Minimartzz/github-issues huggingface dataset:
Add single dataset citations for TweetEval
State: closed

This PR adds single data citations as per request of the original creators of the TweetEval dataset.

This is a recent email from the creator:

> Could I ask you a favor? Would you be able to add at the end of the README the citations of the single datasets as well? You can just copy our readme maybe? https://github.com/cardiffnlp/tweeteval#citing-tweeteval
(just to be sure that the creator of the single datasets also get credits when tweeteval is used)

Please let me know if this looks okay or if any changes are needed.

Thanks,
Gunjan


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

The `test_dataset_cards` method is failing with the error:

```
if error_messages:
>           raise ValueError("\n".join(error_messages))
E           ValueError: The following issues have been found in the dataset cards:
E           YAML tags:
E           The following typing errors are found: {'annotations_creators': "(Expected `typing.List` with length > 0. Found value of type: `<class 'list'>`, with length: 0.\n)\nOR\n(Expected `typing.Dict` with length > 0. Found value of type: `<class 'list'>`, with length: 0.\n)"}
```

Adding `found` as annotation creators.
dataset metadata for reproducibility
State: open
Labels: description: New feature or request
name: enhancement

When pulling a dataset from the hub, it would be useful to have some metadata about the specific dataset and version that is used. The metadata could then be passed to the `Trainer` which could then be saved to a model card. This is useful for people who run many experiments on different versions (commits/branches) of the same dataset. 

The dataset could have a list of “source datasets” metadata and ignore what happens to them before arriving in the Trainer (i.e. ignore mapping, filtering, etc.).

Here is a basic representation (made by @lhoestq )
```python
>>> from datasets import load_dataset
>>> 
>>> my_dataset = load_dataset(...)["train"]
>>> my_dataset = my_dataset.map(...)
>>> 
>>> my_dataset.sources
[HFHubDataset(repo_id=..., revision=..., arguments={...})]
```

Comments:
+1 on this idea. This could be powerful for helping better track datasets used for model training and help with automatic model card creation. 

One possible way of doing this would be to store some/most/all the arguments passed to `load_dataset` if a hub id is passed. i.e. store the Hub ID, configuration, etc. 

cc @tomaarsen
More robust `cast_to_python_objects` in `TypedSequence`
State: closed

Adds a fallback to run an expensive version of `cast_to_python_objects` which exhaustively checks entire lists to avoid the `ArrowInvalid: Could not convert` error in `TypedSequence`. Currently, this error can happen in situations where only some images are decoded in `map`, in which case `cast_to_python_objects` fails to recognize that it needs to cast `PIL.Image` objects if they are not at the beginning of the sequence and stops after the first image dictionary (e.g., if `data` is `[{"bytes": None, "path": "some path"}, PIL.Image(), ...]`)

Fix #4124

Comments:
_The documentation is not available anymore as the PR was closed or merged._
dataset viewer issue for common_voice
State: closed
Labels: description: Related to the dataset viewer on huggingface.co
name: dataset-viewer

name: audio_column

## Dataset viewer issue for 'common_voice'

**Link:** https://huggingface.co/datasets/common_voice

Server Error
Status code:   400
Exception:     TypeError
Message:       __init__() got an unexpected keyword argument 'audio_column'

Am I the one who added this dataset ? No


Comments:
Yes, it's a known issue, and we expect to fix it soon.

Fixed.

<img width="1393" alt="Capture d’écran 2022-04-25 à 15 42 05" src="https://user-images.githubusercontent.com/1676121/165101176-d729d85b-efff-45a8-bad1-b69223edba5f.png">
Building C4 takes forever
State: closed
Labels: description: Something isn't working
name: bug

## Describe the bug
C4-en is a 300 GB dataset. However, when I try to download it through the hub it takes over _six hours_ to generate the train/test split from the downloaded files. This is an absurd amount of time and an unnecessary waste of resources.

## Steps to reproduce the bug
```python
c4 = datasets.load("c4", "en")
```

## Expected results
I would like to be able to download pre-split data.

## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.13.0-35-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 7.0.0
- Pandas version: 1.4.1


Comments:
Hi @StellaAthena, thanks for reporting.

