PORTULAN
/

albertina-900m-portuguese-ptpt-encoder

@@ -76,7 +76,7 @@ The models 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.
 This data set supports the task of semantic text 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.
@@ -87,7 +87,7 @@ This data set supports the task of semantic text similarity (STS), which consist
 | BERTimbau-large     | 0.8913         | 0.8531       |
-## 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:
@@ -113,7 +113,7 @@ We automatically translated the same four tasks from GLUE using [DeepL Translate
 | **Albertina-PT-BR** | 0.7942         | 0.4085         | 0.9048    | **0.8847**      |
-## How to use
 You can use this model directly with a pipeline for masked language modeling:

 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.
 This data set supports the task of semantic text 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.
 | BERTimbau-large     | 0.8913         | 0.8531       |
+### 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:
 | **Albertina-PT-BR** | 0.7942         | 0.4085         | 0.9048    | **0.8847**      |
+# How to use
 You can use this model directly with a pipeline for masked language modeling: