README.md

metadata

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

Mutilingual BioASQ-6B

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.

• 📖 Paper: Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain. In LREC-COLING 2024
• 🌐 Project Website: https://univ-cotedazur.eu/antidote
• Original Dataset: 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

@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}
}