SurAyush/github_issues huggingface dataset:
Issue: Avoid splitting by` .py` for the file.
State: closed
Author: espoirMur

https://github.com/huggingface/datasets/blob/90b3a98065556fc66380cafd780af9b1814b9426/src/datasets/load.py#L272


Hello, 
Thanks you for this library . 

 I was using it and I had one edge case. my home folder name ends with `.py` it is `/home/espoir.py` so anytime I am running the code to load a local module this code here it is failing because after splitting it is trying to save the code to my home directory.


Step to reproduce.

- If you have a home folder which ends with `.py`

- load a module with a local folder 
`qa_dataset = load_dataset("src/data/build_qa_dataset.py")`
it is failed 
A possible workaround would be to use pathlib at the mentioned line

` meta_path = Path(importable_local_file).parent.joinpath("metadata.json")` this can alivate the issue .

Let me what are your thought on this and I can try to fix it by A PR.


Comments:
Hi @espoirMur, thanks for reporting.

You are right: that code line could be improved and made more generically valid.

On the other hand, I would suggest using `os.path.splitext` instead.

Are you willing to open a PR? :)

I will have a look.. 

This weekend .. 

@albertvillanova  , Can you have a look at #4590. 

Thanks 

#self-assign
Issue: Replace deprecated logging.warn with logging.warning
State: closed
Author: hugovk

Replace `logging.warn` (deprecated in [Python 2.7, 2011](https://github.com/python/cpython/commit/04d5bc00a219860c69ea17eaa633d3ab9917409f)) with `logging.warning` (added in [Python 2.3, 2003](https://github.com/python/cpython/commit/6fa635df7aa88ae9fd8b41ae42743341316c90f7)).

* https://docs.python.org/3/library/logging.html#logging.Logger.warning
* https://github.com/python/cpython/issues/57444


Comments:
Issue: Dataset Viewer issue for Pile of Law
State: closed
Author: Breakend

### Link

https://huggingface.co/datasets/pile-of-law/pile-of-law

### Description

Hi, I would like to turn off the dataset viewer for our dataset without enabling access requests. To comply with upstream dataset creator requests/licenses, we would like to make sure that the data is not indexed by search engines and so would like to turn off dataset previews. But we do not want to collect user emails because it would violate single blind review, allowing us to deduce potential reviewers' identities. Is there a way that we can turn off the dataset viewer without collecting identity information?

Thanks so much! 

### Owner

Yes

Comments:
Hi @Breakend, yes – we'll propose a solution today

Thanks so much, I appreciate it!

Thanks so much for adding the docs. I was able to successfully hide the viewer using the 
```
viewer: false
```
flag in the README.md of the dataset. I'm closing the issue because this is resolved. Thanks again!

Awesome! Thanks for confirming. cc @severo 

Just for the record:

- the doc
  
<img width="1430" alt="Capture d’écran 2022-06-27 à 09 29 27" src="https://user-images.githubusercontent.com/1676121/175884089-bca6c0d5-6387-473e-98ca-86a910ede4bd.png">

- the dataset main page

<img width="1134" alt="Capture d’écran 2022-06-27 à 09 29 05" src="https://user-images.githubusercontent.com/1676121/175884152-5f285bf0-3471-45de-927a-e141b00ebb33.png">

- the dataset viewer page

<img width="567" alt="Capture d’écran 2022-06-27 à 09 29 16" src="https://user-images.githubusercontent.com/1676121/175884191-ab6a297b-1c11-417e-bbde-0b7623278a79.png">
Issue: Fix WMT dataset loading issue and docs update
State: closed
Author: khushmeeet

This PR is a fix for #4354 

Changes are made for `wmt14`, `wmt15`, `wmt16`, `wmt17`, `wmt18`, `wmt19` and `wmt_t2t`. And READMEs are updated for the corresponding datasets.

As I am on a M1 Mac, I am not able to create a virtual `dev` environment using `pip install -e ".[dev]"`. Issue is with `tensorflow-text` not supported on M1s and there is no supporting repo by Apple or Google. So, if I was needed to perform local testing, I am not able to do that.

