EmbeddingGemma: Powerful and Lightweight Text Representations
Authors: name: Henrique Schechter Vera, name: Sahil Dua, name: Biao Zhang, name: Daniel Salz, name: Ryan Mullins, name: Sindhu Raghuram Panyam, name: Sara Smoot, name: Iftekhar Naim, name: Joe Zou, name: Feiyang Chen, name: Daniel Cer, name: Alice Lisak, name: Min Choi, name: Lucas Gonzalez, name: Omar Sanseviero, name: Glenn Cameron, name: Ian Ballantyne, name: Kat Black, name: Kaifeng Chen, name: Weiyi Wang, name: Zhe Li, name: Gus Martins, name: Jinhyuk Lee, name: Mark Sherwood, name: Juyeong Ji, name: Renjie Wu, name: Jingxiao Zheng, name: Jyotinder Singh, name: Abheesht Sharma, name: Divya Sreepat, name: Aashi Jain, name: Adham Elarabawy, name: AJ Co, name: Andreas Doumanoglou, name: Babak Samari, name: Ben Hora, name: Brian Potetz, name: Dahun Kim, name: Enrique Alfonseca, name: Fedor Moiseev, name: Feng Han, name: Frank Palma Gomez, name: Gustavo Hernández Ábrego, name: Hesen Zhang, name: Hui Hui, name: Jay Han, name: Karan Gill, name: Ke Chen, name: Koert Chen, name: Madhuri Shanbhogue, name: Michael Boratko, name: Paul Suganthan, name: Sai Meher Karthik Duddu, name: Sandeep Mariserla, name: Setareh Ariafar, name: Shanfeng Zhang, name: Shijie Zhang, name: Simon Baumgartner, name: Sonam Goenka, name: Steve Qiu, name: Tanmaya Dabral, name: Trevor Walker, name: Vikram Rao, name: Waleed Khawaja, name: Wenlei Zhou, name: Xiaoqi Ren, name: Ye Xia, name: Yichang Chen, name: Yi-Ting Chen, name: Zhe Dong, name: Zhongli Ding, name: Francesco Visin, name: Gaël Liu, name: Jiageng Zhang, name: Kathleen Kenealy, name: Michelle Casbon, name: Ravin Kumar, name: Thomas Mesnard, name: Zach Gleicher, name: Cormac Brick, name: Olivier Lacombe, name: Adam Roberts, name: Yunhsuan Sung, name: Raphael Hoffmann, name: Tris Warkentin, name: Armand Joulin, name: Tom Duerig, name: Mojtaba Seyedhosseini

We introduce EmbeddingGemma, a new lightweight, open text embedding model
based on the Gemma 3 language model family. Our innovative training recipe
strategically captures knowledge from larger models via encoder-decoder
initialization and geometric embedding distillation. We improve model
robustness and expressiveness with a spread-out regularizer, and ensure
generalizability by merging checkpoints from varied, optimized mixtures.
Evaluated on the Massive Text Embedding Benchmark (MTEB) across multilingual,
English, and code domains, EmbeddingGemma (300M) achieves state-of-the-art
results. Notably, it outperforms prior top models, both proprietary and open,
with fewer than 500M parameters, and provides performance comparable to models
double its size, offering an exceptional performance-to-cost ratio. Remarkably,
this lead persists when quantizing model weights or truncating embedding
outputs. This makes EmbeddingGemma particularly well-suited for low-latency and
high-throughput use cases such as on-device applications. We provide ablation
studies exploring our key design choices. We release EmbeddingGemma to the
community to promote further research.