title: Qwen3 Embedding: Advancing Text Embedding and Reranking Through
    Foundation Models
summary: In this work, we introduce the Qwen3 Embedding series, a significant
  advancement over its predecessor, the GTE-Qwen series, in text embedding and
  reranking capabilities, built upon the Qwen3 foundation models. Leveraging the
  Qwen3 LLMs' robust capabilities in multilingual text understanding and
  generation, our innovative multi-stage training pipeline combines large-scale
  unsupervised pre-training with supervised fine-tuning on high-quality datasets.
  Effective model merging strategies further ensure the robustness and
  adaptability of the Qwen3 Embedding series. During the training process, the
  Qwen3 LLMs serve not only as backbone models but also play a crucial role in
  synthesizing high-quality, rich, and diverse training data across multiple
  domains and languages, thus enhancing the training pipeline. The Qwen3
  Embedding series offers a spectrum of model sizes (0.6B, 4B, 8B) for both
  embedding and reranking tasks, addressing diverse deployment scenarios where
  users can optimize for either efficiency or effectiveness. Empirical
  evaluations demonstrate that the Qwen3 Embedding series achieves
  state-of-the-art results across diverse benchmarks. Notably, it excels on the
  multilingual evaluation benchmark MTEB for text embedding, as well as in
  various retrieval tasks, including code retrieval, cross-lingual retrieval and
  multilingual retrieval. To facilitate reproducibility and promote
  community-driven research and development, the Qwen3 Embedding models are
  publicly available under the Apache 2.0 license.
paper_authors._id: 6842521939f41e76fd96ae38
paper_authors.name: Yanzhao Zhang
paper_authors._id: 6842521939f41e76fd96ae39
paper_authors.name: Mingxin Li
paper_authors._id: 6842521939f41e76fd96ae3a
paper_authors.name: Dingkun Long
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-06T07:41:13.249Z
paper_authors._id: 616adb8578833ce5997e441a
paper_authors.avatarUrl: /avatars/bf5c04a6032709f35e3fb48e1be6976f.svg
paper_authors.fullname: Dingkun Long
paper_authors.type: user
paper_authors.user: thenlper
paper_authors._id: 6842521939f41e76fd96ae3b
paper_authors.name: Xin Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-06T07:41:10.698Z
paper_authors._id: 63b6dbc8ccebeadccc888456
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1673396893898-63b6dbc8ccebeadccc888456.jpeg
paper_authors.fullname: Xin Zhang
paper_authors.type: user
paper_authors.user: izhx
paper_authors._id: 6842521939f41e76fd96ae3c
paper_authors.name: Huan Lin
paper_authors._id: 6842521939f41e76fd96ae3d
paper_authors.name: Baosong Yang
paper_authors._id: 6842521939f41e76fd96ae3e
paper_authors.name: Pengjun Xie
paper_authors._id: 6842521939f41e76fd96ae3f
paper_authors.name: An Yang
paper_authors._id: 6842521939f41e76fd96ae40
paper_authors.name: Dayiheng Liu
paper_authors._id: 6842521939f41e76fd96ae41
paper_authors.name: Junyang Lin
paper_authors._id: 6842521939f41e76fd96ae42
paper_authors.name: Fei Huang
paper_authors._id: 6842521939f41e76fd96ae43
paper_authors.name: Jingren Zhou
paper_title: Qwen3 Embedding: Advancing Text Embedding and Reranking Through
    Foundation Models
paper_summary: In this work, we introduce the Qwen3 Embedding series, a significant
  advancement over its predecessor, the GTE-Qwen series, in text embedding and
  reranking capabilities, built upon the Qwen3 foundation models. Leveraging the
  Qwen3 LLMs' robust capabilities in multilingual text understanding and
  generation, our innovative multi-stage training pipeline combines large-scale
  unsupervised pre-training with supervised fine-tuning on high-quality datasets.
  Effective model merging strategies further ensure the robustness and
  adaptability of the Qwen3 Embedding series. During the training process, the
  Qwen3 LLMs serve not only as backbone models but also play a crucial role in
  synthesizing high-quality, rich, and diverse training data across multiple
  domains and languages, thus enhancing the training pipeline. The Qwen3
  Embedding series offers a spectrum of model sizes (0.6B, 4B, 8B) for both
  embedding and reranking tasks, addressing diverse deployment scenarios where
  users can optimize for either efficiency or effectiveness. Empirical
  evaluations demonstrate that the Qwen3 Embedding series achieves
  state-of-the-art results across diverse benchmarks. Notably, it excels on the
  multilingual evaluation benchmark MTEB for text embedding, as well as in
  various retrieval tasks, including code retrieval, cross-lingual retrieval and
  multilingual retrieval. To facilitate reproducibility and promote
  community-driven research and development, the Qwen3 Embedding models are
  publicly available under the Apache 2.0 license.
paper_ai_summary: The Qwen3 Embedding series, built on Qwen3 foundation models, offers advanced text embedding and reranking capabilities through a multi-stage training pipeline, achieving state-of-the-art performance across multilingual and retrieval benchmarks.
paper_ai_keywords: Qwen3 Embedding series GTE-Qwen series Qwen3 LLMs multilingual text understanding unsupervised pre-training supervised fine-tuning model merging embedding reranking MTEB code retrieval cross-lingual retrieval multilingual retrieval