josteece/github-issues huggingface dataset:
[SEO improvement] Add Dataset Metadata to make datasets indexable
Opened by cakiki (closed)

Some people who host datasets on github seem to include a table of metadata at the end of their README.md to make the dataset indexable by [Google Dataset Search](https://datasetsearch.research.google.com/) (See [here](https://github.com/google-research/google-research/tree/master/goemotions#dataset-metadata) and [here](https://github.com/cvdfoundation/google-landmark#dataset-metadata)). This could be a useful addition to canonical datasets; perhaps even community datasets.

I'll include a screenshot (as opposed to markdown) as an example so as not to have a github issue indexed as a dataset:

> ![image](https://user-images.githubusercontent.com/3664563/144496173-953428cf-633a-4571-b75b-f099c6b2ed65.png)



**_PS: It might very well be the case that this is already covered by some other markdown magic I'm not aware of._**


Comments:
New: Americas NLI dataset
Opened by fdschmidt93 (closed)

This PR adds the [Americas NLI](https://arxiv.org/abs/2104.08726) dataset, extension of XNLI to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika.

One odd thing (not sure) is that I had to set

`datasets-cli dummy_data ./datasets/americas_nli/ --auto_generate --n_lines 7500`

`n_lines` very large to successfully generate the dummy files for all the subsets. Happy to get some guidance here.

Otherwise, I hope everything is in order :)

e: missed a step, onto fixing the tests
e2: there you go -- hope it's ok to have added more languages with their ISO codes to `languages.json`, need those tests to pass :laughing: 

Comments:
Document a training loop for streaming dataset
Opened by lhoestq (closed)

I added some docs about streaming dataset. In particular I added two subsections:
- one on how to use `map` for preprocessing
- one on how to use a streaming dataset in a pytorch training loop

cc @patrickvonplaten @stevhliu if you have some comments
cc @Rocketknight1 later we can add the one for TF and I might need your help ^^'

Comments:
[Audio] Allow resampling for audio datasets in streaming mode
Opened by patrickvonplaten (closed)

Many audio datasets like Common Voice always need to be resampled. This can very easily be done in non-streaming mode as follows:

```python
from datasets import load_dataset

ds = load_dataset("common_voice", "ab", split="test")

ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
```

However in streaming mode it fails currently:

```python
from datasets import load_dataset

ds = load_dataset("common_voice", "ab", split="test", streaming=True)

ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
```

with the following error:

```
AttributeError: 'IterableDataset' object has no attribute 'cast_column'  
```

It would be great if we could add such a feature (I'm not 100% sure though how complex this would be)

Comments:
This requires implementing `cast_column` for iterable datasets, it could be a very nice addition !


It can also be useful to be able to disable the audio/image decoding for the dataset viewer (see PR https://github.com/huggingface/datasets/pull/3430) cc @severo 

EDIT: actually following https://github.com/huggingface/datasets/issues/3145 the dataset viewer might not need it anymore

Just to clarify a bit. This feature is **always** needed when using the common voice dataset in streaming mode. So I think it's quite important
Add multimodal datasets
Opened by albertvillanova (open)

Epic issue to track the addition of multimodal datasets:
- [ ] #2526
- [x] #1842
- [ ] #1810

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

@VictorSanh feel free to add and sort by priority any interesting dataset. I have added the multimodal dataset requests which were already present as issues.

Comments:
Add task tags for multimodal datasets
Opened by albertvillanova (closed)

## **Is your feature request related to a problem? Please describe.**

Currently, task tags are either exclusively related to text or speech processing:
- https://github.com/huggingface/datasets/blob/master/src/datasets/utils/resources/tasks.json

## **Describe the solution you'd like**

We should also add tasks related to:
- multimodality
- image
- video

CC: @VictorSanh @lewtun @lhoestq @merveenoyan @SBrandeis 

Comments:
The Hub pulls these tags from [here](https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts) (allows multimodal tasks) now, so I'm closing this issue.
Use the Audio feature in the AutomaticSpeechRecognition template
Opened by anton-l (closed)

This updates the ASR template and all supported datasets to use the `Audio` feature

Comments:
Cool !

