jamesLeeeeeee/datasets-github-issues huggingface dataset:
Align question answering tasks with sub-domains
Author: lewtun | Pull request: no | State: closed

As pointed out by @thomwolf in #2255 we should consider breaking with the pipeline taxonomy of `transformers` to account for the various types of question-answering domains:

> `question-answering` exists in two forms: abstractive and extractive question answering.
> 
> we can keep a generic `question-answering` but then it will probably mean diferrent schema of input/output for both (abstractive will have text for both while extractive can use spans indication as well as text).
> 
> Or we can also propose to use `abstractive-question-answering` and `extractive-question-answering` for instance.
> Maybe we could have `question-answering-abstractive` and `question-answering-extractive` if somehow we can use a for a completion or search in the future (detail).
> Actually I see that people are more organizing in terms of general and sub-tasks, for instance on paperwithcode: https://paperswithcode.com/area/natural-language-processing and on nlpprogress: https://github.com/sebastianruder/NLP-progress/blob/master/english/question_answering.md#squad
> 
> Probably the best is to align with one of these in terms of denomination, PaperWithCode is probably the most active and maintained and we work with them as well.
> Maybe you want to check with a few QA datasets that this schema make sense. Typically NaturalQuestions, TriviaQA and can be good second datasets to compare to and be sure of the generality of the schema.
> 
> A good recent list of QA datasets to compare the schemas among, is for instance in the UnitedQA paper: https://arxiv.org/abs/2101.00178

Investigate which grouping of QA is best suited for `datasets` and adapt / extend the QA task template accordingly.
Adding HendrycksTest dataset
Author: andyzoujm | Pull request: yes | State: closed

Adding Hendrycks test from https://arxiv.org/abs/2009.03300.
I'm having a bit of trouble with dummy data creation because some lines in the csv files aren't being loaded properly (only the first entry loaded in a row of length 6). The dataset is loading just fine. Hope you can kindly help!
Thank you!
correct labels of conll2003
Author: philschmid | Pull request: yes | State: closed

# What does this PR

It fixes/extends the `ner_tags` for conll2003 to include all. 
Paper reference https://arxiv.org/pdf/cs/0306050v1.pdf
Model reference https://huggingface.co/elastic/distilbert-base-cased-finetuned-conll03-english/blob/main/config.json
Json loader fails if user-specified features don't match the json data fields order
Author: lhoestq | Pull request: no | State: closed

If you do
```python
dataset = load_dataset("json", data_files=data_files, features=features)
```
Then depending on the order of the features in the json data field it fails:
```python
[...]
~/Desktop/hf/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files)
     94             if self.config.schema:
     95                 # Cast allows str <-> int/float, while parse_option explicit_schema does NOT
---> 96                 pa_table = pa_table.cast(self.config.schema)
     97             yield i, pa_table
[...]
ValueError: Target schema's field names are not matching the table's field names: ['tokens', 'ner_tags'], ['ner_tags', 'tokens']
```

This is because one must first re-order the columns of the table to match the `self.config.schema` before calling cast.

One way to fix the `cast` would be to replace it with:
```python
# reorder the arrays if necessary + cast to schema
# we can't simply use .cast here because we may need to change the order of the columns
pa_table = pa.Table.from_arrays([pa_table[name] for name in schema.names], schema=schema)
```
Missing ClassLabel encoding in Json loader
Author: lhoestq | Pull request: no | State: closed

Currently if you want to load a json dataset this way
```python
dataset = load_dataset("json", data_files=data_files, features=features)
```
Then if your features has ClassLabel types and if your json data needs class label encoding (i.e. if the labels in the json files are strings and not integers), then it would fail:
```python
[...]
~/Desktop/hf/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files)
     94             if self.config.schema:
     95                 # Cast allows str <-> int/float, while parse_option explicit_schema does NOT
---> 96                 pa_table = pa_table.cast(self.config.schema)
     97             yield i, pa_table
[...]
ArrowInvalid: Failed to parse string: 'O' as a scalar of type int64
```

This is because it just tries to cast the string data to integers, without applying the mapping str->int first