Let me know, if any additional changes are required.

Thanks

Comments:
The PR branch now has some commits unrelated to the changes, probably due to rebasing. Can you please close this PR and open a new one from a new branch? You can use  `git cherry-pick` to preserve the relevant changes:
```bash
git checkout master
git remote add upstream git@github.com:huggingface/datasets.git
git pull --ff-only upstream master
git checkout -b wmt-datasets-fix2
git cherry-pick f2d6c995d5153131168f64fc60fe33a7813739a4 a9fdead5f435aeb88c237600be28eb8d4fde4c55
```

Closing this PR due to unwanted commit changes. Will be opening new PR for the same issue.
Issue: Properly raise FileNotFound even if the dataset is private
State: closed
Author: lhoestq

`tests/test_load.py::test_load_streaming_private_dataset` was failing because the hub now returns 401 when getting the HfApi.dataset_info of a dataset without authentication. `load_dataset` was raising ConnectionError, while it should be FileNoteFoundError since it first checks for local files before checking the Hub.

Moreover when use_auth_token is not set (default is False), we should not pass `token=None` to HfApi.dataset_info, or it will use the local token by default - instead it should use no token. It's currently not possible to ask for no token to be used, so as a workaround I simply set token="no-token"

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Issue: Add `batch_size` parameter when calling `add_faiss_index` and `add_faiss_index_from_external_arrays`
State: closed
Author: alvarobartt

Currently, even though the `batch_size` when adding vectors to the FAISS index can be tweaked in `FaissIndex.add_vectors()`, the function `ArrowDataset.add_faiss_index` doesn't have either the parameter `batch_size` to be propagated to the nested `FaissIndex.add_vectors` function or `*args, **kwargs`, so on, this PR adds the `batch_size` parameter to both `ArrowDataset.add_faiss_index` and `ArrowDataset.add_faiss_index_from_external_arrays`.

This is useful so as to tweak the `batch_size` according to the VM specifications.

Comments:
Also, I had a doubt while checking the code related to the indices... 

@lhoestq, there's a value in `config.py` named `DATASET_INDICES_FILENAME` which has the arrow extension (which I assume it should be `indices.faiss`, as the Elastic Search indices are not stored in a file, but not sure), and it's just used before actually saving an `ArrowDataset` in disk, but since those indices are never stored AFAIK, is that actually required?

https://github.com/huggingface/datasets/blob/aec86ea4b790ccccc9b2e0376a496728b1c914cc/src/datasets/config.py#L183

https://github.com/huggingface/datasets/blob/aec86ea4b790ccccc9b2e0376a496728b1c914cc/src/datasets/arrow_dataset.py#L1079-L1092

So should I also remove that?

P.S. I also edited the following code comment which I found misleading as it's not actually storing the indices.

https://github.com/huggingface/datasets/blob/8ddc4bbeb1e2bd307b21f5d21f884649aa2bf640/src/datasets/arrow_dataset.py#L1122

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

> @lhoestq, there's a value in config.py named DATASET_INDICES_FILENAME which has the arrow extension (which I assume it should be indices.faiss, as the Elastic Search indices are not stored in a file, but not sure), and it's just used before actually saving an ArrowDataset in disk, but since those indices are never stored AFAIK, is that actually required?

The arrow file is used to store an indices mapping (when you shuffle the dataset for example) - not for a faiss index ;)

Ok cool thanks a lot for the explanation @lhoestq I was not sure about that :+1: I'll also add it there as you suggested!

CI failures are unrelated to this PR and fixed on master, merging
Issue: Add `tldr_news` dataset
State: closed
Author: JulesBelveze

This PR aims at adding support for a news dataset: `tldr news`.

This dataset is based on the daily [tldr tech newsletter](https://tldr.tech/newsletter) and contains a `headline` as well as a `content` for every piece of news contained in a newsletter.

