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README.md

@@ -1,44 +1,44 @@
-# 🛡️ Phishing Domain Classifier (FastText)
-
-This repository contains a **FastText-based supervised classification model** trained to detect phishing domains.
-
-## 🚀 Model Overview
-
-- **Algorithm**: Facebook's [fastText](https://fasttext.cc/)
-- **Task**: Binary classification (`phishing` vs `clean`)
-- **Input format**: Domain names (e.g., `paypal-login.su`)
-- **Labels**: `__label__phishing`, `__label__clean`
-- **Features**:
-  - Fast and lightweight
-  - Trained with `wordNgrams = 2`
-  - 10 epochs
-
 ---
+tags:
+- fasttext
+- phishing
+- domain-classification
+license: mit
+language:
+- en
+---
+
+# Phishing Detection Model (FastText)
 
-## 📂 Files Included
+This is a lightweight FastText model trained to classify domain names as either phishing or clean. It uses supervised learning with `wordNgrams=2` for better n-gram feature coverage.
 
-```text
-phishing_model.bin         # Trained model file (binary format)
-phishing_model.vec         # Vector embeddings
-fasttext_train.txt         # Training data file
-README.md                  # Documentation
+## Usage
 
+```bash
+# Predict a single domain
+echo "carreeffoursa.site" | ./fasttext predict phishing_model.bin -
+```
 
-🔧 Installation
+## Training Info
 
-Option 1: From Source
+- Framework: FastText
+- Labels: `__label__phishing`, `__label__clean`
+- Epochs: 10
+- Learning rate: 0.5
+- wordNgrams: 2
 
-git clone https://github.com/facebookresearch/fastText.git
-cd fastText
-mkdir build && cd build
-cmake ..
-make
+## Example
 
-Option 2: Using pip (limited support)
+Input:
+```
+carreeffoursa.site
+```
 
-pip install fasttext
+Output:
+```
+__label__phishing
+```
 
-⚠️ The pip version does not support all features. Compiling from source is recommended.
+## License
 
-Usage
-echo "carreeffoursa.site" | ./fasttext predict phishing_model.bin -
+MIT