Update README.md

README.md

@@ -48,17 +48,16 @@ For further details, check the respective publication:
 Please use the above cannonical reference when using or citing this model.
 
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-## Model Description
+# Model Description
 
 **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).
 
 
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 # Training Data
 
@@ -73,15 +72,13 @@ This model is distributed free of charge under the [MIT](https://choosealicense.
 **Albertina PT-BR**, in turn, was trained over the [BrWac](https://huggingface.co/datasets/brwac) data set.
 
 
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 ## 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 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
+## Training
 
 As codebase, we resorted to the [DeBERTa V2 XLarge](https://huggingface.co/microsoft/deberta-v2-xlarge), for English.
 
@@ -98,7 +95,7 @@ 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.
 
 
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 # Evaluation
 
@@ -108,7 +105,7 @@ In one group, we have the two data sets from the [ASSIN 2 benchmark](https://hug
 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
 
 [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.
 This data set supports the task of semantic textual similarity (STS), which consists of assigning a score of how semantically related two sentences are; and the task of recognizing textual entailment (RTE), which given a pair of sentences, consists of determining whether the first entails the second.
@@ -119,7 +116,7 @@ This data set supports the task of semantic textual similarity (STS), which cons
 | BERTimbau-large     | 0.8913         | 0.8531       |
 
 
-### GLUE tasks translated
+## GLUE tasks translated
 
 We resort to [PLUE](https://huggingface.co/datasets/dlb/plue) (Portuguese Language Understanding Evaluation), a data set that was obtained by automatically translating GLUE into **PT-BR**.
 We address four tasks from those in PLUE, namely:
@@ -144,6 +141,7 @@ We automatically translated the same four tasks from GLUE using [DeepL Translate
 | |   |      |  |           |
 | **Albertina-PT-BR** | 0.7942         | 0.4085         | 0.9048    | **0.8847**      |
 
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 # How to use
 
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 ```
 
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 # Citation
 
 When using or citing this model, kindly cite the following publication:
@@ -205,6 +205,8 @@ When using or citing this model, kindly cite the following publication:
 }
 ```
 
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 # Acknowledgments
 
 The research reported here was partially supported by: PORTULAN CLARIN—Research Infrastructure for the Science and Technology of Language,