ai-content-detector-bert
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2676
- Accuracy: 0.9415
- Auc: 0.9913
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: 5e-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
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc |
|---|---|---|---|---|---|
| 0.1651 | 1.0 | 250 | 0.2075 | 0.936 | 0.9867 |
| 0.0927 | 2.0 | 500 | 0.1942 | 0.943 | 0.9920 |
| 0.0159 | 3.0 | 750 | 0.2676 | 0.9415 | 0.9913 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2