josteece/github-issues huggingface dataset:
Fix load_from_disk temporary directory
Opened by lhoestq (closed)

`load_from_disk` uses `tempfile.TemporaryDirectory()` instead of our `get_temporary_cache_files_directory()` function. This can cause the temporary directory to be deleted before the dataset object is garbage collected.

In practice, it prevents anyone from using methods like `shuffle` on a dataset loaded this way, because it can't write the shuffled indices in a directory that doesn't exist anymore.

In this PR I switch to using `get_temporary_cache_files_directory()` and I update the tests.

cc @mariosasko since you worked on `get_temporary_cache_files_directory()`

Comments:
Remove redundant isort module placement
Opened by mariosasko (closed)

`isort` can place modules by itself from [version 5.0.0](https://pycqa.github.io/isort/docs/upgrade_guides/5.0.0.html#module-placement-changes-known_third_party-known_first_party-default_section-etc) onwards, making the `known_first_party` and `known_third_party` fields in `setup.cfg` redundant (this is why our CI works, even though we haven't touched these options in a while).

Comments:
Adding ANERcorp-CAMeLLab dataset
Opened by vitalyshalumov (open)


Comments:
Adding ANERcorp dataset

## Adding a Dataset
- **Name:** *ANERcorp-CAMeLLab*
- **Description:** *Since its creation in 2008, the ANERcorp dataset (Benajiba & Rosso, 2008) has been a standard reference used by Arabic named entity recognition researchers around the world. However, over time, this dataset was copied over from user to user, modified slightly here and there, and split in many different configurations that made it hard to compare fairly across papers and systems.

In 2020, a group of researchers from CAMeL Lab (Habash, Alhafni and Oudah), and Mind Lab (Antoun and Baly) met with the creator of the corpus, Yassine Benajiba, to consult with him and collectively agree on an exact split, and accepted minor corrections from the original dataset. Bashar Alhafni from CAMeL Lab working with Nizar Habash implemented the decisions provided in this release.*

- **Paper:** *(a) Benajiba, Yassine, Paolo Rosso, and José Miguel Benedí Ruiz. "Anersys: An Arabic named entity recognition system based on maximum entropy." In International Conference on Intelligent Text Processing and Computational Linguistics, pp. 143-153. Springer, Berlin, Heidelberg, 2007.

(b)Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, and Nizar Habash. "CAMeL Tools: An Open Source Python Toolkit, for Arabic Natural Language Processing." In Proceedings of the Conference on Language Resources and Evaluation (LREC 2020), Marseille, 2020.*
- **Data:** *https://camel.abudhabi.nyu.edu/anercorp/*
- **Motivation:** This is the standard dataset for evaluating NER performance in Arabic*

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Couldn't reach data file for disaster_response_messages
Opened by pandya6988 (closed)

## Describe the bug
Following command gives an ConnectionError.

## Steps to reproduce the bug
```python
disaster = load_dataset('disaster_response_messages')
```

## Error
```
ConnectionError: Couldn't reach https://datasets.appen.com/appen_datasets/disaster_response_data/disaster_response_messages_training.csv
```
## Expected results
It should load dataset without an error

## 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:
- Platform: Google Colab
- Python version: 3.7
- PyArrow version: 


Comments:
It looks like the dataset isn't available anymore on appen.com

The CSV files appear to still be available at https://www.kaggle.com/landlord/multilingual-disaster-response-messages though. It says that the data are under the CC0 license so I guess we can host the dataset elsewhere instead ?
Reuters21578 Couldn't reach 
Opened by TingNLP (closed)

``## Adding a Dataset
- **Name:** *Reuters21578*
- **Description:** *ConnectionError: Couldn't reach https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz*
- **Data:** *https://huggingface.co/datasets/reuters21578*

`from datasets import load_dataset`
`dataset = load_dataset("reuters21578", 'ModLewis')`

ConnectionError: Couldn't reach https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz

And I try to request the link as follow:
`import requests`
`requests.head('https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz')`

SSLError: HTTPSConnectionPool(host='kdd.ics.uci.edu', port=443): Max retries exceeded with url: /databases/reuters21578/reuters21578.tar.gz (Caused by SSLError(SSLError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:852)'),))

This problem likes #575
What should I do ?


