README.md

---
dataset_info:
- config_name: en
  features:
  - name: id
    dtype: string
  - name: type
    dtype: string
  - name: body
    dtype: string
  - name: ideal_answer
    sequence: string
  - name: exact_answer
    sequence: string
  - name: snippets
    sequence: string
  - name: documents
    sequence: string
  - name: triples
    list:
    - name: p
      dtype: string
    - name: s
      dtype: string
    - name: o
      dtype: string
  - name: concepts
    sequence: string
  splits:
  - name: train
    num_bytes: 10827410
    num_examples: 2251
  - name: test
    num_bytes: 1709411
    num_examples: 500
  download_size: 5185124
  dataset_size: 12536821
- config_name: es
  features:
  - name: id
    dtype: string
  - name: type
    dtype: string
  - name: body
    dtype: string
  - name: ideal_answer
    sequence: string
  - name: exact_answer
    sequence: string
  - name: snippets
    sequence: string
  - name: documents
    sequence: string
  - name: triples
    list:
    - name: p
      dtype: string
    - name: s
      dtype: string
    - name: o
      dtype: string
  - name: concepts
    sequence: string
  splits:
  - name: train
    num_bytes: 11694723
    num_examples: 2251
  - name: test
    num_bytes: 1808733
    num_examples: 500
  download_size: 5417329
  dataset_size: 13503456
- config_name: fr
  features:
  - name: id
    dtype: string
  - name: type
    dtype: string
  - name: body
    dtype: string
  - name: ideal_answer
    sequence: string
  - name: exact_answer
    sequence: string
  - name: snippets
    sequence: string
  - name: documents
    sequence: string
  - name: triples
    list:
    - name: p
      dtype: string
    - name: s
      dtype: string
    - name: o
      dtype: string
  - name: concepts
    sequence: string
  splits:
  - name: train
    num_bytes: 11760491
    num_examples: 2251
  - name: test
    num_bytes: 1799313
    num_examples: 500
  download_size: 5402467
  dataset_size: 13559804
- config_name: it
  features:
  - name: id
    dtype: string
  - name: type
    dtype: string
  - name: body
    dtype: string
  - name: ideal_answer
    sequence: string
  - name: exact_answer
    sequence: string
  - name: snippets
    sequence: string
  - name: documents
    sequence: string
  - name: triples
    list:
    - name: p
      dtype: string
    - name: s
      dtype: string
    - name: o
      dtype: string
  - name: concepts
    sequence: string
  splits:
  - name: train
    num_bytes: 11241823
    num_examples: 2251
  - name: test
    num_bytes: 1737683
    num_examples: 500
  download_size: 5320580
  dataset_size: 12979506
configs:
- config_name: en
  data_files:
  - split: train
    path: en/train-*
  - split: test
    path: en/test-*
- config_name: es
  data_files:
  - split: train
    path: es/train-*
  - split: test
    path: es/test-*
- config_name: fr
  data_files:
  - split: train
    path: fr/train-*
  - split: test
    path: fr/test-*
- config_name: it
  data_files:
  - split: train
    path: it/train-*
  - split: test
    path: it/test-*
license: apache-2.0
task_categories:
- question-answering
- summarization
language:
- en
- es
- fr
- it
tags:
- biology
- medical
pretty_name: Multilingual BioASQ-6B
---


<p align="center">
    <br>
    <img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="width: 30%;">
    <h2 align="center">Mutilingual BioASQ-6B</h2>
    <be>

<p align="justify">
We translate the BioASQ-6B English Question Answering dataset to generate parallel French, Italian and Spanish versions using the NLLB200 3B parameter model. For more info read the original task description: [http://bioasq.org/participate/challenges_year_6](http://bioasq.org/participate/challenges_year_6)

We translate the `body`, `snippets`, `ideal_answer` and `exact_answer` fields. We have validated the quality of the `ideal_answer` field, however, the `exact_answer` field can contain translation artifacts, as NLLB200 often produces low-quality translations of single-word sentences. 
</p>

  - 📖 Paper: [Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain. In LREC-COLING 2024](https://arxiv.org/abs/2404.07613)
  - 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote)
  - Original Dataset: [http://bioasq.org/participate/challenges_year_6](http://bioasq.org/participate/challenges_year_6)
  - Funding: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR

## Citation
```bibtext
@proceedings{garcíaferrero2024medical,
      title={Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain}, 
      author={Iker García-Ferrero and Rodrigo Agerri and Aitziber Atutxa Salazar and Elena Cabrio and Iker de la Iglesia and Alberto Lavelli and Bernardo Magnini and Benjamin Molinet and Johana Ramirez-Romero and German Rigau and Jose Maria Villa-Gonzalez and Serena Villata and Andrea Zaninello},
      year={2024},
      booktitle={Proceedings of LREC-COLING}
}
```