Add UTS2017_Bank model and dual-model usage demonstration

- Upload sklearn_model_uts2017_bank.joblib (UTS2017_Bank model, 70.96% accuracy)
- Update use_this_model.py to support both VNTC and UTS2017_Bank models
- Add separate examples for news and banking text classification
- Include interactive mode selection for both models
- Provide clear usage examples for both model types

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

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

sklearn_model_uts2017_bank.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:729e6e6d7b34dc1057275d15ce0d8475ffc1b614a13fbc0f174155e9dec4795d
+size 3029656

use_this_model.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """
 Demonstration script for using Sonar Core 1 models from Hugging Face Hub.
-Shows how to download and use the pre-trained Vietnamese text classification model.
+Shows how to download and use both VNTC and UTS2017_Bank pre-trained models.
 """
 
 from huggingface_hub import hf_hub_download
@@ -9,10 +9,22 @@ import joblib
 import numpy as np
 
 
-def load_model_from_hub():
-    """Load the pre-trained model from Hugging Face Hub"""
-    print("Downloading model from Hugging Face Hub...")
-    model_path = hf_hub_download("undertheseanlp/sonar_core_1", "sklearn_model.joblib")
+def load_model_from_hub(model_type="vntc"):
+    """Load the pre-trained model from Hugging Face Hub
+
+    Args:
+        model_type: "vntc" for news classification or "uts2017_bank" for banking text
+    """
+    if model_type == "vntc":
+        filename = "sklearn_model.joblib"
+        print("Downloading VNTC (Vietnamese News) model from Hugging Face Hub...")
+    elif model_type == "uts2017_bank":
+        filename = "sklearn_model_uts2017_bank.joblib"
+        print("Downloading UTS2017_Bank (Vietnamese Banking) model from Hugging Face Hub...")
+    else:
+        raise ValueError("model_type must be 'vntc' or 'uts2017_bank'")
+
+    model_path = hf_hub_download("undertheseanlp/sonar_core_1", filename)
     print(f"Model downloaded to: {model_path}")
 
     print("Loading model...")
@@ -23,7 +35,7 @@ def load_model_from_hub():
 def predict_vntc_examples(model):
     """Demonstrate predictions on VNTC (news) examples"""
     print("\n" + "="*60)
-    print("VIETNAMESE NEWS CLASSIFICATION EXAMPLES")
+    print("VIETNAMESE NEWS CLASSIFICATION EXAMPLES (VNTC)")
     print("="*60)
 
     # Vietnamese news examples for different categories
@@ -70,10 +82,65 @@ def predict_vntc_examples(model):
             print("-" * 60)
 
 
-def interactive_mode(model):
+def predict_uts2017_examples(model):
+    """Demonstrate predictions on UTS2017_Bank examples"""
+    print("\n" + "="*60)
+    print("VIETNAMESE BANKING TEXT CLASSIFICATION EXAMPLES (UTS2017_Bank)")
+    print("="*60)
+
+    # Vietnamese banking examples for different categories
+    examples = [
+        ("ACCOUNT", "Tôi muốn mở tài khoản tiết kiệm mới"),
+        ("CARD", "Thẻ tín dụng của tôi bị khóa, làm sao để mở lại?"),
+        ("CUSTOMER_SUPPORT", "Tôi cần hỗ trợ về dịch vụ ngân hàng"),
+        ("DISCOUNT", "Có chương trình giảm giá nào cho khách hàng không?"),
+        ("INTEREST_RATE", "Lãi suất tiết kiệm hiện tại là bao nhiều?"),
+        ("INTERNET_BANKING", "Làm thế nào để đăng ký internet banking?"),
+        ("LOAN", "Tôi muốn vay mua nhà với lãi suất ưu đãi"),
+        ("MONEY_TRANSFER", "Chi phí chuyển tiền ra nước ngoài là bao nhiều?"),
+        ("OTHER", "Tôi có câu hỏi về dịch vụ khác"),
+        ("PAYMENT", "Thanh toán hóa đơn điện nước qua ngân hàng"),
+        ("PROMOTION", "Khuyến mãi tháng này có gì hấp dẫn?"),
+        ("SAVING", "Gói tiết kiệm nào có lãi suất cao nhất?"),
+        ("SECURITY", "Bảo mật tài khoản ngân hàng như thế nào?"),
+        ("TRADEMARK", "Ngân hàng ACB có uy tín không?")
+    ]
+
+    print("Testing Vietnamese banking text classification:")
+    print("-" * 60)
+
+    for expected_category, text in examples:
+        try:
+            prediction = model.predict([text])[0]
+            probabilities = model.predict_proba([text])[0]
+            confidence = np.max(probabilities)
+
+            print(f"Text: {text}")
+            print(f"Expected: {expected_category}")
+            print(f"Predicted: {prediction}")
+            print(f"Confidence: {confidence:.3f}")
+
+            # Show top 3 predictions
+            if hasattr(model, 'classes_'):
+                top_indices = np.argsort(probabilities)[-3:][::-1]
+                print("Top 3 predictions:")
+                for i, idx in enumerate(top_indices, 1):
+                    category = model.classes_[idx]
+                    prob = probabilities[idx]
+                    print(f"  {i}. {category}: {prob:.3f}")
+
+            print("-" * 60)
+
+        except Exception as e:
+            print(f"Error predicting '{text}': {e}")
+            print("-" * 60)
+
+
+def interactive_mode(model, model_type):
     """Interactive mode for testing custom text"""
+    dataset_name = "VNTC (News)" if model_type == "vntc" else "UTS2017_Bank (Banking)"
     print("\n" + "="*60)
-    print("INTERACTIVE MODE - VIETNAMESE TEXT CLASSIFICATION")
+    print(f"INTERACTIVE MODE - {dataset_name.upper()} CLASSIFICATION")
     print("="*60)
     print("Enter Vietnamese text to classify (type 'quit' to exit):")
 
