Minimartzz/github-issues huggingface dataset:
make shape verification to use ArrayXD instead of nested lists for map
State: open
Labels: description: New feature or request
name: enhancement

As describe in https://github.com/huggingface/datasets/issues/2005#issuecomment-793716753 and mentioned by @mariosasko in [image feature example](https://colab.research.google.com/drive/1mIrTnqTVkWLJWoBzT1ABSe-LFelIep1c#scrollTo=ow3XHDvf2I0B&line=1&uniqifier=1), IMO make shape verifcaiton to use ArrayXD instead of nested lists for map can help user reduce unnecessary cast. I notice datasets have done something special for `input_ids` and `attention_mask` which is also unnecessary after this feature added.

Comments:
Hi! 

Yes, this makes sense for numeric values, but first I have to finish https://github.com/huggingface/datasets/pull/3336 because currently ArrayXD only allows the first dimension to be dynamic.
Fix duplicate keys in NewsQA
State: closed
Labels: description: Contribution to a dataset script
name: dataset contribution

* Fix duplicate keys in NewsQA when loading from CSV files.
* Fix s/narqa/newsqa/ in the download manually error message.
* Make the download manually error message show nicely when printed. Otherwise, is hard to read due to spacing issues.
* Fix the format of the license text.
* Reformat the code to make it simpler.

Comments:
Flaky tests?

Thanks for your contribution, @bryant1410.

I think the fix of the duplicate key in this PR was superseded by:
- #3696

I'm closing this because we are moving all dataset scripts from GitHub to the Hugging Face Hub.
Fix overriding of filesystem info
State: closed

Previously, `BaseCompressedFileFileSystem.info` was overridden and transformed from function to dict.

This generated a bug for filesystem methods that use `self.info()`, like e.g. `fs.isfile()`.

This PR:
- Adds tests for `fs.isfile` (that use `fs.info`).
- Fixes custom `BaseCompressedFileFileSystem.info` by removing its overriding.

Comments:
the compression format requested when saving a dataset in json format is not respected
State: closed
Labels: description: Something isn't working
name: bug

## Describe the bug
In the documentation of the `to_json` method, it is stated in the parameters that 
> **to_json_kwargs – Parameters to pass to pandas’s pandas.DataFrame.to_json.

however when we pass for example `compression="gzip"`, the saved file is not compressed.

Would you also have expected compression to be applied? :relaxed: 

## Steps to reproduce the bug
```python
my_dict = {"a": [1, 2, 3], "b": [1, 2, 3]}
```
### Result with datasets
```python
from datasets import Dataset

dataset = Dataset.from_dict(my_dict)
dataset.to_json("dic_with_datasets.jsonl.gz", compression="gzip")
!cat dic_with_datasets.jsonl.gz
```
output
```
{"a":1,"b":1}
{"a":2,"b":2}
{"a":3,"b":3}
```
Note: I would expected to see binary data here

### Result with pandas
```python
import pandas as pd

df = pd.DataFrame(my_dict)
df.to_json("dic_with_pandas.jsonl.gz", lines=True, orient="records", compression="gzip")
!cat dic_with_pandas.jsonl.gz
```
output
```
4��a�dic_with_pandas.jsonl��VJT�2�QJ��\� ��g��yƵ���������)���
```
Note: It looks like binary data

## Expected results

I would have expected that the saved result with datasets would also be a binary file

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1
- Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.11
- PyArrow version: 5.0.0


Comments:
Thanks for reporting @SaulLu.

At first sight I think the problem is caused because `pandas` only takes into account the `compression` parameter if called with a non-null file path or buffer. And in our implementation, we call pandas `to_json` with `None` `path_or_buf`.

