Update README.md

README.md

@@ -31,22 +31,22 @@ widget:
 
 ---
 
-# Albertina PT-PT base
+# Albertina 100M PTPT
 
 
-**Albertina PT-PT base** is a foundation, large language model for European **Portuguese** from **Portugal**.
+**Albertina 100M PTPT** is a foundation, large language model for European **Portuguese** from **Portugal**.
 
 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 is distributed free of charge and under a most permissible license.
 
-You may be also interested in [**Albertina 900m PT-PT**](https://huggingface.co/PORTULAN/albertina-ptpt).
+You may be also interested in [**Albertina 900m PTPT**](https://huggingface.co/PORTULAN/albertina-ptpt).
 This is a larger version, 
 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.
 
-**Albertina PT-PT base** is developed by a joint team from the University of Lisbon and the University of Porto, Portugal. 
+**Albertina 100M 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/?):
 
 ``` latex
@@ -68,7 +68,7 @@ Please use the above cannonical reference when using or citing this model.
 
 # Model Description
 
-**This model card is for Albertina-PT-PT base**, with 100M parameters, 12 layers and a hidden size of 768.
+**This model card is for Albertina 100M PTPT base**, with 100M parameters, 12 layers and a hidden size of 768.
 
 Albertina-PT-PT base is distributed under an [MIT license](https://huggingface.co/PORTULAN/albertina-ptpt/blob/main/LICENSE).
 
@@ -79,7 +79,7 @@ DeBERTa is distributed under an [MIT license](https://github.com/microsoft/DeBER
 
 # Training Data
 
-[**Albertina-PT-PT base**](https://huggingface.co/PORTULAN/albertina-ptpt-base) was trained over a 2.2 billion token data set that resulted from gathering some openly available corpora of European Portuguese from the following sources:
+[**Albertina 100M PTPT**](https://huggingface.co/PORTULAN/albertina-ptpt-base) was trained over a 2.2 billion token 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.
@@ -89,7 +89,7 @@ DeBERTa is distributed under an [MIT license](https://github.com/microsoft/DeBER
 
 ## Preprocessing
 
-We filtered the PT-PT corpora using the [BLOOM pre-processing](https://github.com/bigscience-workshop/data-preparation) pipeline.
+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.
 
 
@@ -97,7 +97,7 @@ We skipped the default filtering of stopwords since it would disrupt the syntact
 
 As codebase, we resorted to the [DeBERTa V1 base](https://huggingface.co/microsoft/deberta-base), for English.
 
-To train [**Albertina-PT-PT base**](https://huggingface.co/PORTULAN/albertina-ptpt-base), the data set was tokenized with the original DeBERTa tokenizer with a 128 token sequence truncation and dynamic padding.
+To train [**Albertina 100M PTPT**](https://huggingface.co/PORTULAN/albertina-ptpt-base), 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 3072 samples (192 samples per GPU).
 We opted for a learning rate of 1e-5 with linear decay and 10k warm-up steps. 
 A total of 200 training epochs were performed resulting in approximately 180k steps.
@@ -114,13 +114,13 @@ The base model version was evaluated on downstream tasks, namely the translation
 ## GLUE tasks translated
 
 
-We resorted to [GLUE-PT](https://huggingface.co/datasets/PORTULAN/glue-ptpt), a **PT-PT version of the GLUE** benchmark. 
+We resorted to [GLUE-PT](https://huggingface.co/datasets/PORTULAN/glue-ptpt), a **PTPT version of the GLUE** benchmark. 
 We automatically translated the same four tasks from GLUE using [DeepL Translate](https://www.deepl.com/), which specifically provides translation from English to PT-PT as an option.
 
 | Model                    | RTE (Accuracy) | WNLI (Accuracy)| MRPC (F1) | STS-B (Pearson) |
 |--------------------------|----------------|----------------|-----------|-----------------|
-| **Albertina 900m PT-PT** | **0.8339**     | 0.4225         | **0.9171**| **0.8801**      | 
-| **Albertina-PT-PT base** |  0.6787        | **0.4507**     | 0.8829    | 0.8581          |
+| **Albertina 900m PTPT** | **0.8339**     | 0.4225         | **0.9171**| **0.8801**      | 
+| **Albertina 100m PTPT** |  0.6787        | **0.4507**     | 0.8829    | 0.8581          |
 
 <br>