I noticed that you removed the `audio_file_path_column` field of the template, note that you also have to update all the dataset_infos.json file that still contain this outdated field. For example in the common_voice you can find this:
```
"task_templates": [{"task": "automatic-speech-recognition", "audio_file_path_column": "path", "transcription_column": "sentence"}]
```

Yes, will do that. I'm just busy with the bigscience task.

After we merge this, we should also update the following dataset scripts: https://huggingface.co/datasets?task_ids=task_ids:automatic-speech-recognition

Closing in favor of https://github.com/huggingface/datasets/pull/4006
Adapt image datasets
Opened by mariosasko (closed)

This PR:
* adapts the ImageClassification template to use the new Image feature
* adapts the following datasets to use the new Image feature:
  * beans (+ fixes streaming)
  * cast_vs_dogs (+ fixes streaming)
  * cifar10
  * cifar100
  * fashion_mnist
  * mnist
  * head_qa

cc @nateraw

Comments:
This PR can be merged after #3163 is merged (this PR is pretty big because I was working on the forked branch).

@lhoestq @albertvillanova Could you please take a look at the changes in `src/datasets/utils/streaming_download_manager.py`? These changes were required to support streaming of the `cats_vs_dogs` and the `beans` datasets.

The CI failures are due to the missing fields in the README files.

and thanks for adding support for Path.name and Path.parent for streaming :)
Add The Pile Free Law subset
Opened by albertvillanova (closed)

Add:
- Free Law subset of The Pile: "free_law" config

Close bigscience-workshop/data_tooling#75.

CC: @StellaAthena

Comments:
@albertvillanova Is there a specific reason you’re adding the Pile under “the” instead of under “pile”? That does not appear to be consistent with other datasets.

Hi @StellaAthena,

I asked myself the same question, but at the end I decided to be consistent with previously added Pile subsets:
- #2817

I guess the reason is to stress that the definite article is always used before the name of the dataset (your site says: "The Pile. An 800GB Dataset of Diverse Text for Language Modeling"). Other datasets are not usually preceded by the definite article, like "the SQuAD" or "the GLUE" or "the Common Voice"...

CC: @lhoestq 

> I guess the reason is to stress that the definite article is always used before the name of the dataset (your site says: "The Pile. An 800GB Dataset of Diverse Text for Language Modeling").

Yes that's because of this that it starts with "the"
add new field, and get errors
Opened by PatricYan (closed)

after adding new field **tokenized_examples["example_id"]**, and get errors below,
I think it is due to changing data to tensor, and **tokenized_examples["example_id"]** is string list 
**all fields**
```
***************** train_dataset 1: Dataset({
    features: ['attention_mask', 'end_positions', 'example_id', 'input_ids', 'start_positions', 'token_type_ids'],
    num_rows: 87714
})
```

**Errors**
```
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 705, in convert_to_tensors
    tensor = as_tensor(value)
ValueError: too many dimensions 'str'
```

Comments:
Hi, 

could you please post this question on our [Forum](https://discuss.huggingface.co/) as we keep issues for bugs and feature requests? 

> Hi,
> 
> could you please post this question on our [Forum](https://discuss.huggingface.co/) as we keep issues for bugs and feature requests?

ok.
to_tf_dataset() refactor
Opened by Rocketknight1 (closed)

This is the promised cleanup to `to_tf_dataset()` now that the course is out of the way! The main changes are:

- A collator is always required (there was way too much hackiness making things like labels work without it)
- Lots of cleanup and a lot of code moved to `_get_output_signature`
- Should now handle it gracefully when the data collator adds unexpected columns

Comments:
Also, please don't merge yet - I need to make sure all the code samples and notebooks have a collate_fn specified, since we're removing the ability for this method to work without one!

