rIsHu009/github-issues-updated huggingface dataset:
Title: `datasets-cli test` should work with datasets without scripts
Author: sashavor (CONTRIBUTOR)
State: open
Created: 2021-10-25T18:52:30Z
Labels: enhancement

It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached  to them (whether the datasets are private or not).

I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!

Comments:
> It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not).
> 
> I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!

why don't you try to share that info with people, so you can also save some days.

Hi ! You can run the command if you download the repository
```
git clone https://huggingface.co/datasets/huggingface/DataMeasurementsTest
```
and run the command
```
datasets-cli test DataMeasurementsTest/DataMeasurementsTest.py
```

(though on my side it doesn't manage to download the data since the dataset is private ^^)

> Hi ! You can run the command if you download the repository
> 
> ```
> git clone https://huggingface.co/datasets/huggingface/DataMeasurementsTest
> ```
> 
> and run the command
> 
> ```
> datasets-cli test DataMeasurementsTest/DataMeasurementsTest.py
> ```
> 
> (though on my side it doesn't manage to download the data since the dataset is private ^^)

Hi! Thanks for the info. 
git cannot find the repository. Do you know if they have depreciated these tests and created a new one?

I think it's become private, but feel free to try with any other dataset like `lhoestq/test` for example at `https://huggingface.co/datasets/lhoestq/test`

> I think it's become private, but feel free to try with any other dataset like `lhoestq/test` for example at `https://huggingface.co/datasets/lhoestq/test`

your example repo and this page `https://huggingface.co/docs/datasets/add_dataset.html` helped me to solve.. thanks a lot
Title: Add riddle_sense dataset
Author: ziyiwu9494 (CONTRIBUTOR)
State: closed
Created: 2021-10-25T18:30:56Z

Adding a new dataset for QA with riddles. I'm confused about the tagging process because it looks like the streamlit app loads data from the current repo, so is it something that should be done after merging or off my fork?

Comments:
@lhoestq 
I address all the comments, I think. Thanks! 


The five test fails are unrelated to this PR and fixed on master so we can ignore them
Title: Better error msg if `len(predictions)` doesn't match `len(references)` in metrics
Author: mariosasko (COLLABORATOR)
State: closed
Created: 2021-10-25T15:25:05Z

Improve the error message in `Metric.add_batch` if `len(predictions)` doesn't match `len(references)`.

cc: @BramVanroy (feel free to test this code on your examples and review this PR)

Comments:
Can't test this now but it may be a good improvement indeed.

I added a function, but it only works with the `list` type. For arrays/tensors, we delegate formatting to the frameworks.
Title: Make inspect.get_dataset_config_names always return a non-empty list
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-10-25T13:59:43Z

Make all named configs cases, so that no special unnamed config case needs to be handled differently.

Fix #3135.

Comments:
This PR is already working (although not very beautiful; see below): the idea was to have the `DatasetModule.builder_kwargs` accessible from the `builder_cls`, so that this can generate the default builder config (at the class level, without requiring the builder to be instantiated).

I have a plan for a follow-up refactoring (same functionality, better implementation, much nicer), but I think we could already merge this, so that @severo can test it in the datasets previewer and report any potential issues.

Yes @lhoestq you are completely right. Indeed I was exclusively using `builder_cls.kwargs` to get the community dataset `name` (nothing else): "lhoestq___demo1"

See et: https://github.com/huggingface/datasets/pull/3159/files#diff-f933ce41f71c6c0d1ce658e27de62cbe0b45d777e9e68056dd012ac3eb9324f7R413-R415

In your example, the `name` I was getting from `builder_cls.kwargs` was:
```python
{"name": "lhoestq___demo1",...}
```

I'm going to refactor all the approach... as I only need the name for this specific case ;)

I think this makes more sense now, @lhoestq @severo 😅 

It works well, thanks!
Title: Fix string encoding for Value type
Author: lhoestq (MEMBER)
State: closed
Created: 2021-10-25T13:44:13Z

Some metrics have `string` features but currently it fails if users pass integers instead. Indeed feature encoding that handles the conversion of the user's objects to the right python type is missing a case for `string`, while it already works as expected for integers, floats and booleans

Here is an example code that didn't work previously, but that works with this fix:
```python
import datasets

# Note that 'id' is an integer while the SQuAD metric uses strings
predictions = [{'prediction_text': '1976', 'id': 5}]
references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': 5}] 

squad_metric = datasets.load_metric("squad") 
squad_metric.add_batch(predictions=predictions, references=references) 
results = squad_metric.compute()
# {'exact_match': 100.0, 'f1': 100.0}
```

cc @sgugger @philschmid

Comments:
That was fast!
Title: Illegal instruction (core dumped) at datasets import
Author: hacobe (CONTRIBUTOR)
State: closed
Created: 2021-10-24T17:21:36Z
Labels: bug

## Describe the bug
I install datasets using conda and when I import datasets I get: "Illegal instruction (core dumped)"

## Steps to reproduce the bug

```
conda create --prefix path/to/env
conda activate path/to/env
conda install -c huggingface -c conda-forge datasets
# exits with output "Illegal instruction (core dumped)"
python -m datasets
```

## Environment info

When I run "datasets-cli env", I also get "Illegal instruction (core dumped)"

If I run the following commands:

```
conda create --prefix path/to/another/new/env
conda activate path/to/another/new/env
conda install -c huggingface transformers
transformers-cli env
```

Then I get:

- `transformers` version: 4.11.3
- Platform: Linux-5.4.0-67-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyTorch version (GPU?): not installed (NA)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

Let me know what additional information you need in order to debug this issue. Thanks in advance!

