metadata
base_model: microsoft/codebert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: microsoft-codebert-base-finetuned-defect-detection
results: []
microsoft-codebert-base-finetuned-defect-detection
This model is a fine-tuned version of 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