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gervasio-7b-portuguese-ptbr-decoder

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jarodrigues commited on Mar 1

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@@ -129,9 +129,9 @@ For testing, we reserved the translated datasets MRPC (similarity) and RTE (infe
 | Model                    | MRPC (F1)      | RTE (F1)       | COPA (F1) |
 |--------------------------|----------------|----------------|-----------|
-| **Gervásio 7B PTBR**    | **0.7822**     | **0.8321**     | 0.2134    |
-| **LLaMA-2**              | 0.0369         | 0.0516         | 0.4867    |
-| **LLaMA-2 Chat**         | 0.5432         | 0.3807         | **0.5493**|
 <br>
 For further testing our decoder, in addition to the testing data described above, we also reused some of the datasets that had been resorted for American Portuguese to test the state-of-the-art Sabiá model and that were originally developed with materials from Portuguese: ASSIN2 RTE (entailment) and ASSIN2 STS (similarity), BLUEX (question answering), ENEM 2022 (question answering) and FaQuAD (extractive question-answering).

 | Model                    | MRPC (F1)      | RTE (F1)       | COPA (F1) |
 |--------------------------|----------------|----------------|-----------|
+| **Gervásio 7B PTBR**     | **0.7822**     | **0.8321**     | 0.2134    |
+| **LLaMA-2 (EN)**         | 0.0369         | 0.0516         | 0.4867    |
+| **LLaMA-2 Chat (EN)**    | 0.5432         | 0.3807         | **0.5493**|
 <br>
 For further testing our decoder, in addition to the testing data described above, we also reused some of the datasets that had been resorted for American Portuguese to test the state-of-the-art Sabiá model and that were originally developed with materials from Portuguese: ASSIN2 RTE (entailment) and ASSIN2 STS (similarity), BLUEX (question answering), ENEM 2022 (question answering) and FaQuAD (extractive question-answering).