Update README.md

README.md

@@ -36,14 +36,16 @@ Please use the above cannonical reference when using or citing this model.
 
 ## Model Description
 
-**This model card is for the Albertina-PT-PT** model with a total of 900M parameters, 24 layers and a hidden size of 1536.
+**This model card is for Albertina-PT-PT**, with 900M parameters, 24 layers and a hidden size of 1536.
+
+This model is distributed free of charge under the [MIT](https://choosealicense.com/licenses/mit/) license (permits commercial use, distribution, modification and private use).
+
 
-The Albertina-PT-PT is distributed free of charge under the [MIT](https://choosealicense.com/licenses/mit/) license (permits commercial use, distribution, modification and private use).
 
 
 # Training Data
 
-The **Albertina PT-PT** resorted to a data set that resulted from gathering some openly available corpora of European Portuguese from the following sources:
+**Albertina PT-PT** 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.
@@ -51,37 +53,44 @@ The **Albertina PT-PT** resorted to a data set that resulted from gathering some
 - [ParlamentoPT](https://www.parlamento.pt/): the ParlamentoPT is a data set we obtained by gathering the publicly available documents with the transcription of the debates in the Portuguese Parliament.
 
 
-The **Albertina PT-BR** resorted to the [BrWac](https://huggingface.co/datasets/brwac) data set.
+**Albertina PT-BR**, in turn, was trained over the [BrWac](https://huggingface.co/datasets/brwac) data set.
+
+
 
 
 ## Preprocessing
 
-We filtered the PT-PT corpora using the [BLOOM pre-processing](https://github.com/bigscience-workshop/data-preparation) pipeline resulting in a data set of 8 million documents, containing around 2.2 billion tokens.
+We filtered the PT-PT corpora using the [BLOOM pre-processing](https://github.com/bigscience-workshop/data-preparation) pipeline, resulting in a data set of 8 million documents, containing around 2.2 billion tokens.
 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 XLarge](https://huggingface.co/microsoft/deberta-v2-xlarge), for English.
 
+To train **Albertina-PT-PT**, 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.
+The model was trained for 3 days on a2-highgpu-8gb Google Cloud A2 VMs with 8 GPUs, 96 vCPUs and 680 GB of RAM.
+
 To train **Albertina-PT-BR** the BrWac 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 896 samples (56 samples per GPU without gradient accumulation steps).
 We chose a learning rate of 1e-5 with linear decay and 10k warm-up steps based on the results of exploratory experiments. 
 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.
-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.
-The model was trained for 3 days on a2-highgpu-8gb Google Cloud A2 VMs with 8 GPUs, 96 vCPUs and 680 GB of RAM.
+
+
 
 # Evaluation
 
-The models were evaluated on downstream tasks organized into two groups.
+The two model versions were evaluated on downstream tasks organized into two groups.
 
 In one group, we have the two data sets from the [ASSIN 2 benchmark](https://huggingface.co/datasets/assin2), namely STS and RTE, that were used to evaluate the previous state-of-the-art model [BERTimbau Large](https://huggingface.co/neuralmind/bert-large-portuguese-cased).
 In the other group of data sets, we have the translations into PT-BR and PT-PT of the English data sets used for a few of the tasks in the widely-used [GLUE benchmark](https://huggingface.co/datasets/glue), which allowed us to test both Albertina-PT-* variants on a wider variety of downstream tasks.
 
+
 ### ASSIN 2
 
 [ASSIN 2](https://huggingface.co/datasets/assin2) is a **PT-BR data** set of approximately 10.000 sentence pairs, split into 6.500 for training, 500 for validation, and 2.448 for testing, annotated with semantic relatedness scores (range 1 to 5) and with binary entailment judgments.