.
- Add a data augmentation with `set_transform`. There is only 1 example here so far, but we can certainly add more. 

Todo:
- [x] Couldn't figure out why my augmentation function works with `set_transform` but not `map` 🥲. Working with @mariosasko on this!

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3882). All of your documentation changes will be reflected on that endpoint.
Change the framework switches to the new syntax

This PR updates the syntax of the framework-specific code samples. With this new syntax, you'll be able to:

- have paragraphs of text be framework-specific instead of just code samples
- have support for Flax code samples if you want.

This should be merged after https://github.com/huggingface/doc-builder/pull/63 and https://github.com/huggingface/doc-builder/pull/130

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3880). All of your documentation changes will be reflected on that endpoint.

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3880). All of your documentation changes will be reflected on that endpoint.
Update cats_vs_dogs size

It seems like 12 new examples have been added to the `cats_vs_dogs`. This PR updates the size in the card and the info file to avoid a verification error (reported by @stevhliu). 

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3878). All of your documentation changes will be reflected on that endpoint.

Maybe `NonMatchingSplitsSizesError` errors should also tell the user to try using a more recent version of the dataset to get the fixes ?

@lhoestq Good idea. Will open a new PR to improve the error messages of NonMatchingSplitsSizesError, NonMatchingChecksumsError, ...

It seems there is still a problem. I am using datasets version 2.5.1. 
I just typed `ds = load_dataset("cats_vs_dogs")` and get the error below.

```
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=3893603, num_examples=23422, dataset_name='cats_vs_dogs'), 'recorded': SplitInfo(name='train', num_bytes=3891612, num_examples=23410, dataset_name='cats_vs_dogs')}]
```
It looks like the dataset still only has 23,410 examples....


Thanks for reporting, I opened https://github.com/huggingface/datasets/pull/5047
Align metadata to DCAT/DCAT-AP

**Is your feature request related to a problem? Please describe.**
Align to DCAT metadata to describe datasets

**Describe the solution you'd like**
Reuse terms and structure from DCAT in the metadata file, ideally generate a json-ld file dcat compliant

**Describe alternatives you've considered**


**Additional context**
DCAT is a W3C standard extended in Europe with DCAT-AP, an example is data.europa.eu publishing datasets metadata in DCAT-AP


Comments:
Fix download_mode in dataset_module_factory

Fix `download_mode` value set in `dataset_module_factory`.

Before the fix, it was set to `bool` (default to `False`).

Also set properly its default value in all public functions.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3876). All of your documentation changes will be reflected on that endpoint.
Module namespace cleanup for v2.0

This is an attempt to make the user-facing `datasets`' submodule namespace cleaner:

In particular, this PR does the following:
* removes the unused `zip_nested` and `flatten_nest_dict` and their accompanying tests
* removes `pyarrow` from the top-level namespace
* properly uses `__all__` and the `from  <module> import *` syntax to avoid importing the `<module>`'s submodules
* cleans up the `utils` namespace
* moves the `temp_seed` context manage from `datasets/utils/file_utils.py` to `datasets/utils/py_utils.py`

Comments:
will it solve https://github.com/huggingface/datasets-preview-backend/blob/4c542a74244045929615640ccbba5a902c344c5a/pyproject.toml#L85-L89?

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3875). All of your documentation changes will be reflected on that endpoint.

@severo No, this PR doesn't fix that issue in the current state. We can fix it by adding `__all__` to `datasets/__init__.py` and `datasets/formatting/__init__.py`. However, this would require updating `__all__` for each new function/class definition, which could become cumbersome, and we can't do this dynamically because `mypy` is a static type checker.

@lhoestq @albertvillanova WDYT?

Feel free to merge this one if it's good for you :)
add MSE and MAE metrics - V2

Created a new pull request to resolve unrelated changes in PR caused due to rebasing.
Ref Older PR :  [#3845](https://github.com/huggingface/datasets/pull/3845) 

Feature request here : Add support for continuous metrics (RMSE, MAE) [#3608](https://github.com/huggingface/datasets/issues/3608)

Comments:
@mariosasko New PR here. I'm not sure how to add you as a co-author here.  Also I see flake8 tests are failing, any inputs on how to resolve this ?
Also, let me know if any other changes are required. Thank you.

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3874). All of your documentation changes will be reflected on that endpoint.

Great. Thank you.

Thanks so much for this 🙏 💯
Create SQuAD metric README.md

Proposal for a metrics card structure (with an example based on the SQuAD metric).
@thomwolf @lhoestq @douwekiela @lewtun -- feel free to comment on structure or content (it's an initial draft, so I realize there's stuff missing!).

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3873). All of your documentation changes will be reflected on that endpoint.

Oh one last thing I almost forgot, I think I would add a section "Examples" with examples of inputs and outputs and in particular: an example giving maximal values, an examples giving minimal values and maybe a standard examples from SQuAD. What do you think?
HTTP error 504 Server Error: Gateway Time-out

I am trying to push a large dataset(450000+) records with the help of `push_to_hub()`
While pushing, it gives some error like this.

```
Traceback (most recent call last):
  File "data_split_speech.py", line 159, in <module>
    data_new_2.push_to_hub("user-name/dataset-name",private=True)
  File "/opt/conda/lib/python3.8/site-packages/datasets/dataset_dict.py", line 951, in push_to_hub
    repo_id, split, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(
  File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3556, in _push_parquet_shards_to_hub
    api.upload_file(
  File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1017, in upload_file
    raise err
  File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1008, in upload_file
    r.raise_for_status()
  File "/opt/conda/lib/python3.8/site-packages/requests/models.py", line 953, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/user-name/dataset-name/upload/main/data/train2-00041-of-00064.parquet

```
Can anyone help me to resolve this issue.


