rIsHu009/github-issues-updated huggingface dataset:
Title: ArrowInvalid when mapping dataset with missing values
Author: uniquefine (NONE)
State: open
Created: 2021-08-24T08:50:42Z
Labels: bug

## Describe the bug
I encountered an `ArrowInvalid` when mapping dataset with missing values. 
Here are the files for a minimal example. The exception is only thrown when the first line in the csv has a missing value (if you move the last line to the top it isn't thrown).
[data_small.csv](https://github.com/huggingface/datasets/files/7037838/data_small.csv)
[data.csv](https://github.com/huggingface/datasets/files/7037842/data.csv)

## Steps to reproduce the bug
```python
from datasets import load_dataset

datasets = load_dataset("csv", data_files=['data_small.csv'])

datasets = datasets.map(lambda e: {'labels': e['match']},
                        remove_columns=['id'])
```

## Expected results
No error

## Actual results
```
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Invalid null value
```

## Environment info
- `datasets` version: 1.5.0
- Platform: Linux-5.11.0-25-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyTorch version (GPU?): 1.7.1+cpu (False)
- Tensorflow version (GPU?): 2.4.1 (False)
- Using GPU in script?: no
- Using distributed or parallel set-up in script?: no

Comments:
Hi ! It fails because of the feature type inference.

Because the first 1000 examples all have null values in the "match" field, then it infers that the type for this field is `null` type before writing the data on disk. But as soon as it tries to map an example with a non-null "match" field, then it fails.

To fix that you can either:
- increase the writer_batch_size to >2000 (default is 1000) so that some non-null values will be in the first batch written to disk
```python
datasets = datasets.map(lambda e: {'labels': e['match']}, remove_columns=['id'], writer_batch_size=2000)
```
- OR force the feature type with:
```python
from datasets import Features, Value

features = Features({
    'conflict': Value('int64'),
    'date': Value('string'),
    'headline': Value('string'),
    'match': Value('float64'),
    'label': Value('float64')
})
datasets = datasets.map(lambda e: {'labels': e['match']}, remove_columns=['id'], features=features)
```
Title: Optimize streaming from TAR archives
Author: lhoestq (MEMBER)
State: closed
Created: 2021-08-23T16:56:40Z
Labels: enhancement

streaming

Hi ! As you know TAR has some constraints for data streaming. While it is optimized for buffering, the files in the TAR archive **need to be streamed in order**. It means that we can't choose which file to stream from, and this notation is to be avoided for TAR archives:
```
tar://books_large_p1.txt::https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2
```
Instead, I suggest we implement `iter_archive` for the `StreamingDownloadManager`.
The regular `DownloadManager` already has it.

Then we will have to update the json/txt/csv/etc. loaders to make them use `iter_archive` on TAR archives.

That's also what Tensorflow Datasets is doing in this case.
See this [dataset](https://github.com/tensorflow/datasets/blob/93895059c80a9e05805e8f32a2e310f66a23fc98/tensorflow_datasets/image_classification/flowers.py) for example.

Therefore instead of doing
```python
uncompressed = dl_manager.extract(tar_archive)
filename = "books_large_p1.txt"
with open(os.path.join(uncompressed, filename)) as f:
    for line in f:
        ...
```
we'll do
```python
for filename, f in dl_manager.iter_archive(tar_archive):
    for line in f:
        ...
```

Comments:
Closed by: 
- #3066
Title: Add code-mixed Kannada Hope speech dataset
Author: adeepH (CONTRIBUTOR)
State: closed
Created: 2021-08-23T15:55:09Z

## Adding a Dataset
- **Name:** *KanHope*
- **Description:** *A code-mixed English-Kannada dataset for Hope speech detection*
- **Paper:** *https://arxiv.org/abs/2108.04616* 
- **Data:** *https://github.com/adeepH/KanHope/tree/main/dataset*
- **Motivation:** *The dataset is amongst the very few resources available for code-mixed low-resourced Dravidian languages of India*

Comments:
Title: HF_DATASETS_CACHE variable in Windows
Author: rp2839 (NONE)
State: closed
Created: 2021-08-21T13:17:44Z

I can't seem to use a custom Cache directory in Windows. I have tried:

set HF_DATASETS_CACHE = "C:\Datasets"
set HF_DATASETS_CACHE = "C:/Datasets"
set HF_DATASETS_CACHE = "C:\\Datasets"
set HF_DATASETS_CACHE = "r'C:\Datasets'"
set HF_DATASETS_CACHE = "\Datasets"
set HF_DATASETS_CACHE = "/Datasets"

In each instance I get the "[WinError 123] The filename, directory name, or volume label syntax is incorrect" error when attempting to load a dataset

Comments:
Agh - I'm a muppet. No quote marks are needed.
set HF_DATASETS_CACHE = C:\Datasets
works as intended.
Title: Add url prefix convention for many compression formats
Author: lhoestq (MEMBER)
State: closed
Created: 2021-08-20T16:11:23Z

## Intro

When doing dataset streaming, the uncompression of compressed files is done on the fly using `fsspec`.

