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This is a Experimental Study study.

October 20, 2025Open Access

EmbeddingGemma: Powerful and Lightweight Text Representations

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Model shows superior performance on the MTEB across multilingual and code domains, highlighting efficiency.

EmbeddingGemma achieves state-of-the-art results on the MTEB, outperforming larger models with fewer parameters.
With only 300M parameters, it remains competitive with models twice its size, pointing to its efficiency.
The innovative training strategy includes geometric embedding distillation, enhancing model robustness and expressiveness.
Released to the community, EmbeddingGemma promotes further research in effective embedding models.

Vera et al. (2025) studied this question.

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