Please note, that our `datasets` library performs several operations in order to load a dataset, among them:
- it downloads all the required files: for C4 "en", 378.69 GB of JSON GZIPped files
- it parses their content to generate the dataset
- it caches the dataset in an Arrow file: for C4 "en", this file size is 1.87 TB
- it memory-maps the Arrow file

If it suits your use case, you might load this dataset in streaming mode:
- no Arrow file is generated
- you can iterate over elements immediately (no need to wait to download all the entire files)

```python
In [45]: from datasets import load_dataset
    ...: ds = load_dataset("c4", "en", split="train", streaming=True)
    ...: for item in ds:
    ...:     print(item)
    ...:     break
    ...: 
{'text': 'Beginners BBQ Class Taking Place in Missoula!\nDo you want to get better at making delicious BBQ? You will have the opportunity, put this on your calendar now. Thursday, September 22nd join World Class BBQ Champion, Tony Balay from Lonestar Smoke Rangers. He will be teaching a beginner level class for everyone who wants to get better with their culinary skills.\nHe will teach you everything you need to know to compete in a KCBS BBQ competition, including techniques, recipes, timelines, meat selection and trimming, plus smoker and fire information.\nThe cost to be in the class is $35 per person, and for spectators it is free. Included in the cost will be either a t-shirt or apron and you will be tasting samples of each meat that is prepared.', 'timestamp': '2019-04-25T12:57:54Z', 'url': 'https://klyq.com/beginners-bbq-class-taking-place-in-missoula/'}
```
I hope this is useful for your use case.
medical_dialog zh has very slow _generate_examples
State: closed
Labels: description: Something isn't working
name: bug

## Describe the bug
After downloading the files from Google Drive, `load_dataset("medical_dialog", "zh", data_dir="./")` takes an unreasonable amount of time. Generating the train/test split for 33% of the dataset takes over 4.5 hours.

## Steps to reproduce the bug
The easiest way I've found to download files from Google Drive is to use `gdown` and use Google Colab because the download speeds will be very high due to the fact that they are both in Google Cloud.

```python
file_ids = [
        "1AnKxGEuzjeQsDHHqL3NqI_aplq2hVL_E",
        "1tt7weAT1SZknzRFyLXOT2fizceUUVRXX",
        "1A64VBbsQ_z8wZ2LDox586JIyyO6mIwWc",
        "1AKntx-ECnrxjB07B6BlVZcFRS4YPTB-J",
        "1xUk8AAua_x27bHUr-vNoAuhEAjTxOvsu",
        "1ezKTfe7BgqVN5o-8Vdtr9iAF0IueCSjP",
        "1tA7bSOxR1RRNqZst8cShzhuNHnayUf7c",
        "1pA3bCFA5nZDhsQutqsJcH3d712giFb0S",
        "1pTLFMdN1A3ro-KYghk4w4sMz6aGaMOdU",
        "1dUSnG0nUPq9TEQyHd6ZWvaxO0OpxVjXD",
        "1UfCH05nuWiIPbDZxQzHHGAHyMh8dmPQH",
]
for i in file_ids:
    url = f"https://drive.google.com/uc?id={i}"
    !gdown $url


from datasets import load_dataset

ds = load_dataset("medical_dialog", "zh", data_dir="./")
```

## Expected results
Faster load time

## Actual results
`Generating train split: 33%: 625519/1921127 [4:31:03<31:39:20, 11.37 examples/s]`

## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5

@vrindaprabhu , could you take a look at this since you implemented it? I think the `_generate_examples` function might need to be rewritten

Comments:
Hi @nbroad1881, thanks for reporting.

Let me have a look to try to improve its performance. 

Thanks @nbroad1881  for reporting! I don't recall it taking so long. I will also have a look at this. 
@albertvillanova please let me know if I am doing something unnecessary or time consuming.

Hi @nbroad1881 and @vrindaprabhu,

As a workaround for the performance of the parsing of the raw data files (this could be addressed in a subsequent PR), I have found that there are also processed data files, that do not require parsing. I have added these as new configurations `processed.en` and `processed.zh`:
```python
ds = load_dataset("medical_dialog", "processed.zh")
```
Representing dictionaries (json) objects as features
State: open
Labels: description: New feature or request
name: enhancement

In the process of adding a new dataset to the hub, I stumbled upon the inability to represent dictionaries that contain different key names, unknown in advance (and may differ between samples), original asked in the [forum](https://discuss.huggingface.co/t/representing-nested-dictionary-with-different-keys/16442).

