SurAyush/github_issues huggingface dataset:
Issue: add story_cloze
State: closed
Author: zaidalyafeai


Comments:
Thanks for pushing this dataset :)

According to the CI, the file `cloze_test_val__spring2016 - cloze_test_ALL_val.csv` is missing in the dummy data zip file (the zip files seem empty). Feel free to add this file with 4-5 lines and it should be good

And you can fix the YAML tags with
```yaml
pretty_name: Story Cloze Test
```
and filling the other tags task_categories and task_ids

If the dataset doesn exist on paperswithcode, you can just leave
```yaml
paperswithcode_id: null
```

@lhoestq can't fix the last test fails.

> Thanks @zaidalyafeai, the failing test is due to an issue in the master branch, that has already been fixed.
> 
> You can include the fix:
> 
> ```
> git checkout add_story_cloze
> git fetch upstream master
> git merge upstream/master
> ```

Thanks @albertvillanova, passed all the tests now. 

Thanks Albert, I fixed the suggested comments. This dataset has no train splits, it is only used for evaluation.
Issue: Add iter_archive
State: closed
Author: lhoestq

Added the `iter_archive` method for the StreamingDownloadManager.

It was already implemented in the regular DownloadManager.
Now it can be used to stream from TAR archives as mentioned in https://github.com/huggingface/datasets/issues/2829

I also updated the `food101` dataset as an example.
Any image/audio dataset using TAR archives can be updated to use `iter_archive` in order to be streamable :)

cc @severo 

Fix #2829.

Comments:
Issue: Make `interleave_datasets` more robust
State: open
Author: sbmaruf

**Is your feature request related to a problem? Please describe.**
Right now there are few hiccups using `interleave_datasets`. Interleaved dataset iterates until the smallest dataset completes it's iterator. In this way larger datasets may not complete full epoch of iteration. 
It creates new problems in calculation of epoch since there are no way to track which dataset from `interleave_datasets` completes how many epoch.

**Describe the solution you'd like**
For `interleave_datasets` module, 
- [ ] Add a boolean argument `--stop-iter` in `interleave_datasets` that enables dataset to either iterate infinite amount of time or not. That means it should not return `StopIterator` exception in case `--stop-iter=False`.
- [ ] Internal list variable `iter_cnt` that explains how many times (in steps/epochs) each dataset iterates at a given point.
- [ ] Add an argument `--max-iter` (list type) that explain maximum amount of time each of the dataset can iterate. After complete  `--max-iter` of one dataset, other dataset should continue sampling and when all the dataset finish their respective `--max-iter`, only then return `StopIterator`

Note: I'm new to `datasets` api. May be these features are already there in the datasets. 

Since multitask training is the latest trends, I believe this feature would make the `datasets` api more popular.

@lhoestq 

Comments:
Hi @lhoestq  Any response on this issue?

Hi ! Sorry for the late response

I agree `interleave_datasets` would benefit a lot from having more flexibility. If I understand correctly it would be nice to be able to define stopping strategies like `stop="first_exhausted"` (default) or `stop="all_exhausted"`. If you'd like to contribute this feature I'd be happy to give you some pointers :)

Also one can already set the max number of iterations per dataset by doing `dataset.take(n)` on the dataset that should only have `n` samples.

Regarding the `iter_cnt` counter, I think this requires a bit more thoughts, since we might have to be able to backpropagate the the counter if `map` or other transforms have been applied after `interleave_datasets`. 

@sbmaruf I just notice that (1)`interleave_datasets` only samples indices once and reuse for all epochs, and (2) it's limited by the smallest dataset. Do you figure out an alternative way to achieve the same purpose?
Issue: Windows CI is unable to test streaming properly because of SSL issues
State: closed
Author: lhoestq

In https://github.com/huggingface/datasets/pull/3041 the windows tests were skipped because of SSL issues with moon-staging.huggingface.co:443

The issue appears only on windows with asyncio. On Linux it works. With requests it works as well. And with the production environment huggingface.co it also works.