Comments:
It seems to be an issue with how conda-forge is building the binaries. It works on some machines, but not a machine with AMD Opteron 8384 processors.
Title: Re-add faiss to windows testing suite
Author: BramVanroy (CONTRIBUTOR)
State: closed
Created: 2021-10-22T19:34:29Z

In recent versions, `faiss-cpu` seems to be available for Windows as well. See the [PyPi page](https://pypi.org/project/faiss-cpu/#files) to confirm. We can therefore included it for Windows in the setup file.

At first tests didn't pass due to problems with permissions as caused by `NamedTemporaryFile` on Windows. This built-in library is notoriously poor in playing nice on Windows. The required change isn't pretty, but it works. First set `delete=False` to not automatically try to delete the file on `exit`. Then, manually delete the file with `unlink`. It's weird, I know, but it works.

```python
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
    # do stuff
os.unlink(tmp_file.name)
```

closes #3150

Comments:
Title: Faiss _is_ available on Windows
Author: BramVanroy (CONTRIBUTOR)
State: closed
Created: 2021-10-22T18:07:16Z

In the setup file, I find the following:

https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/setup.py#L171

However, FAISS does install perfectly fine on Windows on my system. You can also confirm this on the [PyPi page](https://pypi.org/project/faiss-cpu/#files), where Windows wheels are available. Maybe this was true for older versions? For current versions, this can be removed I think.

(This isn't really a bug but didn't know how else to tag.)

If you agree I can do a quick PR and remove that line.

Comments:
Sure, feel free to open a PR.
Title: Add CMU Hinglish DoG Dataset for MT
Author: Ishan-Kumar2 (CONTRIBUTOR)
State: closed
Created: 2021-10-22T16:17:25Z

Address part of #2841 

Added the CMU Hinglish DoG Dataset as in GLUECoS. Added it as a seperate dataset as unlike other tasks of GLUE CoS this can't be evaluated for a BERT like model. 
Consists of parallel dataset between Hinglish (Hindi-English) and English, can be used for Machine Translation between the two. 

The data processing part is inspired from the GLUECoS repo [here](https://github.com/microsoft/GLUECoS/blob/7fdc51653e37a32aee17505c47b7d1da364fa77e/Data/Preprocess_Scripts/preprocess_mt_en_hi.py)
The dummy data part is not working properly, it shows 
``` UnboundLocalError: local variable 'generator_splits' referenced before assignment ``` 
when I run without ``--auto_generate``.

Please let me know how I can fix that.
Thanks

Comments:
Hi @lhoestq, thanks a lot for the help. I have moved the part as suggested. 
Although still while running the dummy data script, I face this issue

```
Traceback (most recent call last):
  File "/home/ishan/anaconda3/bin/datasets-cli", line 8, in <module>
    sys.exit(main())
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/datasets_cli.py", line 33, in main
    service.run()
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/dummy_data.py", line 318, in run
    self._autogenerate_dummy_data(
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/dummy_data.py", line 363, in _autogenerate_dummy_data
    dataset_builder._prepare_split(split_generator)
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/builder.py", line 1103, in _prepare_split
    example = self.info.features.encode_example(record)
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py", line 981, in encode_example
    return encode_nested_example(self, example)
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py", line 775, in encode_nested_example
    return {
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py", line 775, in <dictcomp>
    return {
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 99, in zip_dict
    yield key, tuple(d[key] for d in dicts)
  File "/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 99, in <genexpr>
    yield key, tuple(d[key] for d in dicts)
KeyError: 'status'
```
This KeyError is at times different from 'status' also.
when I run 
```
datasets-cli dummy_data datasets/cmu_hinglish_dog --auto_generate --json_field='history'
```
I have tried removing unnecessary feature type definition, but that didn't help. Please let me know if I am missing something, thanks!

The CI fail is unrelated to this PR and fixed on master. Merging !
Title: Streaming with num_workers != 0
Author: justheuristic (NONE)
State: closed
Created: 2021-10-22T15:07:17Z
Labels: bug

## Describe the bug
When using dataset streaming with pytorch DataLoader, the setting num_workers to anything other than 0 causes the code to freeze forever before yielding the first batch.

The code owner is likely @lhoestq 

## Steps to reproduce the bug

For your convenience, we've prepped a colab notebook that reproduces the bug
https://colab.research.google.com/drive/1Mgl0oTZSNIE3UeGl_oX9wPCOIxRg19h1?usp=sharing
```python
!pip install datasets==1.14.0

should_freeze_forever = True
# ^-- set this to True in order to freeze forever, set to False in order to work normally

import torch
from datasets import load_dataset

data = load_dataset("oscar", "unshuffled_deduplicated_bn", split="train", streaming=True)
data = data.map(lambda x: {"text": x["text"], "orig": f"oscar[{x['id']}]"}, batched=True)
data = data.shuffle(100, seed=1337)

data = data.with_format("torch")
loader = torch.utils.data.DataLoader(data, batch_size=2, num_workers=2 if should_freeze_forever else 0)

