Update README.md

README.md

@@ -20,39 +20,40 @@ tags:
 - portuguese
 - decoder
 - foundation model
-- instruct
 datasets:
 - PORTULAN/glue-ptpt
 ---
 </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 Instruct Decoder. 
+<p style="text-align: center;">&nbsp;&nbsp;&nbsp;&nbsp;This is the model card for Gervásio 7B PT-PT 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 Instruct
+# Gervásio 7B PT-PT
 
-**Gervásio PT-*** is a competitive **fully open** decoder for the **Portuguese language** language. 
+</br>
+
+**Gervásio PT-*** is a **fully open** decoder for the **Portuguese language**. 
 
 
-It is a **decoder** of the GPT family, based on the neural architecture Transformer and developed over the LLaMA~2 7B model.
+It is a **decoder** of the LLaMA family, based on the neural architecture Transformer and developed over the LLaMA~2 7B model.
 Its further improvement through additional training was done over language resources that include new instruction data sets of Portuguese prepared for this purpose.
 
 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**).
+namely the European variant, spoken in Portugal (**PT-PT**), and the American variant, spoken in Brazil (**PT-BR**).
 
-All versions of Gervásio are **distributed for free and under a fully open license**, including for either research or commercial usage, and can 
-be run on consumer-grade hardware, thus seeking to contribute to the advancement of research and innovation in language technology for Portuguese.
+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 PT-PT 7B Instruct** is developed by NLX-Natural Language and Speech Group, at the University of Lisbon, Faculty of Sciences, Department of Informatics, Portugal.
+**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.
 
 For the record, its full name is **Gervásio Produz Textos em Português**, to which corresponds the natural acronym **GPT PT**, 
 and which is know tough more shortly as **Gervásio PT-***, or even more briefly just as **Gervásio**, among his acquaintances.
 
-For further details, check the respective [publication](https://arxiv.org/abs/?):
+These models are fully documented in the respective [publication](https://arxiv.org/abs/?):
 
 ``` latex
 @misc{albertina-pt,
@@ -73,15 +74,15 @@ Please use the above cannonical reference when using or citing this model.
 
 # Model Description
 
-**This model card is for Gervásio-7B-PTPT-Instruct-Decoder**, 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.
-Gervásio-7B-PTPT-Instruct-Decoder is distributed under an [MIT license](https://huggingface.co/PORTULAN/albertina-ptpt/blob/main/LICENSE).
+**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.
+Gervásio-7B-PTPT-Decoder is distributed under an [MIT license](https://huggingface.co/PORTULAN/albertina-ptpt/blob/main/LICENSE).
 
 
 <br>
 
 # Training Data
 
-**Gervásio-7B-PTPT-Instruct-Decoder** 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 PT-PT** 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 Portuguese could preserve, in the target language, the linguistic properties at stake.