to reproduce on windows:
```python
import fsspec

# use any URL to a file in a dataset repo
url = "https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes"

fsspec.open(url).open()
```
raises
```python
FileNotFoundError: https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes
```
because of
```python
aiohttp.client_exceptions.ClientConnectorCertificateError: Cannot connect to host moon-staging.huggingface.co:443 ssl:True [SSLCertVerificationError: (1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1131)')]
```

Comments:
I think this problem is already fixed:
```python
In [4]: import fsspec
   ...:
   ...: url = "https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattributes"
   ...:
   ...: fsspec.open(url).open()
Out[4]: <File-like object HTTPFileSystem, https://moon-staging.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/my-dataset-16242824690709/resolve/main/.gitattribu
```

No I'm still having this issue on my windows, and so does the CI
Issue: Feature request : add leave=True to dataset.map to enable tqdm nested bars (and whilst we're at it couldn't we get a way to access directly tqdm underneath?) 
State: open
Author: BenoitDalFerro

**A clear and concise description of what you want to happen.**

It would be so nice to be able to nest HuggingFace `Datasets.map() ` progress bars in the grander scheme of things and whilst we're at it why not other functions.

**Describe alternatives you've considered**

By the way is there not a way to directly interact with underlying tqdm module ? **kwargs-ish?

**Additional context**

Furthering tqdm integration #2374 and huggingface/transformers#11797 solutioned by huggingface/transformers#12226 provided with tqdm description as `desc=`

@sgugger @bhavitvyamalik

Comments:
@lhoestq, @albertvillanova can we have `**tqdm_kwargs` in `map`? If there are any fields that are important to our tqdm (like iterable or unit), we can pop them before initialising the tqdm object so as to avoid duplicity.

Hi ! Sounds like a good idea :)

Also I think it would be better to have this as an actual parameters instead of kwargs to make it clearer
Issue: Fix task reloading from cache
State: closed
Author: lhoestq

When reloading a dataset from the cache when doing `map`, the tasks templates were kept instead of being updated regarding the output of the `map` function. This is an issue because we drop the tasks templates that are not compatible anymore after `map`, for example if a column of the template was removed.

This PR fixes this and for convenience introduces a decorator `@transmit_tasks` that takes care of doing this verification, similar to the `@transmit_format` decorator.

This should fix issue https://github.com/huggingface/datasets/issues/3047 cc @sgugger 

Comments:
Issue: Error in per class precision computation 
State: closed
Author: tidhamecha2

## Describe the bug
When trying to get the per class precision values by providing `average=None`, following error is thrown `ValueError: can only convert an array of size 1 to a Python scalar`

## Steps to reproduce the bug
```python
from datasets import load_dataset, load_metric
precision_metric = load_metric("precision")
predictions = [0, 2, 1, 0, 0, 1]
references = [0, 1, 2, 0, 1, 2]
results = precision_metric.compute(predictions=predictions, references=references, average=None)
```

## Expected results
`  {'precision': array([0.66666667, 0.        , 0.        ])}`
as per https://github.com/huggingface/datasets/blob/master/metrics/precision/precision.py

## Actual results
```
   output = self._compute(predictions=predictions, references=references, **kwargs)
  File "~/.cache/huggingface/modules/datasets_modules/metrics/precision/94709a71c6fe37171ef49d3466fec24dee9a79846c9f176dff66a649e9811690/precision.py", line 110, in _compute
    sample_weight=sample_weight,
ValueError: can only convert an array of size 1 to a Python scalar
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: linux
- Python version: 3.6.9
- PyArrow version: 5.0.0


Comments:
Hi @tidhamecha2, thanks for reporting.

Indeed, we fixed this issue just one week ago: #3008

The fix will be included in our next version release.

In the meantime, you can incorporate the fix by installing `datasets` from the master branch:
```
pip install -U git+ssh://git@github.com/huggingface/datasets.git@master#egg=datasest
```
or
```
pip install -U git+https://github.com/huggingface/datasets.git@master#egg=datasets
```
Issue: load_dataset('the_pile_openwebtext2') produces ArrowInvalid, value too large to fit in C integer type
State: closed
Author: davidbau

## Describe the bug
When loading `the_pile_openwebtext2`, we get the error `pyarrow.lib.ArrowInvalid: Value 2111 too large to fit in C integer type`

## Steps to reproduce the bug
```python
import datasets
ds = datasets.load_dataset('the_pile_openwebtext2')
```

## Expected results
Should download the dataset, convert it to an arrow file, and return a working Dataset object.

## Actual results
The download works, but conversion to the arrow file fails as follows:

