metadata
language:
- en
base_model:
- google/gemma-2-9b-it
model-index:
- name: google/Gemma-Embeddings-v0.8
results:
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config: default
name: MTEB ArguAna (default)
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
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task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackAndroidRetrieval (default)
revision: f46a197baaae43b4f621051089b82a364682dfeb
split: test
type: mteb/cqadupstack-android
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task:
type: Retrieval
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config: default
name: MTEB CQADupstackEnglishRetrieval (default)
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
split: test
type: mteb/cqadupstack-english
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revision: CQADupstackRetrieval_is_a_combined_dataset
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type: CQADupstackRetrieval_is_a_combined_dataset
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task:
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revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
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type: mteb/cqadupstack-wordpress
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name: MTEB Touche2020 (default)
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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task:
type: Retrieval
tags:
- mteb
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