noamaanMulla-03/datasets-issues huggingface dataset:
ModuleNotFoundError: No module named 'datasets.tasks' while importing common voice datasets

Error traceback:
---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-8-a7b592d3bca0> in <module>()
      1 from datasets import load_dataset, load_metric
      2 
----> 3 common_voice_train = load_dataset("common_voice", "pa-IN", split="train+validation")
      4 common_voice_test = load_dataset("common_voice", "pa-IN", split="test")

9 frames
/root/.cache/huggingface/modules/datasets_modules/datasets/common_voice/078d412587e9efeb0ae2e574da99c31e18844c496008d53dc5c60f4159ed639b/common_voice.py in <module>()
     19 
     20 import datasets
---> 21 from datasets.tasks import AutomaticSpeechRecognition
     22 
     23 

ModuleNotFoundError: No module named 'datasets.tasks'

Labels: name: bug

Hi @profsatwinder.

It looks like you are using an old version of `datasets`. Please update it with `pip install -U datasets` and indicate if the problem persists.

@albertvillanova Thanks for the information. I updated it to 1.9.0 and the issue is resolved. Thanks again.
Crash when using multiprocessing (`num_proc` > 1) on `filter` and all samples are discarded

## Describe the bug
If `filter` is applied to a dataset using multiprocessing (`num_proc` > 1) and all sharded datasets are empty afterwards (due to all samples being discarded), the program crashes.

## Steps to reproduce the bug
```python
from datasets import Dataset
data = Dataset.from_dict({'id': [0,1]})
data.filter(lambda x: False, num_proc=2)
```

## Expected results
An empty table should be returned without crashing.

## Actual results
```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 185, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/home/user/venv/lib/python3.8/site-packages/datasets/fingerprint.py", line 397, in wrapper
    out = func(self, *args, **kwargs)
  File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2143, in filter
    return self.map(
  File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1738, in map
    result = concatenate_datasets(transformed_shards)
  File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3267, in concatenate_datasets
    table = concat_tables(tables_to_concat, axis=axis)
  File "/home/user/venv/lib/python3.8/site-packages/datasets/table.py", line 853, in concat_tables
    return ConcatenationTable.from_tables(tables, axis=axis)
  File "/home/user/venv/lib/python3.8/site-packages/datasets/table.py", line 713, in from_tables
    blocks = to_blocks(tables[0])
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: 1.9.0
- Platform: Linux-5.12.11-300.fc34.x86_64-x86_64-with-glibc2.2.5
- Python version: 3.8.10
- PyArrow version: 3.0.0


Labels: name: bug
Jsonlines export error

## Describe the bug
When exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default

## Steps to reproduce the bug
This what I'm running:

in python:

```
from datasets import load_dataset
ptb = load_dataset("ptb_text_only")
ptb["train"].to_json("ptb.jsonl")
```

then out of python:

```
head -10000 ptb.jsonl
```

## Expected results
Properly separated lines

## Actual results
The last line is a concatenation of two lines

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->

- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyArrow version: 4.0.1

Labels: name: bug

Thanks for reporting @TevenLeScao! I'm having a look...

(not sure what just happened on the assignations sorry)

For some reason this happens (both `datasets` version are on master) only on Python 3.6 and not Python 3.8.

@TevenLeScao we are using `pandas` to serialize the dataset to JSON Lines. So it must be due to pandas. Could you please check the pandas version causing the issue?

@TevenLeScao I have just checked it: this was a bug in `pandas` and it was fixed in version 1.2: https://github.com/pandas-dev/pandas/pull/36898

Thanks ! I'm creating a PR

Well I though it was me who has taken on this issue... 😅 

Sorry, I was also talking to teven offline so I already had the PR ready before noticing x)

I was also already working in my PR... Nevermind. Next time we should pay attention if there is somebody (self-)assigned to an issue and if he/she is still working on it before overtaking it... 😄 

The fix is available on `master` @TevenLeScao , thanks for reporting
Support remote data files

Add support for (streaming) remote data files:

