metadata
language:
- en
base_model:
- google/gemma-2-9b-it
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 63.90.
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