# v-- the code should freeze forever at this line
for i, row in enumerate(loader):
  print(row)
  if i > 10: break
print("DONE!")
```

## Expected results
The code should not freeze forever with num_workers=2

## Actual results
The code freezes forever with num_workers=2

## Environment info
- `datasets` version: 1.14.0 (also found in previous versions)
- Platform: google colab (also locally)
- Python version: 3.7, (also 3.8)
- PyArrow version: 3.0.0

Comments:
I can confirm that I was able to reproduce the bug. This seems odd given that #3423 reports duplicate data retrieval when `num_workers` and `streaming` are used together, which is obviously different from what is reported here. 

Any update? A possible solution is to have multiple arrow files as shards, and handle them like what webdatasets does.
![image](https://user-images.githubusercontent.com/11533479/148176637-72746b2c-c122-47aa-bbfe-224b13ee9a71.png)

Pytorch's new dataset RFC is supporting sharding now, which may helps avoid duplicate data under streaming mode. (https://github.com/pytorch/pytorch/blob/master/torch/utils/data/datapipes/iter/grouping.py#L13)


Hi ! Thanks for the insights :) Note that in streaming mode there're usually no arrow files. The data are streamed from TAR, ZIP, text, etc. files directly from the web. Though for sharded datasets we can definitely adopt a similar strategy !

fixed by #4375
Title: CLI test command throws NonMatchingSplitsSizesError when saving infos
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-10-22T13:50:53Z
Labels: bug

When trying to generate a datset JSON metadata, a `NonMatchingSplitsSizesError` is thrown:
```
$ datasets-cli test datasets/arabic_billion_words --save_infos --all_configs
Testing builder 'Alittihad' (1/10)
Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: Unknown size, post-processed: Unknown size, total: 332.13 MiB) to .cache\arabic_billion_words\Alittihad\1.1.0\8175ff1c9714c6d5d15b1141b6042e5edf048276bb81a9c14e35e149a7a62ae4...
Traceback (most recent call last):
  File "path\huggingface\datasets\.venv\Scripts\datasets-cli-script.py", line 33, in <module>
    sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())
  File "path\huggingface\datasets\src\datasets\commands\datasets_cli.py", line 33, in main
    service.run()
  File "path\huggingface\datasets\src\datasets\commands\test.py", line 144, in run
    builder.download_and_prepare(
  File "path\huggingface\datasets\src\datasets\builder.py", line 607, in download_and_prepare
    self._download_and_prepare(
  File "path\huggingface\datasets\src\datasets\builder.py", line 709, in _download_and_prepare
    verify_splits(self.info.splits, split_dict)
  File "path\huggingface\datasets\src\datasets\utils\info_utils.py", line 74, in verify_splits
    raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='arabic_billion_words'), 'recorded': SplitInfo(name='train', num_bytes=1601790302, num_examples=349342, dataset_name='arabic_billion_words')}]
```

This is due because a previous run generated a wrong `dataset_info.json`.

This error can be avoided by passing `--ignore_verifications`, but I think this should be assumed when passing `--save_infos`.

Comments:
Title: [when Image type will exist] provide a way to get the data as binary + filename
Author: severo (COLLABORATOR)
State: closed
Created: 2021-10-22T13:23:49Z
Labels: enhancement

dataset-viewer

**Is your feature request related to a problem? Please describe.**

When a dataset cell contains a value of type Image (be it from a remote URL, an Array2D/3D, or any other way to represent images), I want to be able to write the image to the disk, with the correct filename, and optionally to know its mimetype, in order to serve it on the web.

Note: this issue would apply exactly the same for the `Audio` type.

**Describe the solution you'd like**

If a "cell" has the type `Image`, provide a way to get the binary content of the file, and the filename, eg as:

