| --- |
| base_model: microsoft/codebert-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| model-index: |
| - name: microsoft-codebert-base-finetuned-defect-detection |
| 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. --> |
|
|
| # microsoft-codebert-base-finetuned-defect-detection |
|
|
| This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6197 |
| - Accuracy: 0.7382 |
| - Roc Auc: 0.7394 |
| - Precision: 0.7070 |
| - Recall: 0.7924 |
|
|
| ## 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: 8 |
| - eval_batch_size: 8 |
| - seed: 4711 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 32 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Roc Auc | Precision | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|:---------:|:------:| |
| | 0.6456 | 1.0 | 996 | 0.5435 | 0.6832 | 0.6810 | 0.7151 | 0.5843 | |
| | 0.5086 | 2.0 | 1993 | 0.5373 | 0.7113 | 0.7139 | 0.6654 | 0.8227 | |
| | 0.4173 | 3.0 | 2989 | 0.5476 | 0.7289 | 0.7293 | 0.7125 | 0.7461 | |
| | 0.3543 | 4.0 | 3986 | 0.5803 | 0.7357 | 0.7369 | 0.7051 | 0.7888 | |
| | 0.3059 | 5.0 | 4980 | 0.6197 | 0.7382 | 0.7394 | 0.7070 | 0.7924 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.37.2 |
| - Pytorch 2.2.0+cu121 |
| - Datasets 2.17.1 |
| - Tokenizers 0.15.2 |
| |
