Update README.md

README.md

@@ -22,17 +22,17 @@ datasets:
 </br>
 </br>
 <img align="left" width="40" height="40" src="https://github.githubassets.com/images/icons/emoji/unicode/1f917.png">
-<p style="text-align: center;">&nbsp;&nbsp;&nbsp;&nbsp;This is the model card for Gervásio 7B PT-PT Decoder. 
+<p style="text-align: center;">&nbsp;&nbsp;&nbsp;&nbsp;This is the model card for Gervásio 7B PTPT Decoder. 
   You may be interested in some of the other models in the <a href="https://huggingface.co/PORTULAN">Albertina (encoders) and Gervásio (decoders) families</a>.
 </p>
 </br>
 </br>
 
-# Gervásio 7B PT-PT
+# Gervásio 7B PTPT
 
 </br>
 
-**Gervásio PT-*** is a **fully open** decoder for the **Portuguese language**. 
+**Gervásio PT*** is a **fully open** decoder for the **Portuguese language**. 
 
 
 It is a **decoder** of the LLaMA family, based on the neural architecture Transformer and developed over the LLaMA-2 7B model.
@@ -45,19 +45,21 @@ namely for the European variant, spoken in Portugal ([**gervasio-7b-portuguese-p
 All versions of Gervásio are **openly distributed for free under an open license**, including thus for research and commercial purposes, and given its size, can 
 be run on consumer-grade hardware.
 
-**Gervásio 7B PT-PT** is developed by NLX-Natural Language and Speech Group, at the University of Lisbon, Faculty of Sciences, Department of Informatics, Portugal.
+**Gervásio 7B PTPT** is developed by NLX-Natural Language and Speech Group, at the University of Lisbon, Faculty of Sciences, Department of Informatics, Portugal.
 
 For the record, its full name is **Gervásio Produz Textos em Português**, to which corresponds the natural acronym **GPT PT**, 
-and which is known more shortly as Gervásio PT-* or, even more briefly, just as Gervásio, among its acquaintances.
+and which is known more shortly as **Gervásio PT*** or, even more briefly, just as **Gervásio**, among its acquaintances.
 
-These models are fully documented in the respective [publication](https://arxiv.org/abs/?):
+These models are fully documented in the respective [publication](https://arxiv.org/abs/2402.18766):
 
 ``` latex
 @misc{gervasio,
-      title={Advancing Generative AI for Portuguese with Open Decoder Gervásio~PT*}, 
-      author={Rodrigo Santos, João Silva, Luís Gomes, João Rodrigues, António Branco},
+      title={Advancing Generative AI for Portuguese with
+             Open Decoder Gervásio PT-*}, 
+      author={Rodrigo Santos, João Silva, Luís Gomes,
+              João Rodrigues, António Branco},
       year={2024},
-      eprint={?},
+      eprint={2402.18766},
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }
@@ -71,16 +73,16 @@ Please use the above cannonical reference when using or citing this model.
 
 # Model Description
 
-**This model card is for Gervásio 7B PT-PT**, with 7 billion parameters, a hidden size of 4096 units, an intermediate size of 11,008 units, 32 attention heads, 32 hidden layers, and a tokenizer obtained using the Byte-Pair Encoding (BPE) algorithm implemented with SentencePiece, featuring a vocabulary size of 32,000.
+**This model card is for Gervásio 7B PTPT**, with 7 billion parameters, a hidden size of 4,096 units, an intermediate size of 11,008 units, 32 attention heads, 32 hidden layers, and a tokenizer obtained using the Byte-Pair Encoding (BPE) algorithm implemented with SentencePiece, featuring a vocabulary size of 32,000.
 
-Gervásio-7B-PTPT-Decoder is distributed under an [MIT license](https://huggingface.co/PORTULAN/gervasio-7b-portuguese-ptbr-decoder/blob/main/LICENSE).
+Gervásio 7B PTPT is distributed under an [MIT license](https://huggingface.co/PORTULAN/gervasio-7b-portuguese-ptbr-decoder/blob/main/LICENSE).
 
 
 <br>
 
 # Training Data
 
-**Gervásio 7B PT-PT** was trained over standard supervised fine-tuning, and to keep some alignment with mainstream benchmarks for English, we resorted to tasks and respective datasets in the GLUE and the SuperGLUE collections.
+**Gervásio 7B PTPT** was trained over standard supervised fine-tuning, and to keep some alignment with mainstream benchmarks for English, we resorted to tasks and respective datasets in the GLUE and the SuperGLUE collections.
 
 
 We selected those datasets where the outcome of their machine translation into European Portuguese could preserve, in the target language, the linguistic properties at stake.