```python
  filename: str
  data: bytes
```

**Describe alternatives you've considered**

A way to write the cell to the disk (passing a local directory), and then return the pathname, filename, and mimetype.

Comments:
@severo, maybe somehow related to this PR ?
- #3129

@severo I'll keep that in mind.

You can track progress on the Image feature in #3163 (still in the early stage).  

Hi ! As discussed with @severo offline it looks like the dataset viewer already supports reading PIL images, so maybe the dataset viewer doesn't need to disable decoding after all

Fixed with https://github.com/huggingface/datasets/pull/3163
Title: Infer the features if missing
Author: severo (COLLABORATOR)
State: closed
Created: 2021-10-22T13:17:33Z
Labels: enhancement

dataset-viewer

**Is your feature request related to a problem? Please describe.**

Some datasets, in particular community datasets, have no info file, thus no features.

**Describe the solution you'd like**

If a dataset has no features, the first loaded data (5-10 rows) could be used to infer the type.

Related: `datasets` would provide a way to load the data, and get the rows AND the features as the result.

**Describe alternatives you've considered**

The HF hub could also provide some UI to help the dataset maintainers to explicit the types of their rows, or automatically infer them as an initial proposal.

Comments:
Done by @lhoestq here: https://github.com/huggingface/datasets/pull/4500 (https://github.com/huggingface/datasets/pull/4500/files#diff-02930e1d966f4b41f9ddf15d961f16f5466d9bee583138657018c7329f71aa43R1255 in particular)
Title: Provide a way to check if the features (in info) match with the data of a split
Author: severo (COLLABORATOR)
State: open
Created: 2021-10-22T13:13:36Z
Labels: enhancement

dataset-viewer

**Is your feature request related to a problem? Please describe.**

I understand that currently the data loaded has not always the type described in the info features

**Describe the solution you'd like**

Provide a way to check if the rows have the type described by info features

**Describe alternatives you've considered**

Always check it, and raise an error when loading the data if their type doesn't match the features.

Comments:
Related: #3144
Title: Provide a way to write a streamed dataset to the disk
Author: severo (COLLABORATOR)
State: open
Created: 2021-10-22T13:09:53Z
Labels: enhancement

dataset-viewer

**Is your feature request related to a problem? Please describe.**

The streaming mode allows to get the 100 first rows of a dataset very quickly. But it does not cache the answer, so a posterior call to get the same 100 rows will send a request to the server again and again.

**Describe the solution you'd like**

Provide a way to write the streamed rows of a dataset on the disk, and to load from it later.

**Describe alternatives you've considered**

Provide a third mode: `lazy`, which would use the local cache for the data that have already been fetched previously, and use streaming to get the rest of the requested data.

Comments:
Yes, I agree this feature is much needed. We could do something similar to what TF does (https://www.tensorflow.org/api_docs/python/tf/data/Dataset#cache). 

Ideally, if the entire streamed dataset is consumed/cached, the generated cache should be reusable for the Arrow dataset.

@mariosasko Hi big brother,any update on this? It's 2024 with large streamed dataset loading consume too much time(exp.  2day..), really need this feature for what TF does
Title: Fix caching bugs
Author: mariosasko (COLLABORATOR)
State: closed
Created: 2021-10-22T12:59:25Z

This PR fixes some caching bugs (most likely introduced in the latest refactor):
* remove ")" added by accident in the dataset dir name
* correctly pass the namespace kwargs in `CachedDatasetModuleFactory`
* improve the warning message if `HF_DATASETS_OFFLINE is `True`

Comments:
Title: Fix file/directory deletion on Windows  
Author: mariosasko (COLLABORATOR)
State: open
Created: 2021-10-22T12:22:08Z
Labels: bug

Currently, on Windows, some attempts to delete a dataset file/directory will fail with the `PerimissionError`. 

Examples:
- download a dataset, then force redownload it in the same session while keeping a reference to the downloaded dataset
```python
from datasets import load_dataset
dset = load_dataset("sst", split="train")
dset = load_dataset("sst", split="train", download_mode="force_redownload")
```
- try to clean up the cache files while keeping a reference to those files (via the mapped dataset):
```python
from datasets import load_dataset
dset = load_dataset("sst", split="train")
dset_mapped = dset.map(lambda _: {"dummy_col": 1})
dset.cleanup_cache_files()
```
We should fix those.

Comments:
Title: More fine-grained taxonomy of error types
Author: severo (COLLABORATOR)
State: open
Created: 2021-10-22T09:35:29Z
Labels: enhancement

dataset-viewer

**Is your feature request related to a problem? Please describe.**

Exceptions like `FileNotFoundError` can be raised by different parts of the code, and it's hard to detect which one did

**Describe the solution you'd like**

Give a specific exception type for every group of similar errors

**Describe alternatives you've considered**

Rely on the error message, using regex

Comments:
related: #4995
Title: Fix numpy deprecation warning for ragged tensors
Author: lhoestq (MEMBER)
State: closed
Created: 2021-10-22T09:17:46Z

Numpy shows a deprecation warning when we call `np.array` on a list of ragged tensors without specifying the `dtype`. If their shapes match, the tensors can be collated together, otherwise the resulting array should have `dtype=np.object`.

