
### Dataset Summary

QASports is the first large sports-themed question answering dataset counting over 1.5 million questions and answers about 54k preprocessed wiki pages, using as documents the wiki of 3 of the most popular sports in the world, Soccer, American Football and Basketball. Each sport can be downloaded individually as a subset, with the train, test and validation splits, or all 3 can be downloaded together.

- 🎲 Complete dataset: https://osf.io/n7r23/
- 🔧 Processing scripts: https://github.com/leomaurodesenv/qasports-dataset-scripts/

### Supported Tasks and Leaderboards

Extractive Question Answering.

### Languages

English.

## Dataset Structure

### Data Instances

An example of 'train' looks as follows.
```
{
    "answer": {
        "offset": [42,44],
        "text": "16"
    },
    "context": "The following is a list of squads for all 16 national teams competing at the Copa América Centenario. Each national team had to submit a squad of 23 players, 3 of whom must be goalkeepers. The provisional squads were announced on 4 May 2016. A final selection was provided to the organisers on 20 May 2016."	,
    "qa_id": "61200579912616854316543272456523433217",
    "question": "How many national teams competed at the Copa América Centenario?",
    "context_id": "171084087809998484545703642399578583178",
    "context_title": "Copa América Centenario squads | Football Wiki | Fandom",
    "url": "https://football.fandom.com/wiki/Copa_Am%C3%A9rica_Centenario_squads"	

}
```
### Data Fields

The data fields are the same among all splits.
- '': int
- `id_qa`: a `string` feature.
- `context_id`: a `string` feature.
- `context_title`: a `string` feature.
- `url`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
  - `text`: a `string` feature.
  - `offset`: a list feature containing:
    - 2 `int32` features for start and end.
   
### Citation

```

@inproceedings{jardim:2023:qasports-dataset,
    author={Pedro Calciolari Jardim and Leonardo Mauro Pereira Moraes and Cristina Dutra Aguiar},
    title = {{QASports}: A Question Answering Dataset about Sports}, 
    booktitle = {Proceedings of the Brazilian Symposium on Databases: Dataset Showcase Workshop},
    address = {Belo Horizonte, MG, Brazil},
    url = {https://github.com/leomaurodesenv/qasports-dataset-scripts},
    publisher = {Brazilian Computer Society},
    pages = {1-12},
    year = {2023}
}

```

task_categories:question-answering size_categories:1M<n<10M language:en license:mit sports open-domain-qa extractive-qa region:us
{"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["question-answering"], "pretty_name": "QASports", "configs": [{"config_name": "all", "data_files": [{"split": "train", "path": ["trainBask.csv", "trainSocc.csv", "trainFoot.csv"]}, {"split": "test", "path": ["testBask.csv", "testSocc.csv", "testFoot.csv"]}, {"split": "validation", "path": ["validationBask.csv", "validationSocc.csv", "validationFoot.csv"]}], "default": true}, {"config_name": "basketball", "data_files": [{"split": "train", "path": "trainBask.csv"}, {"split": "test", "path": "testBask.csv"}, {"split": "validation", "path": "validationBask.csv"}]}, {"config_name": "football", "data_files": [{"split": "train", "path": "trainFoot.csv"}, {"split": "test", "path": "testFoot.csv"}, {"split": "validation", "path": "validationFoot.csv"}]}, {"config_name": "soccer", "data_files": [{"split": "train", "path": "trainSocc.csv"}, {"split": "test", "path": "testSocc.csv"}, {"split": "validation", "path": "validationSocc.csv"}]}], "tags": ["sports", "open-domain-qa", "extractive-qa"]}
TAGS
#task_categories-question-answering #size_categories-1M<n<10M #language-English #license-mit #sports #open-domain-qa #extractive-qa #region-us 


### Dataset Summary

QASports is the first large sports-themed question answering dataset counting over 1.5 million questions and answers about 54k preprocessed wiki pages, using as documents the wiki of 3 of the most popular sports in the world, Soccer, American Football and Basketball. Each sport can be downloaded individually as a subset, with the train, test and validation splits, or all 3 can be downloaded together.

