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microsoft-codebert-base-finetuned-defect-detection

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  1. README.md +16 -10
  2. model.safetensors +1 -1
README.md CHANGED
@@ -4,6 +4,9 @@ tags:
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  - generated_from_trainer
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  metrics:
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  - accuracy
 
 
 
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  model-index:
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  - name: microsoft-codebert-base-finetuned-defect-detection
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  results: []
@@ -16,8 +19,11 @@ 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: 0.5346
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- - Accuracy: 0.7093
 
 
 
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  ## Model description
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@@ -39,23 +45,23 @@ The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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  - train_batch_size: 32
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  - eval_batch_size: 8
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- - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 3.0
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.6157 | 1.0 | 997 | 0.5484 | 0.6883 |
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- | 0.5023 | 2.0 | 1994 | 0.5381 | 0.7061 |
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- | 0.4403 | 3.0 | 2991 | 0.5346 | 0.7093 |
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  ### Framework versions
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- - Transformers 4.37.0.dev0
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  - Pytorch 2.1.2+cu121
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  - Datasets 2.16.1
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  - Tokenizers 0.15.0
 
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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:
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  - name: microsoft-codebert-base-finetuned-defect-detection
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  results: []
 
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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: 0.5498
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+ - Accuracy: 0.7026
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+ - F1: 0.7299
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+ - Precision: 0.6559
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+ - Recall: 0.8227
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  ## Model description
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  - learning_rate: 2e-05
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  - train_batch_size: 32
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  - eval_batch_size: 8
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+ - seed: 4711
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 3
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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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+ | 0.6584 | 1.0 | 997 | 0.5554 | 0.6827 | 0.6347 | 0.7252 | 0.5642 |
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+ | 0.5304 | 2.0 | 1994 | 0.5229 | 0.6975 | 0.7269 | 0.6502 | 0.8243 |
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+ | 0.4572 | 3.0 | 2991 | 0.5498 | 0.7026 | 0.7299 | 0.6559 | 0.8227 |
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  ### Framework versions
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+ - Transformers 4.36.2
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  - Pytorch 2.1.2+cu121
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  - Datasets 2.16.1
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  - Tokenizers 0.15.0
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