Medical Mt5 Large

🚀 Medical mT5: An Open-Source Multilingual Text-to-Text LLM for the Medical Domain

Medical mT5 is the first open - source text - to - text multilingual model for the medical domain. It continues the training of publicly available mT5 checkpoints on medical domain data in English, Spanish, French, and Italian, aiming to improve medical communication and understanding across languages.

🚀 Quick Start

You can load the model using the following code:

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.

✨ Features

• Multilingual Support: Supports English, Spanish, French, and Italian, facilitating cross - language medical communication.
• Open - Source: The data, code, and models are publicly available, promoting collaboration within the research community.
• Medical Domain Focus: Trained on medical domain data, making it suitable for medical text - to - text generation tasks.

📦 Installation

The installation mainly involves using the transformers library. If you haven't installed it yet, you can use the following command:

pip install transformers

📚 Documentation

Model Card for MedMT5 - large

We present Medical mT5, the first open - source text - to - text multilingual model for the medical domain. Medical mT5 is an encoder - decoder model developed by continuing the training of publicly available mT5 checkpoints on medical domain data for English, Spanish, French, and Italian.

• 📖 Paper: Medical mT5: An Open - Source Multilingual Text - to - Text LLM for The Medical Domain
• 🌐 Project Website: [https://univ - cotedazur.eu/antidote](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](https://ikergarcia1996.github.io/Iker - Garcia - Ferrero/) and Rodrigo Agerri
• 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: text2text - generation
• Language(s) (NLP): English, Spanish, French, Italian
• License: apache - 2.0
• Finetuned from model: mT5

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

Our research in developing Medical mT5, a multilingual text - to - text model for the medical domain, has ethical implications that we acknowledge. Firstly, the broader impact of this work lies in its potential to improve medical communication and understanding across languages, which can enhance healthcare access and quality for diverse linguistic communities. However, it also raises ethical considerations related to privacy and data security. To create our multilingual corpus, we have taken measures to anonymize and protect sensitive patient information, adhering to data protection regulations in each language's jurisdiction or deriving our data from sources that explicitly address this issue in line with privacy and safety regulations and guidelines. Furthermore, we are committed to transparency and fairness in our model's development and evaluation. We have worked to ensure that our benchmarks are representative and unbiased, and we will continue to monitor and address any potential biases in the future. Finally, we emphasize our commitment to open source by making our data, code, and models publicly available, with the aim of promoting collaboration within the research community.

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}
}

Pre - Training settings for MedMT5