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arXiv · 2409.10173

jina-embeddings-v3: Multilingual Embeddings With Task LoRA

Saba Sturua·Isabelle Mohr·Mohammad Kalim Akram·Michael Günther·Bo Wang·Markus Krimmel·Feng Wang·Georgios Mastrapas·Andreas Koukounas·Nan Wang·Han Xiao
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Abstract

We introduce jina-embeddings-v3, a novel text embedding model with 570 million parameters, achieves state-of-the-art performance on multilingual data and long-context retrieval tasks, supporting context lengths of up to 8192 tokens. The model includes a set of task-specific Low-Rank Adaptation (LoRA) adapters to generate high-quality embeddings for query-document retrieval, clustering, classification, and text matching. Evaluation on the MTEB benchmark shows that jina-embeddings-v3 outperforms the latest proprietary embeddings from OpenAI and Cohere on English tasks, while achieving superior performance compared to multilingual-e5-large-instruct across all multilingual tasks. With a default output dimension of 1024, users can flexibly reduce the embedding dimensions to as low as 32 without compromising performance, enabled by Matryoshka Representation Learning.

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Saba Sturua, Isabelle Mohr, Mohammad Kalim Akram, Michael Günther, Bo Wang, Markus Krimmel, Feng Wang, Georgios Mastrapas, Andreas Koukounas, Nan Wang, Han Xiao. 2024-09-19. jina-embeddings-v3: Multilingual Embeddings With Task LoRA. https://arxiv.org/abs/2409.10173

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