Comments:
Hi ! The URL works fine on my side today, could you try again ?

thank you @lhoestq 
it works
wikitext description wrong
Opened by hongyuanmei (closed)

## Describe the bug
Descriptions of the wikitext datasests are wrong. 

## Steps to reproduce the bug
Please see: https://github.com/huggingface/datasets/blob/f6dcafce996f39b6a4bbe3a9833287346f4a4b68/datasets/wikitext/wikitext.py#L50

## Expected results
The descriptions for raw-v1 and v1 should be switched. 

Comments:
Hi @hongyuanmei, thanks for reporting.

I'm fixing it.

Duplicate of:
- #795
Addd options to use updated bleurt checkpoints
Opened by jaehlee (closed)

Adds options to use newer recommended checkpoint (as of 2021/10/8) bleurt-20 and its distilled versions. 

Updated checkpoints are described in https://github.com/google-research/bleurt/blob/master/checkpoints.md#the-recommended-checkpoint-bleurt-20

This change won't affect the default behavior of metrics/bleurt. It only adds option to load newer checkpoints as

`datasets.load_metric('bleurt', 'bleurt-20')`

`bluert-20` generates scores roughly between 0 and 1, which wasn't the case for the previous checkpoints. 

Comments:
Avoid PyArrow type optimization if it fails
Opened by mariosasko (closed)

Adds a new variable, `DISABLE_PYARROW_TYPES_OPTIMIZATION`, to `config.py` for easier control of the Arrow type optimization.

Fix #2206  

Comments:
That's good to have a way to disable this easily :)
I just find it a bit unfortunate that users would have to experience the error once and then do `DISABLE_PYARROW_TYPES_OPTIMIZATION=1`. Do you know if there's a way to simply fallback on disabling it automatically when it fails ?

@lhoestq Actually, I agree a fallback makes more sense. The current approach is not very practical indeed and would require a mention in the docs.


Replaced the env variable with a fallback!

Hmm if the fallback automatically happens without the user knowing it, then I don't think we really need to mention it. But if you really wanted to, I think the [Improve performance](https://huggingface.co/docs/datasets/cache.html#improve-performance) section would be a great place for it! 

Yea I think this could just end up in a note that says that `datasets` automatically picks the most optimized integer precision for your tokenized text data to save you disk space. Maybe later if we have a page on text processing we can add this note, but for now I agree it doesn't fit well into the doc.

In particular in the "Improve performance" section we mention what users can do to speed up their computations, while this behavior is just some internal feature that users don't have control over anyway.
The Xsum datasets seems not able to download.
Opened by FYYFU (closed)

## Describe the bug
The download Link of the Xsum dataset provided in the repository is [Link](http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.

## Steps to reproduce the bug
```python
load_dataset('xsum')
```


## Actual results
``` python
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz
```



Comments:
Hi ! On my side the URL is working fine, could you try again ?

> Hi ! On my side the URL is working fine, could you try again ?

I try it again and cannot download the file (might because of my location). Could you please provide another download link(such as google drive)?   :>

I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example.

> I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example.

:> ok. Thanks for your reply.
Add full tagset to conll2003 README
Opened by BramVanroy (closed)

Even though it is possible to manually get the tagset list with

```python
dset.features[field_name].feature.names
```

I think it is useful to have an overview of the used tagset on the dataset card. This is particularly useful in light of the **dataset viewer**: the tags are encoded, so it is not immediately obvious what they are for a given sample. Adding a label-int mapping should make it easier for visitors to get a grasp of what they mean.

From user-experience perspective, I would urge the full tagsets to always be available in the README's but I understand that that would take a lot of work, probably. Perhaps it can be automated?

closes #3189 

Comments:
I also added the missing `pretty_name` tag in the dataset card to fix the CI
Fix URL in CITATION file
Opened by albertvillanova (closed)

