---
library_name: peft
license: apache-2.0
datasets:
- HiTZ/MedExpQA
language:
- en
- fr
- it
- es
metrics:
- accuracy
pipeline_tag: text-generation
---

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    <br>
    <img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="height: 250px;">
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# Mistral 7B fine-tuned for Medical QA in MedExpQA benchmark

We provide a [Mistral7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) fine-tuned model on [MedExpQA, the first multilingual benchmark for Medical QA which includes
reference gold explanations](https://huggingface.co/datasets/HiTZ/MedExpQA).

The model has been fine-tuned using the Clinical Case and Question + automatically obtained RAG using [the MedCorp and MedRAG method](https://arxiv.org/pdf/2402.13178v1)
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](https://arxiv.org/abs/2404.05590v1)
  - 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote)
  - 💻 Code: [https://github.com/hitz-zentroa/MedExpQA/](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](https://hitz.ehu.eus/en/node/282) and [Rodrigo Agerri](https://ragerri.github.io/)
- **Website**: [https://univ-cotedazur.eu/antidote](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**:

```bibtex
@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}
}
````