task_categories:multiple-choice task_categories:visual-question-answering language:es language:en language:gl language:ru language:it license:mit size_categories:10K<n<100K format:parquet modality:image modality:text library:datasets library:pandas library:mlcroissant library:polars region:us biology chemistry medical
---
language:
- es
- en
- gl
- ru
- it
license: mit
size_categories:
- 10K<n<100K
task_categories:
- multiple-choice
- visual-question-answering
pretty_name: HEAD-QA V2
tags:
- biology
- chemistry
- medical
dataset_info:
- config_name: en
  features:
  - name: qid
    dtype: int32
  - name: qtext
    dtype: string
  - name: ra
    dtype: int32
  - name: answers
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    - name: atext
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  - name: year
    dtype: int32
  - name: category
    dtype: string
  - name: name
    dtype: string
  - name: image
    dtype: image
  splits:
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    num_examples: 12751
  download_size: 91401201
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- config_name: es
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    dtype: int32
  - name: qtext
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  - name: ra
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  - name: answers
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    - name: atext
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  - name: category
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  - name: image
    dtype: image
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    num_examples: 12751
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- config_name: gl
  features:
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    dtype: int32
  - name: qtext
    dtype: string
  - name: ra
    dtype: int32
  - name: answers
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    - name: atext
      dtype: string
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    dtype: int32
  - name: category
    dtype: string
  - name: name
    dtype: string
  - name: image
    dtype: image
  splits:
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    num_examples: 12751
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- config_name: it
  features:
  - name: qid
    dtype: int32
  - name: qtext
    dtype: string
  - name: ra
    dtype: int32
  - name: answers
    list:
    - name: aid
      dtype: int32
    - name: atext
      dtype: string
  - name: year
    dtype: int32
  - name: category
    dtype: string
  - name: name
    dtype: string
  - name: image
    dtype: image
  splits:
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    num_examples: 12751
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- config_name: ru
  features:
  - name: qid
    dtype: int32
  - name: qtext
    dtype: string
  - name: ra
    dtype: int32
  - name: answers
    list:
    - name: aid
      dtype: int32
    - name: atext
      dtype: string
  - name: year
    dtype: int32
  - name: category
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  - name: name
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  - name: image
    dtype: image
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configs:
- config_name: en
  data_files:
  - split: train
    path: en/train-*
- config_name: es
  data_files:
  - split: train
    path: es/train-*
- config_name: gl
  data_files:
  - split: train
    path: gl/train-*
- config_name: it
  data_files:
  - split: train
    path: it/train-*
- config_name: ru
  data_files:
  - split: train
    path: ru/train-*
---
### Dataset Summary

HEAD-QA v2 is an updated version of the [HEAD-QA dataset](https://huggingface.co/datasets/dvilares/head_qa), which is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the [Ministerio de Sanidad, Consumo y Bienestar Social](https://www.mscbs.gob.es/), who also provides direct [access](https://fse.mscbs.gob.es/fseweb/view/public/datosanteriores/cuadernosExamen/busquedaConvocatoria.xhtml) to the exams of the last 5 years (in Spanish).

HEAD-QA V2 expands on the original dataset by including additional translations into multiple languages using the **meta-llama/Llama-3.1-8B-Instruct** model. The dataset contains questions about the following topics:
- Medicine
- Nursing
- Psychology
- Chemistry
- Pharmacology
- Biology

### Languages

The dataset is now available in multiple languages:
- Spanish (`es`)
- English (`en`)
- Galician (`gl`)
- Italian (`it`)
- Russian (`ru`)

By default, the dataset is in Spanish:
```python
from datasets import load_dataset
data_es = load_dataset('head_qa_v2', 'es')
data_en = load_dataset('head_qa_v2', 'en')
```
### Citation Information to the V1 paper

```
@inproceedings{vilares-gomez-rodriguez-2019-head,
    title = "{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning",
    author = "Vilares, David  and
      G{\'o}mez-Rodr{\'i}guez, Carlos",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1092",
    doi = "10.18653/v1/P19-1092",
    pages = "960--966",
    abstract = "We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.",
}
```