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