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 .
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Nan Yang
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Rangan Majumder
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Furu Wei
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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 .
Multilingual E5 text embedding models are released in different sizes and include an instruction-tuned variant, achieving performance comparable to state-of-the-art English-only models.
contrastive pre-training fine-tuning instruction-tuned embedding models