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---
license: apache-2.0
datasets:
- cybersectony/PhishingEmailDetection
library_name: transformers
language:
- en
base_model:
- distilbert/distilbert-base-uncased
tags:
- Phishing
- Email
- URL
- Detection
---
# A distilBERT based Phishing Email Detection Model
## Model Overview
This model is specifically fine-tuned for detecting phishing emails using the Hugging Face Trainer API.
## Key Specifications
- __Base Architecture:__ DistilBERT
- __Task:__ Multilabel Classification
- __Fine-tuning Framework:__ Hugging Face Trainer API
- __Training Duration:__ 3 epochs
## Performance Metrics
- __F1-score:__ 97.717
- __Accuracy:__ 97.716
- __Precision:__ 97.736
- __Recall:__ 97.717
## Dataset Details
This model was trained using a [Phishing Email Detection Dataset](https://huggingface.co/datasets/cybersectony/PhishingEmailDetection).
## Usage Guide
## Installation
```bash
pip install transformers
pip install torch
```
## Quick Start
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("your-username/model-name")
model = AutoModelForSequenceClassification.from_pretrained("your-username/model-name")
def predict_email(email_text):
# Preprocess and tokenize
inputs = tokenizer(
email_text,
return_tensors="pt",
truncation=True,
max_length=512
)
# Get prediction
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
# Get probabilities for each class
probs = predictions[0].tolist()
# Create labels dictionary
labels = {
"legitimate_email": probs[0],
"phishing_url": probs[1],
"legitimate_url": probs[2],
"phishing_url_alt": probs[3]
}
# Determine the most likely classification
max_label = max(labels.items(), key=lambda x: x[1])
return {
"prediction": max_label[0],
"confidence": max_label[1],
"all_probabilities": labels
}
```
## Example Usage
```python
# Example usage
email = """
Dear User,
Your account security needs immediate attention. Please verify your credentials.
Click here: http://suspicious-link.com
"""
result = predict_email(email)
print(f"Prediction: {result['prediction']}")
print(f"Confidence: {result['confidence']:.2%}")
print("\nAll probabilities:")
for label, prob in result['all_probabilities'].items():
print(f"{label}: {prob:.2%}")
``` |