cwe-vulnerability-classification-codebert-base
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.8462
- Accuracy: 0.225
- F1: 0.0092
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 4.8896 | 1.0 | 23 | 4.3198 | 0.225 | 0.0092 |
| 4.223 | 2.0 | 46 | 3.9716 | 0.225 | 0.0092 |
| 4.0284 | 3.0 | 69 | 3.8691 | 0.225 | 0.0092 |
| 3.841 | 4.0 | 92 | 3.8462 | 0.225 | 0.0092 |
Framework versions
- Transformers 4.54.1
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
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Model tree for CIRCL/cwe-vulnerability-classification-codebert-base
Base model
distilbert/distilbert-base-uncased