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Ruri: Japanese General Text Embeddings

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arxiv 2409.07737 v1 pith:HKPTG2KQ submitted 2024-09-12 cs.CL

classification cs.CL
keywords developmentembeddingmodelstextjapaneseruridatasetsgeneral
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We report the development of Ruri, a series of Japanese general text embedding models. While the development of general-purpose text embedding models in English and multilingual contexts has been active in recent years, model development in Japanese remains insufficient. The primary reasons for this are the lack of datasets and the absence of necessary expertise. In this report, we provide a detailed account of the development process of Ruri. Specifically, we discuss the training of embedding models using synthesized datasets generated by LLMs, the construction of the reranker for dataset filtering and knowledge distillation, and the performance evaluation of the resulting general-purpose text embedding models.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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

    cs.IR 2026-07 conditional novelty 7.0 of 10

    Bekko a8m, with 7.7M active parameters, scores 56.2 on MMTEB Multilingual v2 Retrieval, beating mE5 models and BGE-M3, while a25m reaches 57.5, on par with gte-multilingual-base.

  2. llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length

    cs.CL 2025-04 conditional novelty 3.0 of 10

    A 187M-parameter Japanese ModernBERT was trained on the public llm-jp corpus with 8192-token context and released with code, showing modest downstream performance but useful checkpoint-level analyses.

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