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--- |
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library_name: transformers |
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base_model: microsoft/codebert-base |
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tags: |
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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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model-index: |
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- name: Vulnerability_Detection_Using_CodeBERT |
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results: [] |
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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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should probably proofread and complete it, then remove this comment. --> |
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# Vulnerability_Detection_Using_CodeBERT |
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This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0740 |
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- Accuracy: 1.0 |
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- Auc: 1.0 |
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- Precision: 1.0 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:---------:| |
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| 0.2971 | 1.0 | 26 | 0.1815 | 0.925 | 1.0 | 0.81 | |
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| 0.2407 | 2.0 | 52 | 0.1349 | 0.981 | 1.0 | 0.944 | |
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| 0.2619 | 3.0 | 78 | 0.1668 | 0.887 | 1.0 | 0.739 | |
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| 0.2207 | 4.0 | 104 | 0.1081 | 1.0 | 1.0 | 1.0 | |
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| 0.1543 | 5.0 | 130 | 0.1037 | 0.981 | 1.0 | 1.0 | |
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| 0.1428 | 6.0 | 156 | 0.0974 | 0.981 | 1.0 | 0.944 | |
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| 0.1598 | 7.0 | 182 | 0.0916 | 0.981 | 1.0 | 1.0 | |
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| 0.1324 | 8.0 | 208 | 0.1024 | 0.981 | 1.0 | 0.944 | |
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| 0.1445 | 9.0 | 234 | 0.0726 | 1.0 | 1.0 | 1.0 | |
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| 0.1287 | 10.0 | 260 | 0.0740 | 1.0 | 1.0 | 1.0 | |
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### Framework versions |
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- Transformers 4.50.0 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
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