The current workaround is to do instead
```python
dataset = load_dataset("json", data_files=data_files)
dataset = dataset.map(features.encode_example, features=features)
```
README updated for SNLI, MNLI
Author: bhavitvyamalik | Pull request: yes | State: closed

Closes #2275. Mentioned about -1 labels in MNLI, SNLI and how they should be removed before training. @lhoestq `check_code_quality` test might fail for MNLI as the license name `other-Open Portion of the American National Corpus`  is not a registered tag for 'licenses'
Allow model labels to be passed during task preparation
Author: lewtun | Pull request: no | State: closed

Models have a config with label2id. And we have the same for datasets with the ClassLabel feature type. At one point either the model or the dataset must sync with the other. It would be great to do that on the dataset side.

For example for sentiment classification on amazon reviews with you could have these labels:
- "1 star", "2 stars", "3 stars", "4 stars", "5 stars"
- "1", "2", "3", "4", "5"

Some models may use the first set, while other models use the second set.

Here in the `TextClassification` class, the user can only specify one set of labels, while many models could actually be compatible but have different sets of labels. Should we allow users to pass a list of compatible labels sets ?

Then in terms of API, users could use `dataset.prepare_for_task("text-classification", labels=model.labels)` or something like that.

The label set could also be the same but not in the same order. For NLI for example, some models use `["neutral", "entailment", "contradiction"]` and some others use `["neutral", "contradiction", "entailment"]`, so we should take care of updating the order of the labels in the dataset to match the labels order of the model.

Let me know what you think ! This can be done in a future PR

_Originally posted by @lhoestq in https://github.com/huggingface/datasets/pull/2255#discussion_r632412792_
Adding Microsoft CodeXGlue Datasets
Author: ncoop57 | Pull request: yes | State: closed

Hi there, this is a new pull request to get the CodeXGlue datasets into the awesome HF datasets lib. Most of the work has been done in this PR #997 by the awesome @madlag. However, that PR has been stale for a while now and so I spoke with @lhoestq about finishing up the final mile and so he told me to open a new PR with the final changes :smile:. 

I believe I've met all of the changes still left in the old PR to do, except for the change to the languages. I believe the READMEs should include the different programming languages used rather than just using the tag "code" as when searching for datasets, SE researchers may specifically be looking only for what type of programming language and so being able to quickly filter will be very valuable. Let me know what you think of that or if you still believe it should be the "code" tag @lhoestq.
normalized TOCs and titles in data cards
Author: yjernite | Pull request: yes | State: closed

I started fixing some of the READMEs that were failing the tests introduced by @gchhablani but then realized that there were some consistent differences between earlier and newer versions of some of the titles (e.g. Data Splits vs Data Splits Sample Size, Supported Tasks vs Supported Tasks and Leaderboards). We also had different versions of the Table of Content

This PR normalizes all of them to the newer version
Document DatasetInfo attributes
Author: lewtun | Pull request: no | State: closed

**Is your feature request related to a problem? Please describe.**
As noted in PR #2255, the attributes of `DatasetInfo` are not documented in the [docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=datasetinfo#datasetinfo). It would be nice to do so :)
simpllify faiss index save
Author: Guitaricet | Pull request: yes | State: closed

Fixes #2350

In some cases, Faiss GPU index objects do not have neither "device" nor "getDevice". Possibly this happens when some part of the index is computed on CPU.

In particular, this would happen with the index `OPQ16_128,IVF512,PQ32` (issue #2350). I did check it, but it is likely that `OPQ` or `PQ` transforms cause it.

