usage_example.py

from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

# Load fine-tuned model
model = SentenceTransformer('ThanhLe0125/e5-small-math')

print("🧪 Testing MRR-optimized fine-tuned model:")
print("="*50)

# Example: Vietnamese math question
query = "query: Định nghĩa hàm số đồng biến"
chunks = [
    "passage: Hàm số đồng biến trên khoảng (a;b) là hàm số mà với mọi x1 < x2 thì f(x1) < f(x2)",
    "passage: Ví dụ: Tìm khoảng đồng biến của hàm số y = x^2 - 2x + 1", 
    "passage: Phương trình bậc hai ax^2 + bx + c = 0 có delta = b^2 - 4ac",
    "passage: Tính đạo hàm của hàm số đa thức",
    "passage: Giới hạn của dãy số"
]

# Encode and rank
query_emb = model.encode([query])
chunk_embs = model.encode(chunks)
similarities = cosine_similarity(query_emb, chunk_embs)[0]
ranked_indices = similarities.argsort()[::-1]

# Display results
print("🎯 MRR-Optimized Rankings:")
chunk_types = ["CORRECT", "RELATED", "IRRELEVANT", "IRRELEVANT", "IRRELEVANT"]
for rank, idx in enumerate(ranked_indices, 1):
    print(f"Rank {rank}: {chunk_types[idx]:>10} (Score: {similarities[idx]:.4f})")
    print(f"        {chunks[idx][:70]}...")
    print()

# Calculate metrics for this query
correct_rank = None
for rank, idx in enumerate(ranked_indices, 1):
    if idx == 0:  # First chunk is correct
        correct_rank = rank
        break

if correct_rank:
    mrr = 1.0 / correct_rank
    recall_at_k = {}
    for k in [1, 2, 3, 4, 5]:
        recall_at_k[k] = 1 if correct_rank <= k else 0
    
    print(f"📊 Query Metrics:")
    print(f"   MRR: {mrr:.4f} (correct chunk at rank #{correct_rank})")
    print(f"   Recall@1: {recall_at_k[1]} | Recall@2: {recall_at_k[2]} | Recall@3: {recall_at_k[3]}")
    print(f"   Recall@4: {recall_at_k[4]} | Recall@5: {recall_at_k[5]}")
    
    if correct_rank == 1:
        print("   🌟 PERFECT! Correct chunk at rank #1!")
    elif correct_rank <= 2:
        print("   🎯 EXCELLENT! Correct chunk in top 2!")
    elif correct_rank <= 3:
        print("   👍 GOOD! Correct chunk in top 3!")
    else:
        print("   📈 Could be better - but still found the answer!")

print("\n" + "="*50)
print("💡 Fine-tuning Benefits:")
print("   ✅ Pushes correct chunks to rank #1")
print("   ✅ Reduces inference cost (need fewer chunks)")
print("   ✅ Improves user experience (instant answers)")
print("   ✅ Specialized for Vietnamese mathematics")