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    Models/Reranking/cross-encoder/ettin-reranker-1b-v1
    HFRerankingApache-2.0

    ettin-reranker-1b-v1

    by cross-encoder

    State-of-the-art cross-encoder reranker matching its 1.5B teacher at 1B parameters

    Identifiers
    Model ID
    cross-encoder/ettin-reranker-1b-v1
    Feature URI
    mixpeek://reranker@v1/cross_encoder_ettin_1b_v1

    Overview

    Ettin Reranker 1B is the flagship model in the Ettin reranker family (17M to 1B parameters), trained via pointwise MSE distillation from the mxbai-rerank-large-v2 teacher. Built on Ettin ModernBERT encoders, it matches the teacher's MTEB Retrieval score within 0.0001 nDCG@10 while being smaller and faster.

    The Ettin family provides a reranker at every size class, letting you trade latency for quality. The 150M variant runs under 10ms per query-document pair on GPU; the 1B variant delivers maximum accuracy for quality-critical retrieval.

    Architecture

    ModernBERT encoder backbone (Ettin variant) with a cross-encoder classification head. Takes concatenated query-document input and outputs a relevance score. Trained via pointwise MSE distillation from mxbai-rerank-large-v2 on diverse retrieval datasets.

    Mixpeek SDK Integration

    // Reranking is a retriever STAGE in Mixpeek, not an ingest-time extractor.
    // The rerank stage runs a cross-encoder inference service; the shipped default
    // is BAAI/bge-reranker-v2-m3. Pointing it at ettin-reranker-1b-v1 means registering that
    // model as a custom reranker plugin and naming it in feature_uri, which is an
    // Enterprise path. Stage contract read from GET /v1/discovery/stages.
    const retriever = await mx.retrievers.create({
      namespace_id: "my-namespace",
      retriever_name: "search-then-rerank",
      stages: [
        {
          stage_name: "candidates",
          stage_id: "feature_search",
          parameters: { limit: 100 },
        },
        {
          stage_name: "rerank_results",
          stage_id: "rerank",
          parameters: {
            inference_name: "BAAI__bge_reranker_v2_m3",
            query: "{{INPUT.query}}",
            document_field: "content",
            top_k: 10,
          },
        },
      ],
    });

    Capabilities

    • SOTA reranking at 1B parameters on MTEB Retrieval
    • Family of 6 sizes (17M-1B) for latency/quality tradeoffs
    • Matches 1.54B teacher within 0.0001 nDCG@10
    • Compatible with any first-stage retriever
    • Apache 2.0 license

    Use Cases on Mixpeek

    Second-stage reranking in Mixpeek multi-stage retriever pipelines
    Precision-critical document retrieval where recall is already high
    Low-latency reranking with smaller family variants (150M, 400M)

    Benchmarks

    DatasetMetricScoreSource
    MTEB Retrieval (eng, v2)nDCG@10≈teacher (0.0001 gap)Ettin blog, May 2026
    NanoBEIR (13 datasets)nDCG@10SOTA at 1BEttin blog, May 2026

    Performance

    Input SizeUp to 8192 tokens (query + document)
    GPU Latency~18ms / pair (A100)
    GPU Throughput~450 pairs/sec (A100, batch 32)
    GPU Memory~2.1 GB

    Specification

    FrameworkHF
    Organizationcross-encoder
    FeatureReranking
    OutputRelevance score per candidate
    Modalities
    RetrieverCross-Modal Reranker
    Parameters1B
    LicenseApache-2.0
    Downloads/mo52K

    Research Paper

    Introducing the Ettin Reranker Family

    arxiv.org

    Build a pipeline with ettin-reranker-1b-v1

    Add this model to a processing pipeline alongside other extractors. Combine with retrieval stages for end-to-end search.

    Run it on your own data, free