title: Gemini Embedding: Generalizable Embeddings from Gemini
summary: 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.
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paper_title: Gemini Embedding: Generalizable Embeddings from Gemini
paper_summary: 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.
paper_ai_summary: Gemini Embedding, utilizing Google's Gemini large language model, generates high-quality multilingual and code embeddings outperforming benchmarks across various tasks.
paper_ai_keywords: 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