```python
data_files = f"https://huggingface.co/datasets/{repo_id}/resolve/main/{relative_file_path}"
ds = load_dataset("json", split="train", data_files=data_files, streaming=True)
```

cc: @thomwolf 

Labels: name: enhancement

@lhoestq maybe we could also use (if available) the ETag of the remote file in `create_config_id`?

> @lhoestq maybe we could also use (if available) the ETag of the remote file in `create_config_id`?

Sure ! We can get the ETag with
```python
headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token)  # auth for private repos
etag = http_head(url, headers=headers).headers.get("ETag")
```

Since the computation of the `config_id` is done in the `DatasetBuilder.__init__`, then this means that we need to add a new parameter `use_auth_token` in `DatasetBuilder.__init__`

Does that sound good ? We can add this in a following PR
can't set verbosity for `metric.py`

## Describe the bug
```
[2021-07-10 20:13:11,528][datasets.utils.filelock][INFO] - Lock 139705371374976 acquired on /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow.lock
[2021-07-10 20:13:11,529][datasets.arrow_writer][INFO] - Done writing 32 examples in 6100 bytes /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow.
[2021-07-10 20:13:11,531][datasets.arrow_dataset][INFO] - Set __getitem__(key) output type to python objects for no columns  (when key is int or slice) and don't output other (un-formatted) columns.
[2021-07-10 20:13:11,543][/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric.py][INFO] - Removing /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow
```
As you can see, `datasets` logging come from different places. 
`filelock`, `arrow_writer` & `arrow_dataset` comes from `datasets.*` which are expected 
However, `metric.py` logging comes from `/conda/envs/myenv/lib/python3.8/site-packages/datasets/`

So when setting `datasets.utils.logging.set_verbosity_error()`,  it still logs the last message which is annoying during evaluation. 

I had to do 
```
logging.getLogger("/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric").setLevel(logging.ERROR)
``` 
to fully mute these messages

## Expected results
it shouldn't log these messages when setting `datasets.utils.logging.set_verbosity_error()`

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: tried both 1.8.0 & 1.9.0
- Platform: Ubuntu 18.04.5 LTS 
- Python version: 3.8.10
- PyArrow version: 3.0.0


Labels: name: bug

Thanks @thomas-happify for reporting and thanks @mariosasko for the fix.
Make any ClientError trigger retry in streaming mode (e.g. ClientOSError)

During the FLAX sprint some users have this error when streaming datasets:
```python
aiohttp.client_exceptions.ClientOSError: [Errno 104] Connection reset by peer
```
This error must trigger a retry instead of directly crashing

Therefore I extended the error type that triggers the retry to be the base aiohttp error type: `ClientError`
In particular both `ClientOSError` and `ServerDisconnectedError` inherit from `ClientError`.
Return Python float instead of numpy.float64 in sklearn metrics

This PR converts the return type of all `sklearn` metrics to be Python `float` instead of `numpy.float64`.

The reason behind this is that our Hub evaluation framework relies on converting benchmark-specific metrics to YAML ([example](https://huggingface.co/datasets/autonlp/autonlp-benchmark-raft-neelalex__raft-test-neelalex__raft-predictions-3/blob/main/README.md#L11)) and the `numpy.float64` format produces garbage like:

