Update README.md

README.md

@@ -21,18 +21,20 @@ tags:
 <p align="center">
     <br>
     <img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="height: 250px;">
-    <h2 align="center">MedMT5: An Open-Source Multilingual Text-to-Text LLM
-for The Medical Domain</h2>
+    <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 MedMT5, the first open-source text-to-text multilingual model for the medical domain. MedMT5 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.
+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: **Coming soon**  
+  - 📖 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)
 
 
@@ -41,8 +43,8 @@ We present MedMT5, the first open-source text-to-text multilingual model for the
     <thead>
         <tr>
             <th></th>
-            <th>MedMT5-Large (<a href="https://huggingface.co/HiTZ/Medical-mT5-large">HiTZ/Medical-mT5-large</a>)</th>
-            <th>MedMT5-XL (<a href="https://huggingface.co/HiTZ/Medical-mT5-xl">HiTZ/Medical-mT5-xl</a>)</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>
@@ -114,7 +116,7 @@ We present MedMT5, the first open-source text-to-text multilingual model for the
 - **Model type**: text2text-generation
 - **Language(s) (NLP)**: English, Spanish, French, Italian
 - **License**: apache-2.0
-- **Finetuned from model**: MT5
+- **Finetuned from model**: mT5
 
 ## How to Get Started with the Model
 
@@ -134,9 +136,9 @@ The model has been trained using the T5 masked language modeling tasks. You need
     <img src="https://miro.medium.com/v2/0*yeXSc6Qs-SGKDzZP.png" style="height: 250px;">
     <br>
 
-### MedMT5 for Sequence Labelling
+### Medical mT5 for Sequence Labelling
 
-If you want to use MedMT5 for Sequence Labeling, we recommend you use this code: https://github.com/ikergarcia1996/Sequence-Labeling-LLMs
+If you want to use Medical mT5 for Sequence Labeling, we recommend you use this code: https://github.com/ikergarcia1996/Sequence-Labeling-LLMs
 
 ## Training Data
 
@@ -292,7 +294,15 @@ If you want to use MedMT5 for Sequence Labeling, we recommend you use this code:
   
 ## Ethical Statement
 <p align="justify">
-Our research in developing MedMT5, 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.
+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
@@ -300,10 +310,10 @@ Our research in developing MedMT5, a multilingual text-to-text model for the med
 We will soon release a paper, but, for now, you can use:
 
 ```bibtext
-@misc{medMt5,
- title     = "{MedMT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain}",
+@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}",
- url       = "https://huggingface.co/collections/HiTZ/medical-mt5-65413b334cb81ed6dea67cfe",    
- year = 2023 }
+ publisher = "Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING)",
+ year = 2024 }
 
 ```