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StaRSE: Compact Russian Sentence Encoder Achieves High Performance with Low Latency

By PulseAugur Editorial · [2 sources] ·

Researchers have developed StaRSE, a novel static sentence encoder designed for Russian language processing. This encoder significantly reduces inference costs by removing the need for Transformer computations at deployment time, storing token directions as sign bits and norms separately. StaRSE, initialized from ruBERT-base and fine-tuned on specific Russian sentence pairs, achieves a mean score of 51.16 on the RuMTEB benchmark, outperforming static baselines by over 7 points. The model is notably compact at 11.3 MiB and boasts a mean CPU latency of 0.08 ms per sentence, making it efficient for CPU-bound applications. AI

IMPACT Offers a more efficient approach to generating sentence embeddings for Russian, potentially enabling wider deployment on resource-constrained devices.

RANK_REASON The cluster contains an academic paper detailing a new model architecture and benchmark results.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

StaRSE: Compact Russian Sentence Encoder Achieves High Performance with Low Latency

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Boris Malashenko, Ivan Jarsky, Egor Soldatov, Vladislav Luzhnov, Sviatoslav Stumpf, Vladislav Shmatkov, Viacheslav Shalamov, Andrey Stankevich, Valeria Efimova ·

    StaRSE: Compact Russian Sentence Embeddings with a Sign-Coded Static Encoder

    arXiv:2611.14236v1 Announce Type: new Abstract: Transformer sentence encoders provide strong retrieval quality, but their inference cost becomes a bottleneck when embeddings must be produced on CPUs or refreshed frequently. We introduce StaRSE, a compact Russian static sentence e…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Valeria Efimova ·

    StaRSE: Compact Russian Sentence Embeddings with a Sign-Coded Static Encoder

    Transformer sentence encoders provide strong retrieval quality, but their inference cost becomes a bottleneck when embeddings must be produced on CPUs or refreshed frequently. We introduce StaRSE, a compact Russian static sentence encoder that removes Transformer inference at dep…

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