We should fix this:
- either handling directly the `compression` parameter ourselves
- or refactoring to pass non-null path or buffer to pandas

CC: @lhoestq

I was thinking if we can handle the `compression` parameter by ourselves? Compression types will be similar to what `pandas` offer. Initially, we can try this with 2-3 compression types and see how good/bad it is? Let me know if it sounds good, I can raise a PR for this next week

Hi ! Thanks for your help @bhavitvyamalik :)
Maybe let's start with `gzip` ? I think it's the most common use case, then if we're fine with it we can add other compression methods
Dataset preview is not available (I think for all Hugging Face datasets)
State: closed
Labels: description: Something isn't working
name: bug

description: Related to the dataset viewer on huggingface.co
name: dataset-viewer

## Dataset viewer issue for '*french_book_reviews*'

**Link:** https://huggingface.co/datasets/Abirate/french_book_reviews

**short description of the issue**
For my dataset, the dataset preview is no longer functional (it used to work: The dataset had been added the day before and it was fine...)  
And, after looking over the datasets, I discovered that this issue affects all Hugging Face datasets (as of yesterday, December 23, 2021, around 10 p.m. (CET)).  
**Am I the one who added this dataset** :  Yes   

**Note**: here a screenshot showing the issue
![Dataset preview is not available for my dataset](https://user-images.githubusercontent.com/66887439/147333078-60734578-420d-4e91-8691-a90afeaa8948.jpg)  

**And here for glue dataset :**  

![Dataset preview is not available for other Hugging Face datasets(glue)](https://user-images.githubusercontent.com/66887439/147333492-26fa530c-befd-4992-8361-70c51397a25a.jpg)



Comments:
You're right, we have an issue today with the datasets preview. We're investigating.

It should be fixed now. Thanks for reporting.

Down again. 

Fixed for good.
Extend support for streaming datasets that use os.walk
State: closed

This PR extends the support in streaming mode for datasets that use `os.walk`, by patching that function.

This PR adds support for streaming mode to datasets:
1. autshumato
1. code_x_glue_cd_code_to_text
1. code_x_glue_tc_nl_code_search_adv
1. nchlt

CC: @severo 

Comments:
Nice. I'll update the dataset viewer once merged, and test on these four datasets
Use `iter_files` instead of `str(Path(...)` in image dataset
State: closed

Use `iter_files` in the `beans` and the `cats_vs_dogs` dataset scripts as suggested by @albertvillanova.

Additional changes:
* Fix `iter_files` in `MockDownloadManager` (see this [CI error](https://app.circleci.com/pipelines/github/huggingface/datasets/9247/workflows/2657ff8a-b531-4fd9-a9fc-6541a72e8d83/jobs/57028))
* Add support for `os.path.isdir` and `os.path.isfile` in streaming (`os.path.isfile` is needed in `StreamingDownloadManager`'s `iter_files` to make `cats_vs_dogs` streamable)

TODO:
- [ ] add tests for `xisdir` and `xisfile`

Comments:
`iter_archive` is about to support ZIP archives. I think we should use this no ?

see #3347 https://github.com/huggingface/datasets/pull/3379

I was interested in the support for isfile/dir in remote.

Anyway, `iter_files` will be available for community users.

I'm not a big fan of having two functions that do the same thing. What do you think ?

They do not do the same thing:
- One iterates over files in a directory
- The other I guess will iterate over the members of an archive

Makes sense ! Sounds good then - sorry for my misunderstanding

Note that `iter_archive` will be more performant for data streaming that `iter_files` thanks to the buffering so maybe in the future we can `iter_archive` for some of these datasets

Yes, @lhoestq I agree with you: once `iter_archive` supports zip files, it will be more suitable than `iter_files` for these 2 datasets.

Anyway, this PR also implements `isfile`/`isdir` in streaming mode, besides fixing `iter_files`. And I'm interested in having those in master.

Maybe, could we merge this PR into master and take note to refactor the datasets to use `iter_archive` once zip is supported?
Other option could be to split this PR into 2...
Extend support for streaming datasets that use ET.parse
State: closed

This PR extends the support in streaming mode for datasets that use `ET.parse`, by patching the function.

This PR adds support for streaming mode to datasets:
1. ami
1. assin
1. assin2
1. counter
1. enriched_web_nlg
1. europarl_bilingual
1. hyperpartisan_news_detection
1. polsum
1. qa4mre
1. quail
1. ted_talks_iwslt
1. udhr
1. web_nlg
1. winograd_wsc

CC: @severo

Comments:
The rotten_tomatoes dataset of movie reviews contains some reviews in Spanish 
State: open
Labels: description: Something isn't working
name: bug

## Describe the bug
See title. I don't think this is intentional and they probably should be removed. If they stay the dataset description should be at least updated to make it clear to the user. 