In particular, the download manager method `download_and_extract` doesn't return a path to the local download and extracted file, but instead a chained URL so that the uncompression can be done when the file is opened. A few examples of chained URLS:
- `gz://file.txt::https://foo.bar/file.txt.gz`
- `bz2://file.txt::https://foo.bar/file.txt.bz2`
- `zip://::https://foo.bar/archive.zip`
- `tar://::https://foo.bar/archive.tar.gz` (the TAR uncompression includes gz, bz2 etc. uncompression in `fsspec`)

This syntax is highly inspired by the `fsspec` URL chaining syntax from https://filesystem-spec.readthedocs.io/en/latest/features.html#url-chaining

This url prefixing allows `open` to know what kind of uncompression to do in a dataset script when doing
```python
def _generate_examples(self, urlpath):
    with open(urlpath) as f:
        ....
```

## What it changes

This changes the previous behavior from https://github.com/huggingface/datasets/pull/2786 , in which `open` was trying to infer the compression automatically. Infering the compression made it impossible to know whether the user wanted `open` to return compressed data (as the default behavior of the buitin open), or the uncompressed data. By adding uncompression prefixes to the URL, `open` know directly if it has to uncompress or not, and also which protocol to use.

## Additional notes

This PR should close https://github.com/huggingface/datasets/issues/2813

It should also close this PR https://github.com/huggingface/datasets/pull/2811 since the oscar dataset script won't try to uncompress twice anymore

Note that I had to temporarily remove the support for passing tar and zip files to `data_files` for streaming to make it work, since it makes it ambiguous whether a zip file passed as `data_files` should be uncompressed or not. IMO we can make it work again by changing the syntax to make the glob explicit:
```python
load_dataset("json", data_files="zip://*.jsonl::https://foo.bar/archive.zip")
```
This is the exact same convention as fsspec and it removes all ambiguities

cc @albertvillanova @lewtun

Comments:
Thanks for the feedback :) I will also complete the documentation to explain this convention

I just added some documentation about how streaming works with chained URLs.

I will also add some docs about how to use chained URLs directly in `load_dataset` in #2662, since #2662 does change the documentation already and to avoid having to resolve conflicts.

Merging this one now, next step is resolve the conflicts in #2662 and update the docs for URL chaining :)

There is also the glob feature of zip files that I need to add, to be able to do this for example:
```python
load_dataset("json", data_files="zip://*::https://foo.bar/archive.zip")
```
Title: Cannot load linnaeus dataset
Author: NielsRogge (CONTRIBUTOR)
State: closed
Created: 2021-08-20T12:15:15Z
Labels: bug

## Describe the bug
The [linnaeus](https://huggingface.co/datasets/linnaeus) dataset cannot be loaded. To reproduce:
```
from datasets import load_dataset

datasets = load_dataset("linnaeus")
```
This results in:
```
Downloading and preparing dataset linnaeus/linnaeus (download: 17.36 MiB, generated: 8.74 MiB, post-processed: Unknown size, total: 26.10 MiB) to /root/.cache/huggingface/datasets/linnaeus/linnaeus/1.0.0/2ff05dbc256108233262f596e09e322dbc3db067202de14286913607cd9cb704...
---------------------------------------------------------------------------
ConnectionError                           Traceback (most recent call last)
<ipython-input-4-7ef3a88f6276> in <module>()
      1 from datasets import load_dataset
      2 
----> 3 datasets = load_dataset("linnaeus")

11 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
    603             raise FileNotFoundError("Couldn't find file at {}".format(url))
    604         _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
--> 605         raise ConnectionError("Couldn't reach {}".format(url))
    606 
    607     # Try a second time

ConnectionError: Couldn't reach https://drive.google.com/u/0/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download/
```

Comments:
Thanks for reporting ! #2852 fixed this error

We'll do a new release of `datasets` soon :)
Title: cannot load data from my loacal path
Author: yang-collect (NONE)
State: closed
Created: 2021-08-19T11:13:30Z
Labels: bug

## Describe the bug
I just want to directly load data from my local path,but find a bug.And I compare it with pandas to provide my local path is real.

here is my code
```python3
# print my local path
print(config.train_path)
# read data and print data length
tarin=pd.read_csv(config.train_path)
print(len(tarin))

# loading data by load_dataset 
data = load_dataset('csv',data_files=config.train_path)

print(len(data))
```


## Steps to reproduce the bug
```python
C:\Users\wie\Documents\项目\文本分类\data\train.csv
7613
Traceback (most recent call last):
  File "c:/Users/wie/Documents/项目/文本分类/lib/DataPrecess.py", line 17, in <module>
    data = load_dataset('csv',data_files=config.train_path)
  File "C:\Users\wie\Miniconda3\lib\site-packages\datasets\load.py", line 830, in load_dataset
    **config_kwargs,
  File "C:\Users\wie\Miniconda3\lib\site-packages\datasets\load.py", line 710, in load_dataset_builder
    **config_kwargs,
  File "C:\Users\wie\Miniconda3\lib\site-packages\datasets\builder.py", line 271, in __init__
    **config_kwargs,
  File "C:\Users\wie\Miniconda3\lib\site-packages\datasets\builder.py", line 386, in _create_builder_config
    config_kwargs, custom_features=custom_features, use_auth_token=self.use_auth_token
  File "C:\Users\wie\Miniconda3\lib\site-packages\datasets\builder.py", line 156, in create_config_id
    raise ValueError("Please provide a valid `data_files` in `DatasetBuilder`")
ValueError: Please provide a valid `data_files` in `DatasetBuilder`
```

## Expected results
A clear and concise description of the expected results.

## 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:  1.11.0
- Platform: win10
- Python version: 3.7.9
- PyArrow version: 5.0.0

Comments:
Hi ! The `data_files` parameter must be a string, a list/tuple or a python dict.