```python
import yaml
from datasets import load_metric

metric = load_metric("accuracy")
score = metric.compute(predictions=[0,1], references=[0,1])
print(yaml.dump(score["accuracy"])) # output below
# !!python/object/apply:numpy.core.multiarray.scalar
# - !!python/object/apply:numpy.dtype
#   args:
#   - f8
#   - false
#   - true
#   state: !!python/tuple
#   - 3
#   - <
#   - null
#   - null
#   - null
#   - -1
#   - -1
#   - 0
# - !!binary |
#   AAAAAAAA8D8=
```


I opened an issue on the `sklearn` repo to understand why `numpy.float64` is the default: https://github.com/scikit-learn/scikit-learn/discussions/20490

It could be surprising at first to use `tolist()` on numpy scalars but it works ^^

did the same for Pearsonr here: https://github.com/huggingface/datasets/pull/2614
Use ndarray.item instead of ndarray.tolist

This PR follows up on #2612 to use `numpy.ndarray.item` instead of `numpy.ndarray.tolist` as the latter is somewhat confusing to the developer (even though it works).

Judging from the `numpy` docs, `ndarray.item` is closer to what we want: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.item.html#numpy-ndarray-item

PS. Sorry for the duplicate work here. I should have read the numpy docs more carefully in #2612
Add speech processing tasks

This PR replaces the `automatic-speech-recognition` task category with a broader `speech-processing` category. 

The tasks associated with this category are derived from the [SUPERB benchmark](https://arxiv.org/abs/2105.01051), and ASR is included in this set.


Are there any `task_categories:automatic-speech-recognition` dataset for which we should update the tags ?

> Are there any `task_categories:automatic-speech-recognition` dataset for which we should update the tags ?

Yes there's a few - I'll fix them tomorrow :)
[Metrics] addition of wiki_split metrics

**Is your feature request related to a problem? Please describe.**
While training the model on sentence split the task in English we require to evaluate the trained model on `Exact Match`, `SARI` and `BLEU` score
like this 
![image](https://user-images.githubusercontent.com/26653468/124746876-ff5a3380-df3e-11eb-9a01-4b48db7a6694.png)
While training we require metrics which can give all the output

Currently, we don't have an exact match for text normalized data

**Describe the solution you'd like**
A custom metrics for wiki_split that can calculate these three values and provide it in the form of a single dictionary
For exact match, we can refer to [this](https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py) 

**Describe alternatives you've considered**
Two metrics are already present one more can be added for an exact match then we can run all three metrics in training script

#self-assign

Labels: name: enhancement

name: metric request

#take
add image-classification task template

Snippet below is the tl;dr, but you can try it out directly here:

[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/nateraw/005c025d41f0e48ae3d4ee61c0f20b70/image-classification-task-template-demo.ipynb)

```python
from datasets import load_dataset

ds = load_dataset('nateraw/image-folder', data_files='PetImages/')
# DatasetDict({
#     train: Dataset({
#         features: ['file', 'labels'],
#         num_rows: 23410
#     })
# })

ds = ds.prepare_for_task('image-classification')
# DatasetDict({
#     train: Dataset({
#         features: ['image_file_path', 'labels'],
#         num_rows: 23410
#     })
# })
```


Awesome!

Thanks for adding a new task template - great work @nateraw  🚀 !
Add web_split dataset for Paraphase and Rephrase benchmark

## Describe:
For getting simple sentences from complex sentence there are dataset and task like wiki_split that is available in hugging face datasets. This web_split is a very similar dataset. There some research paper which states that by combining these two datasets we if we train the model it will yield better results on both tests data.

This dataset is made from web NLG data.

All the dataset related details are provided in the below repository

Github link: https://github.com/shashiongithub/Split-and-Rephrase



Labels: name: enhancement

#take
Add ASR task for SUPERB

This PR starts building up the SUPERB benchmark by including the ASR task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/v0.2.0/downstream#asr-automatic-speech-recognition).

Usage:

