KRadim/github-issues-minimal huggingface dataset:
Iterating over a vision dataset doesn't decode the images
State: closed
Labels: name: bug, name: vision

## Describe the bug

If I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.

## Steps to reproduce the bug
```python
from datasets import load_dataset
import PIL

mnist = load_dataset("mnist", split="train")

first_image = mnist[0]["image"]
assert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes
first_image = next(iter(mnist))["image"]
assert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails
```

## Expected results

The image should be decoded, as a PIL Image

## Actual results

We get a dictionary
```
{'bytes': b'\x89PNG\r\n\x1a\n\x00..., 'path': None}
```

## Environment info

- `datasets` version: 1.17.1.dev0
- Platform: Darwin-20.6.0-x86_64-i386-64bit
- Python version: 3.7.2
- PyArrow version: 6.0.0

The bug also exists in 1.17.0

## Investigation

I think the issue is that decoding is disabled in `__iter__`:

https://github.com/huggingface/datasets/blob/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6/src/datasets/arrow_dataset.py#L1651-L1661

Do you remember why it was disabled in the first place @albertvillanova ?
Also cc @mariosasko @NielsRogge
Add COCO dataset
State: closed
Labels: name: dataset contribution

This PR adds the MS COCO dataset. Compared to the [TFDS](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/object_detection/coco.py) script, this implementation adds 8 additional configs to cover the tasks other than object detection.

Some notes:
* the data exposed by TFDS is contained in the `2014`, `2015`, `2017` and `2017_panoptic_segmentation` configs here
* I've updated `encode_nested_example` for easier handling of missing values (cc @lhoestq @albertvillanova; will add tests if you are OK with the changes in `features.py`)
* this implementation should fix https://github.com/huggingface/datasets/pull/3377#issuecomment-985559427

TODOs:
- [x] dataset card
- [ ] dummy data

cc @merveenoyan 

Closes #2526
Push dataset infos.json to Hub
State: closed

When doing `push_to_hub`, the feature types are lost (see issue https://github.com/huggingface/datasets/issues/3394).
This PR fixes this by also pushing a `dataset_infos.json` file to the Hub, that stores the feature types.

Other minor changes:
- renamed the `___` separator to `--`, since `--` is now disallowed in a name in the back-end.

I tested this feature with datasets like conll2003 that has feature types like `ClassLabel` that were previously lost.

Close https://github.com/huggingface/datasets/issues/3394

I would like to include this in today's release (though not mandatory), so feel free to comment/suggest changes
Unable to load 'cnn_dailymail' dataset
State: closed
Labels: name: bug, name: duplicate, name: dataset bug

## Describe the bug
I wanted to load cnn_dailymail dataset from huggingface datasets on Google Colab, but I am getting an error while loading it.

## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('cnn_dailymail', '3.0.0', ignore_verifications = True)
```

## Expected results
Expecting to load 'cnn_dailymail' dataset.

## Actual results
`NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'`


## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
struct.error: 'i' format requires -2147483648 <= number <= 2147483647
State: open
Labels: name: bug

## Describe the bug
A clear and concise description of what the bug is.

using latest datasets=datasets-1.16.1-py3-none-any.whl
process my own multilingual dataset by following codes, and the number of rows in all dataset is 306000, the max_length of each sentence is 256:
![image](https://user-images.githubusercontent.com/30341159/146865779-3d25d011-1f42-4026-9e1b-76f6e1d172e9.png)

then I get this error:
![image](https://user-images.githubusercontent.com/30341159/146865844-e60a404c-5f3a-403c-b2f1-acd943b5cdb8.png)

I have seen the issue in #2134 and #2150, so I don't understand why latest repo still can't deal with big dataset.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: linux docker
- Python version: 3.6
Update swahili_news dataset
State: closed
Labels: name: dataset request

Please note also: the HuggingFace version at https://huggingface.co/datasets/swahili_news is outdated. An updated version, with deduplicated text and official splits, can be found at https://zenodo.org/record/5514203.

## Adding a Dataset
- **Name:** swahili_news

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

Related to:
- bigscience-workshop/data_tooling#107
Add CMU Graphics Lab Motion Capture dataset
State: open
Labels: name: dataset request, name: vision

## Adding a Dataset
- **Name:** CMU Graphics Lab Motion Capture database
- **Description:** The database contains free motions which you can download and use. 
- **Data:** http://mocap.cs.cmu.edu/
- **Motivation:** Nice motion capture dataset

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
[WER] Better error message for wer
State: closed

Currently we have the following problem when using the WER. When the input format to the WER metric is wrong, instead of throwing an error message a word-error-rate is computed which is incorrect. E.g. when doing the following: 