Comments:
Hey @lhoestq, 
Sorry for opening a PR, I was following the guide [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)! Thanks for the review anyway, I will follow the instructions you sent 😃 

Thanks, we will update the guide ;)
Issue: Timestamp not returned as datetime objects in streaming mode
State: closed
Author: lhoestq

As reported in (internal) https://github.com/huggingface/datasets-server/issues/397

```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("ett", name="h2", split="test", streaming=True)
>>> d = next(iter(dataset))
>>> d['start']
Timestamp('2016-07-01 00:00:00')
```

while loading in non-streaming mode it returns `datetime.datetime(2016, 7, 1, 0, 0)`

Comments:
Issue: Add Video feature
State: closed
Author: nateraw

The following adds a `Video` feature for encoding/decoding videos on the fly from in memory bytes. It uses my own `encoded-video` library which is basically `pytorchvideo`'s encoded video but with all the `torch` specific stuff stripped out. Because of that, and because the tool I used under the hood is not very mature, I leave this as a draft idea that we can use to build off of.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4532). All of your documentation changes will be reflected on that endpoint.

@nateraw do you have any plans to continue this pr? Or should I write a custom loader script to use my video dataset in the hub?

@fcakyon I think we still want this feature in here, but my solution here isn't the right one, I'm afraid. Using my (very hacky) library is not the right move. Let's move to an issue to discuss the feature/workarounds for now.
Issue: Dataset Viewer issue for CSV datasets
State: closed
Author: merveenoyan

### Link

https://huggingface.co/datasets/scikit-learn/breast-cancer-wisconsin

### Description

I'm populating CSV datasets [here](https://huggingface.co/scikit-learn) but the viewer is not enabled and it looks for a dataset loading script, the datasets aren't on queue as well. 

You can replicate the problem by simply uploading any CSV dataset.

### Owner

Yes

Comments:
this should now be fixed

Confirmed, it's fixed now. Thanks for reporting, and thanks @coyotte508 for fixing it

<img width="1123" alt="Capture d’écran 2022-06-21 à 10 28 05" src="https://user-images.githubusercontent.com/1676121/174753833-1b453a5a-6a90-4717-bca1-1b5fc6b75e4a.png">
Issue: Ecoset
State: closed
Author: DiGyt

## Adding a Dataset
- **Name:** *Ecoset*
- **Description:** *https://www.kietzmannlab.org/ecoset/*
- **Paper:** *https://doi.org/10.1073/pnas.2011417118*
- **Data:** *https://codeocean.com/capsule/9570390/tree/v1*
- **Motivation:**

**Ecoset** was created as a clean and ecologically valid alternative to **Imagenet**.

It is a large image recognition dataset, similar to Imagenet in size and structure. However, the authors of ecoset claim several improvements over Imagenet, like:
- more ecologically valid classes (e.g. not over-focussed on distinguishing different dog breeds)
- less NSFW content
- 'pre-packed image recognition models' that come with the dataset and can be used for validation of other models.


I am working for one of the authors of the paper with the aim of bringing Ecoset to huggingface datasets. Therefore I can work on this issue personally, but could use some help from devs and experienced users if the dataset is of interest to them. I phrased some of my questions on [discuss.huggingface](https://discuss.huggingface.co/t/handling-large-image-datasets/19373).


Comments:
Hi! Very cool dataset! I answered your questions on the forum. Also, feel free to comment `#self-assign` on this issue to self-assign it.

The dataset lives on the Hub [here](https://huggingface.co/datasets/kietzmannlab/ecoset), so I'm closing this issue.

Hey There, thanks for closing 🤗 

Forgot the issue existed, so I didn't close it after implementing the downloader :)
Issue: Dataset Viewer issue for vadis/sv-ident
State: closed
Author: albertvillanova

### Link

https://huggingface.co/datasets/vadis/sv-ident

### Description

The dataset preview does not work:
```
Server Error

Status code:   400
Exception:     Status400Error
Message:       The dataset does not exist.
```

However, the dataset is streamable and works locally:
```python
In [1]: from datasets import load_dataset; ds = load_dataset("sv-ident.py", split="train", streaming=True); item = next(iter(ds)); item
Using custom data configuration default
Out[1]: 
{'sentence': 'Our point, however, is that so long as downward (favorable) comparisons overwhelm the potential for unfavorable comparisons, system justification should be a likely outcome amongst the disadvantaged.',
 'is_variable': 1,
 'variable': ['exploredata-ZA5400_VarV66', 'exploredata-ZA5400_VarV53'],
 'research_data': ['ZA5400'],
 'doc_id': '73106',
 'uuid': 'b9fbb80f-3492-4b42-b9d5-0254cc33ac10',
 'lang': 'en'}
```

CC: @e-tornike

### Owner

No

Comments:
Fixed, thanks!
![Uploading Capture d’écran 2022-06-21 à 18.42.40.png…]()
Issue: split cache used when processing different split
State: open
Author: gpucce

## Describe the bug`
```
ds1 = load_dataset('squad', split='validation')
ds2 = load_dataset('squad', split='train')
ds1 = ds1.map(some_function)
ds2 = ds2.map(some_function)
assert ds1 == ds2
```
This happens when ds1 and ds2 are created in `pytorch_lightning.DataModule` through

```
class myDataModule:

    def train_dataloader(self):
         ds = load_dataset('squad', split='train')
         ds = ds.map(some_function)
         return [ds]

    def val_dataloader(self):
         ds = load_dataset('squad', split="validation")
         ds = ds.map(some_function)
         return [ds]
```
I don't know if it depends on `pytorch_lightning` or `datasets` but setting `ds.map(some_function, load_from_cache_file=False)` fixes the issue.

If this is not enough to replicate I will try and provide and MWE, I don't have time now so I thought I wuld open the issue first!

Comments:
I was not able to reproduce this behavior (I tried without using pytorch lightning though, since I don't know what code you ran in pytorch lightning to get this).

If you can provide a MWE that would be perfect ! :)

Hi, I think the issue happened because I was loading datasets under an `if` ... `else` statement and the condition would change the dataset I would need to load but instead the cached one was always returned. However, I believe that is expected behaviour, if so I'll close the issue.

Otherwise I will try to provide a MWE
Issue: Update download url and improve card of `cats_vs_dogs` dataset
State: closed
Author: mariosasko

Improve the download URL (reported here: https://huggingface.co/datasets/cats_vs_dogs/discussions/1), remove the `image_file_path` column (not used in Transformers, so it should be safe) and add more info to the card.

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Issue: Try to reduce the number of datasets that require manual download
State: open
Author: severo

> Currently, 41 canonical datasets require manual download. I checked their scripts and I'm pretty sure this number can be reduced to ≈ 30 by not relying on bash scripts to download data, hosting data directly on the Hub when the license permits, etc. Then, we will mostly be left with datasets with restricted access, which we can ignore

from https://github.com/huggingface/datasets-server/issues/12#issuecomment-1026920432

Comments:
Issue: Datasets method `.map` not hashing
State: closed
Author: sanchit-gandhi

## Describe the bug
Datasets method `.map` not hashing, even with an empty no-op function

## Steps to reproduce the bug
```python
from datasets import load_dataset

# download 9MB dummy dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean")

def prepare_dataset(batch):
     return(batch)

ds = ds.map(
    prepare_dataset,
    num_proc=1,
    desc="preprocess train dataset",
)
```