```
>>> ds = datasets.load_dataset('the_pile_openwebtext2')
Downloading and preparing dataset openwebtext2/plain_text (download: 27.33 GiB, generated: 63.86 GiB
, post-processed: Unknown size, total: 91.19 GiB) to /home/davidbau/.cache/huggingface/datasets/open
webtext2/plain_text/1.0.0/c48ec73ba3483bac673463f48f67e9a4fd8cb49a9d6ec4fb957f0b424b97cf25...
Traceback (most recent call last):
  File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/builder.py", line 1133,
 in _prepare_split
    writer.write(example, key)
  File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line
366, in write
    self.write_examples_on_file()
  File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line
311, in write_examples_on_file
    pa_array = pa.array(typed_sequence)
  File "pyarrow/array.pxi", line 222, in pyarrow.lib.array
  File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
  File "/home/davidbau/.conda/envs/tenv/lib/python3.9/site-packages/datasets/arrow_writer.py", line
115, in __arrow_array__
    out = pa.array(cast_to_python_objects(self.data, only_1d_for_numpy=True), type=type)
  File "pyarrow/array.pxi", line 305, in pyarrow.lib.array
  File "pyarrow/array.pxi", line 39, in pyarrow.lib._sequence_to_array
  File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
  File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Value 2111 too large to fit in C integer type
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
```

- Platform: Ubuntu 20.04
- Python version: python 3.9
- PyArrow version: 3.0.0


Comments:
I encountered the same bug using different datasets.
any suggestions?

+1, can reproduce here!

I get the same error
Platform: Windows 10
Python: python 3.8.8
PyArrow: 5.0

I was getting a similar error `pyarrow.lib.ArrowInvalid: Integer value 528 not in range: -128 to 127` - AFAICT, this is because the type specified for `reddit_scores` is `datasets.Sequence(datasets.Value("int8"))`, but the actual values can be well outside the max range for 8-bit integers.

I worked around this by downloading the `the_pile_openwebtext2.py` and editing it to use local files and drop reddit scores as a column (not needed for my purposes).

Addressed in https://huggingface.co/datasets/the_pile_openwebtext2/discussions/4
Issue: load_dataset cannot download the data and hangs on forever if cache dir specified
State: closed
Author: BenoitDalFerro

## Describe the bug
After updating datasets, a code that ran just fine for ages began to fail. Specifying _datasets.load_dataset_'s _cache_dir_ optional argument on Windows 10 machine results in data download to hang on forever. Same call without cache_dir works just fine. Surprisingly exact same code just runs perfectly fine on Linux docker instance running in cloud.

Unfortunately I updated Windows also at the same time and I can't remember which version of datasets was running in my conda environment prior to the update otherwise I would have tried both to check this out.  :(

## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
cache_dir = 'c:/data/datasets'
dataset = load_dataset('wikipedia', '20200501.en', split='train',cache_dir=cache_dir) 
```
Note that exact same code without specifying _cache_dir_ argument works perfectly fine.
```
cache_dir = 'c:/data/datasets'
dataset = load_dataset('wikipedia', '20200501.en', split='train') 
```

## Expected results
Downloads the dataset and cache is handled in the _cache_dir_ directory
## Actual results
Data download keeps hanging on forever, **NO TRACEBACK**!

