
# Dataset Card for "lmqg/qa_squad"

## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://rajpurkar.github.io/SQuAD-explorer/](https://rajpurkar.github.io/SQuAD-explorer/)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)

### Dataset Summary
This is the SQuAD v1 dataset with the train/validatio/test split used in [qg_squad](https://huggingface.co/datasets/lmqg/qg_squad).


### Supported Tasks and Leaderboards
* `question-answering`

### Languages
English (en)

## Dataset Structure

### Data Fields
The data fields are the same among all splits.

#### plain_text

- `id`: a `string` feature of id
- `title`: a `string` feature of title of the paragraph 
- `context`: a `string` feature of paragraph 
- `question`: a `string` feature of question
- `answers`: a `json` feature of answers

### Data Splits

|train    |validation|test    |
|--------:|---------:|-------:|
|   75,722|    10,570|  11,877|

## Citation Information

```
@article{2016arXiv160605250R,
       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
                 Konstantin and {Liang}, Percy},
        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
      journal = {arXiv e-prints},
         year = 2016,
          eid = {arXiv:1606.05250},
        pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
       eprint = {1606.05250},
}
```
task_categories:question-answering task_ids:extractive-qa multilinguality:monolingual size_categories:1M< source_datasets:extended|wikipedia language:en license:cc-by-4.0 arxiv:1606.05250 region:us
{"language": "en", "license": "cc-by-4.0", "multilinguality": "monolingual", "size_categories": "1M<", "source_datasets": ["extended|wikipedia"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "pretty_name": "SQuAD with QG split."}
TAGS
#task_categories-question-answering #task_ids-extractive-qa #multilinguality-monolingual #size_categories-1M< #source_datasets-extended|wikipedia #language-English #license-cc-by-4.0 #arxiv-1606.05250 #region-us 

Dataset Card for "lmqg/qa\_squad"
=================================


Dataset Description
-------------------


* Repository: URL
* Paper: URL
* Point of Contact: Asahi Ushio


### Dataset Summary


This is the SQuAD v1 dataset with the train/validatio/test split used in qg\_squad.


### Supported Tasks and Leaderboards


* 'question-answering'


### Languages


English (en)


Dataset Structure
-----------------


### Data Fields


The data fields are the same among all splits.


#### plain\_text


* 'id': a 'string' feature of id
* 'title': a 'string' feature of title of the paragraph
* 'context': a 'string' feature of paragraph
* 'question': a 'string' feature of question
* 'answers': a 'json' feature of answers


### Data Splits




### Dataset Summary


This is the SQuAD v1 dataset with the train/validatio/test split used in qg\_squad. ### Supported Tasks and Leaderboards


* 'question-answering' ### Languages


English (en)


Dataset Structure
----------------- ### Data Fields


The data fields are the same among all splits. #### plain\_text


* 'id': a 'string' feature of id
* 'title': a 'string' feature of title of the paragraph
* 'context': a 'string' feature of paragraph
* 'question': a 'string' feature of question
* 'answers': a 'json' feature of answers ### Data Splits
TAGS
#task_categories-question-answering #task_ids-extractive-qa #multilinguality-monolingual #size_categories-1M< #source_datasets-extended|wikipedia #language-English #license-cc-by-4.0 #arxiv-1606.05250 #region-us 
 ### Dataset Summary


This is the SQuAD v1 dataset with the train/validatio/test split used in qg\_squad. ### Supported Tasks and Leaderboards


* 'question-answering' ### Languages


English (en)


Dataset Structure
----------------- ### Data Fields


The data fields are the same among all splits. #### plain\_text


* 'id': a 'string' feature of id
* 'title': a 'string' feature of title of the paragraph
* 'context': a 'string' feature of paragraph
* 'question': a 'string' feature of question
* 'answers': a 'json' feature of answers ### Data Splits