RayenLLM/Vulnerability_Detection_Using_CodeBERT

1---2library_name: transformers3base_model: microsoft/codebert-base4tags:5- generated_from_trainer6metrics:7- accuracy8- precision9model-index:10- name: Vulnerability_Detection_Using_CodeBERT11  results: []12---13 14<!-- This model card has been generated automatically according to the information the Trainer had access to. You15should probably proofread and complete it, then remove this comment. -->16 17# Vulnerability_Detection_Using_CodeBERT18 19This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset.20It achieves the following results on the evaluation set:21- Loss: 0.074022- Accuracy: 1.023- Auc: 1.024- Precision: 1.025 26## Model description27 28More information needed29 30## Intended uses & limitations31 32More information needed33 34## Training and evaluation data35 36More information needed37 38## Training procedure39 40### Training hyperparameters41 42The following hyperparameters were used during training:43- learning_rate: 0.000244- train_batch_size: 845- eval_batch_size: 846- seed: 4247- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments48- lr_scheduler_type: linear49- num_epochs: 1050 51### Training results52 53| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | Precision |54|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:---------:|55| 0.2971        | 1.0   | 26   | 0.1815          | 0.925    | 1.0 | 0.81      |56| 0.2407        | 2.0   | 52   | 0.1349          | 0.981    | 1.0 | 0.944     |57| 0.2619        | 3.0   | 78   | 0.1668          | 0.887    | 1.0 | 0.739     |58| 0.2207        | 4.0   | 104  | 0.1081          | 1.0      | 1.0 | 1.0       |59| 0.1543        | 5.0   | 130  | 0.1037          | 0.981    | 1.0 | 1.0       |60| 0.1428        | 6.0   | 156  | 0.0974          | 0.981    | 1.0 | 0.944     |61| 0.1598        | 7.0   | 182  | 0.0916          | 0.981    | 1.0 | 1.0       |62| 0.1324        | 8.0   | 208  | 0.1024          | 0.981    | 1.0 | 0.944     |63| 0.1445        | 9.0   | 234  | 0.0726          | 1.0      | 1.0 | 1.0       |64| 0.1287        | 10.0  | 260  | 0.0740          | 1.0      | 1.0 | 1.0       |65 66 67### Framework versions68 69- Transformers 4.50.070- Pytorch 2.6.0+cu12471- Datasets 3.5.072- Tokenizers 0.21.173