```python
from datasets import load_metric

wer = load_metric("wer")

target_str = ["hello this is nice", "hello the weather is bloomy"]
pred_str = [["hello it's nice"], ["hello it's the weather"]]

print("Wrong:", wer.compute(predictions=pred_str, references=target_str))
print("Correct", wer.compute(predictions=[x[0] for x in pred_str], references=target_str))
```
We get:
```
Wrong: 1.0
Correct 0.5555555555555556
```

meaning that we get a word-error rate for incorrectly passed input formats. We should raise an error here instead so that people don't spent hours fixing a model while it's their incorrect evaluation metric is the problem for a low WER.
Easier information editing
State: closed
Labels: name: enhancement, name: generic discussion

**Is your feature request related to a problem? Please describe.**
It requires a lot of effort to improve a datasheet. 

**Describe the solution you'd like**
UI or at least a link to the place where the code that needs to be edited is (and an easy way to edit this code directly from the site, without cloning, branching, makefile etc.)

**Describe alternatives you've considered**
The current Ux is to have the 8 steps for contribution while One just wishes to change a line a type etc.
[Staging] Update dataset repos automatically on the Hub
State: closed

Let's have a script that updates the dataset repositories on staging for now. This way we can make sure it works fine before going in prod.

Related to https://github.com/huggingface/datasets/issues/3341

The script runs on each commit on `master`. It checks the datasets that were changed, and it pushes the changes to the corresponding repositories on the Hub.

If there's a new dataset, then a new repository is created.

If the commit is a new release of `datasets`, it also pushes the tag to all the repositories.
Unexpected behavior doing Split + Filter
State: closed
Labels: name: bug

## Describe the bug
I observed unexpected behavior when applying 'train_test_split' followed by 'filter' on dataset. Elements of the training dataset eventually end up in the test dataset (after applying the 'filter') 

## Steps to reproduce the bug
```
from datasets import Dataset
import pandas as pd
dic = {'x': [1,2,3,4,5,6,7,8,9], 'y':['q','w','e','r','t','y','u','i','o']}
df = pd.DataFrame.from_dict(dic)
dataset = Dataset.from_pandas(df)
split_dataset = dataset.train_test_split(test_size=0.5, shuffle=False, seed=42)
train_dataset = split_dataset["train"]
eval_dataset = split_dataset["test"]
eval_dataset_2 = eval_dataset.filter(lambda example: example['x'] % 2 == 0)
print( eval_dataset['x'])
print(eval_dataset_2['x'])
```
One observes that elements in eval_dataset2 are actually coming from the training dataset...

## Expected results
The expected results would be that the filtered eval dataset would only contain elements from the original eval dataset.

## Actual results
Specify the actual results or traceback.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Windows 10
- Python version: 3.7
- PyArrow version: 5.0.0
Add `__add__()`, `__iadd__()` and similar to `Dataset` class
State: closed
Labels: name: enhancement, name: generic discussion

**Is your feature request related to a problem? Please describe.**
No.

**Describe the solution you'd like**
I would like to be able to concatenate datasets as follows:
```python
>>> dataset["train"] += dataset["validation"]
```

... instead of using `concatenate_datasets()`:
```python
>>> raw_datasets["train"] = concatenate_datasets([raw_datasets["train"], raw_datasets["validation"]])
>>> del raw_datasets["validation"]
```

**Describe alternatives you've considered**
Well, I have considered `concatenate_datasets()` 😀

**Additional context**
N.a.
JSONDecodeError with HuggingFace dataset viewer
State: closed
Labels: name: dataset-viewer

## Dataset viewer issue for 'pubmed_neg'

**Link:** https://huggingface.co/datasets/IGESML/pubmed_neg

I am getting the error: 
Status code:   400
Exception:     JSONDecodeError
Message:       Expecting property name enclosed in double quotes: line 61 column 2 (char 1202)

I have checked all files - I am not using single quotes anywhere. Not sure what is causing this issue.

Am I the one who added this dataset ? Yes
Remove redundant local path information in audio/image datasets
State: closed
Labels: name: dataset contribution

Remove the redundant path information in the audio/image dataset as discussed in https://github.com/huggingface/datasets/pull/3430#issuecomment-994734828

TODOs:
* [ ] merge https://github.com/huggingface/datasets/pull/3430
* [ ] merge https://github.com/huggingface/datasets/pull/3364
* [ ] re-generate the info files of the updated audio datasets

cc:  @patrickvonplaten @anton-l @nateraw (I expect this to break the audio/vision examples in Transformers; after this change you'll be able to access underlying paths as follows `dset = dset.cast_column("audio", Audio(..., decode=False)); path = dset[0]["audio"]`)
Add QuALITY dataset
State: open
Labels: name: dataset request

## Adding a Dataset
- **Name:** QuALITY
- **Description:** A challenging question answering with very long contexts (Twitter [thread](https://twitter.com/sleepinyourhat/status/1471225421794529281?s=20))
- **Paper:** No ArXiv link yet, but draft is [here](https://github.com/nyu-mll/quality/blob/main/quality_preprint.pdf)
- **Data:** GitHub repo [here](https://github.com/nyu-mll/quality)
- **Motivation:** This dataset would serve as a nice way to benchmark long-range Transformer models like BigBird, Longformer and their descendants. In particular, it would be very interesting to see how the S4 model fares on this given it's impressive performance on the Long Range Arena

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Update BLEURT hyperlink
