Ruri: Japanese General Text Embeddings
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 25 of its 42 references that are in the map. First posted on arXiv 12 Sep 2024.
Builds on
25 of 42 references.
References · 25
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
- 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,682
- 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,173
- 2015A large annotated corpus for learning natural language inferencecited by 1,847
- 2020GLU Variants Improve Transformercited by 1,804
- 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
- 2021Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationcited by 1,103
- 2024NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Modelscited by 472
- 2024LLM2Vec: Large Language Models Are Secretly Powerful Text Encoderscited by 404
- 2024Nomic Embed: Training a Reproducible Long Context Text Embeddercited by 265
13 more
- 2020MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answeringcited by 153
- 2021Mr. TyDi: A Multi-lingual Benchmark for Dense Retrievalcited by 119
- 2022RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encodercited by 118
- 2021mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Datasetcited by 116
- 2023Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documentscited by 104
- 2022SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrievalcited by 90
- 2022Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languagescited by 47
- 2021Pairwise Supervised Contrastive Learning of Sentence Representationscited by 31
- 2018WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discoursecited by 26
- 2023Contrastive Learning of Sentence Embeddings from Scratchcited by 24
- 2024JaColBERTv2.5: Optimising Multi-Vector Retrievers to Create State-of-the-Art Japanese Retrievers with Constrained Resourcescited by 15
- 2023RetroMAE-2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Modelscited by 13
- 2024Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot Examplescited by 3
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