Update README.md

README.md

@@ -1,3 +1,150 @@
 ---
 license: apache-2.0
+widget:
+- text: >-
+    <Disease> Torsade de pointes ventricular tachycardia during low dose
+    intermittent dobutamine treatment in a patient with dilated cardiomyopathy
+    and congestive heart failure .
+- text: >-
+    <ClinicalEntity> Ecográficamente se observan tres nódulos tumorales
+    independientes y bien delimitados : dos de ellos heterogéneos , sólidos , de
+    20 y 33 mm de diámetros , con áreas quísticas y calcificaciones .
+- text: >-
+    <ClinicalEntity> On notait une hyperlordose lombaire avec une contracture
+    permanente des muscles paravertébraux , de l abdomen et des deux membres
+    inférieurs .
+- text: >-
+    <ClinicalEntity> Nell ’ anamnesi patologica era riferita ipertensione
+    arteriosa controllata con terapia medica
+library_name: transformers
+pipeline_tag: text2text-generation
+tags:
+- medical
+- multilingual
+- medic
+datasets:
+- HiTZ/Multilingual-Medical-Corpus
+language:
+- es
+- en
+- fr
+- it
+base_model: HiTZ/Medical-mT5-large
 ---
+
+<p align="center">
+    <br>
+    <img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="width: 30%;">
+    <h2 align="center">Medical mT5: An Open-Source Multilingual Text-to-Text LLM
+for the Medical Domain</h2>
+    <be>
+
+# Model Card for Medical MT5-large-multitask
+
+
+<p align="justify">
+
+Medical MT5-large-multitask is a version of Medical MT5 finetuned for sequence labelling. It can correctly label a wide range of Medical labels in unstructured text, such as `Disease`, `Disability`, `ClinicalEntity`, `Chemical`...  Medical MT5-large-multitask has been finetuned for English, Spanish, French and Italian, although it may work with a wide range of languages.   
+
+  - 📖 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)
+
+
+<p align="center">
+    <br>
+    <img src="https://raw.githubusercontent.com/ikergarcia1996/Sequence-Labeling-LLMs/main/resources/MedT5-Ner-mtask.png" style="width: 30%;">
+    <be>
+
+# Open Source Models
+<table border="1" cellspacing="0" cellpadding="5">
+    <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>
+            <th>Medical mT5-Large-multitask (<a href="https://huggingface.co/HiTZ/Medical-mT5-large-multitask">HiTZ/Medical-mT5-large</a>)</th>
+            <th>Medical mT5-XL-multitask (<a href="https://huggingface.co/HiTZ/Medical-mT5-xl-multitask">HiTZ/Medical-mT5-xl</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>
+
+
+# Usage
+
+Medical MT5-large-multitask was training using the *Sequence-Labeling-LLMs* library: https://github.com/ikergarcia1996/Sequence-Labeling-LLMs/   
+This library uses constrained decoding to ensure that the output contains the same words as the input and a valid HTML annotation. We recommend using Medical MT5-large-multitask together with this library. 
+Although you can also directly use it with  🤗 huggingface. In order to label a sentence, you need to append the labels you wan to use, for example, if you want to label *dieseases* you should format your input as follows: `<Disease> Torsade de pointes ventricular tachycardia during low dose intermittent dobutamine treatment in a patient with dilated cardiomyopathy and congestive heart failure .`
+
+```python
+import torch
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+model = AutoModelForSeq2SeqLM.from_pretrained("Medical-mT5-large-multitask",torch_dtype=torch.bfloat16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("Medical-mT5-large-multitask")
+
+input_example = "<Disease> Torsade de pointes ventricular tachycardia during low dose intermittent dobutamine treatment in a patient with dilated cardiomyopathy and congestive heart failure ."
+
+model_input = tokenizer(input_example, return_tensors="pt")
+
+output = model.generate(**model_input.to(model.device),max_new_tokens=128,num_beams=1,num_return_sequences=1,do_sample=False)
+
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+# Performance
+<img src="https://raw.githubusercontent.com/ikergarcia1996/Sequence-Labeling-LLMs/main/resources/multitask_performance.png" style="width: 40%;">
+
+# 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**: HiTZ/Medical-mT5-large
+
+
+# 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
+
+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 }
+
+```