State: closed

The description of BLEURT on the hf.co website has a strange use of URL hyperlinking. This PR attempts to fix this, although I am not 100% sure Markdown syntax is allowed on the frontend or not.

![Screen Shot 2021-12-15 at 17 31 27](https://user-images.githubusercontent.com/26859204/146226432-c83cbdaf-f57d-4999-b53c-85da718ff7fb.png)
Improve Wikipedia Loading Script
State: closed

* More structured approach to detecting redirects
* Remove redundant template filter code (covered by strip_code)
* Add language-specific lists of additional media namespace aliases for filtering
* Add language-specific lists of category namespace aliases for new link text cleaning step
* Remove magic words (parser directions like __TOC__ that occasionally occur in text)

Fix #3400

With support from @albertvillanova 
CC @yjernite
Add The People's Speech
State: closed
Labels: name: dataset request, name: speech

## Adding a Dataset
- **Name:** The People's Speech
- **Description:** a massive English-language dataset of audio transcriptions of full sentences. 
- **Paper:** https://openreview.net/pdf?id=R8CwidgJ0yT
- **Data:** https://mlcommons.org/en/peoples-speech/
- **Motivation:** With over 30,000 hours of speech, this dataset is the largest and most diverse freely available English speech recognition corpus today.


[The article](https://thegradient.pub/new-datasets-to-democratize-speech-recognition-technology-2/) which may be useful when working on the dataset.

cc: @anton-l 

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Add Multilingual Spoken Words dataset
State: closed
Labels: name: dataset request, name: speech

## Adding a Dataset
- **Name:** Multilingual Spoken Words
- **Description:** Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken words in 50 languages for academic research and commercial applications in keyword spotting and spoken term search, licensed under CC-BY 4.0. The dataset contains more than 340,000 keywords, totaling 23.4 million 1-second spoken examples (over 6,000 hours). 

  Read more: https://mlcommons.org/en/news/spoken-words-blog/
- **Paper:** https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/fe131d7f5a6b38b23cc967316c13dae2-Paper-round2.pdf
- **Data:** https://mlcommons.org/en/multilingual-spoken-words/
- **Motivation:** 

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Unable to resolve any data file after loading once
State: closed

when I rerun my program, it occurs this error
" Unable to resolve any data file that matches '['**train*']' at /data2/whr/lzy/open_domain_data/retrieval/wiki_dpr with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'zip']", so how could i deal with this problem?
thx.
And below is my code .
![image](https://user-images.githubusercontent.com/84694183/146023446-d75fdec8-65c1-484f-80d8-6c20ff5e994b.png)
Make cast cacheable (again) on Windows
State: closed

`cast` currently emits the following warning when called on Windows:
```
Parameter 'function'=<function Dataset.cast.<locals>.<lambda> at 0x000001C930571EA0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting 
and caching to work. If you reuse this transform, the caching mechanism will consider it to be different 
from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
```

It seems like the issue stems from the `config.PYARROW_VERSION` object not being serializable on Windows (tested with `dumps(lambda: config.PYARROW_VERSION)`), so I'm fixing this by capturing `config.PYARROW_VERSION.major` before the lambda definition.
Getting configs names takes too long
State: open
Labels: name: bug


## Steps to reproduce the bug
```python
from datasets import get_dataset_config_names
get_dataset_config_names("allenai/c4")
```

## Expected results

I would expect to get the answer quickly, at least in less than 10s

## Actual results

It takes about 45s on my environment

## Environment info

- `datasets` version: 1.16.1
- Platform: Linux-5.11.0-1022-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 4.0.1
data duplicate when setting num_works > 1 with streaming data
State: closed
Labels: name: bug, name: streaming

## Describe the bug
The data is repeated num_works times when we load_dataset with streaming and set num_works > 1 when construct dataloader

## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
import pandas as pd
import numpy as np
import os

from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
import shutil

NUM_OF_USER = 1000000
NUM_OF_ACTION = 50000
NUM_OF_SEQUENCE = 10000
NUM_OF_FILES = 32
NUM_OF_WORKERS = 16

if __name__ == "__main__":
    shutil.rmtree("./dataset")
    for i in range(NUM_OF_FILES):
        sequence_data = pd.DataFrame(
            {
                "imei": np.random.randint(1, NUM_OF_USER, size=NUM_OF_SEQUENCE),
                "sequence": np.random.randint(1, NUM_OF_ACTION, size=NUM_OF_SEQUENCE)
            }
        )

        if not os.path.exists("./dataset"):
            os.makedirs("./dataset")

        sequence_data.to_csv(f"./dataset/sequence_data_{i}.csv",
                            
 index=False)

    dataset = load_dataset("csv",
                           data_files=[os.path.join("./dataset",file) for file in os.listdir("./dataset") if file.endswith(".csv")],
                           split="train",
                           streaming=True).with_format("torch")
    data_loader = DataLoader(dataset,
                             batch_size=1024,
                             num_workers=NUM_OF_WORKERS)
    
    result = pd.DataFrame()
    for i, batch in tqdm(enumerate(data_loader)):
        result = pd.concat([result, 
                           pd.DataFrame(batch)],
                           axis=0)
    result.to_csv(f"num_work_{NUM_OF_WORKERS}.csv", index=False)