Hi @lhoestq @mariosasko, the other PRs this was depending on in Transformers and huggingface/notebooks are now merged, so this is ready to go. Do you want to take one more look at it, or are you happy at this point?

The documentation for the method is fine, it doesn't need to be changed, but the tutorial notebook definitely looks a little out of date. Let me see what I can do!

@lhoestq I rewrote the last bit of the notebook - let me know what you think!

Cool thank you ! It's much nicer that what we had :)

I also spotted other things I'd like to update in the notebook (especially the beginning) but it can be fixed later
Extend support for streaming datasets that use pd.read_excel
Opened by albertvillanova (closed)

This PR fixes error:
```
ValueError: Cannot seek streaming HTTP file
```

CC: @severo 

Comments:
TODO in the future: https://github.com/huggingface/datasets/pull/3355#discussion_r761138011
- If we finally find a use case where the `pd.read_excel()` can work in streaming mode (using fsspec), that is, without using the `.read()`, I propose to try this first, catch the ValueError and then try with `.read`, but all implemented in `xpandas_read_excel`.
raise exception instead of using assertions.
Opened by manisnesan (closed)

fix for the remaining files https://github.com/huggingface/datasets/issues/3171

Comments:
@mariosasko - Thanks for the review & suggestions. Updated as per the suggestions. 

@mariosasko - Hello, Are there any additional changes required from my end??. Wondering if this PR can be merged or still pending on additional steps.

@mariosasko - The approved changes in the PR now has conflicts with the master branch. Would you like me to resolve the conflicts??.  Let me know.   

@mariosasko @lhoestq - Gentle reminder about my previous question. 

Hi ! Thanks for the heads up :)
I just resolved the conflicts, it should be alright now

Merging, thanks for the help @manisnesan !
BLEURT: Match key names to correspond with filename
Opened by jaehlee (closed)

In order to properly locate downloaded ckpt files key name needs to match filename. Correcting change introduced in #3235 

Comments:
Thanks for the suggestion! I think the current checked-in `CHECKPOINT_URLS` is already not working. I believe anyone who tried using the new ckpts (`BLEURT-20-X`) can't unless this fix is in. The zip file from bleurt side unzips to directory name matching the filename (capitalized for new ones). For example without current changes I'd get the following error

```
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-5-f6832fe20f84> in <module>()
      1 predictions = ["hello there", "general kenobi"]
      2 references = ["hello there", "general kenobi"]
----> 3 bleurt = datasets.load_metric("bleurt", "bleurt-20")
      4 results = bleurt.compute(predictions=predictions, references=references)

4 frames
/usr/local/lib/python3.7/dist-packages/bleurt/checkpoint.py in read_bleurt_config(path)
     84   """Reads and checks config file from a BLEURT checkpoint."""
     85   assert tf.io.gfile.exists(path), \
---> 86       "Could not find BLEURT checkpoint {}".format(path)
     87   config_path = os.path.join(path, CONFIG_FILE)
     88   assert tf.io.gfile.exists(config_path), \

AssertionError: Could not find BLEURT checkpoint /root/.cache/huggingface/metrics/bleurt/bleurt-20/downloads/extracted/e34c60f1a05394ecda54e253a10413ca7b5d59f9a23f3cc73258c6b78ffa2f50/bleurt-20
```
inspecting specified path I see that directory name is `BLEURT-20` instead of `bleurt-20`. 
Other solution similar to your suggestion is meddle with `dl_manager.download_and_extract` to unzip to paths with lowering all the paths but I imagine this will affect other parts of the library. 

