| --- |
| base_model: openai-community/roberta-large-openai-detector |
| library_name: transformers |
| license: mit |
| metrics: |
| - accuracy |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: phishing-binary-classification |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # phishing-binary-classification |
|
|
| This model is a fine-tuned version of [openai-community/roberta-large-openai-detector](https://huggingface.co/openai-community/roberta-large-openai-detector) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2813 |
| - Accuracy: 0.882 |
| - Auc: 0.954 |
|
|
| ## 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: 0.0002 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----:| |
| | 0.5501 | 1.0 | 1250 | 0.4015 | 0.818 | 0.927 | |
| | 0.4611 | 2.0 | 2500 | 0.3605 | 0.842 | 0.923 | |
| | 0.4445 | 3.0 | 3750 | 0.3759 | 0.827 | 0.939 | |
| | 0.413 | 4.0 | 5000 | 0.3058 | 0.866 | 0.946 | |
| | 0.4152 | 5.0 | 6250 | 0.3554 | 0.837 | 0.953 | |
| | 0.4086 | 6.0 | 7500 | 0.2908 | 0.874 | 0.949 | |
| | 0.4057 | 7.0 | 8750 | 0.3338 | 0.853 | 0.946 | |
| | 0.3966 | 8.0 | 10000 | 0.2807 | 0.88 | 0.953 | |
| | 0.3961 | 9.0 | 11250 | 0.2836 | 0.878 | 0.952 | |
| | 0.3962 | 10.0 | 12500 | 0.2813 | 0.882 | 0.954 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.45.1 |
| - Pytorch 2.4.0 |
| - Datasets 3.0.1 |
| - Tokenizers 0.20.0 |
|
|
