Update README.md

README.md

@@ -14,14 +14,10 @@ tags:
 - bertimbau
 license: other
 datasets:
-- oscar
 - brwac
-- europarl_bilingual
 - PORTULAN/glue-ptpt
-- PORTULAN/parlamento-pt
 - assin2
 - dlb/plue
-- oscar-corpus/OSCAR-2301
 widget:
 - text: >-
     A culinária brasileira é rica em sabores e [MASK], tornando-se um dos
@@ -91,7 +87,7 @@ a license for non-commercial use.
 **Albertina PT-BR** was trained over the [BrWac](https://huggingface.co/datasets/brwac) data set.
 
 
-**Albertina PT-PT**, in turn, was trained over a data set that resulted from gathering some openly available corpora of European Portuguese from the following sources:
+[**Albertina PT-PT**](https://huggingface.co/PORTULAN/albertina-ptpt), in turn, was trained over a data set that resulted from gathering some openly available corpora of European Portuguese from the following sources:
 
 - [OSCAR](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301): the OSCAR data set includes documents in more than one hundred languages, including Portuguese, and it is widely used in the literature. It is the result of a selection performed over the [Common Crawl](https://commoncrawl.org/) data set, crawled from the Web, that retains only pages whose metadata indicates permission to be crawled, that performs deduplication, and that removes some boilerplate, among other filters. Given that it does not discriminate between the Portuguese variants, we performed extra filtering by retaining only documents whose meta-data indicate the Internet country code top-level domain of Portugal. We used the January 2023 version of OSCAR, which is based on the November/December 2022 version of Common Crawl.
 - [DCEP](https://joint-research-centre.ec.europa.eu/language-technology-resources/dcep-digital-corpus-european-parliament_en): the Digital Corpus of the European Parliament is a multilingual corpus including documents in all official EU languages published on the European Parliament's official website. We retained its European Portuguese portion.
@@ -117,7 +113,7 @@ In total, around 200k training steps were taken across 50 epochs.
 The model was trained for 1 day and 11 hours on a2-megagpu-16gb Google Cloud A2 VMs with 16 GPUs, 96 vCPUs and 1.360 GB of RAM.
 
 
-To train **Albertina-PT-PT**, the data set was tokenized with the original DeBERTa tokenizer with a 128 token sequence truncation and dynamic padding.
+To train [**Albertina PT-PT**](https://huggingface.co/PORTULAN/albertina-ptpt), the data set was tokenized with the original DeBERTa tokenizer with a 128 token sequence truncation and dynamic padding.
 The model was trained using the maximum available memory capacity resulting in a batch size of 832 samples (52 samples per GPU and applying gradient accumulation in order to approximate the batch size of the PT-BR model).
 Similarly to the PT-BR variant above, we opted for a learning rate of 1e-5 with linear decay and 10k warm-up steps.
 However, since the number of training examples is approximately twice of that in the PT-BR variant, we reduced the number of training epochs to half and completed only 25 epochs, which resulted in approximately 245k steps.