Multilingual-Medical-Corpus

	---
	language:
	- en
	- es
	- fr
	- it
	license: apache-2.0
	pretty_name: Multilingual Medical Corpus
	tags:
	- medical
	dataset_info:
	features:
	- name: text
	dtype: string
	splits:
	- name: en
	num_bytes: 7672665166
	num_examples: 21226237
	- name: es
	num_bytes: 6245812986
	num_examples: 35444286
	- name: fr
	num_bytes: 4763269707
	num_examples: 7192779
	- name: it
	num_bytes: 1021535232
	num_examples: 3504555
	download_size: 10530951092
	dataset_size: 19703283091
	configs:
	- config_name: default
	data_files:
	- split: en
	path: data/en-*
	- split: es
	path: data/es-*
	- split: fr
	path: data/fr-*
	- split: it
	path: data/it-*
	---

	<p align="center">
	<br>
	<img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="width: 30%;">
	<h2 align="center">Mutilingual Medical Corpus</h2>
	<be>

	<p align="justify">
	Multilingual-Medical-Corpus a 3 billion word multilingual corpus for training LLMs adapted to the medical domain. Multilingual-Medical-Corpus includes four languages, namely, 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)

	# Corpus 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
	- Language(s) (NLP): English, Spanish, French, Italian
	- License: apache-2.0

	<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>

	# Open Source Models trained with Multilingual-Medical-Corpus:
	<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>

	## 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}
	}
	```