Indeed, good catch ! Your solution that fixes `CHECKPOINT_URLS ` is simple and works well, thanks :)

Furthermore to avoid breaking changes though we could also keep the support for the lowercase one:
```python
        if self.config_name.lower() in CHECKPOINT_URLS:
            checkpoint_name = self.config_name.lower()
        elif self.config_name.upper() in CHECKPOINT_URLS:
            checkpoint_name = self.config_name.upper()
        else:
            raise KeyError(
                f"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"
            )
```
and then we can use `checkpoint_name` instead of `self.config_name` to download and instantiate the model:
```python
        model_path = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
        self.scorer = score.BleurtScorer(os.path.join(model_path, checkpoint_name))
```

Please let me know if that sounds reasonable to you !

Thanks for the suggestion! I believe your suggestion should work to make keys case insensitive. Changes are committed to the PR now.
iter_archive for zip files 
Opened by Mehdi2402 (closed)

* In this PR, I added the option to iterate through zipfiles for `download_manager.py` only. 
* Next PR will be the same applied to `streaming_download_manager.py`.
* Related issue #3272.
## Comments : 
* There is no `.isreg()` equivalent in zipfile library to check if file is Regular so I used `.is_dir()` instead to skip directories.
* For now I got `streaming_download_manager.py`  working for local zip files, but not for urls. I get the following error when I test it on an archive in google drive, so still working on it.  `BlockSizeError: Got more bytes so far (>2112) than requested (22)`


## Tasks : 
- [x] download_manager.py
- [ ] streaming_download_manager.py

Comments:
And also don't always try streaming with Google Drive - it can have issues because of how Google Drive works (with quotas, restrictions, etc.) and it can indeed cause `BlockSizeError`.

Feel free to host your test data elsewhere, such as in a dataset repository on https://huggingface.co (see [here](https://huggingface.co/docs/datasets/upload_dataset.html#upload-your-files) for a tutorial on how to upload files)
Better error message when download fails
Opened by lhoestq (closed)

From our discussions in https://github.com/huggingface/datasets/issues/3269 and https://github.com/huggingface/datasets/issues/3282 it would be nice to have better messages if a download fails.

In particular the error now shows:
- the error from the HEAD request if there's one
- otherwise the response code of the HEAD request

I also added an error to tell users to pass `use_auth_token` when the Hugging Face Hub returns 401 (Unauthorized).

While paying around with this I also fixed a minor issue with the `force_download` parameter that was not always taken into account

Comments:
Fix ASSET dataset data URLs
Opened by tianjianjiang (closed)

Change the branch name "master" to "main" in the data URLs, since facebookresearch has changed that.

Comments:
> Hi @tianjianjiang, thanks for the fix.
> The links should also be updated in the `dataset_infos.json` file.
> The failing tests are due to the missing tag in the header of the `README.md` file:

Hi @albertvillanova, thank you for the info! My apologies for the messy PR.
Mirror the canonical datasets to the Hugging Face Hub
Opened by severo (closed)

- [ ] create a repo on https://hf.co/datasets for every canonical dataset
- [ ] on every commit related to a dataset, update the hf.co repo

See https://github.com/huggingface/moon-landing/pull/1562

@SBrandeis: I let you edit this description if needed to precise the intent.

Comments:
I created a GitHub project to keep track of what needs to be done:
https://github.com/huggingface/datasets/projects/3

I also store my code in a (private for now) repository at https://github.com/huggingface/mirror_canonical_datasets_on_hub

I understand that the datasets are mirrored on the Hub now, right? Might I close @lhoestq @SBrandeis?
Fix JSON ClassLabel casting for integers
Opened by lhoestq (closed)

Loading a JSON dataset with ClassLabel feature types currently fails if the JSON data already has integers. Indeed currently it tries to convert the strings to integers without even checking if the data are not integers already.

