Update README.md
Browse files
README.md
CHANGED
@@ -16,16 +16,22 @@ widget:
|
|
16 |
---
|
17 |
|
18 |
|
19 |
-
# Albertina PT
|
20 |
|
21 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
22 |
|
23 |
-
To develop this **encoder**, which we named **Albertina PT-***, a strong model was used as a starting point, DeBERTa, and its pre-training was done over data sets of Portuguese, namely over a data set we gathered for PT-PT and over the BrWaC corpus for PT-BR.
|
24 |
-
The performance of Albertina and competing models was assessed by evaluating them on prominent downstream language processing tasks adapted for Portuguese.
|
25 |
|
26 |
-
Both **Albertina PT-PT and PT-BR versions are distributed free of charge and under the most permissive license possible** and can be run on consumer-grade hardware, thus seeking to contribute to the advancement of research and innovation in language technology for Portuguese.
|
27 |
|
28 |
-
Please check the [Albertina PT-* article]() for more details.
|
29 |
|
30 |
|
31 |
## Model Description
|
|
|
16 |
---
|
17 |
|
18 |
|
19 |
+
# Albertina PT-PT
|
20 |
|
21 |
+
**Albertina PT-*** is a foundation, large language model for the **Portuguese language**.
|
22 |
+
|
23 |
+
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.
|
24 |
+
|
25 |
+
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.
|
26 |
+
|
27 |
+
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:
|
28 |
+
|
29 |
+
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 ###.
|
30 |
+
|
31 |
+
Please use the above cannonical reference when using or citing this model.
|
32 |
|
|
|
|
|
33 |
|
|
|
34 |
|
|
|
35 |
|
36 |
|
37 |
## Model Description
|