For instance:

```
sample1 = {"nps": {
  "a": {"id": 0, "text": "text1"},
  "b": {"id": 1, "text": "text2"},
}}
sample2 = {"nps": {
  "a": {"id": 0, "text": "text1"},
  "b": {"id": 1, "text": "text2"},
  "c": {"id": 2, "text": "text3"},
}}
sample3 = {"nps": {
  "a": {"id": 0, "text": "text1"},
  "b": {"id": 1, "text": "text2"},
  "c": {"id": 2, "text": "text3"},
  "d": {"id": 3, "text": "text4"},
}}
```

the `nps` field cannot be represented as a Feature while maintaining its original structure.
@lhoestq suggested to add JSON as a new feature type, which will solve this problem.


It seems like an alternative solution would be to change the original data format, which isn't an optimal solution in my case. Moreover, JSON is a common structure, that will likely to be useful in future datasets as well.

Comments:
Hotfix failing CI tests on Windows
State: closed

This PR makes a hotfix for our CI Windows tests: https://app.circleci.com/pipelines/github/huggingface/datasets/11092/workflows/9cfdb1dd-0fec-4fe0-8122-5f533192ebdc/jobs/67414

Fix #4118

I guess this issue is related to this PR:
- huggingface/huggingface_hub#815

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Pretty print dataset info files
State: closed

Adds indentation to the `dataset_infos.json` file when saving for nicer diffs.

(suggested by @julien-c)

This PR also updates the info files of the GH datasets. Note that this change adds more than **10 MB** to the repo size (the total file size before the change: 29.672298 MB, after: 41.666475 MB), so I'm not sure this change is a good idea.

`src/datasets/info.py` is the only relevant file for reviewers.


Comments:
maybe just do it from now on no? (i.e. not for existing `dataset_infos.json` files)

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

> maybe just do it from now on no? (i.e. not for existing dataset_infos.json files)

Yes, or do this only for datasets created with `push_to_hub` to (always) keep the GH datasets small? 


yep sounds good too on my side! 

I reverted the change to avoid the size increase and added the `pretty_print` flag, which pretty-prints the JSON, and that flag is only True for datasets created with `push_to_hub`.
ImageFolder add option to ignore some folders like '.ipynb_checkpoints'
State: closed
Labels: description: New feature or request
name: enhancement

**Is your feature request related to a problem? Please describe.**
I sometimes like to peek at the dataset images from jupyterlab. thus '.ipynb_checkpoints' folder appears where my dataset is and (just realized) leads to accidental duplicate image additions. I think this is an easy enough thing to miss especially if the dataset is very large.

**Describe the solution you'd like**
maybe have an option `ignore` or something .gitignore style
`dataset = load_dataset("imagefolder", data_dir="./data/original", ignore="regex?")`

**Describe alternatives you've considered**
Could filter out manually



Comments:
Maybe it would be nice to ignore private dirs like this one (ones starting with `.`) by default. 

CC @mariosasko 

Maybe we can add a `ignore_hidden_files` flag to the builder configs of our packaged loaders (to be consistent across all of them), wdyt @lhoestq @albertvillanova? 

I think they should always ignore them actually ! Not sure if adding a flag would be helpful

@lhoestq But what if the user explicitly requests those files via regex?

`glob.glob` ignores hidden files (files starting with ".") by default unless they are explicitly requested, but fsspec's `glob` doesn't follow this behavior, which is probably a bug, so maybe we can raise an issue or open a PR in their repo?

> @lhoestq But what if the user explicitly requests those files via regex?

Usually hidden files are meant to be ignored. If they are data files, they must be placed outside a hidden directory in the first place right ? I think it's more sensible to explain this than adding a flag.

> glob.glob ignores hidden files (files starting with ".") by default unless they are explicitly requested, but fsspec's glob doesn't follow this behavior, which is probably a bug, so maybe we can raise an issue or open a PR in their repo?