Currently the BibTeX citation parsed from the CITATION file has wrong URL (it shows the repo URL instead of the proceedings paper URL):
```
@inproceedings{Lhoest_Datasets_A_Community_2021,
author = {Lhoest, Quentin and Villanova del Moral, Albert and von Platen, Patrick and Wolf, Thomas and Šaško, Mario and Jernite, Yacine and Thakur, Abhishek and Tunstall, Lewis and Patil, Suraj and Drame, Mariama and Chaumond, Julien and Plu, Julien and Davison, Joe and Brandeis, Simon and Sanh, Victor and Le Scao, Teven and Canwen Xu, Kevin and Patry, Nicolas and Liu, Steven and McMillan-Major, Angelina and Schmid, Philipp and Gugger, Sylvain and Raw, Nathan and Lesage, Sylvain and Lozhkov, Anton and Carrigan, Matthew and Matussière, Théo and von Werra, Leandro and Debut, Lysandre and Bekman, Stas and Delangue, Clément},
booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
month = {11},
pages = {175--184},
publisher = {Association for Computational Linguistics},
title = {{Datasets: A Community Library for Natural Language Processing}},
url = {https://github.com/huggingface/datasets},
year = {2021}
}
```

Comments:
Update tatoeba to v2021-07-22
Opened by KoichiYasuoka (closed)

Tatoeba's latest version is v2021-07-22

Comments:
How about this? @lhoestq @abhishekkrthakur 

Hi ! I think it would be nice if people could still be able to load the old version.
Maybe this can be a parameter ? For example to load the old version they could do
```python
load_dataset("tatoeba", lang1="en", lang2="mr", date="v2020-11-09")
```

If it sounds good to you, we can add this parameter to the TatoebaConfig:
```python
class TatoebaConfig(datasets.BuilderConfig):
    def __init__(self, *args, lang1=None, lang2=None, date="v2021-07-22", **kwargs):
    self.date = date
```
and then pass the date to the URL
```python
_BASE_URL = "https://object.pouta.csc.fi/OPUS-Tatoeba/{}/moses/{}-{}.txt.zip"
```
```python
        def _base_url(lang1, lang2, date):
            return _BASE_URL.format(date, lang1, lang2)
```

What do you think ?

`_DATE = "v" + "-".join(s.zfill(2) for s in _VERSION.split("."))` seems rather tricky but works well. How about this? @lhoestq 


The CI is only failing because of the missing sections in the dataset card, and because of an issue with the CER metric that is unrelated to this PR
Add docs for audio processing
Opened by stevhliu (closed)

This PR adds documentation for the `Audio` feature. It describes:

- The difference between loading `path` and `audio`, as well as use-cases/best practices for each of them.
- Resampling audio files with `cast_column`, and then calling `ds[0]["audio"]` to automatically decode and resample to the desired sampling rate.
- Resampling with `map`.

Preview [here](https://52969-250213286-gh.circle-artifacts.com/0/docs/_build/html/audio_process.html), let me know if I'm missing anything!

Comments:
Nice ! love it this way. I guess you can set this PR to "ready for review" ?

I guess we can merge this one now :)
Eventual Invalid Token Error at setup of private datasets
Opened by albertvillanova (closed)

## Describe the bug
From time to time, there appear Invalid Token errors with private datasets:

- https://app.circleci.com/pipelines/github/huggingface/datasets/8520/workflows/d44629f2-4749-40f8-a657-50931d0b3434/jobs/52534
  ```
  ____________ ERROR at setup of test_load_streaming_private_dataset _____________
  ValueError: Invalid token passed!

  ____ ERROR at setup of test_load_streaming_private_dataset_with_zipped_data ____
  ValueError: Invalid token passed!
  
  =========================== short test summary info ============================
  ERROR tests/test_load.py::test_load_streaming_private_dataset - ValueError: I...
  ERROR tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data
  ```

- https://app.circleci.com/pipelines/github/huggingface/datasets/8557/workflows/a8383181-ba6d-4487-9d0a-f750b6dcb936/jobs/52763
  ```
  ____ ERROR at setup of test_load_streaming_private_dataset_with_zipped_data ____
  [gw1] linux -- Python 3.6.15 /home/circleci/.pyenv/versions/3.6.15/bin/python3.6

  hf_api = <huggingface_hub.hf_api.HfApi object at 0x7f4899bab908>
  hf_token = 'vgNbyuaLNEBuGbgCEtSBCOcPjZnngJufHkTaZvHwkXKGkHpjBPwmLQuJVXRxBuaRzNlGjlMpYRPbthfHPFWXaaEDTLiqTTecYENxukRYVAAdpeApIUPxcgsowadkTkPj'
  zip_csv_path = PosixPath('/tmp/pytest-of-circleci/pytest-0/popen-gw1/data16/dataset.csv.zip')

      @pytest.fixture(scope="session")
      def hf_private_dataset_repo_zipped_txt_data_(hf_api: HfApi, hf_token, zip_csv_path):
          repo_name = "repo_zipped_txt_data-{}".format(int(time.time() * 10e3))
          hf_api.create_repo(token=hf_token, name=repo_name, repo_type="dataset", private=True)
          repo_id = f"{USER}/{repo_name}"
          hf_api.upload_file(
              token=hf_token,
              path_or_fileobj=str(zip_csv_path),
              path_in_repo="data.zip",
              repo_id=repo_id,
  >           repo_type="dataset",
          )

  tests/hub_fixtures.py:68:

  ...

  ValueError: Invalid token passed!
  =========================== short test summary info ============================
  ERROR tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data
  ```