Fix #3084 
cc @Rocketknight1

Comments:
This'll be a really helpful fix, thank you!
Title: Fix script of Arabic Billion Words dataset to return all data
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-10-22T09:14:24Z

The script has a bug and only parses and generates a portion of the entire dataset.

This PR fixes the loading script so that is properly parses the entire dataset. 

Current implementation generates the same number of examples as reported in the [original paper](https://arxiv.org/abs/1611.04033) for all configurations except for one:
- For "Youm7" we generate more examples (1172136) than the ones reported by the paper (1025027)

|                | Number of examples | Number of examples according to the source |
|:---------------|-------------------:|-----:|
| Alittihad      |             349342 |349342 |
| Almasryalyoum  |             291723 |291723 |
| Almustaqbal    |             446873 |446873 |
| Alqabas        |             817274 |817274 |
| Echoroukonline |             139732 |139732 |
| Ryiadh         |             858188 | 858188 |
| Sabanews       |              92149 |92149 |
| SaudiYoum      |             888068 |888068 |
| Techreen       |             314597 |314597 |
| Youm7          |            1172136 |1025027 |

Fix #3126.

Comments:
Title: Make inspect.get_dataset_config_names always return a non-empty list of configs
Author: severo (COLLABORATOR)
State: closed
Created: 2021-10-22T08:02:50Z
Labels: enhancement

dataset-viewer

**Is your feature request related to a problem? Please describe.**

Currently, some datasets have a configuration, while others don't. It would be simpler for the user to always have configuration names to refer to

**Describe the solution you'd like**

In that sense inspect.get_dataset_config_names should always return at least one configuration name, be it `default` or `Check___region_1` (for community datasets like `Check/region_1`).

https://github.com/huggingface/datasets/blob/c5747a5e1dde2670b7f2ca6e79e2ffd99dff85af/src/datasets/inspect.py#L161

Comments:
Hi @severo, I guess this issue requests not only to be able to access the configuration name (by using `inspect.get_dataset_config_names`), but the configuration itself as well (I mean you use the name to get the configuration afterwards, maybe using `builder_cls.builder_configs`), is this right?

Yes, maybe the issue could be reformulated. As a user, I want to avoid having to manage special cases:
- I want to be able to get the names of a dataset's configs, and use them in the rest of the API (get the data, get the split names, etc).
- I don't want to have to manage datasets with named configs (`glue`) differently from datasets without named configs (`acronym_identification`, `Check/region_1`)
Title: Add ADE20k
Author: NielsRogge (CONTRIBUTOR)
State: closed
Created: 2021-10-21T10:13:09Z
Labels: dataset request

vision

## Adding a Dataset
- **Name:** ADE20k (actually it's called the MIT Scene Parsing Benchmark, it's actually a subset of ADE20k but a lot of authors still call it ADE20k)
- **Description:** A semantic segmentation dataset, consisting of 150 classes.
- **Paper:** http://people.csail.mit.edu/bzhou/publication/scene-parse-camera-ready.pdf
- **Data:** http://sceneparsing.csail.mit.edu/
- **Motivation:** I am currently adding Transformer-based semantic segmentation models that achieve SOTA on this dataset. It would be great to directly access this dataset using HuggingFace Datasets, in order to make example scripts in HuggingFace Transformers.

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

Comments:
I think we can close this issue since PR [#3607](https://github.com/huggingface/datasets/pull/3607) solves this.
Title: Support Audio feature for TAR archives in sequential access
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-10-21T08:56:51Z

Add Audio feature support for TAR archived files in sequential access.

Fix #3128.

Comments:
Also do you think we can adapt `cast_column` to keep the same value for this new parameter when the user only wants to change the sampling rate ?

Thanks for your comments, @lhoestq, I will address them afterwards.

But, I think it is more important/urgent first address the current blocking non-passing test: https://github.com/huggingface/datasets/runs/4143579241?check_suite_focus=true
- I am thinking of a way of solving it, but if you have any hint, it will be more than welcome! 😅 

Basically:
```
{'audio': '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_dataset_with_audio_featur1/data/test_audio_44100.wav'}
``` 
becomes
```
{'audio': {'bytes': None, 'path': '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_dataset_with_audio_featur1/data/test_audio_44100.wav'}}
```
after a `map`, which is what was stored in the Arrow file. However we expect it remains invariant after this `map`.

@lhoestq, @mariosasko I finally proposed another implementation different from my last one:
- Before: store Audio always a struct<path: string, bytes: binary>, where bytes can be None
- Now, depending on the examples, either store Audio as a struct (as before), or as a string.

Please note that the main motivation for this change was the issue mentioned above: https://github.com/huggingface/datasets/pull/3129#issuecomment-964347056


Until here we had the assumption that a Features object always has an associated, deterministic, pyarrow schema. This is useful to ensure that we are able to concatenate two datasets that have the same features for example.