After globbing using `fsspec`, we already ignore files that start with a `.` in `_resolve_single_pattern_locally` and `_resolve_single_pattern_in_dataset_repository`, I guess we can just account for parent directories as well ?

We could open an issue on `fsspec` but I think they won't change this since it's an important breaking change for them.
Multiprocessing with FileLock fails in python 3.9
State: closed
Labels: description: Something isn't working
name: bug

On python 3.9, this code hangs:
```python
from multiprocessing import Pool
from filelock import FileLock


def run(i):
    print(f"got the lock in multi process [{i}]")


with FileLock("tmp.lock"):
    with Pool(2) as pool:
        pool.map(run, range(2))

```

This is because the subprocesses try to acquire the lock from the main process for some reason. This is not the case in older versions of python.

This can cause many issues in python 3.9. In particular, we use multiprocessing to fetch data files when you load a dataset (as long as there are >16 data files). Therefore `imagefolder` hangs, and I expect any dataset that needs to download >16 files to hang as well.

Let's see if we can fix this and have a CI that runs on 3.9.

cc @mariosasko @julien-c 

Comments:
Closing this one because it must be used this way actually:
```python
def main():
    with FileLock("tmp.lock"):
        with Pool(2) as pool:
            pool.map(run, range(2))

if __name__ == "__main__":
    main()
```
Support huggingface_hub 0.5
State: closed

Following https://github.com/huggingface/datasets/issues/4105

`huggingface_hub` deprecated some parameters in `HfApi` in 0.5. This PR updates all the calls to HfApi to remove all the deprecations, 
and I set the `hugginface_hub` requirement to `>=0.5.0`


cc @adrinjalali @LysandreJik 

Comments:
Looks like GH actions is not able to resolve `huggingface_hub` 0.5.0, I'm investivating

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

I'm glad to see changes in `huggingface_hub` are simplifying code here.

seems to supersede #4102, feel free to close mine :)

maybe just cherry-pick the docstring fix

I think I've found the issue:
- https://github.com/huggingface/huggingface_hub/pull/790

Good catch, `huggingface_hub` doesn't support python 3.6 anymore indeed, therefore we should keep support for 0.4.0. I'm reverting the requirement version bump for now.

We can update the requirement once we drop support for python 3.6 in `datasets`

@lhoestq, I've opened this PR on `huggingface_hub`: 
- https://github.com/huggingface/huggingface_hub/pull/823

Is there any strong reason why `huggingface_hub` no longer supports Python 3.6? 

I think `datasets` can drop support for 3.6 soon. But for now maybe let's keep support for 0.4.0, python 3.6 users are not affected by https://github.com/huggingface/datasets/issues/4105 anyway.

`huggingface_hub` doesn't not have to support 3.6 again just for the CI IMO

@lhoestq I commented on the PR, that IMO it is not a good practice to drop support for Python 3.6 without a previous deprecation cycle.

Re-added support for older versions. I ended up checking `huggingface_hub` version to use the old, deprecated API for <0.5.0

I find it good practice to have all dependency version related code in a single file so that when you decide to remove support for an old version of a dependency it's easy to find and remove them, hence suggesting `utils/_fixes.py` in https://github.com/huggingface/datasets/issues/4105#issuecomment-1090041204

good idea, thanks !

I used your suggestion @adrinjalali , I just replace the try/except with a check on the version of `huggingface_hub`
How can I download only the train and test split for full numbers using load_dataset()? 
State: open
Labels: description: New feature or request
name: enhancement

How can I download only the train and test split for full numbers using load_dataset()? 

I do not need the extra split and it will take 40 mins just to download in Colab. I have very short time in hand. Please help.

Comments:
Hi! Can you please specify the full name of the dataset? IIRC `full_numbers` is one of the configs of the `svhn` dataset, and its generation is slow due to data being stored in binary Matlab files. Even if you specify a specific split, `datasets` downloads all of them, but we plan to fix that soon and only download the requested split.

If you are in a hurry, download the `svhn` script [here](`https://huggingface.co/datasets/svhn/blob/main/svhn.py`), remove [this code](https://huggingface.co/datasets/svhn/blob/main/svhn.py#L155-L162), and run:
```python
from datasets import load_dataset
dset = load_dataset("path/to/your/local/script.py", "full_numbers")
```

And to make loading easier in Colab, you can create a dataset repo on the Hub and upload the script there. Or push the script to Google Drive and mount the drive in Colab.
Improve RedCaps dataset card
State: closed

This PR modifies the RedCaps card to:
* fix the formatting of the Point of Contact fields on the Hub
* speed up the image fetching logic (aligns it with the [img2dataset](https://github.com/rom1504/img2dataset) tool) and make it more robust (return None if **any** exception is thrown)

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

I find this preprocessing a bit too specific to add it as a method to `datasets` as it's only useful in the context of CV (and we support multiple modalities). However, I agree it would be great to move this code to another lib to avoid code duplication. Maybe we should create a package with preprocessing functions/transforms for this purpose?
Proposing WikiSplit metric card
State: closed

Pinging @lhoestq to ensure that my distinction between the dataset and the metric are clear :sweat_smile:

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

A quick Github tip ;) To avoid running N times the CI, you can push all the changes at once: go to Files Changed tab, and on each suggestion there's a "add to commit batch" and then you can do one commit for all the suggestions you want to approve ;)

Oh thanks for the tip!! Haha I was wondering why it was running a bunch of
times :P

On Tue, Apr 5, 2022 at 11:44 AM Quentin Lhoest ***@***.***>
wrote:

> A quick Github tip ;) To avoid running N times the CI, you can push all
> the changes at once: go to Files Changed tab, and on each suggestion
> there's a "add to commit batch" and then you can do one commit for all the
> suggestions you want to approve ;)
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/datasets/pull/4098#issuecomment-1088894515>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/ADMMIIRZYNVFJRWRWW4VJY3VDRNUBANCNFSM5SS7L5HA>
> .
> You are receiving this because you modified the open/close state.Message
> ID: ***@***.***>
>


-- 
Sasha Luccioni, PhD
Postdoctoral Researcher (Mila, Université de Montréal)
Chercheure postdoctorale (Mila, Université de Montréal)
https://www.sashaluccioni.com/
                [image: Image result for universite de montreal logo]
Helo Mayfrends
State: closed
Labels: description: Requesting to add a new dataset
name: dataset request

## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


Comments:
elena-soare/crawled-ecommerce: missing dataset
State: closed
Labels: description: Related to the dataset viewer on huggingface.co
name: dataset-viewer

elena-soare/crawled-ecommerce

**Link:** *link to the dataset viewer page*

*short description of the issue*

Am I the one who added this dataset ? Yes-No


Comments:
It's a bug! Thanks for reporting, I'm looking at it.

By the way, the error on our part is due to the huge size of every row (~90MB). The dataset viewer does not support such big dataset rows for the moment.
Anyway, we're working to give a hint about this in the dataset viewer.

Fixed. See https://huggingface.co/datasets/elena-soare/crawled-ecommerce/viewer/elena-soare--crawled-ecommerce/train.

<img width="1552" alt="Capture d’écran 2022-04-12 à 11 23 51" src="https://user-images.githubusercontent.com/1676121/162929722-2e2b80e2-154a-4b61-87bd-e341bd6c46e6.png">

Thanks for reporting!
Create metric card for Frugal Score
State: closed

Proposing metric card for Frugal Score.

@albertvillanova or @lhoestq  -- there are certain aspects that I'm not 100% sure on (such as how exactly the distillation between BertScore and FrugalScore is done) -- so if you find that something isn't clear, please let me know!

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Remove unused legacy Beam utils
State: closed

This PR removes unused legacy custom `WriteToParquet`, once official Apache Beam includes the patch since version 2.22.0: 
- Patch PR: https://github.com/apache/beam/pull/11699
- Issue: https://issues.apache.org/jira/browse/BEAM-10022

In relation with:
- #204

Comments:
_The documentation is not available anymore as the PR was closed or merged._
Fix BeamWriter output Parquet file
State: closed

Since now, the `BeamWriter` saved a Parquet file with a simplified schema, where each field value was serialized to JSON. That resulted in Parquet files larger than Arrow files.

This PR:
- writes Parquet file preserving original schema and without serialization, thus avoiding serialization overhead and resulting in a smaller output file size.
- fixes `parquet_to_arrow` function

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