bertin-project
/

bertin-roberta-base-spanish

@@ -216,17 +216,17 @@ For simplicity, we will abbreviate the different models as follows:
 Table 3. Metrics for different downstream tasks, comparing our different models as well as other relevant BERT variations from the literature. Dataset for POS and NER is CoNLL 2002. POS, NER and PAWS-X used max length 512 and batch size 8. Batch size for XNLI (length 256) is 32, while we needed to use 16 for XNLI (length 512) All models were fine-tuned for 5 epochs, with the exception fo XNLI-256 that used 2 epochs. Results marked with * indicate a repetition.
 </caption>
-|     Model    | POS (F1/Acc)            | NER (F1/Acc)         |  PAWS-X (Acc) | XNLI-256 (Acc) |  XNLI-512 (Acc) |
-|--------------|-------------------------|----------------------|--------------|-----------------|--------------|0.
-|   BERT-m     |    0.9629 / 0.9687      | 0.8539 / 0.9779      |  0.5765*      |  0.7852         |  0.7606  |
-|  BERT-wwm    |    0.9642 / 0.9700      | 0.8579 / 0.9783      |  0.8720*     |  **0.8186**     |  0.8012*  |
-|   BSC-BNE    |     0.9659 / 0.9707     |  0.8700 / 0.9807     |  0.5765*     |  0.8178         |  0.3333*  |
-|    Beta      |    0.9638 / 0.9690      |  0.8725 / 0.9812     |  0.5765*     |     —           |  0.7751*  |
-|    Random    |   0.9656 / 0.9704       | 0.8704 / 0.9807      |  0.8800*      |  0.7745         |  0.7795  |
-|  Stepwise    |   0.9656 / 0.9707       |  0.8705 / 0.9809     |  0.8825*      |  0.7820         |  0.7799  |
-|   Gaussian   |   0.9662 / 0.9709       |  **0.8792 / 0.9816** |  0.8875*      |  0.7942         |  0.7843  |
-| Random-512   |   0.9660 /  0.9707      |   0.8616 / 0.9803    |  0.6735*      |  0.7723         |  0.7799  |
-| Gaussian-512 |   **0.9662 / 0.9714**   |  **0.8764 / 0.9819** |  **0.8965** * |  0.7878         |  0.7843  |
 </figure>

 Table 3. Metrics for different downstream tasks, comparing our different models as well as other relevant BERT variations from the literature. Dataset for POS and NER is CoNLL 2002. POS, NER and PAWS-X used max length 512 and batch size 8. Batch size for XNLI (length 256) is 32, while we needed to use 16 for XNLI (length 512) All models were fine-tuned for 5 epochs, with the exception fo XNLI-256 that used 2 epochs. Results marked with * indicate a repetition.
 </caption>
+|     Model    | POS (F1/Acc)         |     NER (F1/Acc)    | PAWS-X (Acc) | XNLI-256 (Acc) | XNLI-512 (Acc) |
+|--------------|----------------------|---------------------|--------------|----------------|--------------|
+|   BERT-m     |  0.9629 / 0.9687     | 0.8539 / 0.9779     |  0.5765*     |  0.7852        |  0.7606  |
+|  BERT-wwm    |  0.9642 / 0.9700     | 0.8579 / 0.9783     |  0.8720*     |  **0.8186**    |  0.8012*  |
+|   BSC-BNE    |  0.9659 / 0.9707     | 0.8700 / 0.9807     |  0.5765*     |  0.8178        |  0.3333*  |
+|    Beta      |  0.9638 / 0.9690     | 0.8725 / 0.9812     |  0.5765*     |     —          |  0.7751*  |
+|    Random    |  0.9656 / 0.9704     | 0.8704 / 0.9807     |  0.8800*     |  0.7745        |  0.7795  |
+|  Stepwise    |  0.9656 / 0.9707     | 0.8705 / 0.9809     |  0.8825*     |  0.7820        |  0.7799  |
+|   Gaussian   |  0.9662 / 0.9709     | **0.8792 / 0.9816** |  0.8875*     |  0.7942        |  0.7843  |
+| Random-512   |  0.9660 /  0.9707    | 0.8616 / 0.9803     |  0.6735*     |  0.7723        |  0.7799  |
+| Gaussian-512 |  **0.9662 / 0.9714** | **0.8764 / 0.9819** | **0.8965** * |  0.7878        |  0.7843  |
 </figure>