Update use_this_model.py for Pulse Core 1 banking aspect sentiment analysis

- Updated script to focus on Vietnamese banking aspect sentiment analysis
- Changed repository from undertheseanlp/sonar_core_1 to undertheseanlp/pulse_core_1
- Updated model filename to uts2017_sentiment_20250928_122636.joblib
- Removed VNTC news classification examples
- Added comprehensive banking aspect sentiment examples with expected outcomes
- Updated interactive mode for aspect-sentiment analysis
- Added proper aspect#sentiment format demonstrations
- Tested successfully with Hugging Face Hub model download

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

use_this_model.py

@@ -0,0 +1,213 @@
+#!/usr/bin/env python3
+"""
+Demonstration script for using Pulse Core 1 - Vietnamese Banking Aspect Sentiment Analysis from Hugging Face Hub.
+Shows how to download and use the pre-trained aspect sentiment model.
+"""
+
+from huggingface_hub import hf_hub_download
+import joblib
+
+
+def predict_text(model, text):
+    """Make prediction on a single text (consistent with inference.py)"""
+    try:
+        probabilities = model.predict_proba([text])[0]
+
+        # Get top 3 predictions sorted by probability
+        top_indices = probabilities.argsort()[-3:][::-1]
+        top_predictions = []
+        for idx in top_indices:
+            category = model.classes_[idx]
+            prob = probabilities[idx]
+            top_predictions.append((category, prob))
+
+        # The prediction should be the top category
+        prediction = top_predictions[0][0]
+        confidence = top_predictions[0][1]
+
+        return prediction, confidence, top_predictions
+    except Exception as e:
+        print(f"Error making prediction: {e}")
+        return None, 0, []
+
+
+def load_model_from_hub():
+    """Load the pre-trained Pulse Core 1 banking aspect sentiment model from Hugging Face Hub"""
+    filename = "uts2017_sentiment_20250928_122636.joblib"
+    print("Downloading Pulse Core 1 (Vietnamese Banking Aspect Sentiment) model from Hugging Face Hub...")
+
+    model_path = hf_hub_download("undertheseanlp/pulse_core_1", filename)
+    print(f"Model downloaded to: {model_path}")
+
+    print("Loading model...")
+    model = joblib.load(model_path)
+    print(f"Model loaded successfully. Classes: {len(model.classes_)} aspect-sentiment combinations")
+    return model
+
+
+def predict_banking_examples(model):
+    """Demonstrate predictions on Vietnamese banking aspect sentiment examples"""
+    print("\n" + "="*60)
+    print("VIETNAMESE BANKING ASPECT SENTIMENT ANALYSIS EXAMPLES")
+    print("="*60)
+
+    # Vietnamese banking examples with expected aspect-sentiment combinations
+    examples = [
+        ("CUSTOMER_SUPPORT#negative", "Dịch vụ chăm sóc khách hàng rất tệ"),
+        ("CUSTOMER_SUPPORT#positive", "Nhân viên hỗ trợ rất nhiệt tình"),
+        ("TRADEMARK#positive", "Ngân hàng ACB có uy tín tốt"),
+        ("TRADEMARK#negative", "Thương hiệu ngân hàng này không đáng tin cậy"),
+        ("LOAN#positive", "Lãi suất vay mua nhà rất ưu đãi"),
+        ("LOAN#negative", "Lãi suất vay quá cao, không chấp nhận được"),
+        ("INTEREST_RATE#negative", "Lãi suất tiết kiệm thấp quá"),
+        ("INTEREST_RATE#positive", "Lãi suất gửi tiết kiệm khá hấp dẫn"),
+        ("CARD#negative", "Thẻ tín dụng bị khóa không rõ lý do"),
+        ("CARD#positive", "Thẻ ATM rất tiện lợi khi sử dụng"),
+        ("INTERNET_BANKING#negative", "Internet banking hay bị lỗi"),
+        ("INTERNET_BANKING#positive", "Ứng dụng ngân hàng điện tử dễ sử dụng"),
+        ("MONEY_TRANSFER#negative", "Phí chuyển tiền quá đắt"),
+        ("PROMOTION#positive", "Chương trình khuyến mãi rất hấp dẫn"),
+        ("SECURITY#positive", "Bảo mật tài khoản rất tốt")
+    ]
+
+    print("Testing Vietnamese banking aspect sentiment analysis:")
+    print("-" * 60)
+
+    for expected_aspect_sentiment, text in examples:
+        try:
+            prediction, confidence, top_predictions = predict_text(model, text)
+
+            if prediction:
+                print(f"Text: {text}")
+                print(f"Expected: {expected_aspect_sentiment}")
+                print(f"Predicted: {prediction}")
+                print(f"Confidence: {confidence:.3f}")
+
+                # Show top 3 predictions