Comments:
Pin version exclusion for tensorflow incompatible with keras
Opened by albertvillanova (closed)

Once `tensorflow` version 2.6.2 is released:
- https://github.com/tensorflow/tensorflow/commit/c1867f3bfdd1042f694df7a9870be51ba80543cb
- https://pypi.org/project/tensorflow/2.6.2/

with the patch:
- tensorflow/tensorflow#52927

we can remove the temporary fix we introduced in:
- #3208

Fix #3209.

Comments:
Add ACAV100M Dataset
Opened by nateraw (open)

## Adding a Dataset
- **Name:** *ACAV100M*
- **Description:** *contains 100 million videos with high audio-visual correspondence, ideal for self-supervised video representation learning.*
- **Paper:** *https://arxiv.org/abs/2101.10803*
- **Data:** *https://github.com/sangho-vision/acav100m*
- **Motivation:** *The largest dataset (to date) for audio-visual learning.*

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


Comments:
Sort files before loading
Opened by lvwerra (closed)

When loading a dataset that consists of several files (e.g. `my_data/data_001.json`, `my_data/data_002.json` etc.) they are not loaded in order when using `load_dataset("my_data")`.

This could lead to counter-intuitive results if, for example, the data files are sorted by date or similar since they would appear in different order in the `Dataset`.

The straightforward solution is to sort the list of files alphabetically before loading them.

cc @lhoestq 


Comments:
This will be fixed by https://github.com/huggingface/datasets/pull/3221
Fix disable_nullable default value to False
Opened by lhoestq (closed)

Currently the `disable_nullable` parameter is not consistent across all dataset transforms. For example it is `False` in `map` but `True` in `flatten_indices`.

This creates unexpected behaviors like this
```python
from datasets import Dataset, concatenate_datasets

d1 = Dataset.from_dict({"a": [0, 1, 2, 3]})
d2 = d1.filter(lambda x: x["a"] < 2).flatten_indices()
d1.data.schema == d2.data.schema  # False
```
This can cause issues when concatenating datasets for example.

For consistency I set `disable_nullable` to `False` in `flatten_indices` and I fixed some docstrings

cc @SBrandeis 

Comments:
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py
Opened by xiuzhilu (closed)

when I use python examples/pytorch/translation/run_translation.py     --model_name_or_path examples/pytorch/translation/opus-mt-en-ro     --do_train     --do_eval     --source_lang en     --target_lang ro     --dataset_name wmt16     --dataset_config_name ro-en     --output_dir /tmp/tst-translation     --per_device_train_batch_size=4     --per_device_eval_batch_size=4     --overwrite_output_dir     --predict_with_generate to finetune translation model on huggingface, I get the issue"ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py".But I can open the https://raw.githubusercontent.com/huggingface/datasets/1.15.1/datasets/wmt16/wmt16.py by using website. What should I do to solve the issue?

Comments:
Hi ! Do you have some kind of proxy in your browser that gives you access to internet ?

Maybe you're having this error because you don't have access to this URL from python ?

