CodeGenDetect-Bert-T1
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1971
- Accuracy: 0.9431
- F1: 0.9430
- Precision: 0.9434
- Recall: 0.9427
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: 64
- 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
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.1703 | 1.0 | 16897 | 0.1624 | 0.9366 | 0.9365 | 0.9364 | 0.9368 |
| 0.1393 | 2.0 | 33794 | 0.1490 | 0.9433 | 0.9432 | 0.9432 | 0.9432 |
| 0.1116 | 3.0 | 50691 | 0.1565 | 0.9448 | 0.9447 | 0.9448 | 0.9446 |
| 0.0910 | 4.0 | 67588 | 0.1745 | 0.9434 | 0.9433 | 0.9435 | 0.9432 |
| 0.0704 | 5.0 | 84485 | 0.1971 | 0.9431 | 0.9430 | 0.9434 | 0.9427 |
Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cu130
- Datasets 4.0.0
- Tokenizers 0.22.2