README.md

---
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
```

```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_phishing(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)
    
    return {
        "is_phishing": bool(predictions[0][1] > 0.5),
        "confidence": float(predictions[0][1])
    }

# Example usage
email = "Your email text here..."
result = predict_phishing(email)
print(f"Is Phishing: {result['is_phishing']}")
print(f"Confidence: {result['confidence']:.2%}")
```