jina-embeddings-v3: Multilingual Embeddings With Task LoRA
<|reference_start|>jina-embeddings-v3: Multilingual Embeddings With Task LoRA: 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.<|reference_end|>
@article{sturua2024jina-embeddings-v3:,
    title={jina-embeddings-v3: Multilingual Embeddings With Task LoRA},
    author={Saba Sturua, Isabelle Mohr, Mohammad Kalim Akram, Michael G"unther,
  Bo Wang, Markus Krimmel, Feng Wang, Georgios Mastrapas, Andreas Koukounas,
  Nan Wang, Han Xiao},
    journal={arXiv preprint arXiv:2409.10173},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10173},
    primaryClass={cs.CL cs.AI cs.IR}
}