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bertimbau-large-lener_br

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  1. README.md +9 -9
  2. pytorch_model.bin +1 -1
  3. runs/Jul07_11-49-43_844dfc41b032/1625658585.0232518/events.out.tfevents.1625658585.844dfc41b032.77.4 +0 -0
  4. runs/Jul07_11-49-43_844dfc41b032/events.out.tfevents.1625658585.844dfc41b032.77.3 +0 -0
  5. runs/Jul07_11-49-43_844dfc41b032/events.out.tfevents.1625660061.844dfc41b032.77.5 +0 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model_index:
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  metric:
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  name: Accuracy
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  type: accuracy
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- value: 0.9744464930592591
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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
@@ -32,11 +32,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the lener_br dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1193
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- - Precision: 0.8287
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- - Recall: 0.9144
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- - F1: 0.8694
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- - Accuracy: 0.9744
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  ## Model description
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@@ -67,9 +67,9 @@ 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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- | 0.0586 | 1.0 | 1957 | 0.1409 | 0.8317 | 0.8725 | 0.8516 | 0.9667 |
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- | 0.0304 | 2.0 | 3914 | 0.1338 | 0.7137 | 0.9172 | 0.8027 | 0.9619 |
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- | 0.0119 | 3.0 | 5871 | 0.1193 | 0.8287 | 0.9144 | 0.8694 | 0.9744 |
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  ### Framework versions
 
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  metric:
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  name: Accuracy
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  type: accuracy
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+ value: 0.9790177823152056
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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 [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the lener_br dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1064
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+ - Precision: 0.8676
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+ - Recall: 0.9204
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+ - F1: 0.8932
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+ - Accuracy: 0.9790
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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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+ | 0.0601 | 1.0 | 1957 | 0.1097 | 0.8223 | 0.9004 | 0.8596 | 0.9700 |
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+ | 0.0301 | 2.0 | 3914 | 0.1148 | 0.8070 | 0.9333 | 0.8656 | 0.9722 |
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+ | 0.0122 | 3.0 | 5871 | 0.1064 | 0.8676 | 0.9204 | 0.8932 | 0.9790 |
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  ### Framework versions
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