Upload example_usage.py with huggingface_hub

example_usage.py

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+"""
+Example usage of Paraformer model for legal document retrieval.
+
+This is a simplified implementation. For full functionality and customization,
+visit: https://github.com/nguyenthanhasia/paraformer
+
+License: Research purposes - free to use. Commercial purposes - at your own risk.
+"""
+
+from transformers import AutoModel
+import torch
+
+def main():
+    print("Paraformer Model - Example Usage")
+    print("=" * 50)
+    
+    # Load the model
+    print("Loading model from Hugging Face Hub...")
+    model = AutoModel.from_pretrained('nguyenthanhasia/paraformer', trust_remote_code=True)
+    print("✓ Model loaded successfully")
+    
+    # Example 1: Single query-article pair
+    print("\n1. Single Query-Article Example:")
+    print("-" * 30)
+    
+    query = "What are the legal requirements for contract formation?"
+    article = [
+        "A contract is a legally binding agreement between two or more parties.",
+        "For a contract to be valid, it must have offer, acceptance, and consideration.",
+        "The parties must have legal capacity to enter into the contract."
+    ]
+    
+    print(f"Query: {query}")
+    print(f"Article: {len(article)} sentences")
+    
+    # Get relevance score
+    relevance_score = model.get_relevance_score(query, article)
+    print(f"Relevance Score: {relevance_score:.4f}")
+    
+    # Get binary prediction
+    prediction = model.predict_relevance(query, article)
+    print(f"Binary Output: {prediction} (0=lower similarity, 1=higher similarity)")
+    
+    # Example 2: Batch processing
+    print("\n2. Batch Processing Example:")
+    print("-" * 30)
+    
+    queries = [
+        "What constitutes a valid contract?",
+        "How can employment be terminated?",
+        "What are the requirements for copyright protection?"
+    ]
+    
+    articles = [
+        ["A contract requires offer, acceptance, and consideration.", "All parties must have legal capacity."],
+        ["Employment can be terminated by mutual agreement.", "Notice period must be respected."],
+        ["Copyright protects original works of authorship.", "The work must be fixed in a tangible medium."]
+    ]
+    
+    # Forward pass for batch processing
+    outputs = model.forward(
+        query_texts=queries,
+        article_texts=articles,
+        return_dict=True
+    )
+    
+    # Get probabilities and predictions
+    probabilities = torch.softmax(outputs.logits, dim=-1)
+    predictions = torch.argmax(outputs.logits, dim=-1)
+    
+    for i, (query, article) in enumerate(zip(queries, articles)):
+        score = probabilities[i, 1].item()
+        pred = predictions[i].item()
+        print(f"\nQuery {i+1}: {query}")
+        print(f"  Similarity Score: {score:.4f}")
+        print(f"  Binary Output: {pred}")
+    
+    # Example 3: Accessing attention weights
+    print("\n3. Attention Weights Example:")
+    print("-" * 30)
+    
+    query = "What is required for a valid contract?"
+    article = [
+        "A contract is an agreement between parties.",
+        "It must have offer and acceptance.",
+        "Consideration is also required.",
+        "The weather is nice today."  # Irrelevant sentence
+    ]
+    
+    outputs = model.forward(
+        query_texts=[query],
+        article_texts=[article],
+        return_dict=True
+    )
+    
+    if outputs.attentions is not None:
+        attention_weights = outputs.attentions[0, 0]  # First batch, first query
+        print(f"Query: {query}")
+        print("Attention weights per sentence:")
+        for i, (sentence, weight) in enumerate(zip(article, attention_weights)):
+            print(f"  Sentence {i+1}: {weight:.4f} - {sentence}")
+    
+    print("\n" + "=" * 50)
+    print("Important Notes:")
+    print("- Scores represent similarity in learned feature space, not absolute relevance")
+    print("- This is a simplified implementation for easy integration")
+    print("- For full functionality: https://github.com/nguyenthanhasia/paraformer")
+    print("- Research use: free | Commercial use: at your own risk")
+
+if __name__ == "__main__":
+    main()
+