Qwen3 Embedding: Advancing Text Embedding and Reranking Through
  Foundation Models
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.
6842521939f41e76fd96ae38
Yanzhao Zhang
6842521939f41e76fd96ae39
Mingxin Li
6842521939f41e76fd96ae3a
Dingkun Long
claimed_verified
2025-06-06T07:41:13.249Z
616adb8578833ce5997e441a
/avatars/bf5c04a6032709f35e3fb48e1be6976f.svg
Dingkun Long
user
thenlper
6842521939f41e76fd96ae3b
Xin Zhang
claimed_verified
2025-06-06T07:41:10.698Z
63b6dbc8ccebeadccc888456
https://cdn-avatars.huggingface.co/v1/production/uploads/1673396893898-63b6dbc8ccebeadccc888456.jpeg
Xin Zhang
user
izhx
6842521939f41e76fd96ae3c
Huan Lin
6842521939f41e76fd96ae3d
Baosong Yang
6842521939f41e76fd96ae3e
Pengjun Xie
6842521939f41e76fd96ae3f
An Yang
claimed_verified
2025-06-09T10:12:32.896Z
62088594a5943c8a8fc94560
https://cdn-avatars.huggingface.co/v1/production/uploads/1644733028938-62088594a5943c8a8fc94560.png
An Yang
user
yangapku
6842521939f41e76fd96ae40
Dayiheng Liu
6842521939f41e76fd96ae41
Junyang Lin
6842521939f41e76fd96ae42
Fei Huang
6842521939f41e76fd96ae43
Jingren Zhou
Qwen3 Embedding: Advancing Text Embedding and Reranking Through
  Foundation Models
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.
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.
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