## Steps to reproduce the bug
Go to the [dataset viewer](https://huggingface.co/datasets/viewer/?dataset=rotten_tomatoes) for the dataset, set the offset to 4160 for the train dataset, and scroll through the results. I found ones at index 4166 and 4173. There's others too (e.g. index 2888) but those two are easy to find like that. 

## Expected results
English movie reviews only. 

## Actual results
Example of a Spanish movie review (4173): 
> "É uma pena que , mais tarde , o próprio filme abandone o tom de paródia e passe a utilizar os mesmos clichês que havia satirizado "



Comments:
Hi @puzzler10, thanks for reporting.

Please note this dataset is not hosted on Hugging Face Hub. See: 
https://github.com/huggingface/datasets/blob/c8f914473b041833fd47178fa4373cdcb56ac522/datasets/rotten_tomatoes/rotten_tomatoes.py#L42

If there are issues with the source data of a dataset, you should contact the data owners/creators instead. In the homepage associated with this dataset (http://www.cs.cornell.edu/people/pabo/movie-review-data/), you can find the authors of the dataset and how to contact them:
> If you have any questions or comments regarding this site, please send email to Bo Pang or Lillian Lee.

P.S.: Please also note that the example you gave of non-English review is in Portuguese (not Spanish). ;)

Maybe best to just put a quick sentence in the dataset description that highlights this?
Add COCO dataset
State: closed
Labels: description: Contribution to a dataset script
name: dataset contribution

This PR adds the MS COCO dataset. Compared to the [TFDS](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/object_detection/coco.py) script, this implementation adds 8 additional configs to cover the tasks other than object detection.

Some notes:
* the data exposed by TFDS is contained in the `2014`, `2015`, `2017` and `2017_panoptic_segmentation` configs here
* I've updated `encode_nested_example` for easier handling of missing values (cc @lhoestq @albertvillanova; will add tests if you are OK with the changes in `features.py`)
* this implementation should fix https://github.com/huggingface/datasets/pull/3377#issuecomment-985559427

TODOs:
- [x] dataset card
- [ ] dummy data

cc @merveenoyan 

Closes #2526 

Comments:
The CI failures other than a missing dummy data file and missing fields in the card are unrelated to this PR. 

Thanks a lot for this great work and fixing TFDS based script @mariosasko 🤗 will generate the dummy dataset and write the model card tomorrow!

@mariosasko I added the dataset card, I'm on the dummy data rn. 

@merveenoyan Let me know if you need any help with the dummy data.

I plan to split the current script/dataset into 4 smaller scripts/datasets to make sure they are properly indexed by Papers With Code later on. In this format:
* the `*_image_captioning` configs will form the [COCO Captions](https://paperswithcode.com/sota/image-captioning-on-coco-captions) dataset (also present in TFDS, but only the 2017 version)
* the `stuff_segmentation` config will form the [COCO Stuff](https://paperswithcode.com/dataset/coco-stuff) dataset
* the `desnepose` config will form the [DensePose-COCO](https://paperswithcode.com/dataset/densepose) dataset
* the rest will be [COCO](https://paperswithcode.com/dataset/coco) (+ will add the `minival` and the `valminusminival` splits to COCO 2014)

Also, if I find the time, I'll add preprocessing examples that rely on `pycocotools` to the README files.

@mariosasko I feel like we can just push main COCO and add Captions + Stuff later, WDYT?

_The documentation is not available anymore as the PR was closed or merged._

Thanks for your contribution, @mariosasko and @merveenoyan. Are you still interested in adding this dataset?

We are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets

We would suggest you create this dataset there. Please, feel free to tell us if you need some help.
Push dataset infos.json to Hub
State: closed

When doing `push_to_hub`, the feature types are lost (see issue https://github.com/huggingface/datasets/issues/3394).
This PR fixes this by also pushing a `dataset_infos.json` file to the Hub, that stores the feature types.

Other minor changes:
- renamed the `___` separator to `--`, since `--` is now disallowed in a name in the back-end.

I tested this feature with datasets like conll2003 that has feature types like `ClassLabel` that were previously lost.

