metadata
library_name: peft
license: apache-2.0
datasets:
- HiTZ/MedExpQA
language:
- en
- fr
- it
- es
metrics:
- accuracy
pipeline_tag: text-generation
Mistral 7B fine-tuned for Medical QA in MedExpQA benchmark
We provide a Mistral7B fine-tuned model on MedExpQA, the first multilingual benchmark for Medical QA which includes reference gold explanations.
The model has been fine-tuned using the Clinical Case and Question + automatically obtained RAG using the MedCorp and MedRAG method with 32 snippets. The model generates as output a prediction of the correct answer to the multiple choice exam and has been evaluated on 4 languages: English, French, Italian and Spanish.
- 📖 Paper: MedExpQA: Multilingual Benchmarking of Large Language Models for Medical Question Answering
- 🌐 Project Website: https://univ-cotedazur.eu/antidote
- 💻 Code: https://github.com/hitz-zentroa/MedExpQA/
For details about fine-tuning and evaluation please check the paper and the repository for usage.
Model Description
- Developed by: Iñigo Alonso, Maite Oronoz, Rodrigo Agerri
- Contact: Iñigo Alonso and Rodrigo Agerri
- Website: https://univ-cotedazur.eu/antidote
- Funding: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR
- Model type: text-generation
- Language(s) (NLP): English, Spanish, French, Italian
- License: apache-2.0
- Finetuned from model: mistralai/Mistral-7B-v0.1
Citation
If you use MedExpQA data then please cite the following paper:
@misc{alonso2024medexpqa,
title={MedExpQA: Multilingual Benchmarking of Large Language Models for Medical Question Answering},
author={Iñigo Alonso and Maite Oronoz and Rodrigo Agerri},
year={2024},
eprint={2404.05590},
archivePrefix={arXiv},
primaryClass={cs.CL}
}