Update README.md

README.md

@@ -25,7 +25,7 @@ widget:
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 <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 Albertina 1.5B PTPT 
+<p style="text-align: center;">&nbsp;&nbsp;&nbsp;&nbsp;This is the model card for Albertina 1.5B PTPT. 
   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>
 
@@ -34,13 +34,13 @@ widget:
 # Albertina 1.5B PTPT
 
 
-**Albertina 1.5B PTPT** is a foundation, large language model for **European Portuguese** from **Portugal**.
+**Albertina 1.5B PTPT** is a foundation, large language model for the **European variant of Portuguese**.
 
 It is an **encoder** of the BERT family, based on the neural architecture Transformer and 
 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 (**PTPT**) and the American variant from Brazil (**PTBR**), 
-and it is distributed free of charge and under a most permissible license.
+namely the European variant, spoken in Portugal (**PTPT**) and the American variant, spoken in Brazil (**PTBR**), 
+and it is openly distributed free of charge under an open license.
 
 | Albertina's Family of Models                                                                             | 
 |----------------------------------------------------------------------------------------------------------|
@@ -53,10 +53,10 @@ and it is distributed free of charge and under a most permissible license.
 | [**Albertina 100M PTPT**](https://huggingface.co/PORTULAN/albertina-100m-portuguese-ptpt-encoder)       |
 | [**Albertina 100M PTBR**](https://huggingface.co/PORTULAN/albertina-100m-portuguese-ptbr-encoder)       |
 
-**Albertina 1.5B PTPT** is the version for **European Portuguese** from **Portugal**, 
+**Albertina 1.5B PTPT** is a version for the **European variant of Portuguese**, 
 and to the best of our knowledge, this is an encoder specifically for this language and variant 
-that,  at the time of its initial distribution, sets a new state of the art for it, and is made publicly available 
-and distributed for reuse.
+that,  at the time of its initial distribution, with its 1.5 billion parameters and performance scores sets a new state of the art for it, 
+and is made publicly available and distributed for reuse.
 
 It is an **encoder** of the BERT family, based on the neural architecture Transformer and 
 developed over the DeBERTa model, with most competitive performance for this language. 
@@ -64,7 +64,7 @@ It is distributed free of charge and under a most permissible license.
 
 
 **Albertina 1.5B PTPT** is developed by a joint team from the University of Lisbon and the University of Porto, Portugal. 
-For further details, check the respective [publication](https://arxiv.org/abs/?):
+For a fully detailed description, check the respective [publication](https://arxiv.org/abs/?):
 
 ``` latex
 @misc{albertina-pt-fostering,
@@ -109,26 +109,30 @@ DeBERTa is distributed under an [MIT license](https://github.com/microsoft/DeBER
 ## Preprocessing
 
 We filtered the PTPT corpora using the [BLOOM pre-processing](https://github.com/bigscience-workshop/data-preparation) pipeline.
-We skipped the default filtering of stopwords since it would disrupt the syntactic structure, and also the filtering for language identification given the corpus was pre-selected as Portuguese.
+We skipped the default filtering of stopwords since it would disrupt the syntactic structure, and also the filtering 
+for language identification given the corpus was pre-selected as Portuguese.
 
 
 ## Training
 
 As codebase, we resorted to the [DeBERTa V2 xxlarge](https://huggingface.co/microsoft/deberta-v2-xxlarge), for English.
 
-To train **Albertina 1.5B PTPT**, the data set was tokenized with the original DeBERTa tokenizer with a 128-token sequence truncation and dynamic padding for 250k steps, 
-a 256-token sequence-truncation for 80k steps ([**Albertina 1.5B PTPT 256**](https://huggingface.co/PORTULAN/albertina-1b5-portuguese-ptpt-encoder-256)) and finally a 512-token sequence-truncation for 60k steps.
+To train **Albertina 1.5B PTPT**, the data set was tokenized with the original DeBERTa tokenizer with a 128-token sequence 
+truncation and dynamic padding for 250k steps, 
+a 256-token sequence-truncation for 80k steps 
+([**Albertina 1.5B PTPT 256**](https://huggingface.co/PORTULAN/albertina-1b5-portuguese-ptpt-encoder-256)) and finally a 512-token sequence-truncation for 60k steps.
 These steps correspond to the equivalent setup of 48 hours on a2-megagpu-16gb Google Cloud A2 node for the 128-token input sequences, 24 hours of computation for the 256-token 
 input sequences and 24 hours of computation for the 512-token input sequences.
 We opted for a learning rate of 1e-5 with linear decay and 10k warm-up steps. 
 
 <br>
 
-# Evaluation
+# Performance
 
 
-We resorted to [ExtraGLUE](https://huggingface.co/datasets/PORTULAN/extraglue), a **PTPT version of the GLUE and SUPERGLUE** benchmark.
-We automatically translated the tasks from GLUE and SUPERGLUE using [DeepL Translate](https://www.deepl.com/), which specifically provides translation from English to PTPT as an option.
+We resorted to [extraGLUE](https://huggingface.co/datasets/PORTULAN/extraglue), a **PTPT version of the GLUE and SUPERGLUE** benchmark.
+We automatically translated the tasks from GLUE and SUPERGLUE using [DeepL Translate](https://www.deepl.com/), which specifically 
+provides translation from English to PTPT or PTBR as possible options.
 
 | Model                         | RTE (Accuracy) | WNLI (Accuracy)| MRPC (F1) | STS-B (Pearson) | COPA (Accuracy) | CB (F1)    | MultiRC (F1) | BoolQ (Accuracy) |
 |-------------------------------|----------------|----------------|-----------|-----------------|-----------------|------------|--------------|------------------|