multi-qa-mpnet-base-dot-v1

multi-qa-mpnet-base-dot-v1 is a bi-encoder built on the MPNet base architecture, fine-tuned explicitly for asymmetric semantic search — mapping questions and candidate passages into a shared embedding space where dot-product similarity ranks relevant answers. At roughly 110 M parameters it runs comfortably on CPU or a single GPU, delivering latency in the low‑millisecond range per query when batched. The model is distributed via the sentence‑transformers library, so integration is a one‑liner: `SentenceTransformer('sentence-transformers/multi-qa-mpnet-base-dot-v1')`. It also exports cleanly to ONNX or TorchScript for production serving with Triton, TorchServe, or custom runtimes. Compared with general‑purpose embedders like all‑mpnet‑base‑v2, this checkpoint shows measurable gains on MS‑MARCO, TREC‑QA, and internal FAQ benchmarks because its training data emphasizes question‑passage pairs rather than symmetric STS tasks. It still trails cross‑encoder rerankers on absolute accuracy, so a common pattern is to retrieve top‑k with this bi‑encoder then rerank with a heavier cross‑encoder. Licensing follows the underlying model card (typically Apache‑2.0), but verify before commercial deployment. Ideal use cases: semantic search engines, support‑ticket deflection, internal knowledge‑base lookup, and any retrieval pipeline where query‑document asymmetry is the norm.