mengta666/huggingface_github huggingface dataset:
Issue: datasets.config.PYARROW_VERSION has no attribute 'major'

In the test_dataset_common.py script, line 288-289

```
if datasets.config.PYARROW_VERSION.major < 3:
   packaged_datasets = [pd for pd in packaged_datasets if pd["dataset_name"] != "parquet"]
```

which throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.

```
import datasets
datasets.config.PYARROW_VERSION.major
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/var/folders/1f/0wqmlgp90qjd5mpj53fnjq440000gn/T/ipykernel_73361/2547517336.py in <module>
      1 import datasets
----> 2 datasets.config.PYARROW_VERSION.major

AttributeError: 'str' object has no attribute 'major'
```

## Environment info
- `datasets` version: 1.11.0
- Platform: Darwin-20.6.0-x86_64-i386-64bit
- Python version: 3.7.11
- PyArrow version: 4.0.1


Comments:
I have changed line 288 to `if int(datasets.config.PYARROW_VERSION.split(".")[0]) < 3:` just to get around it.

Hi @bwang482,

I'm sorry but I'm not able to reproduce your bug.

Please note that in our current master branch, we made a commit (d03223d4d64b89e76b48b00602aba5aa2f817f1e) that simultaneously modified:
- test_dataset_common.py: https://github.com/huggingface/datasets/commit/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-a1bc225bd9a5bade373d1f140e24d09cbbdc97971c2f73bb627daaa803ada002L289 that introduces the usage of `datasets.config.PYARROW_VERSION.major`
- but also changed config.py: https://github.com/huggingface/datasets/commit/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-e021fcfc41811fb970fab889b8d245e68382bca8208e63eaafc9a396a336f8f2L40, so that `datasets.config.PYARROW_VERSION.major` exists


Sorted. Thanks!

Reopening this. Although the `test_dataset_common.py` script works fine now.

Has this got something to do with my pull request not passing `ci/circleci: run_dataset_script_tests_pyarrow` tests?

https://github.com/huggingface/datasets/pull/2873

Hi @bwang482,

If you click on `Details` (on the right of your non passing CI test names: `ci/circleci: run_dataset_script_tests_pyarrow`), you can have more information about the non-passing tests.

For example, for ["ci/circleci: run_dataset_script_tests_pyarrow_1" details](https://circleci.com/gh/huggingface/datasets/46324?utm_campaign=vcs-integration-link&utm_medium=referral&utm_source=github-build-link), you can see that the only non-passing test has to do with the dataset card (missing information in the `README.md` file): `test_changed_dataset_card`
```
=========================== short test summary info ============================
FAILED tests/test_dataset_cards.py::test_changed_dataset_card[swedish_medical_ner]
= 1 failed, 3214 passed, 2874 skipped, 2 xfailed, 1 xpassed, 15 warnings in 175.59s (0:02:55) =
```

Therefore, your PR non-passing test has nothing to do with this issue.
Issue: Cannot download TOTTO dataset

Error: Couldn't find file at https://storage.googleapis.com/totto/totto_data.zip

`datasets version: 1.11.0`
# How to reproduce:

```py
from datasets import load_dataset
dataset = load_dataset('totto')
```




Comments:
Hola @mrm8488, thanks for reporting.

Apparently, the data source host changed their URL one week ago: https://github.com/google-research-datasets/ToTTo/commit/cebeb430ec2a97747e704d16a9354f7d9073ff8f

I'm fixing it.
Issue: Loading allenai/c4 in streaming mode does too many HEAD requests

This does 60,000+ HEAD requests to get all the ETags of all the data files:
```python
from datasets import load_dataset
load_dataset("allenai/c4", streaming=True)
```
It makes loading the dataset completely impractical.

