Update README.md

README.md

@@ -20,18 +20,35 @@ widget:
 
 **Albertina PT-*** is a foundation, large language model for the **Portuguese language**.
 
-It is an **encoder** of the BERT family, based on a Transformer architecture, developed over the DeBERTa model, with most competitive performance for this language.
+It is an **encoder** of the BERT family, based on a Transformer architecture, 
+developed over the DeBERTa model, with most competitive performance for this language. 
+It has different versions that were trained for different variants of Portuguese (PT), 
+namely the European variant from Portugal (PT-PT) and the American variant from Brazil (PT-BR), 
+and it is distributed free of charge and under a most permissible license.
 
-It has different versions that were trained for different variants of Portuguese (PT), namely the European variant from Portugal (PT-PT) and the American variant from Brazil (PT-BR), and it is distributed free of charge and under a most permissible license.
+**Albertina PT-PT** is the version for European Portuguese from Portugal, 
+and to the best of our knowledge, at the time of its initial distribution,
+it was the first competitive encoder for this language and variant 
+that had been made publicly available and distributed for reuse.
 
-It was developped by a joint team from the University of Lisbon and the University of Porto, Portugal. For further details, check the respective publication:
+It was developped by a joint team from the University of Lisbon and the University of Porto, Portugal. 
+For further details, check the respective publication:
 
-Rodrigues, João António, Luís Gomes, João Silva, António Branco, Rodrigo Santos, Henrique Lopes Cardoso, Tomás Osório, 2023, Advancing Neural Encoding of Portuguese with Transformer Albertina PT-*, arXiv ###.
+``` latex
+@misc{albertina-pt,
+      title={Advancing Neural Encoding of Portuguese with Transformer Albertina PT-*}, 
+      author={João Rodrigues and Luís Gomes and João Silva and António Branco and Rodrigo Santos and Henrique Lopes and Tomás Osório},
+      year={2023},
+      eprint={?},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
 
 Please use the above cannonical reference when using or citing this model.
 
-teste
-
+<br>
+<br>
 
 
 ## Model Description