## Environment info
- `datasets` version: 1.12.1
- Platform: Windows-10-10.0.19042-SP0
- Python version: 3.8.11
- PyArrow version: 3.0.0


Comments:
Issue was environment inconsistency, updating packages did the trick

`conda install -c huggingface -c conda-forge datasets`

> Collecting package metadata (current_repodata.json): done
> Solving environment: |
> The environment is inconsistent, please check the package plan carefully
> The following packages are causing the inconsistency:
> 
>   - conda-forge/noarch::datasets==1.12.1=pyhd8ed1ab_1
>   - conda-forge/win-64::multiprocess==0.70.12.2=py38h294d835_0
> done
> 
> Package Plan
> 
>   environment location: C:\xxx\anaconda3\envs\UnBias-94-1
> 
>   added / updated specs:
>     - datasets
> 
> 
> The following NEW packages will be INSTALLED:
> 
>   dill               conda-forge/noarch::dill-0.3.4-pyhd8ed1ab_0
> 
> The following packages will be UPDATED:
> 
>   ca-certificates    pkgs/main::ca-certificates-2021.9.30-~ --> conda-forge::ca-certificates-2021.10.8-h5b45459_0
>   certifi            pkgs/main::certifi-2021.5.30-py38haa9~ --> conda-forge::certifi-2021.10.8-py38haa244fe_0
> 
> The following packages will be SUPERSEDED by a higher-priority channel:
>
Issue: Non-Matching Checksum Error with crd3 dataset
State: closed
Author: RylanSchaeffer

## Describe the bug
When I try loading the crd3 dataset (https://huggingface.co/datasets/crd3), an error is thrown.

## Steps to reproduce the bug
```python
dataset = load_dataset('crd3', split='train')
```

## Expected results
I expect no error to be thrown.


## Actual results
A non-matching checksum error is thrown.

```
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/RevanthRameshkumar/CRD3/archive/master.zip']
```

## Environment info

- `datasets` version: 1.12.1
- Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial
- Python version: 3.6.10
- PyArrow version: 5.0.0



Comments:
I got the same error for another dataset (`multi_woz_v22`):

```
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_001.json']
```

I'm seeing the same issue as @RylanSchaeffer:
Python 3.7.11, macOs 11.4
datasets==1.14.0

fails on:
```python
dataset = datasets.load_dataset("multi_woz_v22")
```
Issue: Identify which shard data belongs to
State: open
Author: borisdayma

**Is your feature request related to a problem? Please describe.**

I'm training on a large dataset made of multiple sub-datasets.
During training I can observe some jumps in loss which may correspond to different shards.

![image](https://user-images.githubusercontent.com/715491/136668758-521263aa-a9b2-4ad2-8d22-060b6bf86a1c.png)

My suspicion is that either:
* some of the sub-datasets are harder for the model than others
* some of the sub-datasets are not formatted properly

I'd like to identify which shards correspond to those jumps.

**Describe the solution you'd like**

It would be nice to have a key associated to each data sample or data batch containing details on where the data comes from (shard idx + item idx within the shard).
This should be supported both in local and streaming mode.

**Describe alternatives you've considered**
A fix would be for me to add myself details (shard id, sample id) as part of each data sample.
The inconvenient is that it requires users to process/reupload every dataset when they need this feature.

Comments:
Independently of this I think it raises the need to allow multiprocessing during streaming so that we get samples from multiple shards in one batch.
Issue: Add PASS dataset
State: closed
Author: osanseviero

## Adding a Dataset
- **Name:** PASS
- **Description:** An ImageNet replacement for self-supervised pretraining without humans
- **Data:** https://www.robots.ox.ac.uk/~vgg/research/pass/ https://github.com/yukimasano/PASS

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


Comments:
Issue: Improving elasticsearch integration
State: open
Author: ggdupont

- adding murmurhash signature to sample in index
- adding optional credentials for remote elasticsearch server
- enabling sample update in index
- upgrade the elasticsearch 7.10.1 python client
- adding ElasticsearchBulider to instantiate a dataset from an index and a filtering query

Comments:
@lhoestq @albertvillanova Iwas trying to fix the failing tests in circleCI but is there a test elasticsearch instance somewhere? If not, can I launch a docker container to have one?
Issue: Load private data files + use glob on ZIP archives for json/csv/etc. module inference
State: closed
Author: lhoestq

As mentioned in https://github.com/huggingface/datasets/issues/3032 loading data files from private repository isn't working correctly because of the data files resolved.

#2986 did a refactor of the data files resolver. I added authentication to it.

I also improved it to glob inside ZIP archives to look for json/csv/etc. files and infer which dataset builder (json/csv/etc.) to use.

