Update README.md

README.md

@@ -22,13 +22,13 @@ datasets:
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-<p style="text-align: center;">&nbsp;&nbsp;&nbsp;&nbsp;This is the model card for Gervásio 7B PT-BR Decoder. 
+<p style="text-align: center;">&nbsp;&nbsp;&nbsp;&nbsp;This is the model card for Gervásio 7B PTBR 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>.
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-# Gervásio 7B PT-BR
+# Gervásio 7B PTBR
 
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@@ -45,7 +45,7 @@ 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-BR** is developed by NLX-Natural Language and Speech Group, at the University of Lisbon, Faculty of Sciences, Department of Informatics, Portugal.
+**Gervásio 7B PTBR** 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.
@@ -73,7 +73,7 @@ Please use the above cannonical reference when using or citing this model.
 
 # Model Description
 
-**This model card is for Gervásio 7B PT-BR**, 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 PTBR**, 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-PTBR-Decoder is distributed under an [MIT license](https://huggingface.co/PORTULAN/gervasio-7b-portuguese-ptpt-decoder/blob/main/LICENSE).
 
@@ -82,7 +82,7 @@ Gervásio-7B-PTBR-Decoder is distributed under an [MIT license](https://huggingf
 
 # Training Data
 
-**Gervásio 7B PT-BR** 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 PTBR** 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 American Portuguese could preserve, in the target language, the linguistic properties at stake.
@@ -128,7 +128,7 @@ This involves repurposing the tasks in various ways, such as generation of answe
 
 | Model                    | MRPC (F1)      | RTE (F1)       | COPA (F1) |
 |--------------------------|----------------|----------------|-----------|
-| **Gervásio 7B PT-BR**    | **0.7822**     | **0.8321**     | 0.2134    | 
+| **Gervásio 7B PTBR**    | **0.7822**     | **0.8321**     | 0.2134    | 
 | **LLaMA-2**              | 0.0369         | 0.0516         | 0.4867    |
 | **LLaMA-2 Chat**         | 0.5432         | 0.3807         | **0.5493**|
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@@ -144,7 +144,7 @@ To evaluate Gervásio, the examples were randomly selected to be included in the
 
 | Model                    | ENEM 2022 (Accuracy) | BLUEX (Accuracy)| RTE (F1)  | STS (Pearson) |
 |--------------------------|----------------------|-----------------|-----------|---------------|
-| **Gervásio 7B PT-BR**    | 0.1977               | 0.2640          | **0.7469**| **0.2136**    |
+| **Gervásio 7B PTBR**    | 0.1977               | 0.2640          | **0.7469**| **0.2136**    |
 | **LLaMA-2**              | 0.2458               | 0.2903          | 0.0913    | 0.1034        |
 | **LLaMA-2 Chat**         | 0.2231               | 0.2959          | 0.5546    | 0.1750        |
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