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--- |
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license: apache-2.0 |
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language: |
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- en |
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- es |
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- fr |
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- it |
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widget: |
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- text: The best cough medicine is <extra_id_0> because <extra_id_1> |
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- text: El mejor medicamento para la tos es <extra_id_0> porque <extra_id_1> |
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- text: Le meilleur médicament contre la toux est <extra_id_0> car <extra_id_1 |
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- text: La migliore medicina per la tosse è la <extra_id_0> perché la <extra_id_1 |
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library_name: transformers |
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pipeline_tag: text2text-generation |
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tags: |
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- medical |
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- multilingual |
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- medic |
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datasets: |
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- HiTZ/Multilingual-Medical-Corpus |
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base_model: google/mt5-large |
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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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<h2 align="center">Medical mT5: An Open-Source Multilingual Text-to-Text LLM |
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for the Medical Domain</h2> |
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<br> |
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# Model Card for MedMT5-large |
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<p align="justify"> |
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We present Medical mT5, the first open-source text-to-text multilingual model for the medical domain. |
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Medical mT5 is an encoder-decoder model developed by continuing the training of publicly available mT5 checkpoints on |
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medical domain data for English, Spanish, French, and Italian. |
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</p> |
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- 📖 Paper: [Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain](https://arxiv.org/abs/2404.07613) |
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- 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote) |
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<table border="1" cellspacing="0" cellpadding="5"> |
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<caption>Pre-Training settings for MedMT5.</caption> |
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<thead> |
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<tr> |
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<th></th> |
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<th>Medical mT5-Large (<a href="https://huggingface.co/HiTZ/Medical-mT5-large">HiTZ/Medical-mT5-large</a>)</th> |
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<th>Medical mT5-XL (<a href="https://huggingface.co/HiTZ/Medical-mT5-xl">HiTZ/Medical-mT5-xl</a>)</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<td>Param. no.</td> |
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<td>738M</td> |
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<td>3B</td> |
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</tr> |
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<tr> |
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<td>Sequence Length</td> |
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<td>1024</td> |
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<td>480</td> |
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</tr> |
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<tr> |
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<td>Token/step</td> |
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<td>65536</td> |
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<td>30720</td> |
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</tr> |
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<tr> |
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<td>Epochs</td> |
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<td>1</td> |
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<td>1</td> |
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</tr> |
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<tr> |
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<td>Total Tokens</td> |
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<td>4.5B</td> |
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<td>4.5B</td> |
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</tr> |
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<tr> |
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<td>Optimizer</td> |
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<td>Adafactor</td> |
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<td>Adafactor</td> |
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</tr> |
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<tr> |
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<td>LR</td> |
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<td>0.001</td> |
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<td>0.001</td> |
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</tr> |
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<tr> |
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<td>Scheduler</td> |
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<td>Constant</td> |
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<td>Constant</td> |
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</tr> |
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<tr> |
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<td>Hardware</td> |
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<td>4xA100</td> |
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<td>4xA100</td> |
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</tr> |
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<tr> |
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<td>Time (h)</td> |
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<td>10.5</td> |
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<td>20.5</td> |
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</tr> |
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<tr> |
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<td>CO<sub>2</sub>eq (kg)</td> |
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<td>2.9</td> |
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<td>5.6</td> |
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</tr> |
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</tbody> |
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</table> |
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# Model Description |
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- **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 |
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- **Contact**: [Iker García-Ferrero](https://ikergarcia1996.github.io/Iker-Garcia-Ferrero/) 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**: text2text-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**: mT5 |
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## How to Get Started with the Model |
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You can load the model using |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("HiTZ/Medical-mT5-large") |
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model = AutoModelForSeq2SeqLM.from_pretrained("HiTZ/Medical-mT5-large") |
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``` |
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The model has been trained using the T5 masked language modelling tasks. You need to finetune the model for your task. |
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<p align="center"> |
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<br> |
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<img src="https://miro.medium.com/v2/0*yeXSc6Qs-SGKDzZP.png" style="height: 250px;"> |
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<br> |
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## Training Data |
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<table border="1" cellspacing="0" cellpadding="5"> |
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<caption>Data sources and word counts by language.</caption> |
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<thead> |
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<tr> |
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<th>Language</th> |
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<th>Source</th> |
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<th>Words</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<td rowspan="3">English</td> |
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<td>ClinicalTrials</td> |
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<td>127.4M</td> |
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</tr> |
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<tr> |
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<td>EMEA</td> |
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<td>12M</td> |
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</tr> |
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<tr> |
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<td>PubMed</td> |
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<td>968.4M</td> |
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</tr> |
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<tr> |
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<td rowspan="6">Spanish</td> |
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<td>EMEA</td> |
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<td>13.6M</td> |
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</tr> |
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<tr> |
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<td>PubMed</td> |
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<td>8.4M</td> |
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</tr> |
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<tr> |
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<td>Medical Crawler</td> |
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<td>918M</td> |
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</tr> |
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<tr> |
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<td>SPACC</td> |
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<td>350K</td> |
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</tr> |
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<tr> |
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<td>UFAL</td> |
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<td>10.5M</td> |
