Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+tags:
+- email
+- spam
+- spamdetection
+---
+ 
+ # 📩 Spam Detection Neural Network (PyTorch)
+
+[![Python](https://img.shields.io/badge/python-3.10-blue.svg)](https://www.python.org/)
+[![PyTorch](https://img.shields.io/badge/pytorch-2.1-red.svg)](https://pytorch.org/)
+[![License](https://img.shields.io/badge/license-MIT-green.svg)](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**.