Gemini Embedding: Generalizable Embeddings from Gemini
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.
67d108c56bd6c57bab0b6f07
Jinhyuk Lee
admin_assigned
2025-03-12T15:42:14.774Z
63fd3edc3c880680af44aa78
/avatars/98759e23e89f9da3ce13266d030e611b.svg
Jinhyuk Lee
user
jinhyuklee
67d108c56bd6c57bab0b6f08
Feiyang Chen
admin_assigned
2025-03-12T15:42:50.034Z
673fd856a45b6f21829a3bf5
/avatars/deb8c5362fad22019cccaed6d03dea09.svg
Feiyang Chen
user
PhilipChen
67d108c56bd6c57bab0b6f09
Sahil Dua
claimed_verified
2025-03-12T14:25:43.248Z
67d198850e00700a6b9f1715
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/oBAOklXUYODJ7XxxNndqu.png
Sahil Dua
user
sahildua2305
67d108c56bd6c57bab0b6f0a
Daniel Cer
admin_assigned
2025-03-12T15:46:21.396Z
610dd291dda0cb4dbfbf32d2
/avatars/6f3fe6d53c37076e2cefc1b4d95743d6.svg
Daniel Cer
user
danielcer
67d108c56bd6c57bab0b6f0b
Madhuri Shanbhogue
admin_assigned
2025-03-12T15:46:29.742Z
64b09a273b6d9c4ef7626b72
/avatars/a5fed033a8d241276059289318b3c49b.svg
Madhuri Shanbhogue
user
madhuris
67d108c56bd6c57bab0b6f0c
Iftekhar Naim
67d108c56bd6c57bab0b6f0d
Gustavo Hernández Ábrego
67d108c56bd6c57bab0b6f0e
Zhe Li
67d108c56bd6c57bab0b6f0f
Kaifeng Chen
67d108c56bd6c57bab0b6f10
Henrique Schechter Vera
67d108c56bd6c57bab0b6f11
Xiaoqi Ren
67d108c56bd6c57bab0b6f12
Shanfeng Zhang
67d108c56bd6c57bab0b6f13
Daniel Salz
67d108c56bd6c57bab0b6f14
Michael Boratko
admin_assigned
2025-03-12T15:47:38.164Z
655ead197b7450fedc485ce9
/avatars/fecccddf67edb87cc1971feeff556511.svg
Michael Boratko
user
Roulette6888
67d108c56bd6c57bab0b6f15
Jay Han
67d108c56bd6c57bab0b6f16
Blair Chen
67d108c56bd6c57bab0b6f17
Shuo Huang
67d108c56bd6c57bab0b6f18
Vikram Rao
67d108c56bd6c57bab0b6f19
Paul Suganthan
67d108c56bd6c57bab0b6f1a
Feng Han
67d108c56bd6c57bab0b6f1b
Andreas Doumanoglou
67d108c56bd6c57bab0b6f1c
Nithi Gupta
67d108c56bd6c57bab0b6f1d
Fedor Moiseev
admin_assigned
2025-03-12T15:45:35.363Z
66df81e4608ec2ea4ab981a7
/avatars/003fce1379a7cf1e412d328128952188.svg
Fedor Moiseev
user
femoiseev
67d108c56bd6c57bab0b6f1e
Cathy Yip
67d108c56bd6c57bab0b6f1f
Aashi Jain
67d108c56bd6c57bab0b6f20
Simon Baumgartner
claimed_verified
2025-05-20T19:29:55.305Z
6560f697e0a7720b6ae377bc
https://cdn-avatars.huggingface.co/v1/production/uploads/6560f697e0a7720b6ae377bc/8b-p-lQ7KV_VygBJ6Vf3_.jpeg
Simon Baumgartner
user
sens3
67d108c56bd6c57bab0b6f21
Shahrokh Shahi
67d108c56bd6c57bab0b6f22
Frank Palma Gomez
67d108c56bd6c57bab0b6f23
Sandeep Mariserla
67d108c56bd6c57bab0b6f24
Min Choi
67d108c56bd6c57bab0b6f25
Parashar Shah
67d108c56bd6c57bab0b6f26
Sonam Goenka
67d108c56bd6c57bab0b6f27
Ke Chen
67d108c56bd6c57bab0b6f28
Ye Xia
67d108c56bd6c57bab0b6f29
Koert Chen
67d108c56bd6c57bab0b6f2a
Sai Meher Karthik Duddu
67d108c56bd6c57bab0b6f2b
Yichang Chen
67d108c56bd6c57bab0b6f2c
Trevor Walker
67d108c56bd6c57bab0b6f2d
Wenlei Zhou
67d108c56bd6c57bab0b6f2e
Rakesh Ghiya
67d108c56bd6c57bab0b6f2f
Zach Gleicher
67d108c56bd6c57bab0b6f30
Karan Gill
67d108c56bd6c57bab0b6f31
Zhe Dong
67d108c56bd6c57bab0b6f32
Mojtaba Seyedhosseini
67d108c56bd6c57bab0b6f33
Yunhsuan Sung
67d108c56bd6c57bab0b6f34
Raphael Hoffmann
admin_assigned
2025-03-12T15:44:50.723Z
65ac1d802f560c70ff74412d
/avatars/763b352cd671ba7ef2637db14de86951.svg
Raphael hoffmann
user
peacemac
67d108c56bd6c57bab0b6f35
Tom Duerig
admin_assigned
2025-03-12T15:44:36.769Z
631a4acfc9f8cd19a735a0ab
/avatars/61cebf77358634876d978d87248a62f3.svg
Tom Duerig
user
tduerig
Gemini Embedding: Generalizable Embeddings from Gemini
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.
Gemini Embedding, utilizing Google's Gemini large language model, generates high-quality multilingual and code embeddings outperforming benchmarks across various tasks.
Gemini Embedding multilingual code understanding embeddings precomputed downstream tasks classification similarity clustering ranking retrieval Massive Multilingual Text Embedding Benchmark (MMTEB) state-of-the-art performance unified model domain-specific models