The ETags are used to compute the config id (it must depend on the data files being used).
Instead of using the ETags, we could simply use the commit hash of the dataset repository on the hub, as well and the glob pattern used to resolve the files (here it's `*` by default, to load all the files of the repository)

Comments:
https://github.com/huggingface/datasets/blob/6c766f9115d686182d76b1b937cb27e099c45d68/src/datasets/builder.py#L179-L186

Thanks a lot!!!
Issue: Negative timezone

## Describe the bug
The load_dataset method do not accept a parquet file with a negative timezone, as it has the following regex:
```
"^(s|ms|us|ns),\s*tz=([a-zA-Z0-9/_+:]*)$"
```
So a valid timestap ```timestamp[us, tz=-03:00]``` returns an error when loading parquet files.

## Steps to reproduce the bug
```python
# Where the timestamp column has a tz of -03:00
datasets = load_dataset('parquet', data_files={'train': train_files, 'validation': validation_files,
                                        'test': test_files}, cache_dir="./cache_teste/")
```

## Expected results
The -03:00 is a valid tz so the regex should accept this without raising an error.

## Actual results
As this regex disaproves a valid tz it raises the following error:
```python
raise ValueError(
                f"{datasets_dtype} is not a validly formatted string representation of a pyarrow timestamp."
                f"Examples include timestamp[us] or timestamp[us, tz=America/New_York]"
                f"See: https://arrow.apache.org/docs/python/generated/pyarrow.timestamp.html#pyarrow.timestamp"
            )
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Ubuntu 20.04
- Python version: 3.8
- PyArrow version: 5.0.0


Comments:
Fixed by #2847.
Issue: always requiring the username in the dataset name when there is one

Me and now another person have been bitten by the `datasets`'s non-strictness on requiring a dataset creator's username when it's due.

So both of us started with `stas/openwebtext-10k`, somewhere along the lines lost `stas/` and continued using `openwebtext-10k` and it all was good until we published the software and things broke, since there is no `openwebtext-10k`

So this feature request is asking to tighten the checking and not allow dataset loading if it was downloaded with the user prefix, but then attempted to be used w/o it.

The same in code:

```
# first run
python -c "from datasets import load_dataset; load_dataset('stas/openwebtext-10k')"
# now run immediately
python -c "from datasets import load_dataset; load_dataset('openwebtext-10k')"
# the second command should fail, but it doesn't fail now.
```

Please let me know if I explained myself clearly.

Thank you!

Comments:
From what I can understand, you want the saved arrow file directory to have username as well instead of just dataset name if it was downloaded with the user prefix?

I don't think the user cares of how this is done, but the 2nd command should fail, IMHO, as its dataset name is invalid:
```
# first run
python -c "from datasets import load_dataset; load_dataset('stas/openwebtext-10k')"
# now run immediately
python -c "from datasets import load_dataset; load_dataset('openwebtext-10k')"
# the second command should fail, but it doesn't fail now.
```

Moreover, if someone were to create `openwebtext-10k` w/o the prefix, they will now get the wrong dataset, if they previously downloaded `stas/openwebtext-10k`.

And if there are 2 users with the same dataset name `foo/ds` and `bar/ds` - currently this won't work to get the correct dataset.

So really there 3 unrelated issues hiding in the current behavior.

This has been fixed now, and we'll do a new release of the library today.

Now the stas/openwebtext-10k dataset is cached at `.cache/huggingface/datasets/stas___openwebtext10k` and openwebtext-10k would be at `.cache/huggingface/datasets/openwebtext10k`. Since they are different, the cache won't fall back on loading the wrong one anymore.

Same for the python script used to generate the dataset: stas/openwebtext-10k is cached at `.cache/huggingface/modules/datasets_modules/datasets/stas___openwebtext10k` and openwebtext-10k would be at `.cache/huggingface/modules/datasets_modules/datasets/openwebtext10k`

Amazing! Thank you for adding this improvement, @lhoestq!

(can be closed?)

Yes indeed :) thanks
Issue: Adding GLUECoS Hinglish and Spanglish code-switching bemchmark

## Adding a Dataset
- **Name:** GLUECoS
- **Description:** a Microsoft Benchmark to evaluate code-switching for only two language pairs but a variety of tasks
- **Paper:** https://aclanthology.org/2020.acl-main.329/
- **Data:** https://github.com/microsoft/GLUECoS
- **Motivation:** We currently only have [one other](https://huggingface.co/datasets/lince) dataset for code-switching

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


Comments:
Hi @yjernite I am interested in adding this dataset. 
In the repo they have also added a code mixed MT task from English to Hinglish [here](https://github.com/microsoft/GLUECoS#code-mixed-machine-translation-task). I think this could be a good dataset addition in itself and then I can add the rest of the GLUECoS tasks as one dataset. What do you think?
Issue: OpenWebText: NonMatchingSplitsSizesError

## Describe the bug

When downloading `openwebtext`, I'm getting:
```
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]
```

I suspect that the file we download from has changed since the size doesn't look like to match with documentation

`Downloading:   0%|          | 0.00/12.9G [00:00<?, ?B/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.

## Steps to reproduce the bug
```python
from datasets import load_dataset

