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Benign10MGPT2_fromP_BFall_10KGen_toP_0.75
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: 0.1046
- Accuracy: 0.9898
- F1: 0.8806
- Precision: 0.9952
- Recall: 0.7896
- Roc Auc Score: 0.8947
- Tpr At Fpr 0.01: 0.7606
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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|---|---|---|---|---|---|---|---|---|---|
| 0.0104 | 1.0 | 13125 | 0.0568 | 0.9869 | 0.8415 | 0.9964 | 0.7282 | 0.8640 | 0.7054 |
| 0.0078 | 2.0 | 26250 | 0.0722 | 0.9871 | 0.8440 | 0.9932 | 0.7338 | 0.8668 | 0.6516 |
| 0.0047 | 3.0 | 39375 | 0.0675 | 0.9900 | 0.8833 | 0.9913 | 0.7966 | 0.8981 | 0.7312 |
| 0.0011 | 4.0 | 52500 | 0.0811 | 0.9904 | 0.8888 | 0.9936 | 0.804 | 0.9019 | 0.7698 |
| 0.0 | 5.0 | 65625 | 0.1046 | 0.9898 | 0.8806 | 0.9952 | 0.7896 | 0.8947 | 0.7606 |
Framework versions
- Transformers 4.29.1
- Pytorch 1.9.0+cu111
- Datasets 2.10.1
- Tokenizers 0.13.2
