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
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Sahil Dua
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Sahil Dua
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Daniel Cer
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Daniel Cer
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Madhuri Shanbhogue
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Madhuri Shanbhogue
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Iftekhar Naim
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Zhe Li
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Kaifeng Chen
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Kaifeng Chen
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Henrique Schechter Vera
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Henrique Schechter Vera
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Daniel Salz
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Daniel Salz
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Michael Boratko
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2025-03-12T15:47:38.164Z
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Michael Boratko
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Vikram Rao
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Vikram Rao Sudarshan
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Paul Suganthan
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Feng Han
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Andreas Doumanoglou
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Nithi Gupta
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Fedor Moiseev
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Fedor Moiseev
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Cathy Yip
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Simon Baumgartner
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Min Choi
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Min Choi
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Parashar Shah
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Ke Chen
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Raphael hoffmann
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Tom Duerig
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Tom Duerig
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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