## Expected results
Hashed and cached dataset preprocessing

## Actual results
Does not hash properly:
```
Parameter 'function'=<function prepare_dataset at 0x7fccb68e9280> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.3.dev0
- Platform: Linux-5.11.0-1028-gcp-x86_64-with-glibc2.31
- Python version: 3.9.12
- PyArrow version: 8.0.0
- Pandas version: 1.4.2

cc @lhoestq 


Comments:
Fix posted: https://github.com/huggingface/datasets/issues/4506#issuecomment-1157417219

Didn't realize it's a bug when I asked the question yesterday! Free free to post an answer if you are sure the cause has been addressed.

https://stackoverflow.com/questions/72664827/can-pickle-dill-foo-but-not-lambda-x-foox

Thank @nalzok . That works for me:

`pip install "dill<0.3.5"`
Issue: Create new sections for audio and vision in guides
State: closed
Author: stevhliu

This PR creates separate sections in the guides for audio, vision, text, and general usage so it is easier for users to find loading, processing, or sharing guides specific to the dataset type they're working with. It'll also allow us to scale the docs to additional dataset types - like time series, tabular, etc. - while keeping our docs information architecture. 

Some other changes include:

- ~Experimented with decorating text with some CSS to highlight guides specific to each modality. Hopefully, it'll be easier for users to find and realize that these different docs exist!~ Will experiment with this in a different PR.
- Added deprecation warning for Metrics and redirect to Evaluate.
- Updated `set_format` section to recommend using the new `to_tf_dataset` function if you need to convert to a TensorFlow dataset.
- Reorganized `toctree` to nest general usage, audio, vision, and text sections under the how-to guides.
- A quick review and edit to the Load and Process docs for clarity.

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

Ready for review!

The `toctree` is a bit longer now with the sections. I think if we keep the audio/vision/text/dataset repository sections collapsed by default, and keep the general usage expanded, it may look a little cleaner and not as overwhelming. Let me know what you think! 😄
Issue: Add tags for task_ids:summarization-* and task_categories:summarization*
State: closed
Author: hobson

yaml header at top of README.md file was edited to add task tags because I couldn't find the existing tags in the json
separate Pull Request will modify dataset_infos.json to add these tags

The Enron dataset (dataset id aeslc) is only tagged with:

    arxiv:1906.03497'
    languages:en
    pretty_name:AESLC

Using the email subject_line field as a label or target variable it possible to create models for the following task_ids (in order of relevance):

    'task_ids:summarization'
    'task_ids:summarization-other-conversations-summarization'
    "task_ids:other-other-query-based-multi-document-summarization"
    'task_ids:summarization-other-aspect-based-summarization'
    'task_ids:summarization--other-headline-generation'

The subject might also be used for the task_category "task_categories:summarization"

E-mail chains might be used for the task category "task_categories:dialogue-system"

Comments:
Associated community discussion is [here](https://huggingface.co/datasets/aeslc/discussions/1).
Paper referenced in the `dataset_infos.json` is [here](https://arxiv.org/pdf/1906.03497.pdf). It mentions the  _email-subject-generation_ task, which is not a tag mentioned in any other dataset so it was not added in this pull request. The _summarization_ task is mentioned as a related task.

_The documentation is not available anymore as the PR was closed or merged._
Issue: Fix hashing for python 3.9
State: closed
Author: lhoestq

In python 3.9, pickle hashes the `glob_ids` dictionary in addition to the `globs` of a function.

Therefore the test at `tests/test_fingerprint.py::RecurseDumpTest::test_recurse_dump_for_function_with_shuffled_globals` is currently failing for python 3.9

To make hashing deterministic when the globals are not in the same order, we also need to make the order of `glob_ids` deterministic.

Right now we don't have a CI to test python 3.9 but we should definitely have one. For this PR in particular I ran the tests locally using python 3.9 and they're passing now.

Fix https://github.com/huggingface/datasets/issues/4506

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

What do you think @albertvillanova ?
Issue: Add uppercased versions of image file extensions for automatic module inference
State: closed
Author: mariosasko

Adds the uppercased versions of the image file extensions to the supported extensions. 

Another approach would be to call `.lower()` on extensions while resolving data files, but uppercased extensions are not something we want to encourage out of the box IMO unless they are commonly used (as they are in the vision domain)

Note that there is a slight discrepancy between the image file resolution and `imagefolder` as the latter calls `.lower()` on file extensions leading to some image file extensions being ignored by the resolution but not by the loader (e.g. `pNg`). Such extensions should also be discouraged, so I'm ignoring that case too.

Fix #4514.  

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