@@ -110,60 +177,89 @@ def interactive_mode(model):
             print(f"Error: {e}")
 
 
-def simple_usage_example():
-    """Show simple usage example"""
+def simple_usage_examples():
+    """Show simple usage examples for both models"""
     print("\n" + "="*60)
-    print("SIMPLE USAGE EXAMPLE")
+    print("SIMPLE USAGE EXAMPLES")
     print("="*60)
 
-    print("Code example:")
+    print("Code examples:")
     print("""
+# VNTC Model (Vietnamese News Classification)
 from huggingface_hub import hf_hub_download
 import joblib
 
-# Download and load model
-model = joblib.load(
+# Download and load VNTC model
+vntc_model = joblib.load(
     hf_hub_download("undertheseanlp/sonar_core_1", "sklearn_model.joblib")
 )
 
-# Make prediction
-text = "Việt Nam giành chiến thắng trong trận bán kết"
-prediction = model.predict([text])[0]
-probabilities = model.predict_proba([text])[0]
+# Make prediction on news text
+news_text = "Đội tuyển bóng đá Việt Nam giành chiến thắng"
+prediction = vntc_model.predict([news_text])[0]
+print(f"News category: {prediction}")
+
+# UTS2017_Bank Model (Vietnamese Banking Text Classification)
+# Download and load UTS2017_Bank model
+bank_model = joblib.load(
+    hf_hub_download("undertheseanlp/sonar_core_1", "sklearn_model_uts2017_bank.joblib")
+)
 
-print(f"Predicted category: {prediction}")
-print(f"Confidence: {max(probabilities):.3f}")
+# Make prediction on banking text
+bank_text = "Tôi muốn mở tài khoản tiết kiệm"
+prediction = bank_model.predict([bank_text])[0]
+print(f"Banking category: {prediction}")
 """)
 
 
 def main():
     """Main demonstration function"""
-    print("Sonar Core 1 - Hugging Face Hub Model Usage")
+    print("Sonar Core 1 - Dual Model Hugging Face Hub Usage")
     print("=" * 60)
 
     try:
-        # Load model from Hugging Face Hub
-        model = load_model_from_hub()
+        # Show simple usage examples
+        simple_usage_examples()
 
-        # Show simple usage example
-        simple_usage_example()
+        # Test VNTC model
+        print("\n" + "="*60)
+        print("TESTING VNTC MODEL (Vietnamese News Classification)")
+        print("="*60)
 
-        # Run prediction examples
-        predict_vntc_examples(model)
+        vntc_model = load_model_from_hub("vntc")
+        predict_vntc_examples(vntc_model)
+
+        # Test UTS2017_Bank model
+        print("\n" + "="*60)
+        print("TESTING UTS2017_BANK MODEL (Vietnamese Banking Text Classification)")
+        print("="*60)
+
+        bank_model = load_model_from_hub("uts2017_bank")
+        predict_uts2017_examples(bank_model)
 
         # Check if we're in an interactive environment
         try:
-            # Try to get input to see if we can run interactive mode
             import sys
             if hasattr(sys, 'ps1') or sys.stdin.isatty():
-                response = input("\nWould you like to try interactive mode? (y/n): ")
-                if response.lower().startswith('y'):
-                    interactive_mode(model)
+                print("\nAvailable interactive modes:")
+                print("1. VNTC (News) classification")
+                print("2. UTS2017_Bank (Banking) classification")
+
+                choice = input("\nSelect model for interactive mode (1/2) or 'n' to skip: ").strip()
+
+                if choice == "1":
+                    interactive_mode(vntc_model, "vntc")
+                elif choice == "2":
+                    interactive_mode(bank_model, "uts2017_bank")
+
         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("\nBoth models are now available on Hugging Face Hub:")
+        print("- VNTC (News): sklearn_model.joblib")
+        print("- UTS2017_Bank (Banking): sklearn_model_uts2017_bank.joblib")
 
     except ImportError:
         print("Error: huggingface_hub is required. Install with:")