Hi，do you fixed this error?
I still have this issue when use "use_auth_token=True"

You don't need authentication to access those github hosted files
Please check that you can access this URL from your browser and also from your terminal
CI error: Another metric with the same name already exists in Keras 2.7.0
Opened by albertvillanova (closed)

## Describe the bug
Release of TensorFlow 2.7.0 contains an incompatibility with Keras. See:
- keras-team/keras#15579

This breaks our CI test suite: https://app.circleci.com/pipelines/github/huggingface/datasets/8493/workflows/055c7ae2-43bc-49b4-9f11-8fc71f35a25c/jobs/52363


Comments:
Add Multidoc2dial Dataset
Opened by sivasankalpp (closed)

This PR adds the MultiDoc2Dial dataset introduced in this [paper](https://arxiv.org/pdf/2109.12595v1.pdf )

Comments:
@songfeng cc

Hi @sivasankalpp, thanks for your PR.

There was a bug in TensorFlow/Keras. We have made a temporary fix in our master branch. Please, merge master into your PR branch, so that the CI tests pass.

```
git checkout multidoc2dial
git fetch upstream master
git merge upstream/master
```

Hi @albertvillanova, I have merged master into my PR branch. All tests are passing. 
Please take a look when you get a chance, thanks! 


Thanks for your feedback @lhoestq. We addressed your comments in the latest commit. Let us know if everything looks okay :)
FileNotFoundError for TupleIE dataste
Opened by arda-vianai (closed)

Hi,
`dataset = datasets.load_dataset('tuple_ie', 'all')`

returns a FileNotFound error. Is the data not available? 

Many thanks.


Comments:
@mariosasko @lhoestq Could you give me an update on how to load the dataset after the fix?
Thanks.

Hi @arda-vianai,

first, you can try:
```python
import datasets
dataset = datasets.load_dataset('tuple_ie', 'all', revision="master")
```
If this doesn't work, your version of `datasets` is missing some features that are required to run the dataset script, so install the master version with the following command:
```
pip install git+https://github.com/huggingface/datasets.git
```
and then:
```python
import datasets
dataset = datasets.load_dataset('tuple_ie', 'all')
```
should work (even without `revision`).

@mariosasko 
Thanks, it is working now. I actually did that before but I didn't restart the kernel. I restarted it and it works now. My bad!!!
Many thanks and great job!
-arda
Updated: DaNE - updated URL for download
Opened by MalteHB (closed)

It seems that DaNLP has updated their download URLs and it therefore also needs to be updated in here...

Comments:
Actually it looks like the old URL is still working, and it's also the one that is mentioned in https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md

What makes you think we should use the new URL ?

@lhoestq Sorry! I might have jumped to conclusions a bit too fast here... 

I was working in Google Colab and got an error that it was unable to use the URL. I then forked the project, updated the URL, ran it locally and it worked. I therefore assumed that my URL update fixed the issue, however, I see now that it might rather be a Google Colab issue... 

Still - this seems to be the official URL for downloading the dataset, and I think that it will be most beneficial to use. :-) 

It looks like they're using these new urls for their new datasets. Maybe let's change to the new URL in case the old one stops working at one point. Thanks
Add mIoU metric
Opened by NielsRogge (closed)

**Is your feature request related to a problem? Please describe.**
Recently, some semantic segmentation models were added to HuggingFace Transformers, including [SegFormer](https://huggingface.co/transformers/model_doc/segformer.html) and [BEiT](https://huggingface.co/transformers/model_doc/beit.html).

Semantic segmentation (which is the task of labeling every pixel of an image with a corresponding class) is typically evaluated using the Mean Intersection and Union (mIoU). Together with the upcoming Image Feature, adding this metric could be very handy when creating example scripts to fine-tune any Transformer-based model on a semantic segmentation dataset.

An implementation can be found [here](https://github.com/open-mmlab/mmsegmentation/blob/504965184c3e6bc9ec43af54237129ef21981a5f/mmseg/core/evaluation/metrics.py#L132) for instance.


Comments:
Resolved via https://github.com/huggingface/datasets/pull/3745.
Add GSM8K dataset
Opened by NielsRogge (closed)

## Adding a Dataset
- **Name:** GSM8K (short for Grade School Math 8k)
- **Description:** GSM8K is a dataset of 8.5K high quality linguistically diverse grade school math word problems created by human problem writers.
- **Paper:** https://openai.com/blog/grade-school-math/
- **Data:** https://github.com/openai/grade-school-math
- **Motivation:** The dataset is useful to investigate the reasoning abilities of large Transformer models, such as GPT-3.

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


Comments:
Closed via https://github.com/huggingface/datasets/pull/4103