Gemini Embedding: Generalizable Embeddings from Gemini
Authors: name: Jinhyuk Lee, name: Feiyang Chen, name: Sahil Dua, name: Daniel Cer, name: Madhuri Shanbhogue, name: Iftekhar Naim, name: Gustavo Hernández Ábrego, name: Zhe Li, name: Kaifeng Chen, name: Henrique Schechter Vera, name: Xiaoqi Ren, name: Shanfeng Zhang, name: Daniel Salz, name: Michael Boratko, name: Jay Han, name: Blair Chen, name: Shuo Huang, name: Vikram Rao, name: Paul Suganthan, name: Feng Han, name: Andreas Doumanoglou, name: Nithi Gupta, name: Fedor Moiseev, name: Cathy Yip, name: Aashi Jain, name: Simon Baumgartner, name: Shahrokh Shahi, name: Frank Palma Gomez, name: Sandeep Mariserla, name: Min Choi, name: Parashar Shah, name: Sonam Goenka, name: Ke Chen, name: Ye Xia, name: Koert Chen, name: Sai Meher Karthik Duddu, name: Yichang Chen, name: Trevor Walker, name: Wenlei Zhou, name: Rakesh Ghiya, name: Zach Gleicher, name: Karan Gill, name: Zhe Dong, name: Mojtaba Seyedhosseini, name: Yunhsuan Sung, name: Raphael Hoffmann, name: Tom Duerig

In this report, we introduce Gemini Embedding, a state-of-the-art embedding
model leveraging the power of Gemini, Google's most capable large language
model. Capitalizing on Gemini's inherent multilingual and code understanding
capabilities, Gemini Embedding produces highly generalizable embeddings for
text spanning numerous languages and textual modalities. The representations
generated by Gemini Embedding can be precomputed and applied to a variety of
downstream tasks including classification, similarity, clustering, ranking, and
retrieval. Evaluated on the Massive Multilingual Text Embedding Benchmark
(MMTEB), which includes over one hundred tasks across 250+ languages, Gemini
Embedding substantially outperforms prior state-of-the-art models,
demonstrating considerable improvements in embedding quality. Achieving
state-of-the-art performance across MMTEB's multilingual, English, and code
benchmarks, our unified model demonstrates strong capabilities across a broad
selection of tasks and surpasses specialized domain-specific models.