Can you check the type of your `config.train_path` please ? Or use `data_files=str(config.train_path)` ?
Title: Rename The Pile subsets
Author: lhoestq (MEMBER)
State: closed
Created: 2021-08-19T09:56:22Z

After discussing with @yjernite we think it's better to have the subsets of The Pile explicitly have "the_pile" in their names.

I'm doing the changes for the subsets that @richarddwang added:
- [x] books3 -> the_pile_books3 https://github.com/huggingface/datasets/pull/2801
- [x] stack_exchange -> the_pile_stack_exchange https://github.com/huggingface/datasets/pull/2803
- [x] openwebtext2 -> the_pile_openwebtext2 https://github.com/huggingface/datasets/pull/2802

For consistency we should also rename `bookcorpusopen` to `the_pile_bookcorpus` IMO, but let me know what you think.
(we can just add a deprecation message to `bookcorpusopen` for now and add `the_pile_bookcorpus`)

Comments:
Sounds good. Should we also have a “the_pile” dataset with the subsets as configuration?

I think the main `the_pile` datasets will be the one that is the mix of all the subsets: https://the-eye.eu/public/AI/pile/

We can also add configurations for each subset, and even allow users to specify the subsets they want:
```python
from datasets import load_dataset

load_dataset("the_pile", subsets=["openwebtext2", "books3", "hn"])
```

We're alrady doing something similar for mC4, where users can specify the list of languages they want to load.
Title: Add Mostly Basic Python Problems Dataset
Author: osanseviero (CONTRIBUTOR)
State: open
Created: 2021-08-18T20:28:39Z
Labels: dataset request

## Adding a Dataset
- **Name:** Mostly Basic Python Problems Dataset
- **Description:** The benchmark consists of around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry level programmers, covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases.
- **Paper:** *link to the dataset paper if available*
- **Data:** https://github.com/google-research/google-research/tree/master/mbpp
- **Motivation:** Simple, small dataset related to coding problems.

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

Comments:
I started working on that.
Title: Bump tqdm version
Author: mariosasko (COLLABORATOR)
State: closed
Created: 2021-08-18T12:51:29Z

The recently released tqdm 4.62.1 includes a fix for PermissionError on Windows (submitted by me in https://github.com/tqdm/tqdm/pull/1207), which means we can remove expensive `gc.collect` calls by bumping tqdm to that version. This PR does exactly that and, additionally, fixes a `disable_tqdm` definition that would previously, if used, raise a PermissionError on Windows.

Comments:
Title: arXiv Dataset verification problem
Author: eladsegal (CONTRIBUTOR)
State: open
Created: 2021-08-17T18:01:48Z
Labels: bug

dataset bug

## Describe the bug
`dataset_infos.json` for `arxiv_dataset` contains a fixed number of training examples, however the data (downloaded from an external source) is updated every week with additional examples.
Therefore, loading the dataset without `ignore_verifications=True` results in a verification error.

Comments:
Title: Fix stream oscar
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-08-17T10:10:59Z

Previously, an additional `open` was added to oscar to make it stream-compatible: 587bbb94e891b22863b312b99696e32708c379f4.

This was argued that might be problematic: https://github.com/huggingface/datasets/pull/2786#discussion_r690045921

This PR:
- removes that additional `open`
- patches `gzip.open` with `xopen` + `compression="gzip"`

Comments:
One additional note: if we can try to not change the code of oscar.py too often, I'm sure users that have it in their cache directory will be happy to not have to redownload it every time they update the library ;)

(since changing the code changes the cache directory of the dataset)

I don't think this is confusing for users because users don't even know we have patched `open`. The only thing users care is that if the pass `streaming=True`, they want to be able to load the dataset in streaming mode.

I don't see any other dataset where patching `open` with `fsspec.open`+`compression` is an "underlying issue". Are there other datasets where this is an issue?

The only dataset where this was an issue is in oscar and the issue is indeed due to the additional `open` you added inside `zip.open`.

Closing this one since https://github.com/huggingface/datasets/pull/2822 reverted the change of behavior of `open`
Title: Enable streaming for Wikipedia corpora
Author: lewtun (MEMBER)
State: closed
Created: 2021-08-16T15:59:12Z
Labels: enhancement

**Is your feature request related to a problem? Please describe.**
Several of the [Wikipedia corpora](https://huggingface.co/datasets?search=wiki) on the Hub involve quite large files that would be a good candidate for streaming. Currently it is not possible to stream these corpora:

```python
from datasets import load_dataset

# Throws ValueError: Builder wikipedia is not streamable.
wiki_dataset_streamed = load_dataset("wikipedia", "20200501.en", split="train", streaming=True)
```

Given that these corpora are derived from Wikipedia dumps in XML format which are then processed with Apache Beam, I am not sure whether streaming is possible in principle. The goal of this issue is to discuss whether this feature even makes sense :)

**Describe the solution you'd like**
It would be nice to be able to stream Wikipedia corpora from the Hub with something like

```python
from datasets import load_dataset

wiki_dataset_streamed = load_dataset("wikipedia", "20200501.en", split="train", streaming=True)
```

Comments:
Closing as this has been addressed in https://github.com/huggingface/datasets/pull/5689.
Title: Fix streaming zip files from canonical datasets
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-08-16T07:11:40Z

Previous PR #2798 fixed streaming remote zip files when passing the parameter `data_files`.

However, that broke streaming zip files used in canonical `datasets` scripts, which normally have a subsequent `join()` (patched with `xjoin()`) after the `StreamingDownloadManager.download_and_extract()` is called.

This PR fixes this issue and allows streaming zip files both from:
- canonical datasets scripts and
- data files.

