CodeDetect-RoBERTa-T2
This model is a fine-tuned version of FacebookAI/roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2475
- Accuracy: 0.6348
- F1: 0.5913
- Precision: 0.5843
- Recall: 0.6057
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 |
|---|---|---|---|---|---|---|---|
| 1.1223 | 1.0 | 13235 | 1.1464 | 0.5887 | 0.5454 | 0.5491 | 0.5840 |
| 0.9116 | 2.0 | 26470 | 1.0812 | 0.6198 | 0.5809 | 0.5791 | 0.6149 |
| 0.7414 | 3.0 | 39705 | 1.1232 | 0.6290 | 0.5835 | 0.5782 | 0.6064 |
| 0.5937 | 4.0 | 52940 | 1.1859 | 0.6312 | 0.5859 | 0.5796 | 0.6029 |
| 0.4897 | 5.0 | 66175 | 1.2475 | 0.6348 | 0.5913 | 0.5843 | 0.6057 |
Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cu130
- Datasets 4.0.0
- Tokenizers 0.22.2