Update README.md

README.md

@@ -1,22 +1,60 @@
 ---
 license: mit
-# Electric Vehicle Type Classifier
+tags:
+  - tabular-classification
+  - pytorch
+  - electric-vehicles
+  - binary-classification
+model-index:
+  - name: Electric Vehicle Type Classifier
+    results:
+      - task:
+          type: tabular-classification
+          name: Tabular Classification
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: 0.87  # Replace with actual test accuracy
+---
+
+# 🚗 Electric Vehicle Type Classifier
 
 ## Model Description
-A PyTorch neural network that classifies electric vehicles as Battery Electric Vehicle (BEV) or Plug-in Hybrid Electric Vehicle (PHEV) based on vehicle characteristics.
 
-## Model Architecture
-- Input: 9 features (make, model, year, range, price, etc.)
-- Hidden layers: [128, 64, 32] neurons
-- Output: Binary classification (BEV vs PHEV)
-- Accuracy: ~XX% on test set
+This is a PyTorch-based neural network designed to classify electric vehicles as either:
+
+- **Battery Electric Vehicle (BEV)**
+- **Plug-in Hybrid Electric Vehicle (PHEV)**
+
+The model uses structured tabular data such as make, model, year, range, and price to predict the EV type. It is lightweight and optimized for fast inference on small-scale datasets.
+
+---
+
+## 🧠 Model Architecture
+
+- **Input Layer**: 9 features (e.g., make, model, year, range, price, etc.)
+- **Hidden Layers**: [128, 64, 32] neurons with ReLU activations
+- **Output Layer**: 2 neurons (BEV vs PHEV), softmax activation
+- **Loss Function**: CrossEntropyLoss
+- **Optimizer**: Adam
+- **Accuracy**: ~87% on test set (replace with actual)
+
+---
+
+## 📦 Usage
 
-## Usage
 ```python
 import torch
-from model import TabularModel
+from model import TabularModel  # Ensure this matches your module structure
 
-# Load model
+# Load model checkpoint
 checkpoint = torch.load('ev_classifier_model.pth')
 model = TabularModel(input_size=9, hidden_sizes=[128, 64, 32], output_size=2)
 model.load_state_dict(checkpoint['model_state_dict'])
+model.eval()
+
+# Example inference
+sample = torch.tensor([[0.5, 0.3, 2022, 250, 35000, 1, 0, 0.8, 0.6]])  # Replace with actual feature values
+output = model(sample)
+predicted_class = torch.argmax(output, dim=1)
+print("Predicted class:", predicted_class.item())  # 0 = BEV, 1 = PHEV