```

## Expected results
data do not duplicate 

## Actual results
data duplicate NUM_OF_WORKERS = 16 
![image](https://user-images.githubusercontent.com/16486492/145748707-9d2df25b-2f4f-4d7b-a83e-242be4fc8934.png)


## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:datasets==1.14.0
- Platform:transformers==4.11.3
- Python version:3.8
- PyArrow version:
Error about load_metric
State: closed
Labels: name: bug

## Describe the bug
  File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1371, in load_metric
    metric = metric_cls(
TypeError: 'NoneType' object is not callable

## Steps to reproduce the bug
```python
metric = load_metric("glue", "sst2")
```


## Environment info
- `datasets` version: 1.16.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 6.0.1
Add eli5_category dataset
State: closed

This pull request adds a categorized Long-form question answering dataset `ELI5_Category`. It's a new variant of the [ELI5](https://huggingface.co/datasets/eli5) dataset that uses the Reddit tags to alleviate the training/validation overlapping in the origin ELI5 dataset.

A [report](https://celeritasml.netlify.app/posts/2021-12-01-eli5c/)(Section 2) on this dataset.
`.to_json` is extremely slow after `.select`
State: open
Labels: name: bug

## Describe the bug
Saving a dataset to JSON with `to_json` is extremely slow after using `.select` on the original dataset.

