Oxidizing Cross-Encoders

Cross-encoders improve search reranking by jointly evaluating a query and document for relevance, but their computational cost makes efficient batching and inference important. The post describes cross-encode-rs, an open-source Rust library built on ONNX Runtime that tokenizes query-document pairs, batches documents by similar token lengths to reduce padding, and applies model-specific sigmoid or softmax scoring. Benchmarks on a MacBook M4 Max against Python libraries fastembed and sentence-transformers found only modest gains for Rust on the small MiniLM model, where all tools rely on the same underlying C++ ONNX Runtime engine, but a more substantial 1.3–1.5x latency advantage for cross-encode-rs on a larger multilingual Jina reranker. Model session creation times were nearly identical when Python startup costs were excluded, indicating that language choice has limited influence on loading performance, while Rust can still offer deployment benefits through a standalone binary, absence of Python runtime and GIL overhead, and greater control over concurrent batching.

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