jina-embeddings-v3: Multilingual Embeddings With Task LoRA

• URL: http://arxiv.org/abs/2409.10173v3
• Date: Thu, 19 Sep 2024 11:21:24 GMT
• Title: jina-embeddings-v3: Multilingual Embeddings With Task LoRA
• Authors: Saba Sturua, Isabelle Mohr, Mohammad Kalim Akram, Michael Günther, Bo Wang, Markus Krimmel, Feng Wang, Georgios Mastrapas, Andreas Koukounas, Nan Wang, Han Xiao,
• Abstract summary: jina-embeddings-v3 is a novel text embedding model with 570 million parameters. It achieves state-of-the-art performance on multilingual data and long-context retrieval tasks. It supports context lengths of up to 8192 tokens.
• Score: 6.926642162309072
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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