| | --- |
| | base_model: neuralsentry/starencoder-git-commits-mlm |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | - f1 |
| | model-index: |
| | - name: vulnfixClassification-StarEncoder-DCMB |
| | 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. --> |
| |
|
| | # vulnfixClassification-StarEncoder-DCMB |
| |
|
| | This model is a fine-tuned version of [neuralsentry/starencoder-git-commits-mlm](https://huggingface.co/neuralsentry/starencoder-git-commits-mlm) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1797 |
| | - Accuracy: 0.9770 |
| | - Precision: 0.9841 |
| | - Recall: 0.9714 |
| | - F1: 0.9777 |
| | - Roc Auc: 0.9772 |
| |
|
| | ## 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: 0.0001 |
| | - train_batch_size: 128 |
| | - eval_batch_size: 128 |
| | - seed: 420 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 10.0 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:| |
| | | 0.2106 | 1.0 | 219 | 0.1196 | 0.9640 | 0.9654 | 0.9654 | 0.9654 | 0.9639 | |
| | | 0.086 | 2.0 | 438 | 0.0883 | 0.9736 | 0.9859 | 0.9629 | 0.9743 | 0.9740 | |
| | | 0.0477 | 3.0 | 657 | 0.0944 | 0.9729 | 0.9776 | 0.9700 | 0.9738 | 0.9730 | |
| | | 0.0269 | 4.0 | 876 | 0.1215 | 0.9723 | 0.9705 | 0.9764 | 0.9734 | 0.9721 | |
| | | 0.0146 | 5.0 | 1095 | 0.1299 | 0.9743 | 0.9854 | 0.9648 | 0.9750 | 0.9747 | |
| | | 0.0069 | 6.0 | 1314 | 0.1504 | 0.9750 | 0.9814 | 0.9703 | 0.9758 | 0.9752 | |
| | | 0.0044 | 7.0 | 1533 | 0.1653 | 0.9743 | 0.9779 | 0.9725 | 0.9752 | 0.9744 | |
| | | 0.0019 | 8.0 | 1752 | 0.1804 | 0.9756 | 0.9817 | 0.9711 | 0.9764 | 0.9758 | |
| | | 0.0008 | 9.0 | 1971 | 0.1827 | 0.9767 | 0.9839 | 0.9711 | 0.9775 | 0.9769 | |
| | | 0.0008 | 10.0 | 2190 | 0.1797 | 0.9770 | 0.9841 | 0.9714 | 0.9777 | 0.9772 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.31.0 |
| | - Pytorch 2.0.1+cu118 |
| | - Datasets 2.14.2 |
| | - Tokenizers 0.13.3 |
| |
|