load_dataset("openwebtext", download_mode="force_redownload")
```

## Expected results

Loading is successful

## Actual results

Loading throws above error.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.2
- Platform: linux (Redhat version 8.1)
- Python version: 3.8
- PyArrow version: 4.0.1


Comments:
Thanks for reporting, I'm updating the verifications metadata

I just regenerated the verifications metadata and noticed that nothing changed: the data file is fine (the checksum didn't change), and the number of examples is still 8013769. Not sure how you managed to get 7982430 examples.

Can you try to delete your cache ( by default at `~/.cache/huggingface/datasets`) and try again please ?
Also, on which platform are you (linux/macos/windows) ?

I'll try without deleting the whole cache (we have large datasets already stored). I was under the impression that `download_mode="force_redownload"` would bypass cache.
Sorry plateform should be linux (Redhat version 8.1)

Hi @thomasw21 , are you still having this issue after clearing your cache ?

Sorry I haven't had time to work on this. I'll close and re-open if I can't figure out why I'm having this issue. Thanks for taking a look !
Issue: prepare_module issue when loading from read-only fs

## Describe the bug

When we use prepare_module from a readonly file system, we create a FileLock using the `local_path`.
This path is not necessarily writable.

`lock_path = local_path + ".lock"`


## Steps to reproduce the bug

Run `load_dataset` on a readonly python loader file.
```python
ds = load_dataset(
        python_loader, data_files={"train": train_path, "test": test_path}
    )
```

where `python_loader` is a path to a file located in a readonly folder.

## Expected results
This should work I think?

## Actual results

```python
    return load_dataset(
  File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 711, in load_dataset
    module_path, hash, resolved_file_path = prepare_module(
  File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 465, in prepare_module
    with FileLock(lock_path):
  File "/usr/local/lib/python3.8/dist-packages/datasets/utils/filelock.py", line 314, in __enter__
    self.acquire()
  File "/usr/local/lib/python3.8/dist-packages/datasets/utils/filelock.py", line 263, in acquire
    self._acquire()
  File "/usr/local/lib/python3.8/dist-packages/datasets/utils/filelock.py", line 378, in _acquire
    fd = os.open(self._lock_file, open_mode)
OSError: [Errno 30] Read-only file system: 'YOUR_FILE.py.lock'
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.7.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 3.0.0


Comments:
Hello, I opened #2887 to fix this.
Issue: Logging levels not taken into account

## Describe the bug

The `logging` module isn't working as intended relative to the levels to set.

## Steps to reproduce the bug

```python
from datasets import logging

logging.set_verbosity_debug()
logger = logging.get_logger()

logger.error("ERROR")
logger.warning("WARNING")
logger.info("INFO")
logger.debug("DEBUG"
```

## Expected results

I expect all logs to be output since I'm putting a `debug` level.