## Steps to reproduce the bug
```python
from datasets import load_dataset

original = load_dataset("squad", split="train")
original.to_json("from_original.json")  # Takes 0 seconds

selected_subset1 = original.select([i for i in range(len(original))])
selected_subset1.to_json("from_select1.json")  # Takes 212 seconds

selected_subset2 = original.select([i for i in range(int(len(original) / 2))])
selected_subset2.to_json("from_select2.json")  # Takes 90 seconds
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master (https://github.com/huggingface/datasets/commit/6090f3cfb5c819f441dd4a4bb635e037c875b044)
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.27
- Python version: 3.9.7
- PyArrow version: 6.0.0
Fix type of bridge field in QED
State: closed

Use `Value("string")` instead of `Value("bool")` for the feature type of the `"bridge"` field in the QED dataset. If the value is `False`, set to `None`.

The following paragraph in the QED repo explains the purpose of this field:
>Each annotation in referential_equalities is a pair of spans, the question_reference and the sentence_reference, corresponding to an entity mention in the question and the selected_sentence respectively. As described in the paper, sentence_references can be "bridged in", in which case they do not correspond with any actual span in the selected_sentence. Hence, sentence_reference spans contain an additional field, bridge, which is a prepositional phrase when a reference is bridged, and is False otherwise. Prepositional phrases serve to link bridged references to an anchoring phrase in the selected_sentence. In the case a sentence_reference is bridged, the start and end, as well as the span string, map to such an anchoring phrase in the selected_sentence.

Fix #3346

cc @VictorSanh
disaster_response_messages unavailable
State: closed
Labels: name: dataset-viewer

## Dataset viewer issue for '* disaster_response_messages*'

**Link:** https://huggingface.co/datasets/disaster_response_messages

Dataset unavailable. Link dead: https://datasets.appen.com/appen_datasets/disaster_response_data/disaster_response_messages_training.csv

Am I the one who added this dataset ?No
Non-deterministic tests: CI tests randomly fail
State: closed
Labels: name: bug

## Describe the bug
Some CI tests fail randomly.

1. In https://github.com/huggingface/datasets/pull/3375/commits/c10275fe36085601cb7bdb9daee9a8f1fc734f48, there were 3 failing tests, only on Linux:
   ```
   =========================== short test summary info ============================
   FAILED tests/test_streaming_download_manager.py::test_streaming_dl_manager_get_extraction_protocol[https://drive.google.com/uc?export=download&id=1k92sUfpHxKq8PXWRr7Y5aNHXwOCNUmqh-zip]
   FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive - Fi...
   FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped
   = 3 failed, 3553 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 192.79s (0:03:12) =
   ```

2. After re-running the CI (without any change in the code) in https://github.com/huggingface/datasets/pull/3375/commits/57bfe1f342cd3c59d2510b992d5f06a0761eb147, there was only 1 failing test (one on Linux and a different one on Windows):
   - On Linux:
      ```
      =========================== short test summary info ============================
      FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped
      = 1 failed, 3555 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 199.76s (0:03:19) =
      ```
   - On Windows:
      ```
      =========================== short test summary info ===========================
      FAILED tests/test_load.py::test_load_dataset_builder_for_community_dataset_without_script
      = 1 failed, 3551 passed, 2954 skipped, 2 xfailed, 1 xpassed, 121 warnings in 478.58s (0:07:58) =
      ```

   The test `tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped` passes locally.

3. After re-running again the CI (without any change in the code) in https://github.com/huggingface/datasets/pull/3375/commits/39f32f2119cf91b86867216bb5c356c586503c6a, ALL the tests passed.
Fix flaky test again for s3 serialization
State: closed

Following https://github.com/huggingface/datasets/pull/3388 that wasn't enough (see CI error [here](https://app.circleci.com/pipelines/github/huggingface/datasets/9080/workflows/b971fb27-ff20-4220-9416-c19acdfdf6f4/jobs/55985))
[chinese wwm] load_datasets behavior not as expected when using run_mlm_wwm.py script
State: open
Labels: name: bug

## Describe the bug

Model I am using (Bert, XLNet ...): bert-base-chinese

The problem arises when using:
* [https://github.com/huggingface/transformers/blob/master/examples/research_projects/mlm_wwm/run_mlm_wwm.py] the official example scripts: `rum_mlm_wwm.py`

The tasks I am working on is: pretraining whole word masking with my own dataset and ref.json file
I tried follow the run_mlm_wwm.py procedure to do whole word masking on pretraining task. my file is in .txt form, where one line represents one sample, with `9,264,784` chinese lines in total. the ref.json file is also contains 9,264,784 lines of whole word masking reference data for my chinese corpus. but when I try to adapt the run_mlm_wwm.py script, it shows that somehow after 
`datasets["train"] = load_dataset(...`
`len(datasets["train"])` returns `9,265,365`
then, after `tokenized_datasets = datasets.map(...`
`len(tokenized_datasets["train"])` returns `9,265,279`
I'm really confused and tried to trace code by myself but can't know what happened after a week trial.

I want to know what happened in the `load_dataset()` function and `datasets.map` here and how did I get more lines of data than I input. so I'm here to ask.
