title: Multilingual E5 Text Embeddings: A Technical Report
summary: 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 .
paper_authors._id: 65c5a6e657c1ce2de61915ad
paper_authors.name: Liang Wang
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paper_authors.name: Nan Yang
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paper_authors.name: Xiaolong Huang
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paper_authors.fullname: Xiaolong Huang
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paper_authors.name: Linjun Yang
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paper_authors.fullname: Linjun Yang
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paper_authors.name: Rangan Majumder
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paper_authors.fullname: Rangan Majumder
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paper_authors.name: Furu Wei
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paper_authors.fullname: Furu Wei
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paper_authors.user: thegenerality
paper_title: Multilingual E5 Text Embeddings: A Technical Report
paper_summary: 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 .
paper_ai_keywords: contrastive pre-training instruction-tuned embedding model state-of-the-art embedding models multilingual text pairs labeled datasets