+                print("Top 3 predictions:")
+                for i, (category, prob) in enumerate(top_predictions, 1):
+                    print(f"  {i}. {category}: {prob:.3f}")
+
+            print("-" * 60)
+
+        except Exception as e:
+            print(f"Error predicting '{text}': {e}")
+            print("-" * 60)
+
+
+def interactive_mode(model):
+    """Interactive mode for testing custom banking text"""
+    print("\n" + "="*60)
+    print("INTERACTIVE MODE - VIETNAMESE BANKING ASPECT SENTIMENT ANALYSIS")
+    print("="*60)
+    print("Enter Vietnamese banking text to analyze aspect and sentiment (type 'quit' to exit):")
+
+    while True:
+        try:
+            user_input = input("\nText: ").strip()
+
+            if user_input.lower() in ['quit', 'exit', 'q']:
+                break
+
+            if not user_input:
+                continue
+
+            prediction, confidence, top_predictions = predict_text(model, user_input)
+
+            if prediction:
+                print(f"Predicted aspect-sentiment: {prediction}")
+                print(f"Confidence: {confidence:.3f}")
+
+                # Show top 3 predictions
+                print("Top 3 predictions:")
+                for i, (category, prob) in enumerate(top_predictions, 1):
+                    print(f"  {i}. {category}: {prob:.3f}")
+
+        except KeyboardInterrupt:
+            print("\nExiting...")
+            break
+        except Exception as e:
+            print(f"Error: {e}")
+
+
+def simple_usage_examples():
+    """Show simple usage examples for HuggingFace Hub models"""
+    print("\n" + "="*60)
+    print("HUGGINGFACE HUB USAGE EXAMPLES")
+    print("="*60)
+
+    print("Code examples:")
+    print("""
+# Pulse Core 1 Model (Vietnamese Banking Aspect Sentiment Analysis)
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download and load Pulse Core 1 model from HuggingFace Hub
+model = joblib.load(
+    hf_hub_download("undertheseanlp/pulse_core_1", "uts2017_sentiment_20250928_122636.joblib")
+)
+
+# Make prediction on banking text
+bank_text = "Tôi muốn mở tài khoản tiết kiệm"
+prediction = model.predict([bank_text])[0]
+print(f"Aspect-Sentiment: {prediction}")
+
+# For detailed predictions with confidence scores
+probabilities = model.predict_proba([bank_text])[0]
+top_indices = probabilities.argsort()[-3:][::-1]
+for idx in top_indices:
+    category = model.classes_[idx]
+    prob = probabilities[idx]
+    print(f"{category}: {prob:.3f}")
+
+# For local file inference, use inference.py instead
+""")
+
+
+def main():
+    """Main demonstration function"""
+    print("Pulse Core 1 - Vietnamese Banking Aspect Sentiment Analysis")
+    print("=" * 60)
+
+    try:
+        # Show simple usage examples
+        simple_usage_examples()
+
+        # Test banking aspect sentiment model
+        print("\n" + "="*60)
+        print("TESTING PULSE CORE 1 MODEL (Vietnamese Banking Aspect Sentiment Analysis)")
+        print("="*60)
+
+        model = load_model_from_hub()
+        predict_banking_examples(model)
+
+        # Check if we're in an interactive environment
+        try:
+            import sys
+            if hasattr(sys, 'ps1') or sys.stdin.isatty():
+                choice = input("\nEnter interactive mode for banking aspect sentiment analysis? (y/n): ").strip().lower()
+
+                if choice in ['y', 'yes']:
+                    interactive_mode(model)
+
+        except (EOFError, OSError):
+            print("\nInteractive mode not available in this environment.")
+            print("Run this script in a regular terminal to use interactive mode.")
+
+        print("\nDemonstration complete!")
+        print("\nPulse Core 1 model is available on Hugging Face Hub:")
+        print("- Repository: undertheseanlp/pulse_core_1")
+        print("- Model file: uts2017_sentiment_20250928_122636.joblib")
+        print("- Task: Vietnamese Banking Aspect Sentiment Analysis")
+        print("- Classes: 35+ aspect-sentiment combinations")
+
+    except ImportError:
+        print("Error: huggingface_hub is required. Install with:")
+        print("  pip install huggingface_hub")
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        print("\nMake sure you have internet connection and try again.")
+
+
+if __name__ == "__main__":
+    main()