Update README.md

README.md

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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 PT-PT Base. 
+<p style="text-align: center;">&nbsp;&nbsp;&nbsp;&nbsp;This is the model card for Albertina PT-PT base. 
   You may be interested in some of the other models in the <a href="https://huggingface.co/PORTULAN">Albertina family</a>.
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-# Albertina PT-PT Base
+# Albertina PT-PT base
 
 
-**Albertina PT-PT Base** is a foundation, large language model for European **Portuguese** from **Portugal**.
+**Albertina PT-PT base** 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. 
@@ -46,7 +46,7 @@ and to the best of our knowledge, this is an encoder specifically for this langu
 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 PT-PT base** 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/2305.06721):
 
 ``` latex
@@ -70,9 +70,9 @@ 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-PT-PT 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).
+Albertina-PT-PT base is distributed under an [MIT license](https://huggingface.co/PORTULAN/albertina-ptpt/blob/main/LICENSE).
 
 DeBERTa is distributed under an [MIT license](https://github.com/microsoft/DeBERTa/blob/master/LICENSE).
 
@@ -81,7 +81,7 @@ 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-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:
 
 - [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&#39;s official website. We retained its European Portuguese portion.
@@ -97,9 +97,9 @@ We skipped the default filtering of stopwords since it would disrupt the syntact
 
 ## Training
 
-As codebase, we resorted to the [DeBERTa V1 Base](https://huggingface.co/microsoft/deberta-base), for English.
+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-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.
 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.
@@ -122,7 +122,7 @@ We automatically translated the same four tasks from GLUE using [DeepL Translate
 | Model                    | RTE (Accuracy) | WNLI (Accuracy)| MRPC (F1) | STS-B (Pearson) |
 |--------------------------|----------------|----------------|-----------|-----------------|
 | **Albertina-PT-PT**      | **0.8339**     | 0.4225         | **0.9171**| **0.8801**      | 
-| **Albertina-PT-PT Base** |  0.6787        | **0.4507**     | 0.8829    | 0.8581          |
+| **Albertina-PT-PT base** |  0.6787        | **0.4507**     | 0.8829    | 0.8581          |
 
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