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</tr> |
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<tr> |
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<td>WikiMed</td> |
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<td>5.2M</td> |
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</tr> |
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<tr> |
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<td rowspan="5">French</td> |
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<td>PubMed</td> |
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<td>1.4M</td> |
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</tr> |
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<tr> |
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<td>Science Direct</td> |
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<td>15.2M</td> |
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</tr> |
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<tr> |
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<td>Wikipedia - Médecine</td> |
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<td>5M</td> |
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</tr> |
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<tr> |
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<td>EDP</td> |
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<td>48K</td> |
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</tr> |
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<tr> |
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<td>Google Patents</td> |
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<td>654M</td> |
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</tr> |
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<tr> |
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<td rowspan="13">Italian</td> |
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<td>Medical Commoncrawl - IT</td> |
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<td>67M</td> |
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</tr> |
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<tr> |
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<td>Drug instructions</td> |
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<td>30.5M</td> |
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</tr> |
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<tr> |
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<td>Wikipedia - Medicina</td> |
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<td>13.3M</td> |
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</tr> |
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<tr> |
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<td>E3C Corpus - IT</td> |
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<td>11.6M</td> |
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</tr> |
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<tr> |
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<td>Medicine descriptions</td> |
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<td>6.3M</td> |
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</tr> |
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<tr> |
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<td>Medical theses</td> |
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<td>5.8M</td> |
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</tr> |
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<tr> |
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<td>Medical websites</td> |
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<td>4M</td> |
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</tr> |
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<tr> |
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<td>PubMed</td> |
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<td>2.3M</td> |
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</tr> |
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<tr> |
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<td>Supplement description</td> |
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<td>1.3M</td> |
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</tr> |
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<tr> |
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<td>Medical notes</td> |
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<td>975K</td> |
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</tr> |
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<tr> |
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<td>Pathologies</td> |
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<td>157K</td> |
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</tr> |
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<tr> |
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<td>Medical test simulations</td> |
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<td>26K</td> |
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</tr> |
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<tr> |
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<td>Clinical cases</td> |
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<td>20K</td> |
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</tr> |
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</tbody> |
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</table> |
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## Evaluation |
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### Medical mT5 for Sequence Labelling |
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We have released two Medical mT5 models finetuned for multilingual sequence labelling. |
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<table border="1" cellspacing="0" cellpadding="5"> |
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<thead> |
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<tr> |
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<th></th> |
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<th><a href="https://huggingface.co/HiTZ/Medical-mT5-large">HiTZ/Medical-mT5-large</a></th> |
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<th><a href="https://huggingface.co/HiTZ/Medical-mT5-xl">HiTZ/Medical-mT5-xl</a></th> |
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<th><a href="https://huggingface.co/HiTZ/Medical-mT5-large-multitask">HiTZ/Medical-mT5-large-multitask</a></th> |
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<th><a href="https://huggingface.co/HiTZ/Medical-mT5-xl-multitask">HiTZ/Medical-mT5-xl-multitask</a></th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<td>Param. no.</td> |
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<td>738M</td> |
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<td>3B</td> |
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<td>738M</td> |
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<td>3B</td> |
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</tr> |
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<tr> |
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<td>Task</td> |
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<td>Language Modeling</td> |
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<td>Language Modeling</td> |
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<td>Multitask Sequence Labeling</td> |
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<td>Multitask Sequence Labeling</td> |
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</tr> |
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<tr> |
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</tbody> |
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</table> |
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### Single-task supervised F1 scores for Sequence Labelling |
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<p align="center"> |
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<br> |
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<img src="https://huggingface.co/HiTZ/Medical-mT5-large/resolve/main/single.png" style="height: 600px;"> |
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<br> |
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### Multi-task supervised F1 scores for Sequence Labelling |
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<p align="center"> |
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<br> |
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<img src="https://huggingface.co/HiTZ/Medical-mT5-large/resolve/main/multi.png" style="height: 600px;"> |
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<br> |
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### Zero-shot F1 scores for Argument Mining. Models have been trained in English and evaluated in Spanish, French and Italian. |
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<p align="center"> |
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<br> |
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<img src="https://huggingface.co/HiTZ/Medical-mT5-large/resolve/main/cross.png" style="height: 320px;"> |
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<br> |
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## Ethical Statement |
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<p align="justify"> |
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Our research in developing Medical mT5, a multilingual text-to-text model for the medical domain, has ethical implications that we acknowledge. |
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Firstly, the broader impact of this work lies in its potential to improve medical communication and understanding across languages, which |
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can enhance healthcare access and quality for diverse linguistic communities. However, it also raises ethical considerations related to privacy and data security. |
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To create our multilingual corpus, we have taken measures to anonymize and protect sensitive patient information, adhering to |
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data protection regulations in each language's jurisdiction or deriving our data from sources that explicitly address this issue in line with |
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privacy and safety regulations and guidelines. Furthermore, we are committed to transparency and fairness in our model's development and evaluation. |
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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. |
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Finally, we emphasize our commitment to open source by making our data, code, and models publicly available, with the aim of promoting collaboration within |
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the research community. |
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</p> |
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## Citation |
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```bibtext |
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@misc{garcíaferrero2024medical, |
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title={Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain}, |
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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}, |
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year={2024}, |
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eprint={2404.07613}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
