Introducing the Ettin Reranker Family

Today I'm releasing six new Sentence Transformers CrossEncoder rerankers, state-of-the-art at their respective sizes, built on top of the Ettin ModernBERT encoders, together with the data and full training recipe that produced them:

The models were trained with a distillation recipe : pointwise MSE on mixedbread-ai/mxbai-rerank-large-v2 scores over cross-encoder/ettin-reranker-v1-data , which is a subset of lightonai/embeddings-pre-training mixed with a reranked subset of lightonai/embeddings-fine-tuning .

Our six rerankers paired with google/embeddinggemma-300m on MTEB(eng, v2) Retrieval. See Results for five more embedder pairings.

A reranker (a.k.a. pointwise cross-encoder) is a neural model that takes a (query, document) pair and outputs a single relevance score. Unlike an embedding model, which encodes the query and document separately and computes their similarity from the two embedding vectors, a reranker lets the two texts attend to each other through every transformer layer. That joint encoding is more accurate but also more expensive: the model has to be run once per (query, document) pair rather than once per text.

All six rerankers share the same architecture and differ only in their backbone size. The backbone is one of the six Ettin encoders from Johns Hopkins University's Ettin suite. These are ModernBERT-style models with unpadded attention, RoPE positional encodings, GeGLU, and 2T tokens of open-license pre-training, supporting up to 8192 tokens of context.

On top of each encoder, the reranker uses a 4-module classification head that mirrors ModernBertForSequenceClassification but is built from Sentence Transformers' modular components. The underlying Transformer is a plain AutoModel rather than AutoModelForSequenceClassification , which lets us use sequence unpadding for variable-length inputs for Flash Attention 2. At medium-document sequence lengths this is a 1.7x-8.3x speedup over fp32+SDPA depending on model size (see Speed for the full benchmark):

The smallest model I'm releasing, our 17M, beats the 33M ms-marco-MiniLM-L12-v2 by +0.051 NDCG@10 (0.5576 vs 0.5066) on MTEB and +0.038 (0.6746 vs 0.6369) on NanoBEIR at roughly half the parameter count. The 32M beats the 568M BAAI/bge-reranker-v2-m3 by +0.025 (0.5779 vs 0.5526) on MTEB, a 17x parameter gap. If you've been using one of the legacy MiniLM rerankers as the default in your retrieve-then-rerank stack, swapping in our 17M (or 32M) is a low-risk drop-in replacement, with a noticeable quality bump on both benchmarks.

All six models are released under the Apache 2.0 license, matching the Ettin encoders.

If you're new to rerankers and want the "why" first, jump to What is a reranker, and why pair one with an embedder? . If you just want to plug a model in, jump to Usage . If you want to train your own, jump to Training .

I bootstrapped the training recipe below with the new train-sentence-transformers Agent Skill shipped in Sentence Transformers v5.5.0 . Install it with hf skills add train-sentence-transformers [--global] [--claude] and ask your AI coding agent (Claude Code, Codex, Cursor, Gemini CLI, ...) to fine-tune a SentenceTransformer , CrossEncoder , or SparseEncoder model on your data.

Because cross-encoders are too expensive to run over a full corpus, the common production pattern is retrieve-then-rerank : a fast embedding model retrieves the top-K candidates (cheap), then a cross-encoder re-orders just those K with high accuracy. The total cost stays bounded while the final ranking is much closer to what an exhaustive cross-encoder pass would produce.