metadata
base_model: microsoft/codebert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: microsoft-codebert-base-finetuned-defect-cwe-group-detection
results: []
microsoft-codebert-base-finetuned-defect-cwe-group-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: 0.6195
- Accuracy: 0.7490
- Precision: 0.5725
- Recall: 0.5159
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 | Precision | Recall |
|---|---|---|---|---|---|---|
| No log | 1.0 | 462 | 0.6077 | 0.7288 | 0.6350 | 0.4460 |
| 0.7284 | 2.0 | 925 | 0.5435 | 0.7485 | 0.6418 | 0.4633 |
| 0.5295 | 3.0 | 1387 | 0.5937 | 0.7209 | 0.5285 | 0.5098 |
| 0.4242 | 4.0 | 1850 | 0.6071 | 0.7400 | 0.5543 | 0.5354 |
| 0.3509 | 4.99 | 2310 | 0.6195 | 0.7490 | 0.5725 | 0.5159 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2