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
Published: 2025-09-24 17:56:51

Abstract: 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.