phishing-email-detection-distilbert_v2.4.1 EU AI Act Compliance Profile

A distilBERT based Phishing Email Detection Model

Model Overview

This model is based on DistilBERT and has been fine-tuned for multilabel classification of Emails and URLs as safe or potentially phishing.

Key Specifications

• Base Architecture: DistilBERT
• Task: Multilabel Classification
• Fine-tuning Framework: Hugging Face Trainer API
• Training Duration: 3 epochs

Performance Metrics

• Accuracy: 99.58
• F1-score: 99.579
• Precision: 99.583
• Recall: 99.58

Dataset Details

The model was trained on a custom dataset of Emails and URLs labeled as legitimate or phishing. The dataset is available at cybersectony/PhishingEmailDetectionv2.0 on the Hugging Face Hub.

Usage Guide

Installation

pip install transformers
pip install torch

Quick Start

from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("cybersectony/phishing-email-detection-distilbert_v2.4.1")
import torch

# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("cybersectony/phishing-email-detection-distilbert_v2.4.1")

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])
    
    retu

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Base Model