pipawo1881/Gemma-Embeddings-v0.8

Gemma Embeddings v0.8

GemmaEmbed is a dense-vector embedding model, trained especially for retrieval. As of December 2, 2024, GemmaEmbed achieves the #1 position overall on the MTEB Retrieval leaderboard, with a score of 63.80.

Important Notes

• This is not an official Google product.
• This is a research project.

Results summary

Results compared to BGE-EN-ICL on several large datasets

Model	DBPedia	FEVER	HotPotQA	MSMARCO	NQ
BGE-EN-ICL	51.63	92.83	85.14	46.79	73.88
Gemma-Embeddings-v0.8	52.58	93.50	87.58	47.13	74.45

Model & Data

Our base encoder model is Gemma2 9B.

We use the BGE-EN-ICL training data.

Research Team

• Nicholas Monath
• Michael Boratko
• Seungyeon Kim
• Andrew McCallum
• Rob Fergus
• Manzil Zaheer