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.

本文介绍了双子嵌入，一种利用Google最先进的大型语言模型双子的强大能力的嵌入模型。双子嵌入能够生成高度可泛化的多语言文本嵌入，表现优于以往的模型，能够有效地应用于分类、相似性、聚类、排序和检索等任务，展现了在MMTEB基准测试中的出色表现。

双子嵌入：来自双子的可泛化嵌入