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bertimbau-base-lener-br-finetuned-lener-br

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update model card README.md

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@@ -24,16 +24,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.8910105127655009
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  - name: Recall
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  type: recall
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- value: 0.8931182795698924
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  - name: F1
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  type: f1
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- value: 0.8920631511115884
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  - name: Accuracy
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  type: accuracy
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- value: 0.9770368903042955
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,10 +44,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [Luciano/bertimbau-base-finetuned-lener-br](https://huggingface.co/Luciano/bertimbau-base-finetuned-lener-br) on the lener_br dataset.
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  It achieves the following results on the evaluation set:
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  - Loss: nan
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- - Precision: 0.8910
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- - Recall: 0.8931
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- - F1: 0.8921
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- - Accuracy: 0.9770
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  ## Model description
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@@ -78,21 +78,21 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.0707 | 1.0 | 1957 | nan | 0.8435 | 0.8798 | 0.8613 | 0.9736 |
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- | 0.0374 | 2.0 | 3914 | nan | 0.8286 | 0.8533 | 0.8408 | 0.9670 |
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- | 0.0278 | 3.0 | 5871 | nan | 0.8635 | 0.9114 | 0.8868 | 0.9774 |
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- | 0.0223 | 4.0 | 7828 | nan | 0.9012 | 0.8714 | 0.8861 | 0.9780 |
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- | 0.0175 | 5.0 | 9785 | nan | 0.8598 | 0.9037 | 0.8812 | 0.9741 |
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- | 0.0105 | 6.0 | 11742 | nan | 0.8903 | 0.8671 | 0.8785 | 0.9746 |
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- | 0.0084 | 7.0 | 13699 | nan | 0.8864 | 0.9045 | 0.8954 | 0.9766 |
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- | 0.0093 | 8.0 | 15656 | nan | 0.8997 | 0.8935 | 0.8966 | 0.9790 |
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- | 0.0035 | 9.0 | 17613 | nan | 0.8901 | 0.8985 | 0.8943 | 0.9774 |
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- | 0.0031 | 10.0 | 19570 | nan | 0.8936 | 0.8955 | 0.8945 | 0.9773 |
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- | 0.0032 | 11.0 | 21527 | nan | 0.8848 | 0.8920 | 0.8884 | 0.9766 |
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- | 0.0009 | 12.0 | 23484 | nan | 0.8883 | 0.8914 | 0.8899 | 0.9769 |
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- | 0.0016 | 13.0 | 25441 | nan | 0.8868 | 0.8912 | 0.8890 | 0.9758 |
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- | 0.002 | 14.0 | 27398 | nan | 0.8920 | 0.8935 | 0.8928 | 0.9773 |
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- | 0.0008 | 15.0 | 29355 | nan | 0.8910 | 0.8931 | 0.8921 | 0.9770 |
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.8942967409948542
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  - name: Recall
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  type: recall
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+ value: 0.8969892473118279
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  - name: F1
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  type: f1
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+ value: 0.8956409705819198
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9696009264479559
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [Luciano/bertimbau-base-finetuned-lener-br](https://huggingface.co/Luciano/bertimbau-base-finetuned-lener-br) on the lener_br dataset.
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  It achieves the following results on the evaluation set:
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  - Loss: nan
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+ - Precision: 0.8943
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+ - Recall: 0.8970
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+ - F1: 0.8956
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+ - Accuracy: 0.9696
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.0678 | 1.0 | 1957 | nan | 0.8148 | 0.8882 | 0.8499 | 0.9689 |
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+ | 0.0371 | 2.0 | 3914 | nan | 0.8347 | 0.9022 | 0.8671 | 0.9671 |
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+ | 0.0242 | 3.0 | 5871 | nan | 0.8491 | 0.8905 | 0.8693 | 0.9716 |
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+ | 0.0197 | 4.0 | 7828 | nan | 0.9014 | 0.8772 | 0.8892 | 0.9780 |
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+ | 0.0135 | 5.0 | 9785 | nan | 0.8651 | 0.9060 | 0.8851 | 0.9765 |
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+ | 0.013 | 6.0 | 11742 | nan | 0.8882 | 0.9054 | 0.8967 | 0.9767 |
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+ | 0.0084 | 7.0 | 13699 | nan | 0.8559 | 0.9097 | 0.8820 | 0.9751 |
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+ | 0.0069 | 8.0 | 15656 | nan | 0.8916 | 0.8828 | 0.8872 | 0.9696 |
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+ | 0.0047 | 9.0 | 17613 | nan | 0.8964 | 0.8931 | 0.8948 | 0.9716 |
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+ | 0.0028 | 10.0 | 19570 | nan | 0.8864 | 0.9047 | 0.8955 | 0.9691 |
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+ | 0.0023 | 11.0 | 21527 | nan | 0.8860 | 0.9011 | 0.8935 | 0.9693 |
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+ | 0.0009 | 12.0 | 23484 | nan | 0.8952 | 0.8987 | 0.8970 | 0.9686 |
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+ | 0.0014 | 13.0 | 25441 | nan | 0.8929 | 0.8985 | 0.8957 | 0.9699 |
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+ | 0.0025 | 14.0 | 27398 | nan | 0.8914 | 0.8981 | 0.8947 | 0.9700 |
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+ | 0.001 | 15.0 | 29355 | nan | 0.8943 | 0.8970 | 0.8956 | 0.9696 |
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  ### Framework versions