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--- |
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library_name: peft |
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license: apache-2.0 |
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datasets: |
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- HiTZ/MedExpQA |
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language: |
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- en |
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- fr |
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- it |
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- es |
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metrics: |
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- accuracy |
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pipeline_tag: text-generation |
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--- |
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<p align="center"> |
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<br> |
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<img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="height: 250px;"> |
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<br> |
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# Mistral 7B fine-tuned for Medical QA in MedExpQA benchmark |
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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 |
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reference gold explanations](https://huggingface.co/datasets/HiTZ/MedExpQA). |
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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) |
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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. |
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- 📖 Paper: [MedExpQA: Multilingual Benchmarking of Large Language Models for Medical Question Answering](https://arxiv.org/abs/2404.05590v1) |
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- 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote) |
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- 💻 Code: [https://github.com/hitz-zentroa/MedExpQA/](https://github.com/hitz-zentroa/MedExpQA/) |
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For details about fine-tuning and evaluation please check the paper and the repository for usage. |
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# Model Description |
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- **Developed by**: Iñigo Alonso, Maite Oronoz, Rodrigo Agerri |
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- **Contact**: [Iñigo Alonso](https://hitz.ehu.eus/en/node/282) and [Rodrigo Agerri](https://ragerri.github.io/) |
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- **Website**: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote) |
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- **Funding**: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR |
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- **Model type**: text-generation |
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- **Language(s) (NLP)**: English, Spanish, French, Italian |
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- **License**: apache-2.0 |
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- **Finetuned from model**: mistralai/Mistral-7B-v0.1 |
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## Citation |
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If you use MedExpQA data then please **cite the following paper**: |
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```bibtex |
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@misc{alonso2024medexpqa, |
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title={MedExpQA: Multilingual Benchmarking of Large Language Models for Medical Question Answering}, |
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author={Iñigo Alonso and Maite Oronoz and Rodrigo Agerri}, |
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year={2024}, |
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eprint={2404.05590}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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```` |