Comments:
Title: add stack exchange
Author: richarddwang (CONTRIBUTOR)
State: closed
Created: 2021-08-14T08:11:02Z

stack exchange is part of EleutherAI/The Pile, but AFAIK, The Pile dataset blend all sub datasets together thus we are not able to use just one of its sub dataset from The Pile data. So I create an independent dataset using The Pile preliminary components.

I also change default `timeout` to 100 seconds instead of 10 seconds, otherwise I keep getting read time out when downloading source data of stack exchange and cc100 dataset.

When I was creating dataset card. I found there is room for creating / editing dataset card. I've made it an issue. #2797

Also I am wondering whether the import of The Pile dataset is actively undertaken (because I may need it recently)? #1675

Comments:
Hi ! Merging this one since it's all good :)

However I think it would also be better to actually rename it `the_pile_stack_exchange` to make things clearer and to avoid name collisions in the future. I would like to do the same for `books3` as well.

If you don't mind I'll open a PR to do the renaming


> If you don't mind I'll open a PR to do the renaming

@lhoestq That will be nice !!
Title: add openwebtext2
Author: richarddwang (CONTRIBUTOR)
State: closed
Created: 2021-08-14T07:09:03Z

openwebtext2 is part of EleutherAI/The Pile, but AFAIK, The Pile dataset blend all sub datasets together thus we are not able to use just one of its sub dataset from The Pile data. So I create an independent dataset using The Pile preliminary components.

When I was creating dataset card. I found there is room for creating / editing dataset card. I've made it an issue. #2797

Also I am wondering whether the import of The Pile dataset is actively undertaken (because I may need it recently)? #1675

Comments:
It seems we need to `pip install jsonlines` to pass the checks ?

Hi ! Do you really need `jsonlines` ? I think it simply uses `json.loads` under the hood.

Currently the test are failing because `jsonlines` is not part of the extra requirements `TESTS_REQUIRE` in setup.py

So either you can replace `jsonlines` with a simple for loop on the lines of the files and use `json.loads`, or you can add `TESTS_REQUIRE` to the test requirements (but in this case users will have to install it as well).

Thanks for your suggestion. I now know `io` and json lines format better and has changed `jsonlines` to just `readlines`.
Title: add books3
Author: richarddwang (CONTRIBUTOR)
State: closed
Created: 2021-08-14T07:04:25Z

books3 is part of EleutherAI/The Pile, but AFAIK, The Pile dataset blend all sub datasets together thus we are not able to use just one of its sub dataset from The Pile data. So I create an independent dataset using The Pile preliminary components.

When I was creating dataset card. I found there is room for creating / editing dataset card. I've made it an issue. #2797 

Also I am wondering whether the import of The Pile dataset is actively undertaken (because I may need it recently)? #1675

Comments:
> When I was creating dataset card. I found there is room for creating / editing dataset card. I've made it an issue. #2797

Thanks for the message, we'll definitely improve this

> Also I am wondering whether the import of The Pile dataset is actively undertaken (because I may need it recently)? #1675

Well currently no, but I think @lewtun was about to do it (though he's currently on vacations)

> > Also I am wondering whether the import of The Pile dataset is actively undertaken (because I may need it recently)? #1675
> 
> Well currently no, but I think @lewtun was about to do it (though he's currently on vacations)

yes i plan to start working on this next week #2185 

one question for @richarddwang - do you know if eleutherai happened to also release the "existing" datasets like enron emails and opensubtitles? 

in appendix c of their paper, they provide details on how they extracted these datasets, but it would be nice if we could just point to a url so we can be as close as possible to original implementation.

@lewtun 

> yes i plan to start working on this next week

Nice! Looking forward to it.

> one question for @richarddwang - do you know if eleutherai happened to also release the "existing" datasets like enron emails and opensubtitles?

Sadly, I don't know any existing dataset of enron emails, but I believe opensubtitles dataset is hosted at here. https://the-eye.eu/public/AI/pile_preliminary_components/
![image](https://user-images.githubusercontent.com/17963619/130061667-8c17985a-1c2f-432f-89f0-66a5288611b8.png)


thanks for the link @richarddwang! i think that corpus is actually the youtube subtitles one and my impression is that eleutherai have only uploaded the 14 new datasets they created. i've contacted one of the authors so hopefully they can share some additional info for us :)

btw it might take a while to put together all the corpora if i also need to preprocess them (e.g. the open subtitles / enron email etc), but i expect no longer than a few weeks.
Title: Support streaming tar files
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-08-14T04:40:17Z

This PR adds support to stream tar files by using the `fsspec` tar protocol.

It also uses the custom `readline` implemented in PR #2786.

The corresponding test is implemented in PR #2786.

Comments:
Hi ! Why do we need the custom `readline` for exactly ? feel free to add a comment to say why it's needed
Title: Fix streaming zip files
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-08-13T15:17:01Z

Currently, streaming remote zip data files gives `FileNotFoundError` message:
```python
data_files = f"https://huggingface.co/datasets/albertvillanova/datasets-tests-compression/resolve/main/sample.zip"
ds = load_dataset("json", split="train", data_files=data_files, streaming=True)
next(iter(ds))
```

This PR fixes it by adding a glob string.

The corresponding test is implemented in PR #2786.

Comments:
Hi ! I don't fully understand this change @albertvillanova 
The `_extract` method used to return the compound URL that points to the root of the inside of the archive.
This way users can use the usual os.path.join or other functions to point to the relevant files. I don't see why you're using a glob pattern ?