By breaking this assumption for the Audio type, how can we ensure that we can concatenate two audio datasets if one has Audio as a struct and the other a string ?

Oh I noticed that the Audio feature type has a private attribute `_storage_dtype`, so the assumption still holds, since they are now different feature types depending on the this attribute :)
(i mean different from the python equal operator point of view)

I think this PR is ready, @lhoestq, @mariosasko.  

Nit: We should also mention the new storage structure in the `Features` docstring [here](https://github.com/huggingface/datasets/blob/b29fb550c31de337b952035a7584147e0f18c0cf/src/datasets/features/features.py#L966) for users to know what type of value to return in their dataset scripts (we also have a link to that docstring in the `ADD_NEW_DATASET` template).
Title: Support Audio feature for TAR archives in sequential access
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-10-21T08:23:01Z
Labels: enhancement

Currently, Audio feature accesses each audio file by their file path.

However, streamed TAR archive files do not allow random access to their archived files.

Therefore, we should enhance the Audio feature to support TAR archived files in sequential access.

Comments:
Title: datasets-cli: convertion of a tfds dataset to a huggingface one.
Author: vitalyshalumov (NONE)
State: open
Created: 2021-10-21T06:14:27Z

### Discussed in https://github.com/huggingface/datasets/discussions/3079

<div type='discussions-op-text'>

<sup>Originally posted by **vitalyshalumov** October 14, 2021</sup>
I'm trying to convert a tfds dataset to a huggingface one.

I've tried:

1.       datasets-cli convert  --tfds_path ~/tensorflow_datasets/mnist/3.0.1/ --datasets_directory ~/.cache/huggingface/datasets/mnist/3.0.1/

2.       datasets-cli convert  --tfds_path ~/tensorflow_datasets/mnist/3.0.1/ --datasets_directory ~/.cache/huggingface/datasets/


and other permutations.
The script appears to be running and finishing without an error but when looking in the  huggingface/datasets/ folder nothing is created.


</div>

Comments:
Hi,

the MNIST dataset is already available on the Hub. You can use it as follows:
```python
import datasets
dataset_dict = datasets.load_dataset("mnist")
```

As for the conversion of TFDS datasets to HF datasets, we will be working on it in the coming months, so stay tuned.
Title: "arabic_billion_words" dataset does not create the full dataset
Author: vitalyshalumov (NONE)
State: closed
Created: 2021-10-21T06:02:38Z
Labels: bug

## Describe the bug
When running: 
raw_dataset = load_dataset('arabic_billion_words','Alittihad')
the correct dataset file is pulled from the url.
But, the generated dataset includes just a small portion of the data included in the file.
This is true for all other portions of the "arabic_billion_words" dataset ('Almasryalyoum',.....)

## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
raw_dataset = load_dataset('arabic_billion_words','Alittihad')

#The screen message
Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: 20.62 MiB, post-processed: Unknown size, total: 352.74 MiB) 

## Expected results
over 100K sentences

## Actual results
only 11K sentences

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.0
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1

Comments:
Thanks for reporting, @vitalyshalumov.

Apparently the script to parse the data has a bug, and does not generate the entire dataset.

I'm fixing it.
Title: More efficient nested features encoding
Author: eladsegal (CONTRIBUTOR)
State: closed
Created: 2021-10-21T01:55:31Z

Nested encoding of features wastes a lot of time on operations which are effectively doing nothing when lists are used.
For example, if in the input we have a list of integers, `encoded_nested_example` will iterate over it and apply `encoded_nested_example` on every element even though it just return the int as is.

A similar issue is handled at an earlier stage when casting pytorch/tensorflow/pandas objects to python lists/numpy arrays:
https://github.com/huggingface/datasets/blob/c98c23c4260edadab00f997d1a5d66b7f2e93ce9/src/datasets/features/features.py#L149-L156
https://github.com/huggingface/datasets/blob/c98c23c4260edadab00f997d1a5d66b7f2e93ce9/src/datasets/features/features.py#L212-L228

In this pull request I suggest to use the same approach in `encoded_nested_example`.
In my setup there was a major speedup with this change: loading the data was at least x4 faster.

Comments:
@lhoestq @albertvillanova @mariosasko
Can you please check this out?

Thanks, done!
Title: Segmentation fault when loading datasets from file
Author: TevenLeScao (CONTRIBUTOR)
State: closed
Created: 2021-10-20T20:16:11Z
Labels: bug

## Describe the bug
Custom dataset loading sometimes segfaults and kills the process if chunks contain a variety of features/

## Steps to reproduce the bug

Download an example file:
```
wget https://gist.githubusercontent.com/TevenLeScao/11e2184394b3fa47d693de2550942c6b/raw/4232704d08fbfcaf93e5b51def9e5051507651ad/tiny_kelm.jsonl
```
Then in Python:
```
import datasets
tiny_kelm = datasets.load_dataset("json", data_files="tiny_kelm.jsonl", chunksize=100000)
```

## Expected results
a `tiny_kelm` functional dataset

## Actual results
☠️ `Segmentation fault (core dumped)` ☠️

## Environment info
- `datasets` version: 1.14.0
- Platform: Linux-5.11.0-38-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 5.0.0

Comments:
Hi ! I created an issue on Arrow's JIRA after making a minimum reproducible example

https://issues.apache.org/jira/browse/ARROW-14439

```python
import io

import pyarrow.json as paj

batch = b'{"a": [], "b": 1}\n{"b": 1}'
block_size = 12

paj.read_json(
    io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
```

I don't see a way to workaround this properly now without hurting the performance of the JSON loader significantly though

The issue has been fixed in pyarrow 6.0.0, please update pyarrow :)

The issue was due to missing fields in the JSON data of type list. Now it's working fine and missing list fields are replaced with empty lists
Title: Use huggingface_hub.HfApi to list datasets/metrics
Author: mariosasko (COLLABORATOR)
State: closed
Created: 2021-10-20T17:48:29Z

Delete `datasets.inspect.HfApi` and use `huggingface_hub.HfApi` instead.

WIP until https://github.com/huggingface/huggingface_hub/pull/429 is merged, then wait for the new release of  `huggingface_hub`, update the `huggingface_hub` version in `setup.py` and merge this PR.

cc: @lhoestq

Comments:
Title: Correctly update metadata to preserve features when concatenating datasets with axis=1
Author: mariosasko (COLLABORATOR)
State: closed
Created: 2021-10-20T15:54:58Z

This PR correctly updates metadata to preserve higher-level feature types (e.g. `ClassLabel`) in `datasets.concatenate_datasets` when `axis=1`. Previously, we would delete the feature metadata in `datasets.concatenate_datasets` if `axis=1` and restore the feature types from the arrow table schema in `Dataset.__init__`. However, this approach only works for simple feature types (e.g. `Value`).

Fixes #3111

Comments:
Title: Add OpenSLR 83 - Crowdsourced high-quality UK and Ireland English Dialect speech
Author: tyrius02 (CONTRIBUTOR)
State: closed
Created: 2021-10-20T12:05:07Z
Labels: dataset request

## Adding a Dataset
- **Name:** *openslr**
- **Description:** *Data set which contains male and female recordings of English from various dialects of the UK and Ireland.*
- **Paper:** *https://www.openslr.org/resources/83/about.html*
- **Data:** *Eleven separate data files can be found via https://www.openslr.org/resources/83/*
- **Motivation:** *Increase english ASR data with UK and Irish dialects*

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

The *openslr* dataset already exists, this will add additional subset, *SLR83*.

Comments:
Ugh. The index files for SLR83 are CSV, not TSV. I need to add logic to process these index files.
Title: CI error at each release commit
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-10-20T11:42:53Z
Labels: bug

After 1.12.0, there is a recurrent CI error at each release commit: https://app.circleci.com/pipelines/github/huggingface/datasets/8289/workflows/665d954d-e409-4602-8202-e678594d2946/jobs/51110

```
____________________ LoadTest.test_load_dataset_canonical _____________________
[gw0] win32 -- Python 3.6.8 C:\tools\miniconda3\python.exe

self = <tests.test_load.LoadTest testMethod=test_load_dataset_canonical>

    def test_load_dataset_canonical(self):
        scripts_version = os.getenv("HF_SCRIPTS_VERSION", SCRIPTS_VERSION)
        with self.assertRaises(FileNotFoundError) as context:
            datasets.load_dataset("_dummy")
        self.assertIn(
            f"https://raw.githubusercontent.com/huggingface/datasets/{scripts_version}/datasets/_dummy/_dummy.py",
>           str(context.exception),
        )
E       AssertionError: 'https://raw.githubusercontent.com/huggingface/datasets/1.14.0/datasets/_dummy/_dummy.py' not found in "Couldn't find a dataset script at C:\\Users\\circleci\\datasets\\_dummy\\_dummy.py or any data file in the same directory. Couldn't find '_dummy' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/_dummy/_dummy.py"

tests\test_load.py:358: AssertionError
```

Comments:
Title: Update doc links to point to new docs
Author: mariosasko (COLLABORATOR)
State: closed
Created: 2021-10-20T11:00:47Z
Labels: documentation

This PR:
* updates the README links and the ADD_NEW_DATASET template to point to the new docs (the new docs don't have a section with the list of all the possible features, so I added that info to the `Features` docstring, which is then referenced in the ADD_NEW_DATASET template)
* fixes some broken links in the `.rst` files (fixed with the `make linkcheck` tool)

Comments:
Title: Loading Data from HDF files
Author: FeryET (NONE)
State: open
Created: 2021-10-19T19:26:46Z
Labels: enhancement

good second issue

**Is your feature request related to a problem? Please describe.**
More often than not I come along big HDF datasets, and currently there is no straight forward way to feed them to a dataset.

**Describe the solution you'd like**
I would love to see a `from_h5` method that gets an interface implemented by the user on how items are extracted from dataset (in case of multiple datasets containing elements like arrays and metadata and etc).

**Describe alternatives you've considered**
Currently I manually load hdf files using `h5py` and implement PyTorch dataset interface. For small h5 files I load them into a pandas dataframe and use `from_pandas` function in the `datasets` package to load them, but for big datasets this is not feasible.

**Additional context**
HDF files are widespread throughout different domains and are one of the go to's for many researchers/scientists/engineers who work with numerical data. Given `datasets`' usecases have outgrown NLP use cases, it will make a lot of sense focusing on things like supporting HDF files.

Comments:
I'm currently working on bringing [Ecoset](https://www.pnas.org/doi/10.1073/pnas.2011417118) to huggingface datasets and I would second this request...

I would also like this support or something similar. Geospatial datasets come in netcdf which is derived from hdf5, or zarr. I've gotten zarr stores to work with datasets and streaming, but it takes awhile to convert the data to zarr if it's not stored in that natively. 

@mariosasko , I would like to contribute on this "good second issue" . Is there anything in the works for this Issue or can I go ahead ? 


Hi @VijayKalmath! As far as I know, nobody is working on it, so feel free to take over. Also, before you start, I suggest you comment `#self-assign` on this issue to assign it to yourself.

#self-assign

Hey @mariosasko can you assign this issue to me !!

So basically, we just need to load HDF5 files to Parquet?

e.g. Like this? https://stackoverflow.com/questions/46157709/converting-hdf5-to-parquet-without-loading-into-memory

#self-assign
Hi! I'd love to take a shot at implementing this feature to allow loading HDF5 datasets via Hugging Face Datasets. Will keep you updated here!

Hey @mariosasko,
I've opened a PR to address this: #7625
It adds a new h5folder loader to support .h5 files via load_dataset("h5folder", data_dir=...).
Feedback welcome!
Title: concatenate_datasets removes ClassLabel typing.
Author: Dref360 (CONTRIBUTOR)
State: closed
Created: 2021-10-19T18:05:31Z
Labels: bug

## Describe the bug

When concatenating two datasets, we lose typing of ClassLabel columns.

I can work on this if this is a legitimate bug,

## Steps to reproduce the bug
```python
import datasets
from datasets import Dataset, ClassLabel, Value, concatenate_datasets

