ThanhLe0125/e5-math-smart-binary

E5-Math-Vietnamese-Smart-Binary: Intelligent 1:2 Ratio Training

Model Overview

Fine-tuned E5-base model optimized với Smart Binary Training approach cho Vietnamese mathematics:

• 🎯 Smart 1:2 Ratio: 1 Positive : 1 Hard Negative : 1 Easy Negative
• 🧠 Intelligent Negative Selection: Hard negatives từ related chunks, easy negatives từ irrelevant chunks
• ⚖️ Balanced Precision/Recall: Tối ưu cho better user experience
• ⏰ Loss-based Early Stopping: Prevents overfitting với validation loss monitoring

Performance Summary

Training Results

• Training Strategy: smart_binary_1_to_2_ratio
• Best Validation Loss: 0.33194339065103007
• Training Epochs: 5
• Early Stopping: ❌ Not triggered
• Training Time: 1528.63378572464

Test Performance 🌟 EXCELLENT

Outstanding balanced performance với smart binary approach

Metric	Base E5	Smart Binary FT	Improvement	% Change
MRR	0.9112	0.9526	+0.0414	+4.5%
Accuracy@1	0.8248	0.9051	+0.0803	+9.7%
Hit@1	0.8248	0.9051	+0.0803	+9.7%
Hit@3	1.0000	1.0000	+0.0000	+0.0%
Hit@5	1.0000	1.0000	+0.0000	+0.0%

Total Test Queries: 137

Smart Binary Training Innovation

🎯 Intelligent 1:2 Ratio Strategy

Traditional Approach (1:3 ratio):
❌ 1 Correct : 3 Random Negatives
❌ Often too aggressive, hurts recall
❌ No intelligence in negative selection

Smart Binary Approach (1:2 ratio):
✅ 1 Correct : 1 Hard Negative (from related) : 1 Easy Negative (from irrelevant)
✅ Better precision/recall balance
✅ Intelligent negative selection
✅ Enhanced user experience

🧠 Intelligent Negative Selection

• Hard Negatives: Randomly selected từ related chunks (educational content)

  • Forces model to learn fine-grained distinctions
  • Improves semantic understanding
  • Reduces false positives on similar content

• Easy Negatives: Randomly selected từ irrelevant chunks

  • Maintains clear boundaries
  • Prevents overgeneralization
  • Ensures robust performance

⚖️ Precision/Recall Balance Benefits

Previous 1:3 Ratio Results:
- High Precision (Accuracy@1: ~76%)
- Lower Recall (Hit@3: ~92%)
- User frustration với missed relevant results

Smart Binary 1:2 Ratio Results:
- Maintained Precision (Accuracy@1: ~77%+)
- Improved Recall (Hit@3: ~95%+)
- Better overall user satisfaction

Usage

Basic Usage

from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

# Load smart binary trained model
model = SentenceTransformer('ThanhLe0125/e5-math-smart-binary')

# ⚠️ CRITICAL: Must use E5 prefixes
query = "query: Cách tính đạo hàm của hàm hợp"
chunks = [
    "passage: Đạo hàm hàm hợp: (f(g(x)))' = f'(g(x)) × g'(x)",     # Should rank #1
    "passage: Ví dụ tính đạo hàm hàm hợp với x²+1",                 # Related (hard negative during training)
    "passage: Định nghĩa tích phân xác định trên đoạn [a,b]"        # Irrelevant (easy negative)
]

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

# Smart binary model provides balanced ranking
ranked_indices = similarities.argsort()[::-1]
for rank, idx in enumerate(ranked_indices, 1):
    print(f"Rank {rank}: Score {similarities[idx]:.4f} - {chunks[idx][:60]}...")

# Expected with smart binary training:
# Rank 1: Correct answer (score ~0.87+)
# Rank 2: Related content (score ~0.65+) 
# Rank 3: Irrelevant content (score ~0.20+)

Production-Ready Retrieval

class SmartBinaryMathRetriever:
    def __init__(self):
        self.model = SentenceTransformer('ThanhLe0125/e5-math-smart-binary')
    
    def retrieve_balanced(self, query, chunks, top_k=5):
        """Balanced retrieval với smart binary model"""
        # Format inputs
        formatted_query = f"query: {query}" if not query.startswith("query:") else query
        formatted_chunks = [f"passage: {chunk}" if not chunk.startswith("passage:") else chunk 
                          for chunk in chunks]
        
        # Encode
        query_emb = self.model.encode([formatted_query])
        chunk_embs = self.model.encode(formatted_chunks)
        similarities = cosine_similarity(query_emb, chunk_embs)[0]
        
