Spate219/phishing-email-detector-model

Phishing Email Detector

A transformer-based text classification model for detecting phishing emails from email text content. This model was developed as part of an AI and cybersecurity project focused on applying natural language processing to email threat detection.

Overview

Phishing remains one of the most common and damaging forms of cyberattack. This model is designed to classify email text into one of two categories:

• safe
• phishing

It can be used in educational projects, cybersecurity demonstrations, browser extensions, or lightweight email screening tools.

Model Details

• Developer: Shubh Patel
• Model Type: Text Classification
• Task: Phishing Email Detection
• Language: English
• Framework: Hugging Face Transformers
• License: MIT

Intended Use

This model is intended to help identify suspicious email content based on text alone.

Suitable use cases

• Email text screening
• Cybersecurity learning projects
• Browser extensions for pasted email analysis
• Lightweight phishing detection prototypes
• Security awareness demonstrations

Not intended for

This model should not be used as the sole basis for critical security decisions. It is not a full email security solution and does not replace enterprise-grade protections.

It is not designed for:

• attachment scanning
• sender authentication analysis
• header analysis
• standalone URL reputation analysis
• legal or forensic decision-making
• high-risk production environments without human review

How It Works

The model analyzes email text and predicts whether the content is more likely to be legitimate or phishing-related.

Typical inputs may include:

• email subject lines
• email body text
• combined subject and body content

Typical outputs:

• safe
• phishing

Citation

If you reference this project, you may cite it as:

APA

Patel, S. (2026). Phishing Email Detector [Machine learning model]. Hugging Face.

BibTeX @misc{patel2026phishingdetector, author = {Shubh Patel}, title = {Phishing Email Detector}, year = {2026}, publisher = {Hugging Face}, note = {Transformer-based text classification model for phishing email detection} }

Quick Start

Installation

pip install transformers