I propose, instead of using the index object to get the device, to infer it form the `FaissIndex.device` field as it is done in `.add_vectors`. Here we assume that `.device` always corresponds to the index placement and it seems reasonable.
`FaissIndex.save` throws error on GPU
Author: Guitaricet | Pull request: no | State: closed

## Describe the bug

After training an index with a factory string `OPQ16_128,IVF512,PQ32` on GPU, `.save_faiss_index` throws this error.

```
  File "index_wikipedia.py", line 119, in <module>
    data["train"].save_faiss_index("text_emb", index_save_path)
  File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/datasets/search.py", line 470, in save_faiss_index
    index.save(file)
  File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/datasets/search.py", line 334, in save
    faiss.write_index(index, str(file))
  File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/faiss/swigfaiss_avx2.py", line 5654, in write_index
    return _swigfaiss.write_index(*args)
RuntimeError: Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at /root/miniconda3/conda-bld/faiss-pkg_1613235005464/work/faiss/impl/index_write.cpp:453: don't know how to serialize this type of index
```

## Steps to reproduce the bug

Any dataset will do, I just selected a familiar one.

```python
import numpy as np
import datasets
INDEX_STR = "OPQ16_128,IVF512,PQ32"
INDEX_SAVE_PATH = "will_not_save.faiss"

data = datasets.load_dataset("Fraser/news-category-dataset", split=f"train[:10000]")

def encode(item):
    return {"text_emb": np.random.randn(768).astype(np.float32)}

data = data.map(encode)

data.add_faiss_index(column="text_emb", string_factory=INDEX_STR, train_size=10_000, device=0)
data.save_faiss_index("text_emb", INDEX_SAVE_PATH)
```

## Expected results
Saving the index

## Actual results
Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) ... don't know how to serialize this type of index

## Environment info
- `datasets` version: 1.6.2
- Platform: Linux-4.15.0-142-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyTorch version (GPU?): 1.8.1+cu111 (True)
- Tensorflow version (GPU?): 2.2.0 (False)
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No


I will be proposing a fix in a couple of minutes
Add tests for dataset cards
Author: gchhablani | Pull request: yes | State: closed

Adding tests for dataset cards

This PR will potentially remove the scripts being used for dataset tags and readme validation.

Additionally, this will allow testing dataset readmes by providing the name as follows:

```bash
pytest tests/test_dataset_cards.py::test_dataset_tags[fashion_mnist]
```
and

```bash
pytest tests/test_dataset_cards.py::test_readme_content[fashion_mnist]
```
or a combined test as:

```bash
pytest tests/test_dataset_cards.py::test_dataset_card[fashion_mnist]
```
@lhoestq
Add Qasper Dataset
Author: cceyda | Pull request: yes | State: closed

[Question Answering on Scientific Research Papers](https://allenai.org/project/qasper/home)

Doing NLP on NLP papers to do NLP ♻️ I had to add it~

- [x] Add README (just gotta fill out some more )
- [x] Dataloader code
- [x] Make dummy dataset
- [x] generate dataset infos
- [x] Tests
[Question] How to move and reuse preprocessed dataset? 
Author: AtmaHou | Pull request: no | State: closed

Hi, I am training a gpt-2 from scratch using run_clm.py.

I want to move and reuse the preprocessed dataset (It take 2 hour to preprocess),

I tried to :

copy path_to_cache_dir/datasets to new_cache_dir/datasets
set export HF_DATASETS_CACHE="new_cache_dir/"
but the program still re-preprocess the whole dataset without loading cache.

I also tried to torch.save(lm_datasets, fw), but the saved file is only 14M.

What is the proper way to do this?
Is there a way to join multiple datasets in one?
Author: avacaondata | Pull request: no | State: open

**Is your feature request related to a problem? Please describe.**
I need to join 2 datasets, one that is in the hub and another I've created from my files. Is there an easy way to join these 2? 

**Describe the solution you'd like**
Id like to join them with a merge or join method, just like pandas dataframes. 

**Additional context**
If you want to extend an existing dataset with more data, for example for training a language model, you need that functionality. I've not found it in the documentation.
Columns are removed before or after map function applied?
Author: taghizad3h | Pull request: no | State: open

## Describe the bug
According to the documentation when applying map function the [remove_columns ](https://huggingface.co/docs/datasets/processing.html#removing-columns) will be removed after they are passed to the function, but in the [source code](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map) it's documented that they are removed before applying function. I thinks the source code doc is more accurate, right?
Added the Ascent KB
Author: phongnt570 | Pull request: yes | State: closed

Added the Ascent Commonsense KB of 8.9M assertions.