This change is to allow this:
```python
data_files = f"https://huggingface.co/datasets/albertvillanova/datasets-tests-compression/resolve/main/sample.zip"
ds = load_dataset("json", split="train", data_files=data_files, streaming=True)
assert isinstance(ds, IterableDataset)
```
Note that in this case the user will not call os.path.join.

Before this PR it gave error because pointing to the root, without any subsequent join, gives error:
```python
fsspec.open("zip://::https://huggingface.co/datasets/albertvillanova/datasets-tests-compression/resolve/main/sample.zip")
```
Title: Make creating/editing dataset cards easier, by editing on site and dumping info from test command.
Author: richarddwang (CONTRIBUTOR)
State: open
Created: 2021-08-13T11:54:49Z
Labels: enhancement

**Is your feature request related to a problem? Please describe.**

Creating and editing dataset cards should be but not that easy
- If other else know Some information I don't know (bias of dataset, dataset curation, supported dataset, ...), he/she should know the description on hf.co comes from README.md under github huggingface/datasets/datasets/the dataset, and willing to make a pr to add or fix information.
- Many information is also saved in `dataset_info.json` (citaion, description), but still need to write it down to README.md again.
- Contributor need to pip install and start a local server just for tagging the dataset's size. And contributor may be creating the dataset on lab's server, which can't open a browser.  
- if any one proposes a new tag, it doesn't show in the list that another creator see. (a stackoverflow way may be ideal)
- dataset card generator web app doesn't generate the necessary subsecion `Contributions` for us.

**Describe the solution you'd like**
- Everyone (or at least the author/contributor) can edit the description, information, tags of the dataset, on hf.co website. Just like wikipedia+stackoverflow
-  We can infer the actual data size, citation, data instance, ... from `dataset_info.json` and `dataset.arrow`  via `dataset-cli test`

Comments:
Title: Warnings and documentation about pickling incorrect
Author: mbforbes (NONE)
State: open
Created: 2021-08-12T23:09:13Z
Labels: bug

## Describe the bug

I have a docs bug and a closely related docs enhancement suggestion!

### Bug

The warning and documentation say "either `dill` or `pickle`" for fingerprinting. But it seems that `dill`, which is installed by `datasets` by default, _must_ work, or else the fingerprinting fails.

Warning:

https://github.com/huggingface/datasets/blob/450b9174765374111e5c6daab0ed294bc3d9b639/src/datasets/fingerprint.py#L262

Docs:

> For a transform to be hashable, it needs to be pickleable using dill or pickle.
> – [docs](https://huggingface.co/docs/datasets/processing.html#fingerprinting)

For my code, `pickle` works, but `dill` fails. The `dill` failure has already been reported in https://github.com/huggingface/datasets/issues/2643. However, the `dill` failure causes a hashing failure in the datasets library, without any backing off to `pickle`. This implies that it's not the case that either `dill` **or** `pickle` can work, but that `dill` must work if it is installed. I think this is more accurate wording, since it is installed and used by default:

https://github.com/huggingface/datasets/blob/c93525dc291346e54212567fa72d7d607befe937/setup.py#L83

... and the hashing will fail if it fails.

### Enhancement

I think it'd be very helpful to add to the documentation how to debug hashing failures. It took me a while to figure out how to diagnose this. There is a very nice two-liner by @lhoestq in https://github.com/huggingface/datasets/issues/2516#issuecomment-865173139:

```python
from datasets.fingerprint import Hasher
Hasher.hash(my_object)
```

I think add this to the docs will help future users quickly debug any hashing troubles of their own :-)

## Steps to reproduce the bug

`dill` but not `pickle` hashing failure in https://github.com/huggingface/datasets/issues/2643

## Expected results
If either `dill` or `pickle` can successfully hash, the hashing will succeed.

## Actual results
If `dill` or `pickle` cannot hash, the hashing fails.

## Environment info

- `datasets` version: 1.9.0
- Platform: Linux-5.8.0-1038-gcp-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 4.0.1

Comments:
Title: Update: GooAQ - add train/val/test splits
Author: bhavitvyamalik (CONTRIBUTOR)
State: closed
Created: 2021-08-12T11:40:18Z

[GooAQ](https://github.com/allenai/gooaq) dataset was recently updated after splits were added for the same. This PR contains new updated GooAQ with train/val/test splits and updated README as well.

Comments:
@albertvillanova my tests are failing here:
```
dataset_name = 'gooaq'

    def test_load_dataset(self, dataset_name):
        configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]
>       self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True, use_local_dummy_data=True)

tests/test_dataset_common.py:234: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_dataset_common.py:187: in check_load_dataset
    self.parent.assertTrue(len(dataset[split]) > 0)
E   AssertionError: False is not true
```
When I try loading dataset on local machine it works fine. Any suggestions on how can I avoid this error?

Thanks for the help, @albertvillanova! All tests are passing now.
Title: How to sample every file in a list of files making up a split in a dataset when loading?
Author: brijow (NONE)
State: closed
Created: 2021-08-11T17:43:21Z

I am loading a dataset with multiple train, test, and validation files like this:

```
data_files_dict = {
    "train": [train_file1, train_file2],
    "test": [test_file1, test_file2],
    "val": [val_file1, val_file2]
}
dataset = datasets.load_dataset(
    "csv",
    data_files=data_files_dict,
    split=['train[:8]', 'test[:8]', 'val[:8]']
)

```

However, this only selects the first 8 rows from train_file1, test_file1, val_file1, since they are the first files in the lists.

I'm trying to formulate a split argument that can sample from each file specified in my list of files that make up each split.

