metadata
license: apache-2.0
base_model: bert-large-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: toan_phishing
results: []
toan_phishing
This model is a fine-tuned version of bert-large-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3307
- Accuracy: 0.9825
- Precision: 0.9822
- Recall: 0.9828
- False Positive Rate: 0.0178
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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | False Positive Rate |
|---|---|---|---|---|---|---|---|
| 0.355 | 1.0 | 3025 | 0.3506 | 0.9625 | 0.9753 | 0.9491 | 0.0240 |
| 0.3533 | 2.0 | 6050 | 0.3486 | 0.9641 | 0.9837 | 0.9438 | 0.0156 |
| 0.393 | 3.0 | 9075 | 0.3523 | 0.9609 | 0.9863 | 0.9348 | 0.0130 |
| 0.3311 | 4.0 | 12100 | 0.3307 | 0.9825 | 0.9822 | 0.9828 | 0.0178 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1