- Paper: [Advanced Semantics for Commonsense Knowledge Extraction (WWW'21)](https://arxiv.org/abs/2011.00905)
- Website: https://ascent.mpi-inf.mpg.de/

(I am the author of the dataset)
NonMatchingChecksumError for web_of_science dataset
Author: nbroad1881 | Pull request: no | State: closed

NonMatchingChecksumError when trying to download the web_of_science dataset. 

>NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://data.mendeley.com/datasets/9rw3vkcfy4/6/files/c9ea673d-5542-44c0-ab7b-f1311f7d61df/WebOfScience.zip?dl=1']

Setting `ignore_verfications=True` results in OSError.

>OSError: Cannot find data file. 
Original error:
[Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/37ab2c42f50d553c1d0ea432baca3e9e11fedea4aeec63a81e6b7e25dd10d4e7/WOS5736/X.txt'

```python
dataset = load_dataset('web_of_science', 'WOS5736')
```
There are 3 data instances and they all don't work. 'WOS5736', 'WOS11967', 'WOS46985'

datasets 1.6.2
python 3.7.10
Ubuntu 18.04.5 LTS
Index error in Dataset.map
Author: mariosasko | Pull request: no | State: closed

The following code, if executed on master, raises an IndexError (due to overflow):
```python
>>> from datasets import *
>>> d = load_dataset("bookcorpus", split="train")
Reusing dataset bookcorpus (C:\Users\Mario\.cache\huggingface\datasets\bookcorpus\plain_text\1.0.0\44662c4a114441c35200992bea923b170e6f13f2f0beb7c14e43759cec498700)
2021-05-08 21:23:46.859818: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
>>> d.map(lambda ex: ex)
  0%|▎                                                                    | 289430/74004228 [00:13<58:41, 20935.33ex/s]c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py:84: RuntimeWarning: overflow encountered in int_scalars
  k = i + ((j - i) * (x - arr[i]) // (arr[j] - arr[i]))
  0%|▎                                                                    | 290162/74004228 [00:13<59:11, 20757.23ex/s]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1498, in map
    new_fingerprint=new_fingerprint,
  File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 174, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "c:\users\mario\desktop\projects\datasets-1\src\datasets\fingerprint.py", line 340, in wrapper
    out = func(self, *args, **kwargs)
  File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1799, in _map_single
    for i, example in enumerate(pbar):
  File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\site-packages\tqdm\std.py", line 1133, in __iter__
    for obj in iterable:
  File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1145, in __iter__
    format_kwargs=format_kwargs,
  File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1337, in _getitem
    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
  File "c:\users\mario\desktop\projects\datasets-1\src\datasets\formatting\formatting.py", line 368, in query_table
    pa_subtable = _query_table(table, key)
  File "c:\users\mario\desktop\projects\datasets-1\src\datasets\formatting\formatting.py", line 79, in _query_table
    return table.fast_slice(key % table.num_rows, 1)
  File "c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py", line 128, in fast_slice
    i = _interpolation_search(self._offsets, offset)
  File "c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py", line 91, in _interpolation_search
    raise IndexError(f"Invalid query '{x}' for size {arr[-1] if len(arr) else 'none'}.")
IndexError: Invalid query '290162' for size 74004228.
```
Tested on Windows, can run on Linux if needed.

EDIT:
It seems like for this to happen, the default NumPy dtype has to be np.int32.
Add Topical-Chat
Author: ktangri | Pull request: no | State: open

## Adding a Dataset
- **Name:** Topical-Chat
- **Description:** a knowledge-grounded human-human conversation dataset where the underlying knowledge spans 8 broad topics and conversation partners don’t have explicitly defined roles
- **Paper:** https://www.isca-speech.org/archive/Interspeech_2019/pdfs/3079.pdf
- **Data:** https://github.com/alexa/Topical-Chat
- **Motivation:** Good quality, knowledge-grounded dataset that spans a broad range of topics

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Enable auto-download for PAN-X / Wikiann domain in XTREME
Author: lewtun | Pull request: yes | State: closed

This PR replaces the manual download of the `PAN-X.lang` domains with an auto-download from a Dropbox link provided by the Wikiann author. We also add the relevant dummy data for these domains.

