Update README.md

README.md

@@ -6,44 +6,48 @@ language:
 - 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"
+- 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-xl
 ---
 
 <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>
+    <h2 align="center">Medical mT5: An Open-Source Multilingual Text-to-Text LLM
+for the Medical Domain</h2>
     <br>
 
 
-# Model Card for Medical mT5-large
+# Model Card for MedMT5-xl
 
 <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.
+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]() 
+  - 📖 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)
 
 
-
 <table border="1" cellspacing="0" cellpadding="5">
-    <caption>Pre-Training settings for Medical MT5.</caption>
+    <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>Meical mT5-XL (<a href="https://huggingface.co/HiTZ/Medical-mT5-xl">HiTZ/Medical-mT5-xl</a>)</th>
+            <th>Medical mT5-XL (<a href="https://huggingface.co/HiTZ/Medical-mT5-xl">HiTZ/Medical-mT5-xl</a>)</th>
         </tr>
     </thead>
     <tbody>
@@ -128,16 +132,16 @@ tokenizer = AutoTokenizer.from_pretrained("HiTZ/Medical-mT5-xl")
 model = AutoModelForSeq2SeqLM.from_pretrained("HiTZ/Medical-mT5-xl")
 ```
 
-The model has been trained using the T5 masked language modeling tasks. You need to finetune the model for your task. 
+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>
 
-### Medical mT5 for Sequence Labelling
 
-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
 
@@ -267,10 +271,42 @@ If you want to use Medical mT5 for Sequence Labeling, we recommend you use this
     </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>
 
 
 
-## Evaluation
 
 ### Single-task supervised F1 scores for Sequence Labelling
 <p align="center">
@@ -293,11 +329,21 @@ If you want to use Medical mT5 for Sequence Labeling, we recommend you use this
   
 ## 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.
+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}}",