rachel521/github-issues-simple huggingface dataset:
Dataset Viewer issue for super_glue
Opened by wzsxxa (closed)

### Link

https://huggingface.co/datasets/super_glue

### Description

can't view super_glue dataset on the web page

### Owner

_No response_

Comments: Thanks for reporting @wzsxxa.

However the "super_glue" dataset is rendered properly by the Dataset preview: https://huggingface.co/datasets/super_glue
Trouble loading image folder with additional features -  metadata file ignored
Opened by linoytsaban (closed)

### Describe the bug

Loading image folder with a caption column using `load_dataset(<image_folder_path>)` doesn't load the captions. 

When loading a local image folder with captions using `datasets==2.13.0`
```
from datasets import load_dataset
data = load_dataset(<image_folder_path>)
data.column_names
```
yields 
`{'train': ['image', 'prompt']}`

but when using `datasets==2.15.0`
yeilds 
`{'train': ['image']}`

Putting the images and `metadata.jsonl` file into a nested `train` folder **or** loading with `load_dataset("imagefolder", data_dir=<image_folder_path>)` solves the issue and
yields 
`{'train': ['image', 'prompt']}`

### Steps to reproduce the bug

1. create a folder `<image_folder_path>` that contains images and a metadata file with additional features- e.g. "prompt"
2. run:
```
from datasets import load_dataset
data = load_dataset("<image_folder_path>")
data.column_names
```

### Expected behavior

`{'train': ['image', 'prompt']}`

### Environment info

- `datasets` version: 2.15.0
- Platform: Linux-5.15.120+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.19.4
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
- `fsspec` version: 2023.6.0

Comments: I reproduced too:
- root: metadata file is ignored (https://huggingface.co/datasets/severo/doc-image-3)
- data/ dir: metadata file is ignored (https://huggingface.co/datasets/severo/doc-image-4)
- train/ dir: works (https://huggingface.co/datasets/severo/doc-image-5)
BBox type
Opened by lhoestq (closed)

see [internal discussion](https://huggingface.slack.com/archives/C02EK7C3SHW/p1703097195609209)

Draft to get some feedback on a possible `BBox` feature type that can be used to get object detection bounding boxes data in one format or another.

```python
>>> from datasets import load_dataset, BBox
>>> ds = load_dataset("svhn", "full_numbers", split="train")
>>> ds[0]
{
  'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=107x46 at 0x126409BE0>,
  'digits': {'bbox': [[38, 1, 21, 40], [57, 3, 16, 40]], 'label': [4, 6]}
}
>>> ds = ds.rename_column("digits", "annotations").cast_column("annotations", BBox(format="coco"))
>>> ds[0]
{
  'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=107x46 at 0x147730070>,
  'annotations': [{'bbox': [38, 1, 21, 40], 'category_id': 4}, {'bbox': [57, 3, 16, 40], 'category_id': 6}]
}
```

note that it's a type for a list of bounding boxes, not just one - which would be needed to switch from a format to another using type casting.

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6525). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

closing in favor of other ideas that would not involve any typing
CI is broken for faiss tests on Windows: node down: Not properly terminated
Opened by albertvillanova (closed)

Faiss tests on Windows make the CI run indefinitely until maximum execution time (360 minutes) is reached.

See: https://github.com/huggingface/datasets/actions/runs/9712659783
```
test (integration, windows-latest, deps-minimum)
The job running on runner GitHub Actions 60 has exceeded the maximum execution time of 360 minutes.
test (integration, windows-latest, deps-latest)
The job running on runner GitHub Actions 238 has exceeded the maximum execution time of 360 minutes.
```
```
____________________________ tests/test_search.py _____________________________
[gw1] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe
worker 'gw1' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index'
____________________________ tests/test_search.py _____________________________
[gw2] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe
worker 'gw2' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index'
```
```
tests/test_search.py::IndexableDatasetTest::test_add_faiss_index 
[gw0] node down: Not properly terminated
[gw0] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index 

replacing crashed worker gw0

tests/test_search.py::IndexableDatasetTest::test_add_faiss_index 
[gw1] node down: Not properly terminated
[gw1] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index 

replacing crashed worker gw1

tests/test_search.py::IndexableDatasetTest::test_add_faiss_index 
[gw2] node down: Not properly terminated
[gw2] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index 

replacing crashed worker gw2
```
Improve task api code quality
Opened by mariosasko (closed)

Improves the code quality of the `TaskTemplate` dataclasses.