DS_LEN = 100
my_dataset = Dataset.from_dict(
    {
        "sentence": [f"{chr(i % 10)}" for i in range(DS_LEN)],
        "label": [i % 2 for i in range(DS_LEN)]
    }
)
my_predictions = Dataset.from_dict(
    {
        "pred": [(i + 1) % 2 for i in range(DS_LEN)]
    }
)

my_dataset = my_dataset.cast(datasets.Features({"sentence": Value("string"), "label": ClassLabel(2, names=["POS", "NEG"])}))
print("Original")
print(my_dataset)
print(my_dataset.features)


concat_ds = concatenate_datasets([my_dataset, my_predictions], axis=1)
print("Concatenated")
print(concat_ds)
print(concat_ds.features)


```

## Expected results
The features of `concat_ds` should contain ClassLabel.

## Actual results

On master, I get: 
```
{'sentence': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None), 'pred': Value(dtype='int64', id=None)}
```

## Environment info
- `datasets` version: 1.14.1.dev0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1

Comments:
Something like this would fix it I think: https://github.com/huggingface/datasets/compare/master...Dref360:HF-3111/concatenate_types?expand=1
Title: Stream TAR-based dataset using iter_archive
Author: lhoestq (MEMBER)
State: closed
Created: 2021-10-19T17:16:24Z

I converted all the dataset based on TAR archive to use iter_archive instead, so that they can be streamable.

It means that around 80 datasets become streamable :)

Comments:
I'm creating a new branch `stream-tar-audio` just for the audio datasets since they need https://github.com/huggingface/datasets/pull/3129 to be merged first

The CI fails are only related to missing sections or tags in the dataset cards - which is unrelated to this PR
Title: Add Google BLEU (aka GLEU) metric
Author: slowwavesleep (CONTRIBUTOR)
State: closed
Created: 2021-10-19T14:48:38Z

This PR adds the NLTK implementation of Google BLEU metric. This is also a part of an effort to resolve an unfortunate naming collision between GLEU for machine translation and GLEU for grammatical error correction.
I used [this page](https://huggingface.co/docs/datasets/add_metric.html)  for reference. Please, point me to the right direction if I missed anything.

Comments:
Title: download_mode=`force_redownload` does not work on removed datasets
Author: severo (COLLABORATOR)
State: open
Created: 2021-10-18T13:12:38Z
Labels: bug

dataset-viewer

## Describe the bug

If a cached dataset is removed from the library, I don't see how to delete it programmatically. I thought that using `force_redownload` would try to refresh the cache, then raise an exception, but it reuses the cache instead.

## Steps to reproduce the bug

_requires to already have `wit` in the cache_: see https://github.com/huggingface/datasets/pull/2981

```python
import datasets as ds
dataset = ds.load_dataset("wit", split="train", download_mode='force_redownload')
```

## Expected results

It should raise an exception, since the dataset does not exist anymore.

## Actual results

It uses the cached result

```
Using the latest cached version of the module from /home/slesage/.cache/huggingface/modules/datasets_modules/datasets/wit/107afbffd48e058b19101bddc47fbee25fa68eb6d50a733e262875f1285a5171 (last modified on Wed Sep 29 08:21:10 2021) since it couldn't be found locally at wit, or remotely on the Hugging Face Hub.
```

## Environment info

- `datasets` version: 1.13.4.dev0
- Platform: Linux-5.11.0-1019-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 4.0.1

Comments:
Title: Missing Zenodo 1.13.3 release
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-10-18T12:57:18Z
Labels: bug

After `datasets` 1.13.3 release, this does not appear in Zenodo releases: https://zenodo.org/record/5570305

TODO:
- [x] Contact Zenodo support
- [x] Check it is fixed

Comments:
Zenodo has fixed on their side the 1.13.3 release: https://zenodo.org/record/5589150
Title: Fix project description in PyPI
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-10-18T12:47:29Z

Fix project description appearing in PyPI, so that it contains the content of the README.md file (like transformers).

Currently, `datasets` project description appearing in PyPI shows the release instructions addressed to core maintainers: https://pypi.org/project/datasets/1.13.3/

Fix #3102.

Comments:
Title: Replace FSTimeoutError with parent TimeoutError
Author: albertvillanova (MEMBER)
State: closed
Created: 2021-10-18T07:37:09Z

PR #3050 introduced a dependency on `fsspec.FSTiemoutError`. Note that this error only exists from `fsspec` version `2021.06.0` (June 2021).

To fix #3097, there are 2 alternatives:
- Either pinning `fsspec` to versions newer or equal to `2021.06.0`
- Or replacing `fsspec.FSTimeoutError` wth its parent `asyncio.TimeoutError`, which exists from Python 3.8.0 (Sep 2018).

This PR implements the second approach.

Fix #3097.

Comments: