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--- |
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library_name: transformers |
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tags: |
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- legal |
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datasets: |
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- ealvaradob/phishing-dataset |
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language: |
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- en |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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base_model: |
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- distilbert/distilbert-base-uncased |
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--- |
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# 📧 distilbert-finetuned-phishing |
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A fine-tuned `distilbert-base-uncased` model for phishing email classification. This model is designed to distinguish between **safe** and **phishing** emails using natural language content. |
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[Colab Notebook](https://colab.research.google.com/drive/1_M5BVn9agRHUSN3wBPebfxfOpBqTJcwh?usp=sharing) |
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--- |
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## 🧪 Evaluation Results |
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The model was trained on 77,677 emails and evaluated with the following results: |
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| Metric | Value | |
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|---------------|---------| |
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| Accuracy | 0.9639 | |
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| Precision | 0.9648 | |
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| Recall | 0.9489 | |
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| F1 Score | 0.9568 | |
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| Eval Loss | 0.1326 | |
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--- |
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### ⚙️ Training Configuration |
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TrainingArguments( |
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output_dir="./hf-phishing-model", |
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evaluation_strategy="epoch", |
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save_strategy="epoch", |
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learning_rate=2e-5, |
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per_device_train_batch_size=16, |
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per_device_eval_batch_size=64, |
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num_train_epochs=3, |
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weight_decay=0.01, |
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logging_dir="./logs", |
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load_best_model_at_end=True, |
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fp16=torch.cuda.is_available(), |
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) |