## To reproduce
Sorry that I can't provide my data here since it did not belong to me. but I'm sure I remove the blank lines.

## Expected behavior
I expect the code run as it should. but the AssertionError in line 167 keeps raise as the line of reference json and datasets['train'] differs.

Thanks for your patient reading!

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.29
- Python version: 3.8.10 
- PyArrow version: 3.0.0
Pass new_fingerprint in multiprocessing
State: closed

Following https://github.com/huggingface/datasets/pull/3045

Currently one can pass `new_fingerprint` to `.map()` to use a custom fingerprint instead of the one computed by hashing the map transform. However it's ignored if `num_proc>1`.

In this PR I fixed that by passing `new_fingerprint` to `._map_single()` when `num_proc>1`.
More specifically, `new_fingerprint` with a suffix based on the process `rank` is passed, so that each process has a different `new_fingerprint`

cc @TevenLeScao @vlievin
Typo in Dataset viewer error message
State: closed
Labels: name: dataset-viewer

## Dataset viewer issue for '*name of the dataset*'

**Link:** *link to the dataset viewer page*

*short description of the issue*

When creating an empty dataset repo, the Dataset Preview provides a helpful message that no files were found. There is a tiny typo in that message: "ressource" should be "resource"

![Screen Shot 2021-12-09 at 15 31 31](https://user-images.githubusercontent.com/26859204/145415725-9cd728f0-c2c8-4b4e-a8e1-4f4d7841c94a.png)


Am I the one who added this dataset ? 

N/A
Use max number of data files to infer module
State: closed

When inferring the module for datasets without script, set a maximum number of iterations over data files.

This PR fixes the issue of taking too long when hundred of data files present.

Please, feel free to agree on both numbers:
```
# Datasets without script
DATA_FILES_MAX_NUMBER = 10
ARCHIVED_DATA_FILES_MAX_NUMBER = 5
```

Fix #3404.
ZIP format inference does not work when files located in a dir inside the archive
State: closed
Labels: name: bug

## Describe the bug
When a zipped file contains archived files within a directory, the function `infer_module_for_data_files_in_archives` does not work.

It only works for files located in the root directory of the ZIP file.

## Steps to reproduce the bug
```python
infer_module_for_data_files_in_archives(["path/to/zip/file.zip"], False)
```
Optimize ZIP format inference
State: closed
Labels: name: enhancement

**Is your feature request related to a problem? Please describe.**
When hundreds of ZIP files are present in a dataset, format inference takes too long.

See: https://github.com/bigscience-workshop/data_tooling/issues/232#issuecomment-986685497

**Describe the solution you'd like**
Iterate over a maximum number of files.

CC: @lhoestq
Cannot import name 'maybe_sync'
State: closed
Labels: name: bug

## Describe the bug
Cannot seem to import datasets when running run_summarizer.py script on a VM set up on ovhcloud

## Steps to reproduce the bug
```python
from datasets import load_dataset
```

## Expected results
No error

## Actual results
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/conda/lib/python3.7/site-packages/datasets/__init__.py", line 34, in <module>
    from .arrow_dataset import Dataset, concatenate_datasets
  File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 48, in <module>
    from .arrow_writer import ArrowWriter, OptimizedTypedSequence
  File "/opt/conda/lib/python3.7/site-packages/datasets/arrow_writer.py", line 27, in <module>
    from .features import (
  File "/opt/conda/lib/python3.7/site-packages/datasets/features/__init__.py", line 2, in <module>
    from .audio import Audio
  File "/opt/conda/lib/python3.7/site-packages/datasets/features/audio.py", line 8, in <module>
    from ..utils.streaming_download_manager import xopen
  File "/opt/conda/lib/python3.7/site-packages/datasets/utils/streaming_download_manager.py", line 16, in <module>
    from ..filesystems import COMPRESSION_FILESYSTEMS
  File "/opt/conda/lib/python3.7/site-packages/datasets/filesystems/__init__.py", line 13, in <module>
    from .s3filesystem import S3FileSystem  # noqa: F401
  File "/opt/conda/lib/python3.7/site-packages/datasets/filesystems/s3filesystem.py", line 1, in <module>
    import s3fs
  File "/opt/conda/lib/python3.7/site-packages/s3fs/__init__.py", line 1, in <module>
    from .core import S3FileSystem, S3File
  File "/opt/conda/lib/python3.7/site-packages/s3fs/core.py", line 11, in <module>
    from fsspec.asyn import AsyncFileSystem, sync, sync_wrapper, maybe_sync
ImportError: cannot import name 'maybe_sync' from 'fsspec.asyn' (/opt/conda/lib/python3.7/site-packages/fsspec/asyn.py)

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.0
- Platform: OVH Cloud Tesla V100 Machine
- Python version: 3.7.9
- PyArrow version: 6.0.1
Add Wikimedia pre-processed datasets
State: closed
Labels: name: dataset request

## Adding a Dataset
- **Name:** Add pre-processed data to:
  - *wikimedia/wikipedia*: https://huggingface.co/datasets/wikimedia/wikipedia
  - *wikimedia/wikisource*: https://huggingface.co/datasets/wikimedia/wikisource
- **Description:** Add pre-processed data to the Hub for all languages
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** This will be very useful for the NLP community, as the pre-processing has a high cost for lot of researchers (both in computation and in knowledge)

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

CC: @geohci, @yjernite
Improve Wikipedia loading script
State: closed
Labels: name: dataset request