For example this currently fails:
```python
from datasets import load_dataset, Features, ClassLabel

path = "data.json"
f = Features({"a": ClassLabel(names=["neg", "pos"])})
d = load_dataset("json", data_files=path, features=f)
```
data.json
```json
{"a": 0}
{"a": 1}
```

I fixed that by adding a line that checks the type of the JSON data before trying to convert them

cc @albertvillanova let me know if it sounds good to you

Comments:
Typing of Dataset.__getitem__ could be improved.
Opened by Dref360 (closed)

## Describe the bug

The newly added typing for Dataset.__getitem__ is Union[Dict, List]. This makes tools like mypy a bit awkward to use as we need to check the type manually. We could use type overloading to make this easier. [Documentation](https://docs.python.org/3/library/typing.html#typing.overload)

## Steps to reproduce the bug
Let's have a file `test.py`

```python
from typing import List, Dict, Any

from datasets import Dataset

ds = Dataset.from_dict({
    'a': [1,2,3],
    'b': ["1", "2", "3"]
})

one_colum: List[str] = ds['a']
some_index: Dict[Any, Any] = ds[1]
```

## Expected results

Running `mypy test.py` should not give any error.


## Actual results

```
test.py:10: error: Incompatible types in assignment (expression has type "Union[Dict[Any, Any], List[Any]]", variable has type "List[str]")
test.py:11: error: Incompatible types in assignment (expression has type "Union[Dict[Any, Any], List[Any]]", variable has type "Dict[Any, Any]")
Found 2 errors in 1 file (checked 1 source file)
```


## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.13.3
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 6.0.1


Comments:
Hi ! Thanks for the suggestion, I didn't know about this decorator.

If you are interesting in contributing, feel free to open a pull request to add the overload methods for each typing combination :) To assign you to this issue, you can comment `#self-assign` in this thread.

`Dataset.__getitem__` is defined right here: https://github.com/huggingface/datasets/blob/e6f1352fe19679de897f3d962e616936a17094f5/src/datasets/arrow_dataset.py#L1840

#self-assign
Add support for multiple dynamic dimensions and to_pandas conversion for dynamic arrays
Opened by mariosasko (closed)

Add support for multiple dynamic dimensions (e.g. `(None, None, 3)` for arbitrary sized images) and `to_pandas()` conversion for dynamic arrays.

TODOs:
* [ ] Cleaner code
* [ ] Formatting issues (if NumPy doesn't allow broadcasting even though dtype is np.object)
* [ ] Fix some issues with zero-dim tensors 
* [ ] Tests


Comments:
Integrate Polars library
Opened by albertvillanova (closed)

Check potential integration of the Polars library: https://github.com/pola-rs/polars
- Benchmark: https://h2oai.github.io/db-benchmark/

CC: @thomwolf @lewtun 


Comments:
If possible, a neat API could be something like `Dataset.to_polars()`, as well as `Dataset.set_format("polars")`

Note they use a "custom" implementation of Arrow: [Arrow2](https://github.com/jorgecarleitao/arrow2).

Polars has grown rapidly in popularity over the last year - could you consider integrating the Polars functionality again?

I don't think the "custom" implementation should be a barrier, it still conforms to the Arrow specification 

Is there some direction regarding this from the HF team @lewtun ? Can conversion from polars to HF dataset be implemented with limited/zero copy? So, something like ``Dataset.from_polars()`` and ``Dataset.to_polars()`` like you mentioned. Happy to contribute if I can get some pointers on how this may be implemented.

Hi, is there any updates? Thanks!

> Hi, is there any updates? Thanks!

The feature has been there for a bit 😊 You can call `dataset.to_polars()` (on a `Dataset`, not a `DatasetDict`). The issue can be closed, I guess! @lhoestq 

Looks great and thanks!

Thank you.
Fix error message and add extension fallback
Opened by mariosasko (closed)

Fix the error message raised if `infered_module_name` is `None` in `CommunityDatasetModuleFactoryWithoutScript.get_module` and make `infer_module_for_data_files` more robust. 

In the linked issue, `infer_module_for_data_files` returns `None` because `json` is the second most common extension due to the suffix ordering. Now,  we go from the most common to the least common extension and try to map it or return `None`.

Fix #3331

Comments: