jamesLeeeeeee/datasets-github-issues huggingface dataset:
equal operation to perform unbatch for huggingface datasets 
Author: dorooddorood606 | Pull request: no | State: closed

Hi
I need to use "unbatch" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:

I am considering "record" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:

https://github.com/google-research/text-to-text-transfer-transformer/blob/3c58859b8fe72c2dbca6a43bc775aa510ba7e706/t5/data/preprocessors.py#L925

Here please find an example:

  For example, a typical example from ReCoRD might look like
  {
      'passsage': 'This is the passage.',
      'query': 'A @placeholder is a bird.',
      'entities': ['penguin', 'potato', 'pigeon'],
      'answers': ['penguin', 'pigeon'],
  }
  and I need a prosessor which would turn this example into the following two examples:
  {
      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
                'potato, pigeon passage: This is the passage.',
      'targets': 'penguin',
  }
  and
  {
      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
                'potato, pigeon passage: This is the passage.',
      'targets': 'pigeon',
  }


For doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with  huggingface datasets library and greatly appreciate your help

@lhoestq 

Thank you very much.
BERTScore Error
Author: gagan3012 | Pull request: no | State: closed

## Describe the bug
A clear and concise description of what the bug is.

## Steps to reproduce the bug
```python
predictions = ["hello there", "general kenobi"]
references = ["hello there", "general kenobi"]
bert = load_metric('bertscore')
bert.compute(predictions=predictions, references=references,lang='en')
```

# Bug
`TypeError: get_hash() missing 1 required positional argument: 'use_fast_tokenizer'`


## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Colab 
- Python version:
- PyArrow version:
Add RVL-CDIP dataset
Author: NielsRogge | Pull request: no | State: closed

## Adding a Dataset
- **Name:** RVL-CDIP
- **Description:** The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images. The images are sized so their largest dimension does not exceed 1000 pixels.
- **Paper:** https://www.cs.cmu.edu/~aharley/icdar15/
- **Data:** https://www.cs.cmu.edu/~aharley/rvl-cdip/
- **Motivation:** I'm currently adding LayoutLMv2 and LayoutXLM to HuggingFace Transformers. LayoutLM (v1) already exists in the library. This dataset has a large value for document image classification (i.e. classifying scanned documents). LayoutLM models obtain SOTA on this dataset, so would be great to directly use it in notebooks.

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Add Nuswide dataset
Author: shivangibithel | Pull request: no | State: open

## Adding a Dataset
- **Name:** *NUSWIDE*
- **Description:** *[A Real-World Web Image Dataset from National University of Singapore](https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html)*
- **Paper:** *[here](https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/nuswide-civr2009.pdf)*
- **Data:** *[here](https://github.com/wenting-zhao/nuswide)*
- **Motivation:** *This dataset is a benchmark in the Text Retrieval task.*

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Unexpected type after `concatenate_datasets`
Author: JulesBelveze | Pull request: no | State: closed

## Describe the bug
I am trying to concatenate two `Dataset` using `concatenate_datasets` but it turns out that after concatenation the features are casted from `torch.Tensor` to `list`. 
It then leads to a weird tensors when trying to convert it to a `DataLoader`. However, if I use each `Dataset` separately everything behave as expected.

## Steps to reproduce the bug
```python
>>> featurized_teacher
Dataset({
    features: ['t_labels', 't_input_ids', 't_token_type_ids', 't_attention_mask'],
    num_rows: 502
})
>>> for f in featurized_teacher.features:
     print(featurized_teacher[f].shape)
torch.Size([502])
torch.Size([502, 300])
torch.Size([502, 300])
torch.Size([502, 300])

>>> featurized_student
Dataset({
    features: ['s_features', 's_labels'],
    num_rows: 502
})
>>> for f in featurized_student.features:
     print(featurized_student[f].shape)
torch.Size([502, 64])
torch.Size([502])
```
The shapes seem alright to me. Then the results after concatenation are as follow:
```python
>>> concat_dataset = datasets.concatenate_datasets([featurized_student, featurized_teacher], axis=1)
>>> type(concat_dataset["t_labels"])
<class 'list'>
```
One would expect to obtain the same type as the one before concatenation.

Am I doing something wrong here? Any idea on how to fix this unexpected behavior?

## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-10.14.6-x86_64-i386-64bit
- Python version: 3.9.5
- PyArrow version: 3.0.0
Second concatenation of datasets produces errors
Author: Aktsvigun | Pull request: no | State: closed

Hi,

I am need to concatenate my dataset with others several times, and after I concatenate it for the second time, the features of features (e.g. tags names) are collapsed. This hinders, for instance, the usage of tokenize function with `data.map`.

```
from datasets import load_dataset, concatenate_datasets

data = load_dataset('trec')['train']
concatenated = concatenate_datasets([data, data])
concatenated_2 = concatenate_datasets([concatenated, concatenated])
print('True features of features:', concatenated.features)
print('\nProduced features of features:', concatenated_2.features)
```
outputs 

```
True features of features: {'label-coarse': ClassLabel(num_classes=6, names=['DESC', 'ENTY', 'ABBR', 'HUM', 'NUM', 'LOC'], names_file=None, id=None), 'label-fine': ClassLabel(num_classes=47, names=['manner', 'cremat', 'animal', 'exp', 'ind', 'gr', 'title', 'def', 'date', 'reason', 'event', 'state', 'desc', 'count', 'other', 'letter', 'religion', 'food', 'country', 'color', 'termeq', 'city', 'body', 'dismed', 'mount', 'money', 'product', 'period', 'substance', 'sport', 'plant', 'techmeth', 'volsize', 'instru', 'abb', 'speed', 'word', 'lang', 'perc', 'code', 'dist', 'temp', 'symbol', 'ord', 'veh', 'weight', 'currency'], names_file=None, id=None), 'text': Value(dtype='string', id=None)}

Produced features of features: {'label-coarse': Value(dtype='int64', id=None), 'label-fine': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)}
```

I am using `datasets` v.1.11.0
Raise a proper exception when trying to stream a dataset that requires to manually download files
Author: severo | Pull request: no | State: closed

## Describe the bug

At least for 'reclor', 'telugu_books', 'turkish_movie_sentiment', 'ubuntu_dialogs_corpus', 'wikihow', trying to `load_dataset` in streaming mode raises a `TypeError` without any detail about why it fails.

## Steps to reproduce the bug

```python
from datasets import load_dataset
dataset = load_dataset("reclor", streaming=True)
```

## Expected results

Ideally: raise a specific exception, something like `ManualDownloadError`.

Or at least give the reason in the message, as when we load in normal mode:

```python
from datasets import load_dataset
dataset = load_dataset("reclor")
```

```
AssertionError: The dataset reclor with config default requires manual data.
 Please follow the manual download instructions:   to use ReClor you need to download it manually. Please go to its homepage (http://whyu.me/reclor/) fill the google
  form and you will receive a download link and a password to extract it.Please extract all files in one folder and use the path folder in datasets.load_dataset('reclor', data_dir='path/to/folder/folder_name')
  .
 Manual data can be loaded with `datasets.load_dataset(reclor, data_dir='<path/to/manual/data>')
```

## Actual results

```
TypeError: expected str, bytes or os.PathLike object, not NoneType
```

## Environment info

- `datasets` version: 1.11.0
- Platform: macOS-11.5-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
add multi-proc in `to_json`
Author: bhavitvyamalik | Pull request: yes | State: closed

Closes #2663. I've tried adding multiprocessing in `to_json`. Here's some benchmarking I did to compare the timings of current version (say v1) and multi-proc version (say v2). I did this with `cpu_count` 4 (2015 Macbook Air)

1. Dataset name: `ascent_kb` - 8.9M samples (all samples were used, reporting this for a single run)
v1- ~225 seconds for converting whole dataset to json
v2- ~200 seconds for converting whole dataset to json

2. Dataset name: `lama` - 1.3M samples (all samples were used, reporting this for 2 runs)
v1- ~26 seconds for converting whole dataset to json
v2- ~23.6 seconds for converting whole dataset to json

I think it's safe to say that v2 is 10% faster as compared to v1. Timings may improve further with better configuration.

The only bottleneck I feel is writing to file from the output list. If we can improve that aspect then timings may improve further. 

Let me know if any changes/improvements can be done in this @stas00, @lhoestq, @albertvillanova. @lhoestq even suggested to extend this work with other export methods as well like `csv` or `parquet`.
Cannot load `few-nerd` dataset
Author: Mehrad0711 | Pull request: no | State: closed

## Describe the bug

Cannot load `few-nerd` dataset.

## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset('few-nerd', 'supervised')
```

## Actual results

Executing above code will give the following error:

```
Using the latest cached version of the module from /Users/Mehrad/.cache/huggingface/modules/datasets_modules/datasets/few-nerd/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53 (last modified on Wed Jun  2 11:34:25 2021) since it couldn't be found locally at /Users/Mehrad/Documents/GitHub/genienlp/few-nerd/few-nerd.py, or remotely (FileNotFoundError).
Downloading and preparing dataset few_nerd/supervised (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/Mehrad/.cache/huggingface/datasets/few_nerd/supervised/0.0.0/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53...
Traceback (most recent call last):
  File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 693, in _download_and_prepare
    self._prepare_split(split_generator, **prepare_split_kwargs)
  File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1107, in _prepare_split
    disable=bool(logging.get_verbosity() == logging.NOTSET),
  File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1133, in __iter__
    for obj in iterable:
  File "/Users/Mehrad/.cache/huggingface/modules/datasets_modules/datasets/few-nerd/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53/few-nerd.py", line 196, in _generate_examples
    with open(filepath, encoding="utf-8") as f:
FileNotFoundError: [Errno 2] No such file or directory: '/Users/Mehrad/.cache/huggingface/datasets/downloads/supervised/train.json'
```
The bug is probably in identifying and downloading the dataset. If I download the json splits directly from [link](https://github.com/nbroad1881/few-nerd/tree/main/uncompressed) and put them under the downloads directory, they will be processed into arrow format correctly. 

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Python version: 3.8
- PyArrow version: 1.0.1
added semeval18_emotion_classification dataset
Author: maxpel | Pull request: yes | State: closed

I added the data set of SemEval 2018 Task 1 (Subtask 5) for emotion detection in three languages.

```
datasets-cli test datasets/semeval18_emotion_classification/ --save_infos --all_configs

RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_semeval18_emotion_classification
```
Both commands ran successfully.

I couldn't create the dummy data (the files are tsvs but have .txt ending, maybe that's the problem?) and therefore the test on the dummy data fails, maybe someone can help here.

I also formatted the code:
```
black --line-length 119 --target-version py36 datasets/semeval18_emotion_classification/
isort datasets/semeval18_emotion_classification/
flake8 datasets/semeval18_emotion_classification/
```
That's the publication for reference:

Mohammad, S., Bravo-Marquez, F., Salameh, M., & Kiritchenko, S. (2018). SemEval-2018 task 1: Affect in tweets. Proceedings of the 12th International Workshop on Semantic Evaluation, 1–17. https://doi.org/10.18653/v1/S18-1001
Dataset JSON is incorrect
Author: severo | Pull request: no | State: closed

## Describe the bug

The JSON file generated for https://github.com/huggingface/datasets/blob/573f3d35081cee239d1b962878206e9abe6cde91/datasets/journalists_questions/journalists_questions.py is https://github.com/huggingface/datasets/blob/573f3d35081cee239d1b962878206e9abe6cde91/datasets/journalists_questions/dataset_infos.json.

The only config should be `plain_text`, but the first key in the JSON is `journalists_questions` (the dataset id) instead.

```json
{
  "journalists_questions": {
    "description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
    ...
```

## Steps to reproduce the bug

Look at the files.

## Expected results

The first key should be `plain_text`:

```json
{
  "plain_text": {
    "description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
    ...
```

## Actual results

```json
{
  "journalists_questions": {
    "description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
    ...
```
Improve detection of streamable file types
Author: severo | Pull request: no | State: closed

**Is your feature request related to a problem? Please describe.**

```python
from datasets import load_dataset_builder
from datasets.utils.streaming_download_manager import StreamingDownloadManager
builder = load_dataset_builder("journalists_questions", name="plain_text")
builder._split_generators(StreamingDownloadManager(base_path=builder.base_path))
```

raises

```
NotImplementedError: Extraction protocol for file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet
```

But the file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is a text file and it can be streamed:

```bash
curl --header "Range: bytes=0-100" -L https://drive.google.com/uc\?export\=download\&id\=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U
506938088174940160      yes     1
302221719412830209      yes     1
289761704907268096      yes     1
513820885032378369      yes     %
```

Yet, it's wrongly categorized as a file type that cannot be streamed because the test is currently based on 1. the presence of a file extension at the end of the URL (here: no extension), and 2. the inclusion of this extension in a list of supported formats.

**Describe the solution you'd like**

In the case of an URL (instead of a local path), ask for the MIME type, and decide on that value? Note that it would not work in that case, because the value of `content_type` is `text/html; charset=UTF-8`.

**Describe alternatives you've considered**

Add a variable in the dataset script to set the data format by hand.
Add Hypersim dataset
Author: osanseviero | Pull request: no | State: open

## Adding a Dataset
- **Name:** Hypersim
- **Description:** photorealistic synthetic dataset for holistic indoor scene understanding
- **Paper:** *link to the dataset paper if available*
- **Data:** https://github.com/apple/ml-hypersim

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Pass tokenize to sacrebleu only if explicitly passed by user
Author: albertvillanova | Pull request: yes | State: closed

Next `sacrebleu` release (v2.0.0) will remove `sacrebleu.DEFAULT_TOKENIZER`: https://github.com/mjpost/sacrebleu/pull/152/files#diff-2553a315bb1f7e68c9c1b00d56eaeb74f5205aeb3a189bc3e527b122c6078795L17-R15

This PR passes `tokenize` to `sacrebleu` only if explicitly passed by the user, otherwise it will not pass it (and `sacrebleu` will use its default, no matter where it is and how it is called).

Close: #2737.
SacreBLEU update
Author: devrimcavusoglu | Pull request: no | State: closed

With the latest release of [sacrebleu](https://github.com/mjpost/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.

    AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'

this happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`

## Steps to reproduce the bug
```python
sacrebleu= datasets.load_metric('sacrebleu')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
results = sacrebleu.compute(predictions=predictions, references=references)
print(results)
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: Python 3.8.0
- PyArrow version: 5.0.0
Add Microsoft Building Footprints dataset
Author: albertvillanova | Pull request: no | State: open