As reported by @geohci, the "wikipedia" processing/loading script could be improved by some additional small suggested processing functions:
- _extract_content(filepath):
  - Replace .startswith("#redirect") with more structured approach: if elem.find(f"./{namespace}redirect") is None: continue
- _parse_and_clean_wikicode(raw_content, parser):
  - Remove rm_template from cleaning -- this is redundant with .strip_code() from mwparserformhell
  - Build a language-specific list of namespace prefixes to filter out per below get_namespace_prefixes
  - Optional: strip prefixes like categories -- e.g., Category:Towns in Tianjin becomes Towns in Tianjin
  - Optional: strip magic words
Add Wikisource dataset
State: closed
Labels: name: dataset request

## Adding a Dataset
- **Name:** *wikisource*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** Additional high quality textual data, besides Wikipedia.

Add loading script as "canonical" dataset (as it is the case for ""wikipedia").

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

CC: @geohci, @yjernite
Add URL field to Wikimedia dataset instances: wikipedia,...
State: closed
Labels: name: dataset request

As reported by @geohci, in order to host pre-processed data in the Hub, we should add the full URL to data instances (new field "url"), so that we conform to proper attribution from license requirement. See, e.g.: https://fair-trec.github.io/docs/Fair_Ranking_2021_Participant_Instructions.pdf#subsection.3.2

This should be done for all pre-processed datasets under "wikimedia" org in the Hub: https://huggingface.co/wikimedia
add BNL newspapers 
State: closed

This pull request adds the BNL's [processed newspaper collections](https://data.bnl.lu/data/historical-newspapers/) as a dataset. This is partly done to support BigScience see: https://github.com/bigscience-workshop/data_tooling/issues/192. 

The Datacard is more sparse than I would like but I plan to make a separate pull request to try and make this more complete at a later date. 

I had to manually add the `dummy_data` but I believe I've done this correctly (the tests pass locally).
Install Audio dependencies to support audio decoding
State: closed
Labels: name: dataset-viewer, name: audio_column

## Dataset viewer issue for '*openslr*', '*projecte-aina/parlament_parla*'

**Link:** *https://huggingface.co/datasets/openslr*
**Link:** *https://huggingface.co/datasets/projecte-aina/parlament_parla*

Error:
```
Status code:   400
Exception:     ImportError
Message:       To support decoding audio files, please install 'librosa'.
```

Am I the one who added this dataset ? Yes-No
- openslr: No
- projecte-aina/parlament_parla: Yes
Preserve all feature types when saving a dataset on the Hub with `push_to_hub`
State: closed
Labels: name: bug

Currently, if one of the dataset features is of type `ClassLabel`, saving the dataset with `push_to_hub` and reloading the dataset with `load_dataset` will return the feature of type `Value`. To fix this, we should do something similar to `save_to_disk` (which correctly preserves the types) and not only push the parquet files in `push_to_hub`, but also the dataset `info` (stored in a JSON file).
Common Voice Belarusian Dataset
State: open
Labels: name: dataset request, name: speech

## Adding a Dataset
- **Name:** *Common Voice Belarusian Dataset*
- **Description:** *[commonvoice.mozilla.org/be](https://commonvoice.mozilla.org/be)*
- **Data:** *[commonvoice.mozilla.org/be/datasets](https://commonvoice.mozilla.org/be/datasets)*
- **Motivation:** *It has more than 7GB of data, so it will be great to have it in this package so anyone can try to train something for Belarusian language.*

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Dataset viewer issue for `dansbecker/hackernews_hiring_posts`
State: closed
Labels: name: dataset-viewer

## Dataset viewer issue for `dansbecker/hackernews_hiring_posts`

**Link:** https://huggingface.co/datasets/dansbecker/hackernews_hiring_posts

*short description of the issue*

Dataset preview not showing for uploaded DatasetDict. See https://discuss.huggingface.co/t/dataset-preview-not-showing-for-uploaded-datasetdict/12603

Am I the one who added this dataset ?

No -> @dansbecker
method to select columns
State: closed
Labels: name: enhancement

**Is your feature request related to a problem? Please describe.**

* There is currently no way to select some columns of a dataset. In pandas, one can use `df[['col1', 'col2']]` to select columns, but in `datasets`, it results in error.

**Describe the solution you'd like**

* A new method that can be used to create a new dataset with only a list of specified columns.

**Describe alternatives you've considered**

`.remove_columns(self, columns: Union[str, List[str]], inverse: bool = False)`
Or 
`.select(self,  indices: Iterable = None, columns: List[str] = None)`
Loading dataset throws "KeyError: 'Field "builder_name" does not exist in table schema'"
State: closed
Labels: name: bug

## Describe the bug
I have prepared dataset to datasets and now I am trying to load it back Finnish-NLP/voxpopuli_fi
I get "KeyError: 'Field "builder_name" does not exist in table schema'"

My dataset folder and files should be like @patrickvonplaten  has here https://huggingface.co/datasets/flax-community/german-common-voice-processed

How my voxpopuli dataset looks like:
![image](https://user-images.githubusercontent.com/25264037/144895598-b7d9ae91-b04a-4046-9f06-b71ff0824d13.png)

Part of the processing (path column is the absolute path to audio files)
```
def add_audio_column(example):
    example['audio'] = example['path']
    return example

voxpopuli = voxpopuli.map(add_audio_column)
voxpopuli.cast_column("audio", Audio())
voxpopuli["audio"] <-- to my knowledge this does load the local files and prepares those arrays
voxpopuli = voxpopuli.cast_column("audio", Audio(sampling_rate=16_000)) resampling 16kHz
```

I have then saved it to disk_
`voxpopuli.save_to_disk('/asr_disk/datasets_processed_new/voxpopuli')`

and made folder structure same as @patrickvonplaten 
I also get same error while trying to load_dataset from his repo:
![image](https://user-images.githubusercontent.com/25264037/144895872-e9b8f326-cf2b-46cf-9417-606a0ce14077.png)


## Steps to reproduce the bug
```python
dataset = load_dataset("Finnish-NLP/voxpopuli_fi")
```

## Expected results
Dataset is loaded correctly and looks like in the first picture

## Actual results
Loading throws keyError:
KeyError: 'Field "builder_name" does not exist in table schema'


Resources I have been trying to follow:
https://huggingface.co/docs/datasets/audio_process.html 
https://huggingface.co/docs/datasets/share_dataset.html

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.2.dev0
- Platform: Ubuntu 20.04.2 LTS
- Python version: 3.8.12
- PyArrow version: 6.0.1
Add EDGAR
State: open
Labels: name: dataset request

## Adding a Dataset
- **Name:** EDGAR Database
- **Description:** https://www.sec.gov/edgar/about EDGAR, the Electronic Data Gathering, Analysis, and Retrieval system, is the primary system for companies and others submitting documents under the Securities Act of 1933, the Securities Exchange Act of 1934, the Trust Indenture Act of 1939, and the Investment Company Act of 1940. Containing millions of company and individual filings, EDGAR benefits investors, corporations, and the U.S. economy overall by increasing the efficiency, transparency, and fairness of the securities markets. The system processes about 3,000 filings per day, serves up 3,000 terabytes of data to the public annually, and accommodates 40,000 new filers per year on average. EDGAR® and EDGARLink® are registered trademarks of the SEC.
- **Data:** https://www.sec.gov/os/accessing-edgar-data
- **Motivation:** Enabling and improving FSI (Financial Services Industry) datasets to increase ease of use 

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Create Language Modeling task
State: closed

Create Language Modeling task to be able to specify the input "text" column in a dataset.

This can be useful for datasets which are not exclusively used for language modeling and have more than one column:
- for text classification datasets (with columns "review" and "rating", for example), the Language Modeling task can be used to specify the "text" column ("review" in this case).

TODO:
- [ ] Add the LanguageModeling task to all dataset scripts which can be used for language modeling
None batched `with_transform`, `set_transform`
State: open
Labels: name: enhancement

**Is your feature request related to a problem? Please describe.**

A `torch.utils.data.Dataset.__getitem__` operates on a single example.
But 🤗 `Datasets.with_transform` doesn't seem to allow non-batched transform.

**Describe the solution you'd like**

Have a `batched=True` argument in `Datasets.with_transform`

**Describe alternatives you've considered**

* Convert a non-batched transform function to batched one myself. 
* Wrap a 🤗 Dataset with torch Dataset, and add a `__getitem__`. 🙄
* Have `lazy=False` in `Dataset.map`, and returns a `LazyDataset` if `lazy=True`. This way the same `map` interface can be used, and existing code can be updated with one argument change.
#3337 Add typing overloads to Dataset.__getitem__ for mypy
State: closed

Add typing overloads to Dataset.__getitem__ for mypy

Fixes #3337 

**Iterable**
Iterable from `collections` cannot have a type, so you can't do `Iterable[int]` for example. `typing` has a Generic version that builds upon the one from `collections`.

**Flake8**
I had to add `# noqa: F811`, this is a bug from Flake8.

datasets uses flake8==3.7.9 which released in October 2019 if I update flake8 (4.0.1), I no longer get these errors, but I did not want to make the update without your approval. (It also triggers other errors like no args in f-strings.)
[Quick poll] Give your opinion on the future of the Hugging Face Open Source ecosystem!
State: closed

Thanks to all of you, `datasets` will pass 11.5k stars :star2: this week!

If you have a couple of minutes and want to participate in shaping the future of the ecosystem, please share your thoughts: 

[**hf.co/oss-survey**](https://hf.co/oss-survey)

(please reply in the above feedback form rather than to this thread)

Thank you all on behalf of the HuggingFace team! 🤗
iter_archive on zipfiles with better compression type check
State: closed

Hello @lhoestq , thank you for your detailed answer on previous PR ! 
I made this new PR because I misused git on the previous one #3347.
Related issue #3272.

# Comments  : 

* For extension check I used the `_get_extraction_protocol` function in **download_manager.py** with a slight change and called it `_get_extraction_protocol_local`: 

**I removed this part :**
 ```python
    elif path.endswith(".tar.gz") or path.endswith(".tgz"):
        raise NotImplementedError(
            f"Extraction protocol for TAR archives like '{urlpath}' is not implemented in streaming mode. Please use `dl_manager.iter_archive` instead."
        )
```
**And also changed :** 
```diff
- extension = path.split(".")[-1]
+ extension = "tar" if path.endswith(".tar.gz") else path.split(".")[-1]
```
The reason for this is a compression like **.tar.gz** will be considered a **.gz** which is handled with **zipfile**, though **tar.gz** can only be opened using **tarfile**.

Please tell me if there's anything to change.

# Tasks : 
- [x] download_manager.py
- [x] streaming_download_manager.py
COCO 🥥 on the 🤗 Hub?
State: closed

This is a draft PR since I ran into few small problems. 
I referred to this TFDS code: https://github.com/tensorflow/datasets/blob/2538a08c184d53b37bfcf52cc21dd382572a88f4/tensorflow_datasets/object_detection/coco.py
cc: @mariosasko
Support streaming zipped CSV dataset repo by passing only repo name
State: closed
Labels: name: enhancement

Given a community 🤗 dataset repository containing only a zipped CSV file (only raw data, no loading script), I would like to load it in streaming mode without passing `data_files`:
```
ds_name = "bigscience-catalogue-data/vietnamese_poetry_from_fsoft_ai_lab"
ds = load_dataset(ds_name, split="train", streaming=True, use_auth_token=True)
item = next(iter(ds))
```

Currently, it gives a `FileNotFoundError` because there is no glob (no "\*" after "zip://": "zip://*") in the passed URL:
```
'zip://::https://huggingface.co/datasets/bigscience-catalogue-data/vietnamese_poetry_from_fsoft_ai_lab/resolve/e5d45f1bd9a8a798cc14f0a45ebc1ce91907c792/poems_dataset.zip'
```
[SEO improvement] Add Dataset Metadata to make datasets indexable
State: closed
Labels: name: enhancement

Some people who host datasets on github seem to include a table of metadata at the end of their README.md to make the dataset indexable by [Google Dataset Search](https://datasetsearch.research.google.com/) (See [here](https://github.com/google-research/google-research/tree/master/goemotions#dataset-metadata) and [here](https://github.com/cvdfoundation/google-landmark#dataset-metadata)). This could be a useful addition to canonical datasets; perhaps even community datasets.

I'll include a screenshot (as opposed to markdown) as an example so as not to have a github issue indexed as a dataset:

> ![image](https://user-images.githubusercontent.com/3664563/144496173-953428cf-633a-4571-b75b-f099c6b2ed65.png)



**_PS: It might very well be the case that this is already covered by some other markdown magic I'm not aware of._**
New: Americas NLI dataset
State: closed

This PR adds the [Americas NLI](https://arxiv.org/abs/2104.08726) dataset, extension of XNLI to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika.

One odd thing (not sure) is that I had to set

`datasets-cli dummy_data ./datasets/americas_nli/ --auto_generate --n_lines 7500`

`n_lines` very large to successfully generate the dummy files for all the subsets. Happy to get some guidance here.

Otherwise, I hope everything is in order :)

e: missed a step, onto fixing the tests
e2: there you go -- hope it's ok to have added more languages with their ISO codes to `languages.json`, need those tests to pass :laughing:
[Audio] Allow resampling for audio datasets in streaming mode
State: closed
Labels: name: enhancement

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

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

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