-  Complete dataset: URL
-  Processing scripts: URL

### Supported Tasks and Leaderboards

Extractive Question Answering.

### Languages

English.

## Dataset Structure

### Data Instances

An example of 'train' looks as follows.

### Data Fields

The data fields are the same among all splits.
- '': int
- 'id_qa': a 'string' feature.
- 'context_id': a 'string' feature.
- 'context_title': a 'string' feature.
- 'url': a 'string' feature.
- 'context': a 'string' feature.
- 'question': a 'string' feature.
- 'answers': a dictionary feature containing:
  - 'text': a 'string' feature.
  - 'offset': a list feature containing:
    - 2 'int32' features for start and end.
   

### Dataset Summary

QASports is the first large sports-themed question answering dataset counting over 1.5 million questions and answers about 54k preprocessed wiki pages, using as documents the wiki of 3 of the most popular sports in the world, Soccer, American Football and Basketball. Each sport can be downloaded individually as a subset, with the train, test and validation splits, or all 3 can be downloaded together.

-  Complete dataset: URL
-  Processing scripts: URL ### Supported Tasks and Leaderboards

Extractive Question Answering. ### Languages

English. ## Dataset Structure ### Data Instances

An example of 'train' looks as follows. ### Data Fields

The data fields are the same among all splits.
- '': int
- 'id_qa': a 'string' feature.
- 'context_id': a 'string' feature.
- 'context_title': a 'string' feature.
- 'url': a 'string' feature.
- 'context': a 'string' feature.
- 'question': a 'string' feature.
- 'answers': a dictionary feature containing:
  - 'text': a 'string' feature.
  - 'offset': a list feature containing:
    - 2 'int32' features for start and end.
TAGS
#task_categories-question-answering #size_categories-1M<n<10M #language-English #license-mit #sports #open-domain-qa #extractive-qa #region-us 
 ### Dataset Summary

QASports is the first large sports-themed question answering dataset counting over 1.5 million questions and answers about 54k preprocessed wiki pages, using as documents the wiki of 3 of the most popular sports in the world, Soccer, American Football and Basketball. Each sport can be downloaded individually as a subset, with the train, test and validation splits, or all 3 can be downloaded together.

-  Complete dataset: URL
-  Processing scripts: URL ### Supported Tasks and Leaderboards

Extractive Question Answering. ### Languages

English. ## Dataset Structure ### Data Instances

An example of 'train' looks as follows. ### Data Fields

The data fields are the same among all splits.
- '': int
- 'id_qa': a 'string' feature.
- 'context_id': a 'string' feature.
- 'context_title': a 'string' feature.
- 'url': a 'string' feature.
- 'context': a 'string' feature.
- 'question': a 'string' feature.
- 'answers': a dictionary feature containing:
  - 'text': a 'string' feature.
  - 'offset': a list feature containing:
    - 2 'int32' features for start and end.
passage: TAGS
#task_categories-question-answering #size_categories-1M<n<10M #language-English #license-mit #sports #open-domain-qa #extractive-qa #region-us 
### Dataset Summary

QASports is the first large sports-themed question answering dataset counting over 1.5 million questions and answers about 54k preprocessed wiki pages, using as documents the wiki of 3 of the most popular sports in the world, Soccer, American Football and Basketball. Each sport can be downloaded individually as a subset, with the train, test and validation splits, or all 3 can be downloaded together.

-  Complete dataset: URL
-  Processing scripts: URL### Supported Tasks and Leaderboards

Extractive Question Answering.### Languages

English.## Dataset Structure### Data Instances

An example of 'train' looks as follows.### Data Fields

The data fields are the same among all splits.
- '': int
- 'id_qa': a 'string' feature.
- 'context_id': a 'string' feature.
- 'context_title': a 'string' feature.
- 'url': a 'string' feature.
- 'context': a 'string' feature.
- 'question': a 'string' feature.
- 'answers': a dictionary feature containing:
  - 'text': a 'string' feature.
  - 'offset': a list feature containing:
    - 2 'int32' features for start and end.