Qwen3 Embedding: Advancing Text Embedding and Reranking Through
  Foundation Models
Authors: name: Yanzhao Zhang, name: Mingxin Li, name: Dingkun Long, name: Xin Zhang, name: Huan Lin, name: Baosong Yang, name: Pengjun Xie, name: An Yang, name: Dayiheng Liu, name: Junyang Lin, name: Fei Huang, name: Jingren Zhou

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.