Close https://github.com/huggingface/datasets/issues/3394

I would like to include this in today's release (though not mandatory), so feel free to comment/suggest changes

Comments:
The change from `___` to `--` was allowed by https://github.com/huggingface/moon-landing/pull/1657
Add CRASS dataset
State: closed
Labels: description: Contribution to a dataset script
name: dataset contribution

Added crass dataset

Comments:
Hi Albert,
Thank you for your comments.
I hope I have uploaded my local git repo to include the dummy files and style reworkings.
Added YAML in Readme as well.

Please check again.

Hope it works now :)

Thanks for your contribution, @apergo-ai. 

We are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets

We would suggest you create this dataset there. It's OK for you? Please, feel free to tell us if you need some help.
Unable to load 'cnn_dailymail' dataset
State: closed
Labels: description: Something isn't working
name: bug

description: This issue or pull request already exists
name: duplicate

description: A bug in a dataset script provided in the library
name: dataset bug

## Describe the bug
I wanted to load cnn_dailymail dataset from huggingface datasets on Google Colab, but I am getting an error while loading it.

## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('cnn_dailymail', '3.0.0', ignore_verifications = True)
```

## Expected results
Expecting to load 'cnn_dailymail' dataset.

## Actual results
`NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'`


## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0


Comments:
Hi @talha1503, thanks for reporting.

It seems there is an issue with one of the data files hosted at Google Drive:
```
Google Drive - Quota exceeded

Sorry, you can't view or download this file at this time.

Too many users have viewed or downloaded this file recently. Please try accessing the file again later. If the file you are trying to access is particularly large or is shared with many people, it may take up to 24 hours to be able to view or download the file. If you still can't access a file after 24 hours, contact your domain administrator.
```

As you probably know, Hugging Face does not host the data, and in this case the data owner decided to host their data at Google Drive, which has quota limits.

Is there anything we could do, @lhoestq @mariosasko?

This looks related to https://github.com/huggingface/datasets/issues/996

It seems that [this](https://huggingface.co/datasets/ccdv/cnn_dailymail) copy of the dataset has fixed the problem

thank you @AyhamAlom ...
it resolved the error
struct.error: 'i' format requires -2147483648 <= number <= 2147483647
State: open
Labels: description: Something isn't working
name: bug

## Describe the bug
A clear and concise description of what the bug is.

using latest datasets=datasets-1.16.1-py3-none-any.whl
process my own multilingual dataset by following codes, and the number of rows in all dataset is 306000, the max_length of each sentence is 256:
![image](https://user-images.githubusercontent.com/30341159/146865779-3d25d011-1f42-4026-9e1b-76f6e1d172e9.png)

then I get this error:
![image](https://user-images.githubusercontent.com/30341159/146865844-e60a404c-5f3a-403c-b2f1-acd943b5cdb8.png)

I have seen the issue in #2134 and #2150, so I don't understand why latest repo still can't deal with big dataset.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: linux docker
- Python version: 3.6



Comments:
Hi ! Can you try setting `datasets.config.MAX_TABLE_NBYTES_FOR_PICKLING` to a smaller value than `4 << 30` (4GiB), for example `500 << 20` (500MiB) ? It should reduce the maximum size of the arrow table being pickled during multiprocessing.

If it fixes the issue, we can consider lowering the default value for everyone.

@lhoestq I tried that just now but didn't seem to help.
Update swahili_news dataset
State: closed
Labels: description: Requesting to add a new dataset
name: dataset request

Please note also: the HuggingFace version at https://huggingface.co/datasets/swahili_news is outdated. An updated version, with deduplicated text and official splits, can be found at https://zenodo.org/record/5514203.

## Adding a Dataset
- **Name:** swahili_news

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

Related to:
- bigscience-workshop/data_tooling#107


Comments:
Don't encode lists as strings when using `Value("string")`
State: closed

Following https://github.com/huggingface/datasets/pull/3456#event-5792250497 it looks like `datasets` can silently convert lists to strings using `str()`, instead of raising an error.

This PR fixes this and should fix the issue with WER showing low values if the input format is not right.

Comments:
Should we close this PR?

since the original issue has to do with metrics that have been moved to `evaludate` I think we can close this one

_The documentation is not available anymore as the PR was closed or merged._
[WER] Better error message for wer
State: closed

Currently we have the following problem when using the WER. When the input format to the WER metric is wrong, instead of throwing an error message a word-error-rate is computed which is incorrect. E.g. when doing the following: 