While re-generating `dataset_infos.json` I ran into a `KeyError` in the `udpos.Arabic` domain so have included a fix for this as well.
Added the HLGD dataset
Author: tingofurro | Pull request: yes | State: closed

Added the Headline Grouping Dataset (HLGD), from the NAACL2021 paper: News Headline Grouping as a Challenging NLU Task
Dataset Link: https://github.com/tingofurro/headline_grouping
Paper link: https://people.eecs.berkeley.edu/~phillab/pdfs/NAACL2021_HLG.pdf
Create Audio feature
Author: albertvillanova | Pull request: yes | State: closed

Create `Audio` feature to handle raw audio files.

Some decisions to be further discussed:
- I have chosen `soundfile` as the audio library; another interesting library is `librosa`, but this requires `soundfile` (see [here](https://github.com/librosa/librosa/blob/main/setup.cfg#L53)). If we require some more advanced functionalities, we could eventually switch the library.
- I have implemented the audio feature as an extra: `pip install datasets[audio]`. For the moment, the typical datasets user uses only text datasets, and there is no need for them for additional package requirements for audio/image if they do not need them.
- For tests, I require audio dependencies (so that all audio functionalities are checked with our CI test suite); I exclude Linux platforms, which require an additional library to be installed with the distribution package manager
  - I also require `pytest-datadir`, which allow to have (audio) data files for tests
- The audio data contain: array and sample_rate.
- The array is reshaped as 1D array (expected input for `Wav2Vec2`).

Note that to install `soundfile` on Linux, you need to install `libsndfile` using your distribution’s package manager, for example `sudo apt-get install libsndfile1`.

## Requirements Specification
- Access example with audio loading and resampling:
  ```python
  ds[0]["audio"]
  ```
- Map with audio loading & resampling:
  ```python
  def preprocess(batch):
       batch["input_values"] = processor(batch["audio"]).input_values
       return batch

  ds = ds.map(preprocess)
  ```
- Map without audio loading and resampling:
  ```python
  def preprocess(batch):
       batch["labels"] = processor(batch["target_text"]).input_values
       return batch

  ds = ds.map(preprocess)
  ```
- Additional requirement specification (see https://github.com/huggingface/datasets/pull/2324#pullrequestreview-768864998): Cast audio column to change sampling sate:
  ```python
  ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
  ```
load_dataset("timit_asr") gives back duplicates of just one sample text
Author: ekeleshian | Pull request: no | State: closed

## Describe the bug
When you look up on key ["train"] and then ['text'], you get back a list  with just one sentence duplicated 4620 times. Namely, the sentence "Would such an act of refusal be useful?". Similarly when you look up ['test'] and then ['text'], the list is one sentence repeated "The bungalow was pleasantly situated near the shore." 1680 times. 

I tried to work around the issue by downgrading to datasets version 1.3.0, inspired by [this post](https://www.gitmemory.com/issue/huggingface/datasets/2052/798904836) and removing the entire huggingface directory from ~/.cache, but I still get the same issue.  

## Steps to reproduce the bug
```python
from datasets import load_dataset
timit = load_dataset("timit_asr")
print(timit['train']['text'])
print(timit['test']['text'])
```

## Expected Result
Rows of diverse text, like how it is shown in the [wav2vec2.0 tutorial](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb)
<img width="485" alt="Screen Shot 2021-05-05 at 9 09 57 AM" src="https://user-images.githubusercontent.com/33647474/117146094-d9b77f00-ad81-11eb-8306-f281850c127a.png">


## Actual results
Rows of repeated text.
<img width="319" alt="Screen Shot 2021-05-05 at 9 11 53 AM" src="https://user-images.githubusercontent.com/33647474/117146231-f8b61100-ad81-11eb-834a-fc10410b0c9c.png">


## Versions
- Datasets: 1.3.0
- Python: 3.9.1
- Platform: macOS-11.2.1-x86_64-i386-64bit}