## Actual results

Only the two first logs are output.

## Environment info

- `datasets` version: 1.11.0
- Platform: Linux-5.13.9-arch1-1-x86_64-with-glibc2.33
- Python version: 3.9.6
- PyArrow version: 5.0.0

## To go further

This logging issue appears in `datasets` but not in `transformers`. It happens because there is no handler defined for the logger. When no handler is defined, the `logging` library will output a one-off error to stderr, using a `StderrHandler` with level `WARNING`.

`transformers` sets a default `StreamHandler` [here](https://github.com/huggingface/transformers/blob/5c6eca71a983bae2589eed01e5c04fcf88ba5690/src/transformers/utils/logging.py#L86)

Comments:
I just take a look at all the outputs produced by `datasets` using the different log-levels.
As far as i can tell using `datasets==1.17.0` they overall issue seems to be fixed.

However, I noticed that there is one tqdm based progress indicator appearing on STDERR that I can simply not suppress.
```
Resolving data files: 100%|██████████| 652/652 [00:00<00:00, 1604.52it/s]
```

According to _get_origin_metadata_locally_or_by_urls it shold be supressable by using the `NOTSET` log-level
https://github.com/huggingface/datasets/blob/1406a04c3e911cec2680d8bc513653e0cafcaaa4/src/datasets/data_files.py#L491-L501
Sadly when specifiing the log-level `NOTSET` it seems to has no effect.

But appart from it not having any effect I must admit that it seems unintuitive to me.
I would suggest changing this such that it is only shown when the log-level is greater or equal to INFO.

This would conform better to INFO according to the [documentation](https://huggingface.co/docs/datasets/v1.0.0/package_reference/logging_methods.html#datasets.logging.set_verbosity_info).
> This will display most of the logging information and tqdm bars.

Any inputs on this?
I will be happy to supply a PR if desired 👍 

Hi! This should disable the tqdm output:
```python
import datasets
datasets.set_progress_bar_enabled(False)
```

On a side note: I believe the issue with logging (not tqdm) is still relevant on master.
Issue: ArrowInvalid when mapping dataset with missing values

## 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)
```
Issue: Optimize streaming from TAR archives

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
Issue: HF_DATASETS_CACHE variable in Windows

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.
Issue: Cannot load linnaeus dataset

## 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 :)
Issue: cannot load data from my loacal path

## 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)` ?
Issue: Add Mostly Basic Python Problems Dataset

## 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.
Issue: Enable streaming for Wikipedia corpora

**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.
Issue: How to sample every file in a list of files making up a split in a dataset when loading?

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.
Issue: Remove dataset_infos.json

**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
Issue: BERTScore Error

## 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"
```
Issue: Add RVL-CDIP dataset

## 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.
Issue: Unexpected type after `concatenate_datasets`

## 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!
Issue: Second concatenation of datasets produces errors

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.
Issue: Raise a proper exception when trying to stream a dataset that requires to manually download files

## 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
Issue: Dataset JSON is incorrect

## Describe the bug

The JSON file generated for https://github.com/huggingface/datasets/blob/573f3d35081cee239d1b962878206e9abe6cde91/datasets/journalists_questions/journalists_questions.py is https://github.com/huggingface/datasets/blob/573f3d35081cee239d1b962878206e9abe6cde91/datasets/journalists_questions/dataset_infos.json.

The only config should be `plain_text`, but the first key in the JSON is `journalists_questions` (the dataset id) instead.

```json
{
  "journalists_questions": {
    "description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
    ...
```

## Steps to reproduce the bug

Look at the files.

## Expected results

The first key should be `plain_text`:

```json
{
  "plain_text": {
    "description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
    ...
```

## Actual results

```json
{
  "journalists_questions": {
    "description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
    ...
```



Comments:
As discussed, the metadata JSON files must be regenerated because the keys were nor properly generated and they will not be read by the builder:
> Indeed there is some problem/bug while reading the datasets_info.json file: there is a mismatch with the config.name keys in the file...
In the meanwhile, in order to be able to use the datasets_info.json file content, you can create the builder without passing the name :
```
In [25]: builder = datasets.load_dataset_builder("journalists_questions")
In [26]: builder.info.splits
Out[26]: {'train': SplitInfo(name='train', num_bytes=342296, num_examples=10077, dataset_name='journalists_questions')}
```

After regenerating the metadata JSON file for this dataset, I get the right key:
```
{"plain_text": {"description": "The journalists_questions corpus (
```

Thanks!
Issue: Improve detection of streamable file types

**Is your feature request related to a problem? Please describe.**

```python
from datasets import load_dataset_builder
from datasets.utils.streaming_download_manager import StreamingDownloadManager
builder = load_dataset_builder("journalists_questions", name="plain_text")
builder._split_generators(StreamingDownloadManager(base_path=builder.base_path))
```

raises

```
NotImplementedError: Extraction protocol for file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet
```

But the file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is a text file and it can be streamed:

```bash
curl --header "Range: bytes=0-100" -L https://drive.google.com/uc\?export\=download\&id\=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U
506938088174940160      yes     1
302221719412830209      yes     1
289761704907268096      yes     1
513820885032378369      yes     %
```

Yet, it's wrongly categorized as a file type that cannot be streamed because the test is currently based on 1. the presence of a file extension at the end of the URL (here: no extension), and 2. the inclusion of this extension in a list of supported formats.

**Describe the solution you'd like**

In the case of an URL (instead of a local path), ask for the MIME type, and decide on that value? Note that it would not work in that case, because the value of `content_type` is `text/html; charset=UTF-8`.

**Describe alternatives you've considered**

Add a variable in the dataset script to set the data format by hand.


Comments:
maybe we should rather attempt to download a `Range` from the server and see if it works?
Issue: SacreBLEU update

With the latest release of [sacrebleu](https://github.com/mjpost/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.

    AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'

this happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`

## Steps to reproduce the bug
```python
sacrebleu= datasets.load_metric('sacrebleu')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
results = sacrebleu.compute(predictions=predictions, references=references)
print(results)
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: Python 3.8.0
- PyArrow version: 5.0.0


Comments:
Hi @devrimcavusoglu, 
I tried your code with latest version of `datasets`and `sacrebleu==1.5.1` and it's running fine after changing one small thing:
```
sacrebleu = datasets.load_metric('sacrebleu')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = [["It is a guide to action that ensures that the military will forever heed Party commands"]]  # double brackets here should do the work
results = sacrebleu.compute(predictions=predictions, references=references)
print(results)
output: {'score': 41.180376356915765, 'counts': [11, 8, 6, 4], 'totals': [18, 17, 16, 15], 'precisions': [61.111111111111114, 47.05882352941177, 37.5, 26.666666666666668], 'bp': 1.0, 'sys_len': 18, 'ref_len': 16}
```

@bhavitvyamalik hmm. I forgot double brackets, but still didn't work when used it with double brackets. It may be an isseu with platform (using win-10 currently), or versions. What is your platform and your version info for datasets, python, and sacrebleu ?

You can check that here, I've reproduced your code in [Google colab](https://colab.research.google.com/drive/1X90fHRgMLKczOVgVk7NDEw_ciZFDjaCM?usp=sharing). Looks like there was some issue in `sacrebleu` which was fixed later from what I've found [here](https://github.com/pytorch/fairseq/issues/2049#issuecomment-622367967). Upgrading `sacrebleu` to latest version should work.

It seems that next release of `sacrebleu` (v2.0.0) will break our `datasets` implementation to compute it. See my Google Colab: https://colab.research.google.com/drive/1SKmvvjQi6k_3OHsX5NPkZdiaJIfXyv9X?usp=sharing

I'm reopening this Issue and making a Pull Request to fix it.

> It seems that next release of `sacrebleu` (v2.0.0) will break our `datasets` implementation to compute it. See my Google Colab: https://colab.research.google.com/drive/1SKmvvjQi6k_3OHsX5NPkZdiaJIfXyv9X?usp=sharing
> 
> I'm reopening this Issue and making a Pull Request to fix it.

How did you solve him
Issue: Add Microsoft Building Footprints dataset

## Adding a Dataset
- **Name:** Microsoft Building Footprints
- **Description:** With the goal to increase the coverage of building footprint data available as open data for OpenStreetMap and humanitarian efforts, we have released millions of building footprints as open data available to download free of charge.
- **Paper:** *link to the dataset paper if available*
- **Data:** https://www.microsoft.com/en-us/maps/building-footprints
- **Motivation:** this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc.

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

Reported by: @sashavor

Comments:
Motivation: this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc. I'll see if I can figure out how to add it!
Issue: Update CommonVoice with new release

## Adding a Dataset
- **Name:** CommonVoice mid-2021 release
- **Description:** more data in CommonVoice: Languages that have increased the most by percentage are Thai (almost 20x growth, from 12 hours to 250 hours), Luganda (almost 9x growth, from 8 to 80), Esperanto (7x growth, from 100 to 840), and Tamil (almost 8x, from 24 to 220).
- **Paper:** https://discourse.mozilla.org/t/common-voice-2021-mid-year-dataset-release/83812
- **Data:** https://commonvoice.mozilla.org/en/datasets
- **Motivation:** More data and more varied. I think we just need to add configs in the existing dataset script.

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


Comments:
cc @patrickvonplaten?

Does anybody know if there is a bundled link, which would allow direct data download instead of manual? 
Something similar to: `https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/ab.tar.gz` ? cc @patil-suraj 


Also see: https://github.com/common-voice/common-voice-bundler/issues/15
Issue: Error in loading the Arabic Billion Words Corpus

## Describe the bug
I get `IndexError: list index out of range` when trying to load the `Techreen` and `Almustaqbal` configs of the dataset.

## Steps to reproduce the bug
```python
load_dataset("arabic_billion_words", "Techreen")
load_dataset("arabic_billion_words", "Almustaqbal")
```

## Expected results
The datasets load succefully.

## Actual results
```python
_extract_tags(self, sample, tag)
    139             if len(out) > 0:
    140                 break
--> 141         return out[0]
    142 
    143     def _clean_text(self, text):

IndexError: list index out of range
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.2
- Platform: Ubuntu 18.04.5 LTS
- Python version: 3.7.11
- PyArrow version: 3.0.0


Comments:
I modified the dataset loading script to catch the `IndexError` and inspect the records at which the error is happening, and I found this:
For the `Techreen` config, the error happens in 36 records when trying to find the `Text` or `Dateline` tags. All these 36 records look something like:
```
<Techreen>
 <ID>TRN_ARB_0248167</ID>
 <URL>http://tishreen.news.sy/tishreen/public/read/248240</URL>
 <Headline>Removed, because the original articles was in English</Headline>
</Techreen>
```

and all the 288 faulty records in the `Almustaqbal` config look like:
```
<Almustaqbal>
 <ID>MTL_ARB_0028398</ID>
 
 <URL>http://www.almustaqbal.com/v4/article.aspx?type=NP&ArticleID=179015</URL>
 <Headline> Removed because it is not available in the original site</Headline>
</Almustaqbal>
```

so the error is happening because the articles were removed and so the associated records lack the `Text` tag.

In this case, I think we just need to catch the `IndexError` and ignore (pass) it.


Thanks @M-Salti for reporting this issue and for your investigation.

Indeed, those `IndexError` should be catched and the corresponding record should be ignored.

I'm opening a Pull Request to fix it.