Changes:
* replaces `return NotImplemented` with raise `NotImplementedError` 
* replaces `sorted` with `len` in the uniqueness check 
* defines `label2id` and `id2label` in the `TextClassification` template as properties
* replaces the `object.__setattr__(self, attr, value)` syntax with (IMO nicer) `self.__dict__[attr] = value`

Comments: Looks good thanks, what do you think @lewtun ?

thanks for including the lazy `ClassLabel` class @mariosasko ! from my side this LGTM!
Dataset preview is not available (I think for all Hugging Face datasets)
Opened by Abirate (closed)

## 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.
ExpectedMoreSplits error on load_dataset when upgrading to 2.19.0
Opened by ebsmothers (open)

### Describe the bug

Hi there, thanks for the great library! We have been using it a lot in torchtune and it's been a huge help for us. 

Regarding the bug: the same call to `load_dataset` errors with `ExpectedMoreSplits` in 2.19.0 after working fine in 2.18.0. Full details given in the repro below.

### Steps to reproduce the bug

On 2.18.0, things work fine:
```
# First clear the locally cached dataset
rm -r ~/.cache/huggingface/datasets/lvwerra___stack-exchange-paired
pip install "datasets==2.18.0"
python3
>>> from datasets import load_dataset
>>> dataset = load_dataset('lvwerra/stack-exchange-paired', split='train', data_dir='data/rl')
```

On 2.19.0, they do not:
```
# First clear the locally cached dataset
rm -r ~/.cache/huggingface/datasets/lvwerra___stack-exchange-paired
pip install "datasets==2.19.0"
python3
>>> from datasets import load_dataset
>>> dataset = load_dataset('lvwerra/stack-exchange-paired', split='train', data_dir='data/rl')
```

The stack trace I see from the 2.19.0 version of load_dataset can be seen [here](https://gist.github.com/ebsmothers/f9b1f1949bee7030a8d7bb8a491550d2).

(Maybe unsurprising but) notably if I do not delete the cache first I am able to load the dataset successfully. So based on this I suspect the cause is somewhere in the download logic.

### Expected behavior

Download the dataset successfully :)

### Environment info

- `datasets` version: 2.19.0
- Platform: Linux-5.12.0-0_fbk16_zion_7661_geb00762ce6d2-x86_64-with-glibc2.34
- Python version: 3.11.9
- `huggingface_hub` version: 0.22.2
- PyArrow version: 16.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1

Comments: Get same error on same datasets too.

+1

same error
CI is broken after ruff-0.5.0: E721
Opened by albertvillanova (closed)

After ruff-0.5.0 release (https://github.com/astral-sh/ruff/releases/tag/0.5.0), our CI is broken due to E721 rule. 

See: https://github.com/huggingface/datasets/actions/runs/9707641618/job/26793170961?pr=6983
> src/datasets/features/features.py:844:12: E721 Use `is` and `is not` for type comparisons, or `isinstance()` for isinstance checks
Invalid download URL of WMT17 `zh-en` data 
Opened by Ririkoo (closed)

## Describe the bug
Partial data (wmt17 zh-en) cannot be downloaded due to an invalid URL.

## Steps to reproduce the bug
```python
from datasets import load_dataset
 dataset = load_dataset('wmt17','zh-en')
```

## Expected results
ConnectionError: Couldn't reach ftp://cwmt-wmt:cwmt-wmt@datasets.nju.edu.cn/parallel/casia2015.zip

Comments: Fixed in the current release. Close this issue.
Add NewsPH_NLI dataset
Opened by anaerobeth (closed)

This PR adds the NewsPH-NLI Dataset, the first benchmark dataset for sentence entailment in the low-resource Filipino language. Constructed through exploting the structure of news articles. Contains 600,000 premise-hypothesis pairs, in 70-15-15 split for training, validation, and testing.

Link to the paper: https://arxiv.org/pdf/2010.11574.pdf

Link to the dataset/repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
Fix NestedDataStructure.data for empty dict
Opened by albertvillanova (closed)

Fix #1981

Comments: I validated that this fixed the problem, thank you, @albertvillanova!


still facing the same issue or similar:
from datasets import load_dataset
wtm14_test = load_dataset('wmt14',"de-en",cache_dir='./datasets')

~\.cache\huggingface\modules\datasets_modules\datasets\wmt14\43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e\wmt_utils.py in _split_generators(self, dl_manager)
    758         # Extract manually downloaded files.
    759         manual_files = dl_manager.extract(manual_paths_dict)
--> 760         extraction_map = dict(downloaded_files, **manual_files)
    761 
    762         for language in self.config.language_pair:

TypeError: type object argument after ** must be a mapping, not list

Hi @sabania 
We released a patch version that fixes this issue (1.4.1), can you try with the new version please ?
```
pip install --upgrade datasets
```

I re-validated with the hotfix and the problem is no more.

