Datasets:
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---
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
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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
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- name: test
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num_examples: 500
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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
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- name: test
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- 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}
}
``` |