Model Card for MedMT5-large
- 📖 Paper: Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain
- 🌐 Project Website: https://univ-cotedazur.eu/antidote
Model Description
- Developed by: Iker García-Ferrero, Rodrigo Agerri, Aitziber Atutxa Salazar, Elena Cabrio, Iker de la Iglesia, Alberto Lavelli, Bernardo Magnini, Benjamin Molinet, Johana Ramirez-Romero, German Rigau, Jose Maria Villa-Gonzalez, Serena Villata and Andrea Zaninello
- Contact: Iker García-Ferrero 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: text2text-generation
- Language(s) (NLP): English, Spanish, French, Italian
- License: apache-2.0
- Finetuned from model: mT5
How to Get Started with the Model
You can load the model using
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("HiTZ/Medical-mT5-large")
model = AutoModelForSeq2SeqLM.from_pretrained("HiTZ/Medical-mT5-large")
The model has been trained using the T5 masked language modelling tasks. You need to finetune the model for your task.
Training Data
Evaluation
Medical mT5 for Sequence Labelling
We have released two Medical mT5 models finetuned for multilingual sequence labelling.
Single-task supervised F1 scores for Sequence Labelling
Multi-task supervised F1 scores for Sequence Labelling
Zero-shot F1 scores for Argument Mining. Models have been trained in English and evaluated in Spanish, French and Italian.
Ethical Statement
Citation
@misc{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},
eprint={2404.07613},
archivePrefix={arXiv},
primaryClass={cs.CL}
}