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Gemma-Embeddings-v1.0
pipawo1881/Gemma-Embeddings-v1.0
Gemma-Embeddings-v1.0 is a machine learning model from pipawo1881. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as gemma.
GemmaEmbed is a dense-vector embedding model, trained especially for retrieval. As of December 12, 2024, GemmaEmbed achieves the 1 position overall on the MTEB leaderboard, with a score of 72.72.
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At a glance
- License
- gemma
- Access
- Public
- Created
- May 19, 2025
- Updated
- May 19, 2025
- SHA
- a6f3f3ea
Base models
Model card
From the Hugging Face model README
Gemma Embeddings v1.0
GemmaEmbed is a dense-vector embedding model, trained especially for retrieval. As of December 12, 2024, GemmaEmbed achieves the #1 position overall on the MTEB leaderboard, with a score of 72.72.
Important Notes
- This is not an official Google product.
- This is a research project.
Results summary
Results comparing with BGE-EN-ICL and NV-Embed-v2 on each task in MTEB:
| Model | Total (56) | Classification (12) | Classification Pair (3) | STS (10) | Clustering (11) | Reranking (4) | Retrieval (15) | Summary (1) |
|---|---|---|---|---|---|---|---|---|
| bge-en-icl | 0.7167 | 0.8895 | 0.8814 | 0.8425 | 0.5789 | 0.5986 | 0.6216 | 0.3077 |
| NV-Embed-v2 | 0.7231 | 0.9037 | 0.8867 | 0.8431 | 0.5846 | 0.6065 | 0.6265 | 0.3070 |
| Gemma-Embeddings-v1.0 | 0.7272 | 0.9000 | 0.8809 | 0.8423 | 0.5826 | 0.6214 | 0.6371 | 0.4052 |
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
- License
- gemma
- Languages
- en
- Created
- May 19, 2025
- Updated
- May 19, 2025