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devxyasir/
Email_Spam_Detection

Name: Email_Spam_Detection
Rating: 2.5 (5 reviews)

spam

spamdetection

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📩 Spam Detection Neural Network (PyTorch)

Python

PyTorch

License

A simple, real-world spam detection neural network built from scratch in PyTorch.
This model classifies SMS / short text messages as Spam or Ham (Not Spam).

The project is small, easy to understand, and perfect for learning.
You can fork it, fine-tune it, and use it as a starting point for your own projects.

🧠 Model Overview

Framework: PyTorch
Architecture: Fully Connected Neural Network (MLP)
Input: Bag-of-Words text vectors
Output: Binary classification (Spam / Ham)
Training: From scratch, small dataset (~5,500 messages)

⚠️ Note: The dataset is intentionally small to keep things simple.
You are encouraged to fork the repo, add more data, and fine-tune the model.

📂 Repository Structure


.
├── spam_nn.pth        # Trained PyTorch model weights
├── vectorizer.pkl     # CountVectorizer for text preprocessing
├── model.py           # Neural network architecture
├── config.json        # Model configuration
├── inference.py       # Inference / prediction script
├── README.md          # Documentation

🚀 Usage

Load Model

python

import torch
from model import SpamNN
import pickle

# Load model architecture + weights
model = SpamNN()
model.load_state_dict(torch.load("spam_nn.pth"))
model.eval()

# Load vectorizer
with open("vectorizer.pkl", "rb") as f:
    vectorizer = pickle.load(f)

Predict Messages

python

def predict(text):
    vec = vectorizer.transform([text]).toarray()
    vec = torch.tensor(vec, dtype=torch.float32)
    
    with torch.no_grad():
        output = model(vec)
    
    return "Spam" if output.item() > 0.35 else "Ham"

# Example
print(predict("Congratulations! You won $1000. Click now!"))

🔧 Training & Fine-Tuning

The model can be improved and fine-tuned by:

Adding more data (larger SMS datasets)
Increasing n-grams (ngram_range=(1,2))
Adjusting class weights in BCEWithLogitsLoss
Training with more epochs
Using embeddings or LSTM for contextual understanding

💡 Fork this repo and experiment freely. Make it your own!

🌟 Support the Project

If this project is helpful:

⭐ Give this repository a star 🍴 Fork it and improve it 📢 Share it with others learning PyTorch

Following and starring helps me keep releasing open-source projects!

📌 Source Code & Updates

For the full source code, training scripts, and future updates, please visit the GitHub repository linked to this project.

📜 License

This project is open-source and intended for educational purposes. MIT License applies.

🤗 Hugging Face Friendly

You can also upload this model to Hugging Face Model Hub. Include spam_nn.pth, vectorizer.pkl, config.json, and inference.py to make it ready for inference online.

devxyasir/Email_Spam_Detection