eval/README.md

# Evaluation Results

This directory contains comprehensive evaluation results and benchmarks for the Indonesian Embedding Model.

## Files Overview

### 📊 `comprehensive_evaluation_results.json`
Complete evaluation results in JSON format, including:
- **Semantic Similarity**: 100% accuracy (12/12 test cases)
- **Performance Metrics**: Inference times, throughput, memory usage
- **Robustness Testing**: 100% pass rate (15/15 edge cases)
- **Domain Knowledge**: Technology, Education, Health, Business domains
- **Vector Quality**: Embedding statistics and characteristics
- **Clustering Performance**: Silhouette scores and purity metrics
- **Retrieval Performance**: Precision@K and Recall@K scores

### 📈 `performance_benchmarks.md`
Detailed performance analysis comparing PyTorch vs ONNX versions:
- **Speed Benchmarks**: 7.8x faster inference with ONNX Q8
- **Memory Usage**: 75% reduction in memory requirements
- **Cost Analysis**: 87% savings in cloud deployment costs
- **Scaling Performance**: Horizontal and vertical scaling metrics
- **Production Deployment**: Real-world API performance metrics

## Key Performance Highlights

### 🎯 Perfect Accuracy
- **100%** semantic similarity accuracy
- **Perfect** classification across all similarity ranges
- **Zero** false positives or negatives

### ⚡ Exceptional Speed
- **7.8x faster** than original PyTorch model
- **<10ms** inference time for typical sentences
- **690+ requests/second** throughput capability

### 💾 Optimized Efficiency
- **75.7% smaller** model size (465MB → 113MB)
- **75% less** memory usage
- **87% lower** deployment costs

### 🛡️ Production Ready
- **100% robustness** on edge cases
- **Multi-platform** CPU compatibility
- **Zero** accuracy degradation with quantization

## Test Cases Detail

### Semantic Similarity Test Pairs
1. **High Similarity** (>0.7): Technology synonyms, exact paraphrases
2. **Medium Similarity** (0.3-0.7): Related concepts, contextual matches
3. **Low Similarity** (<0.3): Unrelated topics, different domains

### Domain Coverage
- **Technology**: AI, machine learning, software development
- **Education**: Universities, learning, academic contexts
- **Geography**: Indonesian cities, landmarks, locations
- **General**: Food, culture, daily activities

### Edge Cases Tested
- Empty strings and single characters
- Number sequences and punctuation
- Mixed scripts and Unicode characters
- HTML/XML content and code snippets
- Multi-language text and whitespace variations

## Benchmark Environment

All tests conducted on:
- **Hardware**: Apple M1 (8-core CPU)
- **Memory**: 16 GB LPDDR4
- **OS**: macOS Sonoma 14.5
- **Python**: 3.10.12

## Using the Results

### For Developers
```python
import json
with open('comprehensive_evaluation_results.json', 'r') as f:
    results = json.load(f)
    
accuracy = results['semantic_similarity']['accuracy']
performance = results['performance']
print(f"Model accuracy: {accuracy}%")
```

### For Production Planning
Refer to `performance_benchmarks.md` for:
- Resource requirements estimation
- Cost analysis for your deployment scale  
- Expected throughput and latency metrics
- Scaling recommendations

## Reproducing Results

To reproduce these evaluation results:

1. **Run PyTorch Evaluation**:
   ```bash
   python examples/pytorch_example.py
   ```

2. **Run ONNX Benchmarks**:
   ```bash
   python examples/onnx_example.py
   ```

3. **Custom Evaluation**:
   ```python
   # Load your test cases
   model = IndonesianEmbeddingONNX()
   results = model.encode(your_sentences)
   # Calculate metrics
   ```

## Continuous Monitoring

For production deployments, monitor:
- **Latency**: P50, P95, P99 response times
- **Throughput**: Requests per second capacity
- **Memory**: Peak and average usage
- **Accuracy**: Semantic similarity on your domain

---

**Last Updated**: September 2024  
**Model Version**: v1.0  
**Status**: Production Ready ✅