Unverified50% confidenceFactExact time
EmbeddingGemma2 is based on the Gemma 2 language model architecture and was trained with multimodal contrastive learning.
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50%
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Long-term
Relevance
10/7/2026
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Unverified专用embedding模型通常基于双编码器(Bi-Encoder)架构训练,通过对比学习优化问题-段落向量距离,这是检索专用模型召回率优于通用模型的根本原因73% similarUnverifiedEmbeddingGemma2 employs Matryoshka Representation Learning, encoding the most important information at the front of the vector to support vector truncation.70% similarUnverifiedEmbedding模型本质上是Encoder-only Transformer架构,代表为BERT及其变体,通过对比学习或掩码语言模型任务预训练70% similarUnverifiedEmbedding model fine-tuning typically adopts a contrastive learning framework, training the model to bring positive sample pairs closer together and push negative sample pairs apart by constructing positive and negative sample pairs.68% similarUnverified现代多模态大模型采用统一的端到端训练框架,图像编码器与语言模型联合优化67% similar
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