Multilingual E5 Text Embeddings: A Technical Report
<|reference_start|>Multilingual E5 Text Embeddings: A Technical Report: This technical report presents the training methodology and evaluation results of the open-source multilingual E5 text embedding models, released in mid-2023. Three embedding models of different sizes (small / base / large) are provided, offering a balance between the inference efficiency and embedding quality. The training procedure adheres to the English E5 model recipe, involving contrastive pre-training on 1 billion multilingual text pairs, followed by fine-tuning on a combination of labeled datasets. Additionally, we introduce a new instruction-tuned embedding model, whose performance is on par with state-of-the-art, English-only models of similar sizes. Information regarding the model release can be found at https://github.com/microsoft/unilm/tree/master/e5 .<|reference_end|>
@article{wang2024multilingual,
    title={Multilingual E5 Text Embeddings: A Technical Report},
    author={Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder,
  Furu Wei},
    journal={arXiv preprint arXiv:2402.05672},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05672},
    primaryClass={cs.CL cs.IR}
}