Comments:
is pushing directly with git (and git-lfs) an option for you?

I have installed git-lfs and doing this push with that


yes but is there any way you could try pushing with `git` command line directly instead of `push_to_hub`?

Okay. I didnt saved the dataset to my local machine. So, I processed the dataset and pushed it directly to the hub. I think I should try saving those dataset to my local machine by `save_to_disk` and then push it with git command line

cc @lhoestq @albertvillanova @LysandreJik because maybe I'm giving dumb advice here 😅 

`push_to_hub` is the preferred way of uploading a dataset to the Hub, which can then be reloaded with `load_dataset`. Feel free to try again and see if the server is working as expected now. Maybe we can add a retry mechanism in the meantime to workaround 504 errors.

Regarding `save_to_disk`, this must only be used for local serialization (because it's uncompressed and compatible with memory-mapping). If you upload a dataset saved with `save_to_disk` to the Hub, then to reload it you will have to download/clone the repository locally by yourself and use `load_from_disk`.
add pandas to env command

Pandas is a required packages and used quite a bit. I don't see any downside with adding its version to the `datasets-cli env` command.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3871). All of your documentation changes will be reflected on that endpoint.

Think failures are unrelated - feel free to merge whenever you want :-)
Add wikitablequestions dataset


Comments:
@lhoestq Would you mind reviewing it when you're available? Thanks!


> Awesome thanks for adding this dataset ! :) The dataset script and dataset cards look pretty good
> 
> It looks like your `dummy_data.zip` files are quite big though (>1MB each), do you think we can reduce their sizes ? This way this git repository doesn't become too big

I have manually reduced the `dummy_data.zip` and its current size is about 54KB. Hope it is fine for you!

@lhoestq I think the dataset is ready to merge now. Any follow-up question is welcome :-D

> Thanks ! It looks all good now :)

Awesome! Thanks for your quick response!
Making the Hub the place for datasets in Portuguese 

Let's make Hugging Face Datasets the central hub for datasets in Portuguese :)

**Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the Portuguese speaking community.

What are some datasets in Portuguese worth integrating into the Hugging Face hub?

Special thanks to @augusnunes for his collaboration on identifying the first ones:

-  [NILC - USP](http://www.nilc.icmc.usp.br/nilc/index.php/tools-and-resources).

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

cc @osanseviero




Comments:
Hi @omarespejel! I think the philosophy for `datasets` issues is to create concrete issues with proposals to add a specific, individual dataset rather than umbrella issues for things such as datasets for a language, since we could end up with hundreds of issues (one per language). I see NILC - USP has many datasets, I would suggest to either create an issue for their datasets, or even better, we are trying to push to upload datasets as community datasets instead of adding them to the core library as guided in https://huggingface.co/docs/datasets/share. That would have the additional benefit that the dataset would live under the NILC organization.

@lhoestq correct me if I'm wrong please 😄
Ignore duplicate keys if `ignore_verifications=True`

Currently, it's impossible to generate a dataset if some keys from `_generate_examples` are duplicated. This PR allows skipping the check for duplicate keys if `ignore_verifications` is set to `True`.  

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3868). All of your documentation changes will be reflected on that endpoint.

Cool thanks ! Could you add a test please ?
Update for the rename doc-builder -> hf-doc-utils

This PR adapts the job to the upcoming change of name of `doc-builder`.

Comments:
why utils? it's a builder no?

~~@julien-c there was a vote 🙂 https://huggingface.slack.com/archives/C021H1P1HKR/p1646405136644739~~

oh I see you already commeented in the thread as well

Thanks ! It looks all good to me (provided `hf-doc-utils` is the name we keep in the end). I'm fine with this name, and `hf-doc-builder` is also fine IMHO

ok, this is definitely not a hill I'll die on =) @mishig25 @sgugger
Bring back imgs so that forsk dont get broken


Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3866). All of your documentation changes will be reflected on that endpoint.

I think we just need to keep `datasets_logo_name.jpg` and `course_banner.png` because they appear in the README.md of the forks of `datasets`. The other images can be removed

Force pushed those two imgs only
Update code blocks

Following https://github.com/huggingface/datasets/pull/3860#issuecomment-1061756712 and https://github.com/huggingface/datasets/pull/3690 we need to update the code blocks to use markdown instead of sphinx

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3863). All of your documentation changes will be reflected on that endpoint.
Manipulate columns on IterableDataset (rename columns, cast, etc.)

I added:
- add_column
- cast
- rename_column
- rename_columns

related to https://github.com/huggingface/datasets/issues/3444

TODO:
- [x] docs
- [x] tests

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3862). All of your documentation changes will be reflected on that endpoint.

> IIUC we check if columns are present/not present directly in the yielded examples and not in info.features because info.features can be None (after map, for instance)?

Yes exactly

> We should develop a solution that ensures info.features is never None. For example, one approach would be to infer them from examples in map and make them promotable from Value("null") to a specific type, in case of None values.

I agree this would be useful. Though inferring the type requires to start streaming some data, which takes a few seconds (compared to being instantaneous right now).

Let's discuss this in a new issue maybe ?