metadata
library_name: transformers
base_model: huggingface/CodeBERTa-small-v1
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: v1
results: []
v1
This model is a fine-tuned version of huggingface/CodeBERTa-small-v1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6553
- Accuracy: 0.6354
- F1 Weighted: 0.6336
- F1 Vuln: 0.5847
- Precision: 0.6339
- Recall: 0.6354
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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Weighted | F1 Vuln | Precision | Recall |
|---|---|---|---|---|---|---|---|---|
| 0.6492 | 1.0 | 682 | 0.6336 | 0.6032 | 0.6045 | 0.559 | 0.6068 | 0.6032 |
| 0.5907 | 2.0 | 1364 | 0.6094 | 0.6296 | 0.631 | 0.5942 | 0.6348 | 0.6296 |
| 0.5385 | 3.0 | 2046 | 0.6289 | 0.6442 | 0.6426 | 0.577 | 0.642 | 0.6442 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2