## Adding a Dataset
- **Name:** Microsoft Building Footprints
- **Description:** With the goal to increase the coverage of building footprint data available as open data for OpenStreetMap and humanitarian efforts, we have released millions of building footprints as open data available to download free of charge.
- **Paper:** *link to the dataset paper if available*
- **Data:** https://www.microsoft.com/en-us/maps/building-footprints
- **Motivation:** this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc.

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

Reported by: @sashavor
Add Open Buildings dataset
Author: albertvillanova | Pull request: no | State: open

## Adding a Dataset
- **Name:** Open Buildings
- **Description:** A dataset of building footprints to support social good applications.

  Building footprints are useful for a range of important applications, from population estimation, urban planning and humanitarian response, to environmental and climate science. This large-scale open dataset contains the outlines of buildings derived from high-resolution satellite imagery in order to support these types of uses. The project being based in Ghana, the current focus is on the continent of Africa.

  See: "Mapping Africa's Buildings with Satellite Imagery" https://ai.googleblog.com/2021/07/mapping-africas-buildings-with.html
- **Paper:** https://arxiv.org/abs/2107.12283
- **Data:** https://sites.research.google/open-buildings/
- **Motivation:** *what are some good reasons to have this dataset*

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

Reported by: @osanseviero
Add missing parquet known extension
Author: lhoestq | Pull request: yes | State: closed

This code was failing because the parquet extension wasn't recognized:

```python
from datasets import load_dataset
base_url = "https://storage.googleapis.com/huggingface-nlp/cache/datasets/wikipedia/20200501.en/1.0.0/"
data_files = {"train": base_url + "wikipedia-train.parquet"}
wiki = load_dataset("parquet", data_files=data_files, split="train", streaming=True)
```

It raises
```python
NotImplementedError: Extraction protocol for file at https://storage.googleapis.com/huggingface-nlp/cache/datasets/wikipedia/20200501.en/1.0.0/wikipedia-train.parquet is not implemented yet
```

I added `parquet` to the list of known extensions

EDIT: added pickle, conllu, xml extensions as well
Adding to_tf_dataset method
Author: Rocketknight1 | Pull request: yes | State: closed

Oh my **god** do not merge this yet, it's just a draft.

I've added a method (via a mixin) to the `arrow_dataset.Dataset` class that automatically converts our Dataset classes to TF Dataset classes ready for training. It hopefully has most of the features we want, including streaming from disk (no need to load the whole dataset in memory!), correct shuffling, variable-length batches to reduce compute, and correct support for unusual padding. It achieves that by calling the tokenizer `pad` method in the middle of a TF compute graph via a very hacky call to `tf.py_function`, which is heretical but seems to work.

A number of issues need to be resolved before it's ready to merge, though:

1) Is a MixIn the right way to do this? Do other classes besides `arrow_dataset.Dataset` need this method too?
2) Needs an argument to support constant-length batches for TPU training - this is easy to add and I'll do it soon.
3) Needs the user to supply the list of columns to drop from the arrow `Dataset`. Is there some automatic way to get the columns we want, or see which columns were added by the tokenizer?
4) Assumes the label column is always present and always called "label" - this is probably not great, but I'm not sure what the 'correct' thing to do here is.
Update CommonVoice with new release
Author: yjernite | Pull request: no | State: open

## Adding a Dataset
- **Name:** CommonVoice mid-2021 release
- **Description:** more data in CommonVoice: Languages that have increased the most by percentage are Thai (almost 20x growth, from 12 hours to 250 hours), Luganda (almost 9x growth, from 8 to 80), Esperanto (7x growth, from 100 to 840), and Tamil (almost 8x, from 24 to 220).
- **Paper:** https://discourse.mozilla.org/t/common-voice-2021-mid-year-dataset-release/83812
- **Data:** https://commonvoice.mozilla.org/en/datasets
- **Motivation:** More data and more varied. I think we just need to add configs in the existing dataset script.

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).