Is this type of splitting supported? If so, how can I do it?

Comments:
Hi ! This is not possible just with `load_dataset`.

You can do something like this instead:
```python
seed=42
data_files_dict = {
    "train": [train_file1, train_file2],
    "test": [test_file1, test_file2],
    "val": [val_file1, val_file2]
}
dataset = datasets.load_dataset(
    "csv",
    data_files=data_files_dict,
).shuffle(seed=seed)

sample_dataset = {splitname: split.select(range(8)) for splitname, split in dataset.items()}
```

Another alternative is loading each file separately with `split="train[:8]"` and then use `concatenate_datasets` to merge the sample of each file.
Title: Fix renaming of corpus_bleu args
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-08-10T11:02:34Z

Last `sacrebleu` release (v2.0.0) has renamed `sacrebleu.corpus_bleu` args from `(sys_stream, ref_streams)` to `(hipotheses, references)`: https://github.com/mjpost/sacrebleu/pull/152/files#diff-2553a315bb1f7e68c9c1b00d56eaeb74f5205aeb3a189bc3e527b122c6078795L17-R15

This PR passes the args without parameter names, so that it is valid for all versions of `sacrebleu`.

This is a partial hotfix of #2781.

Close #2781.

Comments:
Title: Latest v2.0.0 release of sacrebleu has broken some metrics
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-08-10T09:59:41Z
Labels: bug

## Describe the bug
After `sacrebleu` v2.0.0 release (see changes here: https://github.com/mjpost/sacrebleu/pull/152/files#diff-2553a315bb1f7e68c9c1b00d56eaeb74f5205aeb3a189bc3e527b122c6078795L17-R15), some of `datasets` metrics are broken:
- Default tokenizer `sacrebleu.DEFAULT_TOKENIZER` no longer exists:
  - #2739
  - #2778
- Bleu tokenizers are no longer accessible with `sacrebleu.TOKENIZERS`:
  - #2779
- `corpus_bleu` args have been renamed from `(sys_stream, ref_streams)` to `(hipotheses, references)`: 
  - #2782

Comments:
Title: Fix sacrebleu tokenizers
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-08-10T09:24:27Z

Last `sacrebleu` release (v2.0.0) has removed `sacrebleu.TOKENIZERS`: https://github.com/mjpost/sacrebleu/pull/152/files#diff-2553a315bb1f7e68c9c1b00d56eaeb74f5205aeb3a189bc3e527b122c6078795L17-R15

This PR makes a hot fix of the bug by using a private function in `sacrebleu`: `sacrebleu.metrics.bleu._get_tokenizer()`.

Eventually, this should be further fixed in order to use only public functions.

This is a partial hotfix of #2781.

Comments:
Title: Do not pass tokenize to sacrebleu
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-08-10T08:40:37Z

Last `sacrebleu` release (v2.0.0) has removed `sacrebleu.DEFAULT_TOKENIZER`: https://github.com/mjpost/sacrebleu/pull/152/files#diff-2553a315bb1f7e68c9c1b00d56eaeb74f5205aeb3a189bc3e527b122c6078795L17-R15

This PR does not pass `tokenize` to `sacrebleu` (note that the user cannot pass it anyway) and `sacrebleu` will use its default, no matter where it is and how it is called.

Related to #2739.

This is a partial hotfix of #2781.

Comments:
Title: document `config.HF_DATASETS_OFFLINE` and precedence
Author: stas00 (CONTRIBUTOR)
State: open
Created: 2021-08-09T21:23:17Z
Labels: enhancement

https://github.com/huggingface/datasets/pull/1976 implemented `HF_DATASETS_OFFLINE`, but:
1. `config.HF_DATASETS_OFFLINE` is not documented
2. the precedence is not documented (env, config)

I'm thinking it probably should be similar to what it says https://huggingface.co/docs/datasets/loading_datasets.html#from-the-huggingface-hub about `datasets.config.IN_MEMORY_MAX_SIZE`:

Quote:
> The default in 🤗 Datasets is to memory-map the dataset on disk unless you set datasets.config.IN_MEMORY_MAX_SIZE different from 0 bytes (default). In that case, the dataset will be copied in-memory if its size is smaller than datasets.config.IN_MEMORY_MAX_SIZE bytes, and memory-mapped otherwise. This behavior can be enabled by setting either the configuration option datasets.config.IN_MEMORY_MAX_SIZE (higher precedence) or the environment variable HF_DATASETS_IN_MEMORY_MAX_SIZE (lower precedence) to nonzero.

Context: trying to use `config.HF_DATASETS_OFFLINE` here:
https://github.com/bigscience-workshop/Megatron-DeepSpeed/pull/48
but are uncertain if it's safe, since it's not documented as a public API.

Thank you!

@lhoestq, @albertvillanova

Comments:
Title: Remove dataset_infos.json
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-08-09T07:43:19Z
Labels: enhancement

generic discussion

**Is your feature request related to a problem? Please describe.**
As discussed, there are infos in the `dataset_infos.json` which are redundant and we could have them only in the README file.

Others could be migrated to the README, like: "dataset_size", "size_in_bytes", "download_size", "splits.split_name.[num_bytes, num_examples]",...

However, there are others that do not seem too meaningful in the README, like the checksums.

**Describe the solution you'd like**
Open a discussion to decide what to do with the `dataset_infos.json` files: which information to be migrated and/or which information to be kept.

cc: @julien-c @lhoestq

Comments:
This was closed by:
- #4926
Title: Remove returned feature constrain
Author: PosoSAgapo (NONE)
State: open
Created: 2021-08-08T04:01:30Z
Labels: enhancement

In the current version, the returned value of the map function has to be list or ndarray. However, this makes it unsuitable for many tasks. In NLP, many features are sparse like verb words, noun chunks, if we want to assign different values to different words, which will result in a large sparse matrix if we only score useful words like verb words.  

Mostly, when using it on large scale, saving it as a whole takes a lot of disk storage and making it hard to read,  the normal method is saving it in sparse form. However, the NumPy does not support sparse, therefore I have to use PyTorch or scipy to transform a matrix into special sparse form, which is not a form that can be transformed into list or ndarry. This violates the feature constraints of the map function. 

I do appreciate the convenience of Datasets package, but I do not think the compulsory datatype constrain is necessary, in some cases, we just cannot transform it into a list or ndarray due to some reasons.  Any way to fix this? Or what I can do to disable the compulsory datatype constrain?

Comments:
Title: Allow PyArrow from source
Author: patrickvonplaten (CONTRIBUTOR)
State: closed
Created: 2021-08-07T14:26:44Z

When installing pyarrow from source the version is:

```python
>>> import pyarrow; pyarrow.__version__
'2.1.0.dev612'
```

-> however this breaks the install check at init of `datasets`. This PR makes sure that everything coming after the last `'.'` is removed.

Comments:
Title: BERTScore Error
Author: gagan3012 (NONE)
State: closed
Created: 2021-08-06T15:58:57Z
Labels: bug

## Describe the bug
A clear and concise description of what the bug is.

## Steps to reproduce the bug
```python
predictions = ["hello there", "general kenobi"]
references = ["hello there", "general kenobi"]
bert = load_metric('bertscore')
bert.compute(predictions=predictions, references=references,lang='en')
```

# Bug
`TypeError: get_hash() missing 1 required positional argument: 'use_fast_tokenizer'`


## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Colab 
- Python version:
- PyArrow version:

Comments:
Hi,

The `use_fast_tokenizer` argument has been recently added to the bert-score lib. I've opened a PR with the fix. In the meantime, you can try to downgrade the version of bert-score with the following command to make the code work:
```
pip uninstall bert-score
pip install "bert-score<0.3.10"
```
Title: Add RVL-CDIP dataset
Author: NielsRogge (CONTRIBUTOR)
State: closed
Created: 2021-08-05T09:57:05Z
Labels: dataset request

vision

## Adding a Dataset
- **Name:** RVL-CDIP
- **Description:** The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images. The images are sized so their largest dimension does not exceed 1000 pixels.
- **Paper:** https://www.cs.cmu.edu/~aharley/icdar15/
- **Data:** https://www.cs.cmu.edu/~aharley/rvl-cdip/
- **Motivation:** I'm currently adding LayoutLMv2 and LayoutXLM to HuggingFace Transformers. LayoutLM (v1) already exists in the library. This dataset has a large value for document image classification (i.e. classifying scanned documents). LayoutLM models obtain SOTA on this dataset, so would be great to directly use it in notebooks.

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

Comments:
cc @nateraw 

#self-assign

[labels_only.tar.gz](https://docs.google.com/uc?authuser=0&id=0B0NKIRwUL9KYcXo3bV9LU0t3SGs&export=download) on the RVL-CDIP website does not work for me.

> 404. That’s an error. The requested URL was not found on this server.

I contacted the author ( Adam Harley) regarding this, and he told me that the link works for him. Not sure what the issue is. But Adam shared the file (labels_only.tar.gz) with me as an attachment.

Are we allowed to host this file(labels_only.tar.gz) elsewhere and use that link instead ?

Thank you.
Title: Add Nuswide dataset
Author: shivangibithel (NONE)
State: open
Created: 2021-08-05T03:00:41Z
Labels: dataset request

vision

## Adding a Dataset
- **Name:** *NUSWIDE*
- **Description:** *[A Real-World Web Image Dataset from National University of Singapore](https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html)*
- **Paper:** *[here](https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/nuswide-civr2009.pdf)*
- **Data:** *[here](https://github.com/wenting-zhao/nuswide)*
- **Motivation:** *This dataset is a benchmark in the Text Retrieval task.*

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

Comments:
Title: Raise ManualDownloadError when loading a dataset that requires previous manual download
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-08-04T10:19:55Z

This PR implements the raising of a `ManualDownloadError` when loading a dataset that requires previous manual download, and this is missing.

The `ManualDownloadError` is raised whether the dataset is loaded in normal or streaming mode.

Close #2749.

cc: @severo

Comments:
Title: Unexpected type after `concatenate_datasets`
Author: JulesBelveze (NONE)
State: closed
Created: 2021-08-04T07:10:39Z
Labels: bug

## Describe the bug
I am trying to concatenate two `Dataset` using `concatenate_datasets` but it turns out that after concatenation the features are casted from `torch.Tensor` to `list`. 
It then leads to a weird tensors when trying to convert it to a `DataLoader`. However, if I use each `Dataset` separately everything behave as expected.

## Steps to reproduce the bug
```python
>>> featurized_teacher
Dataset({
    features: ['t_labels', 't_input_ids', 't_token_type_ids', 't_attention_mask'],
    num_rows: 502
})
>>> for f in featurized_teacher.features:
     print(featurized_teacher[f].shape)
torch.Size([502])
torch.Size([502, 300])
torch.Size([502, 300])
torch.Size([502, 300])

>>> featurized_student
Dataset({
    features: ['s_features', 's_labels'],
    num_rows: 502
})
>>> for f in featurized_student.features:
     print(featurized_student[f].shape)
torch.Size([502, 64])
torch.Size([502])
```
The shapes seem alright to me. Then the results after concatenation are as follow:
```python
>>> concat_dataset = datasets.concatenate_datasets([featurized_student, featurized_teacher], axis=1)
>>> type(concat_dataset["t_labels"])
<class 'list'>
```
One would expect to obtain the same type as the one before concatenation.

Am I doing something wrong here? Any idea on how to fix this unexpected behavior?

## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-10.14.6-x86_64-i386-64bit
- Python version: 3.9.5
- PyArrow version: 3.0.0

Comments:
Hi @JulesBelveze, thanks for your question.

Note that 🤗  `datasets` internally store their data in Apache Arrow format.

However, when accessing dataset columns, by default they are returned as native Python objects (lists in this case).

If you would like their columns to be returned in a more suitable format for your use case (torch arrays), you can use the method `set_format()`:
```python
concat_dataset.set_format(type="torch")
```

You have detailed information in our docs:
- [Using a Dataset with PyTorch/Tensorflow](https://huggingface.co/docs/datasets/torch_tensorflow.html)
- [Dataset.set_format()](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.set_format)

Thanks @albertvillanova it indeed did the job 😃 
Thanks for your answer!
Title: Second concatenation of datasets produces errors
Author: Aktsvigun (NONE)
State: closed
Created: 2021-08-03T10:47:04Z
Labels: bug

Hi,

I am need to concatenate my dataset with others several times, and after I concatenate it for the second time, the features of features (e.g. tags names) are collapsed. This hinders, for instance, the usage of tokenize function with `data.map`.

```
from datasets import load_dataset, concatenate_datasets

data = load_dataset('trec')['train']
concatenated = concatenate_datasets([data, data])
concatenated_2 = concatenate_datasets([concatenated, concatenated])
print('True features of features:', concatenated.features)
print('\nProduced features of features:', concatenated_2.features)
```
outputs 

```
True features of features: {'label-coarse': ClassLabel(num_classes=6, names=['DESC', 'ENTY', 'ABBR', 'HUM', 'NUM', 'LOC'], names_file=None, id=None), 'label-fine': ClassLabel(num_classes=47, names=['manner', 'cremat', 'animal', 'exp', 'ind', 'gr', 'title', 'def', 'date', 'reason', 'event', 'state', 'desc', 'count', 'other', 'letter', 'religion', 'food', 'country', 'color', 'termeq', 'city', 'body', 'dismed', 'mount', 'money', 'product', 'period', 'substance', 'sport', 'plant', 'techmeth', 'volsize', 'instru', 'abb', 'speed', 'word', 'lang', 'perc', 'code', 'dist', 'temp', 'symbol', 'ord', 'veh', 'weight', 'currency'], names_file=None, id=None), 'text': Value(dtype='string', id=None)}

Produced features of features: {'label-coarse': Value(dtype='int64', id=None), 'label-fine': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)}
```

I am using `datasets` v.1.11.0

Comments:
@albertvillanova 

Hi @Aktsvigun, thanks for reporting.

I'm investigating this.

Hi @albertvillanova ,
any update on this? Can I probably help in some way?

Hi @Aktsvigun! We are planning to address this issue before our next release, in a couple of weeks at most. 😅 

In the meantime, if you would like to contribute, feel free to open a Pull Request. You are welcome. Here you can find more information: [How to contribute to Datasets?](CONTRIBUTING.md)

I can't reproduce the bug on master. I believe this issue was fixed by https://github.com/huggingface/datasets/pull/3551.
Title: Raise a proper exception when trying to stream a dataset that requires to manually download files
Author: severo (COLLABORATOR)
State: closed
Created: 2021-08-03T10:26:27Z
Labels: bug

## Describe the bug

At least for 'reclor', 'telugu_books', 'turkish_movie_sentiment', 'ubuntu_dialogs_corpus', 'wikihow', trying to `load_dataset` in streaming mode raises a `TypeError` without any detail about why it fails.

## Steps to reproduce the bug

```python
from datasets import load_dataset
dataset = load_dataset("reclor", streaming=True)
```

## Expected results

Ideally: raise a specific exception, something like `ManualDownloadError`.

Or at least give the reason in the message, as when we load in normal mode:

```python
from datasets import load_dataset
dataset = load_dataset("reclor")
```

```
AssertionError: The dataset reclor with config default requires manual data.
 Please follow the manual download instructions:   to use ReClor you need to download it manually. Please go to its homepage (http://whyu.me/reclor/) fill the google
  form and you will receive a download link and a password to extract it.Please extract all files in one folder and use the path folder in datasets.load_dataset('reclor', data_dir='path/to/folder/folder_name')
  .
 Manual data can be loaded with `datasets.load_dataset(reclor, data_dir='<path/to/manual/data>')
```

## Actual results

```
TypeError: expected str, bytes or os.PathLike object, not NoneType
```

## Environment info

- `datasets` version: 1.11.0
- Platform: macOS-11.5-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1

Comments:
Hi @severo, thanks for reporting.

As discussed, datasets requiring manual download should be:
- programmatically identifiable
- properly handled with more clear error message when trying to load them with streaming

In relation with programmatically identifiability, note that for datasets requiring manual download, their builder have a property `manual_download_instructions` which is not None:
```python
# Dataset requiring manual download:
builder.manual_download_instructions is not None
```

Thanks @albertvillanova