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mcanoglu
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microsoft-codebert-base-finetuned-defect-detection

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End of training

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  1. README.md +12 -13
  2. model.safetensors +1 -1
README.md CHANGED
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  - generated_from_trainer
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  metrics:
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  - accuracy
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- - f1
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  - precision
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  - recall
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  model-index:
@@ -19,11 +18,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6534
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- - Accuracy: 0.7342
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- - F1: 0.7413
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- - Precision: 0.7066
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- - Recall: 0.7795
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  ## Model description
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | 0.6396 | 1.0 | 996 | 0.5277 | 0.6905 | 0.6502 | 0.7258 | 0.5889 |
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- | 0.4862 | 2.0 | 1993 | 0.5331 | 0.7176 | 0.7393 | 0.6733 | 0.8196 |
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- | 0.4043 | 3.0 | 2989 | 0.5521 | 0.7339 | 0.7343 | 0.7167 | 0.7528 |
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- | 0.3439 | 4.0 | 3986 | 0.5945 | 0.7357 | 0.7422 | 0.7087 | 0.7790 |
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- | 0.2946 | 5.0 | 4980 | 0.6534 | 0.7342 | 0.7413 | 0.7066 | 0.7795 |
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  ### Framework versions
 
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  - generated_from_trainer
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  metrics:
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  - accuracy
 
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  - precision
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  - recall
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  model-index:
 
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  This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6197
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+ - Accuracy: 0.7382
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+ - Roc Auc: 0.7394
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+ - Precision: 0.7070
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+ - Recall: 0.7924
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Roc Auc | Precision | Recall |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|:---------:|:------:|
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+ | 0.6456 | 1.0 | 996 | 0.5435 | 0.6832 | 0.6810 | 0.7151 | 0.5843 |
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+ | 0.5086 | 2.0 | 1993 | 0.5373 | 0.7113 | 0.7139 | 0.6654 | 0.8227 |
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+ | 0.4173 | 3.0 | 2989 | 0.5476 | 0.7289 | 0.7293 | 0.7125 | 0.7461 |
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+ | 0.3543 | 4.0 | 3986 | 0.5803 | 0.7357 | 0.7369 | 0.7051 | 0.7888 |
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+ | 0.3059 | 5.0 | 4980 | 0.6197 | 0.7382 | 0.7394 | 0.7070 | 0.7924 |
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  ### Framework versions
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