```python
from datasets import load_dataset 

asr = load_dataset("superb", "asr")
# DatasetDict({
#     train: Dataset({
#         features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'],
#         num_rows: 28539
#     })
#     validation: Dataset({
#         features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'],
#         num_rows: 2703
#     })
#     test: Dataset({
#         features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'],
#         num_rows: 2620
#     })
# })
```

I've used the GLUE benchmark as a guide for filling out the README.

To move fast during the evaluation PoC I propose to merge one task at a time, so we can continue building the training / evaluation framework in parallel.

Note: codewise this PR is ready for review - I'll add the missing YAML tags once #2620 is merged :)


Wait until #2620 is merged before pushing the README tags in this PR

> Thanks!
> 
> One question: aren't you adding `task_templates` to the `_info` method (and to the `dataset_infos.json`?

great catch! i've now added the asr task template (along with a mapping from superb task -> template) and updated the `dataset_infos.json` :) 

> Good!
> 
> I have a suggested refactoring... Tell me what you think! :)

your approach is much more elegant - i've included your suggestions 🙏
load_dataset processing failed with OS error after downloading a dataset

## Describe the bug
After downloading a dataset like opus100, there is a bug that 
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS

## Steps to reproduce the bug
```python
from datasets import load_dataset
this_dataset = load_dataset('opus100', 'af-en')
```

## Expected results
there is no error when running load_dataset.

## Actual results
Specify the actual results or traceback.

Traceback (most recent call last):
  File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prep
    self._prepare_split(split_generator, **prepare_split_kwargs)
  File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 989, in _prepare_split
    example = self.info.features.encode_example(record)
  File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 952, in encode_example
    example = cast_to_python_objects(example)
  File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 219, in cast_to_python_ob
    return _cast_to_python_objects(obj)[0]
  File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 165, in _cast_to_python_o
    import torch
  File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 188, in <module>
    _load_global_deps()
  File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 141, in _load_global_deps
    ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
  File "/home/anaconda3/lib/python3.6/ctypes/__init__.py", line 348, in __init__
    self._handle = _dlopen(self._name, mode)
OSError: dlopen: cannot load any more object with static TLS

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "download_hub_opus100.py", line 9, in <module>
    this_dataset = load_dataset('opus100', language_pair)
  File "/home/anaconda3/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset
    use_auth_token=use_auth_token,
  File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepa
    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
  File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 658, in _download_and_prep
    + str(e)
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS


## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-3.13.0-32-generic-x86_64-with-debian-jessie-sid
- Python version: 3.6.6
- PyArrow version: 3.0.0




Labels: name: bug

Hi ! It looks like an issue with pytorch.

Could you try to run `import torch` and see if it raises an error ?

> Hi ! It looks like an issue with pytorch.
> 
> Could you try to run `import torch` and see if it raises an error ?

It works. Thank you!
`to_json` reporting enhancements

While using `to_json` 2 things came to mind that would have made the experience easier on the user:

1. Could we have a `desc` arg for the tqdm use and a fallback to just `to_json` so that it'd be clear to the user what's happening? Surely, one can just print the description before calling json, but I thought perhaps it'd help to have it self-identify like you did for other progress bars recently.

2. It took me a while to make sense of the reported numbers:
```
 22%|██▏       | 1536/7076 [12:30:57<44:09:42, 28.70s/it]
```
So iteration here happens to be 10K samples, and the total is 70M records. But the user does't know that, so the progress bar is perfect, but the numbers it reports are meaningless until one discovers that 1it=10K samples. And one still has to convert these in the head - so it's not quick. Not exactly sure what's the best way to approach this, perhaps it can be part of `desc`? or report M or K, so it'd be built-in if it were to print, e.g.:
```
 22%|██▏       | 15360K/70760K [12:30:57<44:09:42, 28.70s/it]
```
or 
```
 22%|██▏       | 15.36M/70.76M [12:30:57<44:09:42, 28.70s/it]
```
