Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models
<|reference_start|>Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models: This report describes the training dataset creation and recipe behind the family of \texttt{arctic-embed} text embedding models (a set of five models ranging from 22 to 334 million parameters with weights open-sourced under an Apache-2 license). At the time of their release, each model achieved state-of-the-art retrieval accuracy for models of their size on the MTEB Retrieval leaderboard, with the largest model, arctic-embed-l outperforming closed source embedding models such as Cohere's embed-v3 and Open AI's text-embed-3-large. In addition to the details of our training recipe, we have provided several informative ablation studies, which we believe are the cause of our model performance.<|reference_end|>
@article{merrick2024arctic-embed:,
    title={Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models},
    author={Luke Merrick, Danmei Xu, Gaurav Nuti, Daniel Campos},
    journal={arXiv preprint arXiv:2405.05374},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05374},
    primaryClass={cs.CL cs.AI cs.IR}
}