ILDC-Appeal-Prediction-Model

ILDC Appeal Prediction Model ⚖️

Model Description

This is a fine-tuned Machine Learning model trained to predict the outcome of appeal cases in the Indian Supreme Court. It takes the text of a court judgment as input and predicts whether the appeal will be Accepted or Rejected.

• Task: Binary Text Classification (Legal Judgment Prediction)
• Dataset: ILDC (Indian Legal Documents Corpus)
• Architecture: [Insert Architecture, e.g., InLegalBERT / Bi-LSTM / XLNet]
• Input: Legal case text (pre-processed/cleaned)
• Output: Class 0 (Rejected) or Class 1 (Accepted)

📊 Performance Metrics

• Accuracy: [INSERT SCORE, e.g., 78.5%]
• F1-Score: [INSERT SCORE, e.g., 0.76]
• Loss: [INSERT LOSS, e.g., 0.45]

💻 How to Use

1. Load the Model (PyTorch)

Use the following code to load the entire_model.pt file into your Python environment.

import torch
# If using a specific architecture class, import it first
# from model_architecture import MyCustomModel

# Path to the downloaded model file
model_path = "entire_model.pt"

# Load the model
# Option A: If you saved the entire model
model = torch.load(model_path)

# Option B: If you saved only state_dict (Recommended)
# model = MyCustomModel()
# model.load_state_dict(torch.load(model_path))

model.eval()
print("Model loaded successfully!")