It's working. thanks a lot.
nlp.Features does not distinguish between nullable and non-nullable types in PyArrow schema
Opened by jarednielsen (closed)

Here's the code I'm trying to run:

```python
dset_wikipedia = nlp.load_dataset("wikipedia", "20200501.en", split="train", cache_dir=args.cache_dir)
dset_wikipedia.drop(columns=["title"])
dset_wikipedia.features.pop("title")
dset_books = nlp.load_dataset("bookcorpus", split="train", cache_dir=args.cache_dir)
dset = nlp.concatenate_datasets([dset_wikipedia, dset_books])
```

This fails because they have different schemas, despite having identical features.

```python
assert dset_wikipedia.features == dset_books.features # True
assert dset_wikipedia._data.schema == dset_books._data.schema # False
```

The Wikipedia dataset has 'text: string', while the BookCorpus dataset has 'text: string not null'. Currently I hack together a working schema match with the following line, but it would be better if this was handled in Features themselves.

```python
dset_wikipedia._data = dset_wikipedia.data.cast(dset_books._data.schema)
```


Comments: In 0.4.0, the assertion in `concatenate_datasets ` is on the features, and not the schema.
Could you try to update `nlp` ?

Also, since 0.4.0, you can use `dset_wikipedia.cast_(dset_books.features)` to avoid the schema cast hack.

Or maybe the assertion comes from elsewhere ?

I'm using the master branch. The assertion failure comes from the underlying `pa.concat_tables()`, which is in the pyarrow package. That method does check schemas.

Since `features.type` does not contain information about nullable vs non-nullable features, the `cast_()` method won't resolve the schema mismatch. There is information in a schema which is not stored in features.

I'm doing a refactor of type inference in #363 . Both text fields should match after that

By default nullable will be set to True

It should be good now. I was able to run

```python
>>> from nlp import concatenate_datasets, load_dataset
>>>
>>> bookcorpus = load_dataset("bookcorpus", split="train")
>>> wiki = load_dataset("wikipedia", "20200501.en", split="train")
>>> wiki.remove_columns_("title")  # only keep the text
>>>
>>> assert bookcorpus.features.type == wiki.features.type
>>> bert_dataset = concatenate_datasets([bookcorpus, wiki])
```

Thanks!
add thaiqa_squad
Opened by cstorm125 (closed)

Example format is a little different from SQuAD since `thaiqa` always have one answer per question so I added a check to convert answers to lists if they are not already one to future-proof additional questions that might have multiple answers.

`thaiqa_squad` is an open-domain, extractive question answering dataset (4,000 questions in `train` and 74 questions in `dev`) in [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, originally created by [NECTEC](https://www.nectec.or.th/en/) from Wikipedia articles and adapted to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format by [PyThaiNLP](https://github.com/PyThaiNLP/).
Add Hippocorpus Dataset
Opened by manandey (closed)


Comments: > Before we merge can you try to reduce the size of the dummy_data.zip file ?
> 
> To do so feel free to only keep a few lines of the csv files ans also remove unnecessary chunks of texts (for example keep only the first sentences of a story).

Hi @lhoestq, I have reduced the size of the dummy_data.zip file by making the necessary changes you had suggested. 

merging since the CI is fixed on master
Support downloading specific splits in load_dataset
Opened by ArjunJagdale (closed)

This PR builds on #6832 by @mariosasko.

May close - #4101, #2538

Discussion - https://github.com/huggingface/datasets/pull/7648#issuecomment-3084050130

---
### Note - This PR is under work and frequent changes will be pushed.

Comments: I’ve completed the following steps to continue the partial split download support (from PR #6832):

I did changes on top of what has been done by mario. Here are some of those changes: 
- Restored support for writing multiple split shards:

- In _prepare_split_single, we now correctly replace JJJJJ and SSSSS placeholders in the fpath for job/shard IDs before creating the writer.

- Added os.makedirs(os.path.dirname(path), exist_ok=True) after placeholder substitution to prevent FileNotFoundError.

- Applied the fix to both split writers:

       1] self._generate_examples version (used by most modules).

       2] self._generate_tables version (used by IterableDatasetBuilder).

- Confirmed 109/113 tests passing, meaning the general logic is working across the board.

What’s still failing
4 integration tests fail:

`test_load_hub_dataset_with_single_config_in_metadata`

`test_load_hub_dataset_with_two_config_in_metadata`

`test_load_hub_dataset_with_metadata_config_in_parallel`

`test_reload_old_cache_from_2_15`

All are due to FileNotFoundError from uncreated output paths, which I'm currently finalizing by ensuring os.makedirs() is correctly applied before every writer instantiation.

I will update about these fixes after running tests!

