Cross-Encoder Reranker Implementation

Implement a cross-encoder reranker for improving retrieval precision.

## Retrieval System
{{retrieval_system}}

## Reranker Model Options
{{model_options}}

## Latency Budget
{{latency_budget}}ms

Build the reranker:

```python
class CrossEncoderReranker:
    def __init__(self, model_name: str, max_length: int = 512):
        pass
    
    def rerank(self, query: str, documents: List[str], top_k: int) -> List[ScoredDocument]:
        """Score and rerank documents"""
        pass
    
    def rerank_batched(self, query: str, documents: List[str], batch_size: int) -> List[ScoredDocument]:
        """Batched inference for efficiency"""
        pass
    
    def calibrate_scores(self, raw_scores: List[float]) -> List[float]:
        """Normalize scores to 0-1 range"""
        pass
```

Include:
- Model selection guidance
- Batching optimization
- GPU vs CPU trade-offs
- Integration patterns
- Caching reranked results