Multilingual E5 Text Embeddings: A Technical Report
Liang Wang Nan Yang Xiaolong Huang Linjun Yang Rangan Majumder Furu Wei
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 .