```python
from datasets import load_metric

wer = load_metric("wer")

target_str = ["hello this is nice", "hello the weather is bloomy"]
pred_str = [["hello it's nice"], ["hello it's the weather"]]

print("Wrong:", wer.compute(predictions=pred_str, references=target_str))
print("Correct", wer.compute(predictions=[x[0] for x in pred_str], references=target_str))
```
We get:
```
Wrong: 1.0
Correct 0.5555555555555556
```

meaning that we get a word-error rate for incorrectly passed input formats. We should raise an error here instead so that people don't spent hours fixing a model while it's their incorrect evaluation metric is the problem for a low WER.

Comments:
Hi ! I don't think this would solve this issue.
Currently it looks like there's a bug that converts the list `["hello it's nice"]` to a string `'["hello it's nice"]'` since this is what the metric expects as input. The conversion is done before the data are passed to `_compute()`.

This is `Value("string").encode_example` that is called to do the conversion. Since `str()` encoding is too permissive we should consider raising an error if the example is not a string (even though it can be converted to string). 

> called

Ah yeah you're right

I just opened https://github.com/huggingface/datasets/pull/3460 to fix that. It now raises an error instead of computing the wrong WER

Thank you - that should be good enough!
Easier information editing
State: closed
Labels: description: New feature or request
name: enhancement

description: Generic discussion on the library
name: generic discussion

**Is your feature request related to a problem? Please describe.**
It requires a lot of effort to improve a datasheet. 

**Describe the solution you'd like**
UI or at least a link to the place where the code that needs to be edited is (and an easy way to edit this code directly from the site, without cloning, branching, makefile etc.)

**Describe alternatives you've considered**
The current Ux is to have the 8 steps for contribution while One just wishes to change a line a type etc.




Comments:
Hi ! I guess you are talking about the dataset cards that are in this repository on github ?

I think github allows to submit a PR even for 1 line though the `Edit file` button on the page of the dataset card.

Maybe let's mention this in `CONTRIBUTING.md` ?

We now host all the datasets on the HF Hub, where you can easily edit them through UI (for single file changes) or Git workflow (for single/multiple file changes)
why the stratify option is omitted from test_train_split function?
State: closed
Labels: description: New feature or request
name: enhancement

description: Issues a bit more difficult than "Good First" issues
name: good second issue

why the stratify option is omitted from test_train_split function?

is there any other way implement the stratify option while splitting the dataset? as it is important point to be considered while splitting the dataset.

Comments:
Hi ! It's simply not added yet :)

If someone wants to contribute to add the `stratify` parameter I'd be happy to give some pointers.

In the meantime, I guess you can use `sklearn` or other tools to do a stratified train/test split over the **indices** of your dataset and then do
```
train_dataset = dataset.select(train_indices)
test_dataset = dataset.select(test_indices)
```

Hi @lhoestq I would like to add `stratify` parameter, can you give me some pointers for adding the same ?

Hi ! Sure :)

The `train_test_split` method is defined here: 

https://github.com/huggingface/datasets/blob/dc62232fa1b3bcfe2fbddcb721f2d141f8908943/src/datasets/arrow_dataset.py#L3253-L3253

and inside `train_test_split ` we need to create the right `train_indices` and `test_indices` that are passed here to `.select()`:

https://github.com/huggingface/datasets/blob/dc62232fa1b3bcfe2fbddcb721f2d141f8908943/src/datasets/arrow_dataset.py#L3450-L3464

For example if your dataset is like
|    |   label |
|---:|--------:|
|  0 |       1 |
|  1 |       1 |
|  2 |       0 |
|  3 |       0 |

and the user passes `stratify=dataset["label"]`, then you should get indices that look like this
```
train_indices = [0, 2]
test_indices = [1, 3]
```

these indices will be passed to `.select` to return the stratified train and test splits :)

Feel free to îng me if you have any question !

@lhoestq 
I just added the implementation for `stratify` option here #4322