Fix https://github.com/huggingface/datasets/issues/3032

Note that #2986 needs to get merged first

Comments:
I have an error on windows:
```python
aiohttp.client_exceptions.ClientConnectorCertificateError: Cannot connect to host moon-staging.huggingface.co:443 ssl:True [SSLCertVerificationError: (1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1131)')]
```
at the `fsspec` call in `xglob`:
```python
fs, *_ = fsspec.get_fs_token_paths(urlpath, storage_options=storage_options)
```

Looks like the windows CI has an SSL issue... 

I can reproduce it on my windows machine. On linux it works fine though

I'm just skipping the windows test for now

The Windows CI failure seems unrelated to this PR
```python
ERROR tests/test_arrow_dataset.py::test_dummy_dataset_serialize_s3
```
Issue: [save_to_disk] Using `select()` followed by `save_to_disk` saves complete dataset making it hard to create dummy dataset
State: closed
Author: patrickvonplaten

## Describe the bug

When only keeping a dummy size of a dataset (say the first 100 samples), and then saving it  to disk to upload it in the following to the hub for easy demo/use - not just the small dataset is saved but the whole dataset with an indices file. The problem with this is that the dataset is still very big.

## Steps to reproduce the bug

E.g. run the following:

```python
from datasets import load_dataset, save_to_disk

nlp = load_dataset("glue", "mnli", split="train")
nlp.save_to_disk("full")

nlp = nlp.select(range(100))
nlp.save_to_disk("dummy")
```

Now one can see that both `"dummy"` and `"full"` have the same size. This shouldn't be the case IMO.

## Expected results

IMO `"dummy"` should be much smaller so that one can easily play around with the dataset on the hub.

## Actual results
Specify the actual results or traceback.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->

- `datasets` version: 1.12.2.dev0
- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 5.0.0



Comments:
Hi,

the `save_to_disk` docstring explains that `flatten_indices` has to be called on a dataset before saving it to save only the shard/slice of the dataset.

That works! Thansk!

Might be worth doing that automatically actually in case the `save_to_disk` is called on a dataset that has an indices mapping :-)

I agree with @patrickvonplaten: this issue is reported recurrently, so better if we implement the `.flatten_indices()` automatically?

That would be great indeed - I don't really see a use case where one would not like to call `.flatten_indices()` before calling `save_to_disk`

+1 on this !
Issue: Protect master branch to force contributions via Pull Requests
State: closed
Author: albertvillanova

In order to have a clearer Git history in the master branch, I propose to protect it so that all contributions must be done through a Pull Request and no direct commits to master are allowed.
- The Pull Request allows to give context, discuss any potential issues and improve the quality of the contribution
- The Pull Request will eventually be squashed and merged into master with a single commit that links to the Pull Request page (with all the context/discussions)

Note that we already implemented a protection in the master branch to avoid *merge* commits and ensure a linear history. This proposal goes one step further by avoiding all kind of direct commits and forcing contributions **only** through Pull Requests.

Please note that we can temporarily deactivate this protection if we need to make a direct commit, e.g. at each new version release.

The only way GitHub allows this kind or protection is by requiring a minimal number (at least one) of approvals of the Pull Request. The inconvenient is that the PR creator cannot approve their own PR: another person must approve it before it can be merged into master. To circumvent this, we could eventually disable this protection in the master branch when an urgent commit is needed (e.g. for a hotfix) and there is no other person available at that time to approve the PR.


Comments:
It would be nice to protect the master from direct commits, but still having a way to merge our own PRs when no review is required (for example when updating a dataset_infos.json file, or minor bug fixes - things that happen quite often actually).
Do you know if there's a way ?

you can if you're an admin of the repo

This is done. Now the master branch is protected:
- [x] Require a pull request before merging: all commits must be made to a non-protected branch and submitted via a pull request
  - Required number of approvals before merging: 1 
- [x] Require linear history: prevent merge commits from being pushed
- [x] These requirements are not enforced for administrators
- [x] Additionally, the master branch is also protected against deletion and force pushes

CC: @lhoestq @julien-c @thomwolf