ettin-reranker-150m-v1

This is a Cross Encoder model finetuned from jhu-clsp/ettin-encoder-150m on the cross-encoder/ettin-reranker-v1-data dataset using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search. See the release blogpost for details on the training recipe, evaluation results, and speed benchmarks against other public rerankers. The Evaluation section below also has the headline numbers. Each model in the ettin-reranker-v1 family was evaluated on the full `MTEB(eng, v2)` Retrieval benchmark (10 tasks, top-100 reranked) using MTEB's two-stage reranking flow, pairing each reranker with six embedding models that span the speed/quality spectrum. The dashed retriever-only line in each chart below is the headline number to beat. Anything below it means the reranker actively hurts the pipeline on average:

번역 준비 중입니다. 원문을 표시합니다.