(while of course remaining friendly to small datasets)

I forget if tqdm lets you add a magnitude identifier to the running count.

Thank you!

Labels: name: enhancement
Can't pass `sep=None` to load_dataset("csv", ...) to infer the separator via pandas.read_csv

When doing `load_dataset("csv", sep=None)`, the `sep` passed to `pd.read_csv` is still the default `sep=","` instead, which makes it impossible to make the csv loader infer the separator.

Related to https://github.com/huggingface/datasets/pull/2656

cc @SBrandeis
Use prefix to allow exceed Windows MAX_PATH

By using this prefix, you can exceed the Windows MAX_PATH limit.

See: https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces

Related to #2524, #2220.


Does this mean the `FileNotFoundError` that avoids infinite loop can be removed?

Yes, I think so...

Or maybe we could leave it in case a relative path exceeds the MAX_PATH limit?

 > Or maybe we could leave it in case a relative path exceeds the MAX_PATH limit?

What about converting relative paths to absolute?

Nice ! Have you had a chance to test it on a windows machine with the max path limit enabled ? Afaik the CI doesn't have the path limit

Sure @lhoestq: I've tested on my machine... And this fixes most of the tests... 😅
Streaming for the Json loader

It was not using `open` in the builder. Therefore `pyarrow.json.read_json` was downloading the full file to start yielding rows.

Moreover, it appeared that `pyarrow.json.read_json` was not really suited for streaming as it was downloading too much data and failing if `block_size` was not properly configured (related to #2573).

So I switched to using `open` which is extended to support reading from remote file progressively, and I removed the pyarrow json reader which was not practical.
Instead, I'm using the classical `json.loads` from the standard library.


A note is that I think we should add a few indicator of status (as mentioned by @stas00 in #2649), probably at the (1) downloading, (2) extracting and (3) reading steps. In particular when loading many very large files it's interesting to know a bit where we are in the process.

I tested locally, and the builtin `json` loader is 4x slower than `pyarrow.json`. Thanks for the comment @albertvillanova !

Therefore I switched back to using `pyarrow.json`, but only on the batch that is read. This way we don't have to deal with its `block_size`, and it only loads in memory one batch at a time.
Mesinesp development and training data sets have been added.

https://zenodo.org/search?page=1&size=20&q=mesinesp, Mesinesp has Medical Semantic Indexed records in Spanish.  Indexing is done using DeCS codes, a sort of Spanish equivalent to MeSH terms.
The Mesinesp (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) development set has a total of 750 records.
The Mesinesp (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) training set has a total of 369,368 records. 




It'll be new pull request with new commits.
Delete extracted files to save disk space

As discussed with @stas00 and @lhoestq, allowing the deletion of extracted files would save a great amount of disk space to typical user.

Labels: name: enhancement

My suggestion for this would be to have this enabled by default.

Plus I don't know if there should be a dedicated issue to that is another functionality. But I propose layered building rather than all at once. That is:

1. uncompress a handful of files via a generator enough to generate one arrow file
2. process arrow file 1
3. delete all the files that went in and aren't needed anymore.

rinse and repeat.

1. This way much less disc space will be required - e.g. on JZ we won't be running into inode limitation, also it'd help with the collaborative hub training project
2. The user doesn't need to go and manually clean up all the huge files that were left after pre-processing
3. It would already include deleting temp files this issue is talking about

I wonder if the new streaming API would be of help, except here the streaming would be into arrow files as the destination, rather than dataloaders.
Use tqdm from tqdm_utils

This PR replaces `tqdm` from the `tqdm` lib with `tqdm` from `datasets.utils.tqdm_utils`. With this change, it's possible to disable progress bars just by calling `disable_progress_bar`. Note this doesn't work on Windows when using multiprocessing due to how global variables are shared between processes. Currently, there is no easy way to disable progress bars in a multiprocess setting on Windows (patching logging with `datasets.utils.logging.get_verbosity = lambda: datasets.utils.logging.NOTSET` doesn't seem to work as well), so adding support for this is a future goal. Additionally, this PR adds a unit ("ba" for batches) to the bar printed by `Dataset.to_json` (this change is motivated by https://github.com/huggingface/datasets/issues/2657).


The current CI failure is due to modifications in the dataset script.

Merging since the CI is only failing because of dataset card issues, which is unrelated to this PR