---
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]()
  - 🌐 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

We will soon release a paper, but, for now, you can use:

```bibtext
@inproceedings{medical-mt5,
 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}",
 publisher = "Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING)",
 year = 2024 }

```