| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: cybersectony/phishing-email-detection-distilbert_v2.4.1 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: results |
| results: [] |
|
|
| inference_providers: |
| - name: "CustomProvider" |
| type: "python" |
| path: "provider.py" |
| description: "Runs inference using my custom backend" |
| --- |
| |
| <!-- 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. --> |
|
|
| # results |
|
|
| This model is a fine-tuned version of [cybersectony/phishing-email-detection-distilbert_v2.4.1](https://huggingface.co/cybersectony/phishing-email-detection-distilbert_v2.4.1) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0222 |
| - Accuracy: 0.9964 |
| - Precision: 0.9965 |
| - Recall: 0.9964 |
| - F1: 0.9964 |
|
|
| ## 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: 16 |
| - eval_batch_size: 16 |
| - 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_ratio: 0.1 |
| - num_epochs: 2 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 0.0213 | 1.0 | 12716 | 0.0208 | 0.9964 | 0.9964 | 0.9964 | 0.9964 | |
| | 0.0071 | 2.0 | 25432 | 0.0222 | 0.9964 | 0.9965 | 0.9964 | 0.9964 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.57.2 |
| - Pytorch 2.9.0+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.1 |
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