        # Smart binary ranking
        top_indices = similarities.argsort()[::-1][:top_k]
        
        results = []
        for rank, idx in enumerate(top_indices):
            # Smart binary model provides confidence scores
            confidence = "high" if similarities[idx] > 0.8 else "medium" if similarities[idx] > 0.5 else "low"
            
            results.append({
                'chunk': chunks[idx],
                'similarity': float(similarities[idx]),
                'rank': rank + 1,
                'confidence': confidence
            })
        
        return results

# Usage
retriever = SmartBinaryMathRetriever()
results = retriever.retrieve_balanced(
    "Công thức tính diện tích hình tròn", 
    math_chunks,
    top_k=3
)

# Smart binary ensures balanced precision/recall
for result in results:
    print(f"Rank {result['rank']}: {result['confidence']} confidence")
    print(f"Score: {result['similarity']:.4f} - {result['chunk'][:50]}...")

Training Methodology

Smart Binary Data Composition

Training Strategy:
- Total Examples: ~2000 triplets
- Ratio: 1 Positive : 2 Negatives
- Hard Negatives: 50% (from related educational content)
- Easy Negatives: 50% (from irrelevant content)
- Target: Balanced precision/recall performance

Training Configuration

Smart Binary Config:
    base_model = "intfloat/multilingual-e5-base"
    training_approach = "smart_binary_1_to_2_ratio"
    negative_selection = "intelligent_hard_easy_split"
    train_batch_size = 4
    learning_rate = 2e-5
    max_epochs = 20
    early_stopping = "loss_based_patience_5"
    loss_function = "MultipleNegativesRankingLoss"

Evaluation Methodology

1. Smart Binary Training: 1:2 ratio với intelligent negative selection
2. Loss-based Early Stopping: Prevents overfitting
3. Comprehensive Testing: 3-level hierarchy restoration for evaluation
4. Balanced Metrics: MRR, Accuracy@1, Hit@K for complete assessment

Key Advantages

🎯 Better User Experience

• Maintained Precision: High-quality top results
• Improved Recall: Better coverage of relevant content
• Balanced Performance: Neither too strict nor too lenient

🧠 Intelligent Training

• Smart Negatives: Hard negatives teach fine distinctions
• Efficient Ratio: 1:2 optimal cho Vietnamese math content
• Loss Monitoring: Comprehensive training insights

⚡ Production Benefits

Smart Binary Model Benefits:
✅ 95%+ of correct answers trong top 3 results
✅ 77%+ precision cho top-1 results
✅ Reduced user frustration với missed content
✅ Better educational outcome
✅ Efficient inference (fewer API calls needed)

Model Architecture

• Base: intfloat/multilingual-e5-base (multilingual support)
• Fine-tuning: Smart binary approach với intelligent negatives
• Max Sequence Length: 256 tokens
• Output Dimension: 768
• Similarity Metric: Cosine similarity
• Training Loss: MultipleNegativesRankingLoss

Use Cases

• ✅ Vietnamese Math Education: Balanced retrieval cho học sinh
• ✅ Tutoring Systems: Intelligent content recommendation
• ✅ Knowledge Base: Efficient mathematical concept search
• ✅ Q&A Platforms: Balanced precision/recall cho user satisfaction
• ✅ Content Management: Smart categorization và retrieval

Performance Insights

Smart Binary vs Traditional Approaches

Comparison với other training approaches:

1:3 Traditional Ratio:
- High precision, lower recall
- User frustration với missed content
- Overly strict ranking

1:1 Equal Ratio:
- Good recall, lower precision  
- Too many irrelevant results
- User confusion

Smart Binary 1:2:
- Balanced precision/recall ✅
- Optimal user experience ✅
- Intelligent negative selection ✅

Limitations

• Vietnamese-optimized: Best performance on Vietnamese mathematical content
• Domain-specific: Optimized cho educational mathematics
• E5 format dependency: Requires "query:" và "passage:" prefixes
• Sequence length: 256 token limit

Future Enhancements

• Ensemble với larger models cho even better performance
• Multi-task learning với additional mathematical domains
• Adaptive ratio selection based on query complexity
• Real-time performance optimization

Citation

@model{e5-math-vietnamese-smart-binary,
  title={E5-Math-Vietnamese-Smart-Binary: Intelligent 1:2 Ratio Training for Balanced Retrieval},
  author={ThanhLe0125},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/ThanhLe0125/e5-math-smart-binary},
  note={Smart binary approach với intelligent negative selection for optimal precision/recall balance}
}

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Trained on July 02, 2025 using smart binary 1:2 ratio approach với intelligent hard/easy negative selection for optimal user experience in Vietnamese mathematical content retrieval.