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