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Bekko Embedding achieves competitive multilingual retrieval with ultra-compact models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Bekko Embedding, a new family of parameter-efficient multilingual retrieval models. The smallest version, bekko-embedding-v1-a8m, with under 8 million active parameters, achieves a score of 56.2 on the MMTEB Multilingual v2 Retrieval benchmark, outperforming larger models like multilingual-e5 and BGE-M3. A slightly larger model, a25m, matches the performance of gte-multilingual-base. These models support inputs up to 8192 tokens and are notably faster on both CPU and GPU, with the a8m model being the fastest among those tested. The models feature a compact 384-dimensional output and can be quantized to reduce size, making them suitable for applications like browser-based search. AI

IMPACT Sets a new standard for efficiency in multilingual retrieval, potentially enabling faster and more accessible search applications.

RANK_REASON The cluster describes a new research paper detailing a novel model architecture and its performance on retrieval benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Bekko Embedding achieves competitive multilingual retrieval with ultra-compact models

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The cluster describes a new research paper detailing a novel model architecture and its performance on retrieval benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Yuichi Tateno ·

    Bekko Embedding: Parameter-Efficient Multilingual Retrieval with Ultra-Compact Encoders

    How small can a competitive multilingual retrieval model be? We present Bekko Embedding: its smallest model, bekko-embedding-v1-a8m, has just under 8M Active Parameters (AP) -- the non-embedding parameters that dominate inference compute -- yet on official MMTEB Multilingual v2 R…

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