| library_name: transformers | |
| base_model: huggingface/CodeBERTa-small-v1 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - precision | |
| - recall | |
| model-index: | |
| - name: v1 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # v1 | |
| This model is a fine-tuned version of [huggingface/CodeBERTa-small-v1](https://huggingface.co/huggingface/CodeBERTa-small-v1) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.6553 | |
| - Accuracy: 0.6354 | |
| - F1 Weighted: 0.6336 | |
| - F1 Vuln: 0.5847 | |
| - Precision: 0.6339 | |
| - Recall: 0.6354 | |
| ## 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: 2e-05 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 64 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Weighted | F1 Vuln | Precision | Recall | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:-------:|:---------:|:------:| | |
| | 0.6492 | 1.0 | 682 | 0.6336 | 0.6032 | 0.6045 | 0.559 | 0.6068 | 0.6032 | | |
| | 0.5907 | 2.0 | 1364 | 0.6094 | 0.6296 | 0.631 | 0.5942 | 0.6348 | 0.6296 | | |
| | 0.5385 | 3.0 | 2046 | 0.6289 | 0.6442 | 0.6426 | 0.577 | 0.642 | 0.6442 | | |
| ### Framework versions | |
| - Transformers 5.0.0 | |
| - Pytorch 2.10.0+cu128 | |
| - Datasets 4.8.5 | |
| - Tokenizers 0.22.2 | |