@lhoestq this was just an update

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7695). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Local DIR wasn't doing well, dk actually what happened, will PR again! Sorry :)
[save_to_disk] Using `select()` followed by `save_to_disk` saves complete dataset making it hard to create dummy dataset
Opened by patrickvonplaten (closed)

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


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

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

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

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

+1 on this !
Fix type of bridge field in QED
Opened by mariosasko (closed)

Use `Value("string")` instead of `Value("bool")` for the feature type of the `"bridge"` field in the QED dataset. If the value is `False`, set to `None`.

The following paragraph in the QED repo explains the purpose of this field:
>Each annotation in referential_equalities is a pair of spans, the question_reference and the sentence_reference, corresponding to an entity mention in the question and the selected_sentence respectively. As described in the paper, sentence_references can be "bridged in", in which case they do not correspond with any actual span in the selected_sentence. Hence, sentence_reference spans contain an additional field, bridge, which is a prepositional phrase when a reference is bridged, and is False otherwise. Prepositional phrases serve to link bridged references to an anchoring phrase in the selected_sentence. In the case a sentence_reference is bridged, the start and end, as well as the span string, map to such an anchoring phrase in the selected_sentence.

Fix #3346

cc @VictorSanh
Add citation to ro_sts and ro_sts_parallel datasets
Opened by albertvillanova (closed)

This PR adds the citation information to `ro_sts_parallel` and `ro_sts_parallel` datasets, once they have replied our request for that information:
- https://github.com/dumitrescustefan/RO-STS/issues/4

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4892). All of your documentation changes will be reflected on that endpoint.
Fix map() example in datasets documentation: define tokenizer before use
Opened by Sanjaykumar030 (closed)

## Problem
The current datasets.Dataset.map() example in the documentation demonstrates batched processing using a tokenizer object without defining or importing it. This causes a NameError when users copy and run the example as-is, breaking the expected seamless experience.

## Correction
This PR fixes the issue by explicitly importing and initializing the tokenizer using the Transformers library (AutoTokenizer.from_pretrained("bert-base-uncased")), making the example self-contained and runnable without errors.
This will help new users understand the workflow and apply the method correctly.

Closes #7703 



Comments: Hi @lhoestq, just a gentle follow-up on this doc fix PR (#7704). Let me know if any changes are needed — happy to update.
Hope this improvement helps users run the example without confusion!

the modified file is the readme of the docs, not about map() specifically
While importing load_dataset from datasets 
Opened by suprith-hub (closed)

### Describe the bug

cannot import name 'DEFAULT_CIPHERS' from 'urllib3.util.ssl_' this is the error i received

### Steps to reproduce the bug

from datasets import load_dataset

### Expected behavior

No errors

### Environment info

python 3.11.5

Comments: Hi! Can you please share the error's stack trace so we can see where it comes from?

We cannot reproduce the issue and we do not have enough information: environment info (need to run `datasets-cli env`), stack trace,...

I am closing the issue. Feel free to reopen it (with additional information) if the problem persists.

Yeah 👍

On Tue, 6 Feb 2024 at 2:56 PM, Albert Villanova del Moral <
***@***.***> wrote:

> We cannot reproduce the issue and we do not have enough information:
> environment info (need to run datasets-cli env), stack trace,...
>
> I am closing the issue. Feel free to reopen it (with additional
> information) if the problem persists.
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/datasets/issues/6618#issuecomment-1929102334>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/ASS4PJ3XOIIWISPY3VX3QRTYSHZK5AVCNFSM6AAAAABCL3BT4SVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSMRZGEYDEMZTGQ>
> .
> You are receiving this because you authored the thread.Message ID:
> ***@***.***>
>


Please downgrade the version of urllib3 if you have the same issue:

!pip install urllib3==1.25.11

> Please downgrade the version of urllib3 if you have the same issue:
> 
> !pip install urllib3==1.25.11

This worked for me. Thanks.

I use python 3.11 and datasets==2.20.0. Downgrading urllib3 to 1.25.11 worked in my case.
COCO 🥥 on the 🤗 Hub?
Opened by merveenoyan (closed)

This is a draft PR since I ran into few small problems. 
I referred to this TFDS code: https://github.com/tensorflow/datasets/blob/2538a08c184d53b37bfcf52cc21dd382572a88f4/tensorflow_datasets/object_detection/coco.py
cc: @mariosasko 

Comments: @mariosasko I fixed couple of bugs

TO-DO: 
- [x] Add unlabeled 2017 splits, train and validation splits of 2015
- [x] Add Class Labels as list instead

@mariosasko added fine & coarse grained labels, will fix the bugs (currently getting set up with VM, my internet is too slow to run the tests and download the data 🥲)

migrated to here https://github.com/huggingface/datasets/tree/coco