File size: 10,944 Bytes
5b55b79 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 |
---
license: apache-2.0
language:
- en
- es
- fr
- it
widget:
- text: The best cough medicine is <extra_id_0> because <extra_id_1>
- text: El mejor medicamento para la tos es <extra_id_0> porque <extra_id_1>
- text: Le meilleur médicament contre la toux est <extra_id_0> car <extra_id_1
- text: La migliore medicina per la tosse è la <extra_id_0> perché la <extra_id_1
library_name: transformers
pipeline_tag: text2text-generation
tags:
- medical
- multilingual
- medic
datasets:
- HiTZ/Multilingual-Medical-Corpus
base_model: google/mt5-large
---
<p align="center">
<br>
<img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="height: 250px;">
<h2 align="center">Medical mT5: An Open-Source Multilingual Text-to-Text LLM
for the Medical Domain</h2>
<br>
# Model Card for MedMT5-large
<p align="justify">
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.
</p>
- 📖 Paper: [Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain](https://arxiv.org/abs/2404.07613)
- 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote)
<table border="1" cellspacing="0" cellpadding="5">
<caption>Pre-Training settings for MedMT5.</caption>
<thead>
<tr>
<th></th>
<th>Medical mT5-Large (<a href="https://huggingface.co/HiTZ/Medical-mT5-large">HiTZ/Medical-mT5-large</a>)</th>
<th>Medical mT5-XL (<a href="https://huggingface.co/HiTZ/Medical-mT5-xl">HiTZ/Medical-mT5-xl</a>)</th>
</tr>
</thead>
<tbody>
<tr>
<td>Param. no.</td>
<td>738M</td>
<td>3B</td>
</tr>
<tr>
<td>Sequence Length</td>
<td>1024</td>
<td>480</td>
</tr>
<tr>
<td>Token/step</td>
<td>65536</td>
<td>30720</td>
</tr>
<tr>
<td>Epochs</td>
<td>1</td>
<td>1</td>
</tr>
<tr>
<td>Total Tokens</td>
<td>4.5B</td>
<td>4.5B</td>
</tr>
<tr>
<td>Optimizer</td>
<td>Adafactor</td>
<td>Adafactor</td>
</tr>
<tr>
<td>LR</td>
<td>0.001</td>
<td>0.001</td>
</tr>
<tr>
<td>Scheduler</td>
<td>Constant</td>
<td>Constant</td>
</tr>
<tr>
<td>Hardware</td>
<td>4xA100</td>
<td>4xA100</td>
</tr>
<tr>
<td>Time (h)</td>
<td>10.5</td>
<td>20.5</td>
</tr>
<tr>
<td>CO<sub>2</sub>eq (kg)</td>
<td>2.9</td>
<td>5.6</td>
</tr>
</tbody>
</table>
# 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](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**: 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
```python
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.
<p align="center">
<br>
<img src="https://miro.medium.com/v2/0*yeXSc6Qs-SGKDzZP.png" style="height: 250px;">
<br>
## Training Data
<table border="1" cellspacing="0" cellpadding="5">
<caption>Data sources and word counts by language.</caption>
<thead>
<tr>
<th>Language</th>
<th>Source</th>
<th>Words</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="3">English</td>
<td>ClinicalTrials</td>
<td>127.4M</td>
</tr>
<tr>
<td>EMEA</td>
<td>12M</td>
</tr>
<tr>
<td>PubMed</td>
<td>968.4M</td>
</tr>
<tr>
<td rowspan="6">Spanish</td>
<td>EMEA</td>
<td>13.6M</td>
</tr>
<tr>
<td>PubMed</td>
<td>8.4M</td>
</tr>
<tr>
<td>Medical Crawler</td>
<td>918M</td>
</tr>
<tr>
<td>SPACC</td>
<td>350K</td>
</tr>
<tr>
<td>UFAL</td>
<td>10.5M</td>
</tr>
<tr>
<td>WikiMed</td>
<td>5.2M</td>
</tr>
<tr>
<td rowspan="5">French</td>
<td>PubMed</td>
<td>1.4M</td>
</tr>
<tr>
<td>Science Direct</td>
<td>15.2M</td>
</tr>
<tr>
<td>Wikipedia - Médecine</td>
<td>5M</td>
</tr>
<tr>
<td>EDP</td>
<td>48K</td>
</tr>
<tr>
<td>Google Patents</td>
<td>654M</td>
</tr>
<tr>
<td rowspan="13">Italian</td>
<td>Medical Commoncrawl - IT</td>
<td>67M</td>
</tr>
<tr>
<td>Drug instructions</td>
<td>30.5M</td>
</tr>
<tr>
<td>Wikipedia - Medicina</td>
<td>13.3M</td>
</tr>
<tr>
<td>E3C Corpus - IT</td>
<td>11.6M</td>
</tr>
<tr>
<td>Medicine descriptions</td>
<td>6.3M</td>
</tr>
<tr>
<td>Medical theses</td>
<td>5.8M</td>
</tr>
<tr>
<td>Medical websites</td>
<td>4M</td>
</tr>
<tr>
<td>PubMed</td>
<td>2.3M</td>
</tr>
<tr>
<td>Supplement description</td>
<td>1.3M</td>
</tr>
<tr>
<td>Medical notes</td>
<td>975K</td>
</tr>
<tr>
<td>Pathologies</td>
<td>157K</td>
</tr>
<tr>
<td>Medical test simulations</td>
<td>26K</td>
</tr>
<tr>
<td>Clinical cases</td>
<td>20K</td>
</tr>
</tbody>
</table>
## Evaluation
### Medical mT5 for Sequence Labelling
We have released two Medical mT5 models finetuned for multilingual sequence labelling.
<table border="1" cellspacing="0" cellpadding="5">
<thead>
<tr>
<th></th>
<th><a href="https://huggingface.co/HiTZ/Medical-mT5-large">HiTZ/Medical-mT5-large</a></th>
<th><a href="https://huggingface.co/HiTZ/Medical-mT5-xl">HiTZ/Medical-mT5-xl</a></th>
<th><a href="https://huggingface.co/HiTZ/Medical-mT5-large-multitask">HiTZ/Medical-mT5-large-multitask</a></th>
<th><a href="https://huggingface.co/HiTZ/Medical-mT5-xl-multitask">HiTZ/Medical-mT5-xl-multitask</a></th>
</tr>
</thead>
<tbody>
<tr>
<td>Param. no.</td>
<td>738M</td>
<td>3B</td>
<td>738M</td>
<td>3B</td>
</tr>
<tr>
<td>Task</td>
<td>Language Modeling</td>
<td>Language Modeling</td>
<td>Multitask Sequence Labeling</td>
<td>Multitask Sequence Labeling</td>
</tr>
<tr>
</tbody>
</table>
### Single-task supervised F1 scores for Sequence Labelling
<p align="center">
<br>
<img src="https://huggingface.co/HiTZ/Medical-mT5-large/resolve/main/single.png" style="height: 600px;">
<br>
### Multi-task supervised F1 scores for Sequence Labelling
<p align="center">
<br>
<img src="https://huggingface.co/HiTZ/Medical-mT5-large/resolve/main/multi.png" style="height: 600px;">
<br>
### Zero-shot F1 scores for Argument Mining. Models have been trained in English and evaluated in Spanish, French and Italian.
<p align="center">
<br>
<img src="https://huggingface.co/HiTZ/Medical-mT5-large/resolve/main/cross.png" style="height: 320px;">
<br>
## Ethical Statement
<p align="justify">
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.
</p>
## Citation
```bibtext
@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}
}
``` |