README.md

---
language:
- en
base_model:
- google/gemma-2-9b-it
model-index:
- name: google/Gemma-Embeddings-v0.8
  results:
  - dataset:
      config: default
      name: MTEB ArguAna (default)
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
      split: test
      type: mteb/arguana
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      value: 68.35
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      value: 80.894
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      value: 82.664
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      value: 83.828
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      value: 84.084
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      value: 84.28
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      value: 84.28
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      value: 77.786
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    - type: map_at_20
      value: 79.349
    - type: map_at_100
      value: 79.38
    - type: map_at_1000
      value: 79.38
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    - type: recall_at_1000
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      value: 4.932
    - type: precision_at_100
      value: 0.996
    - type: precision_at_1000
      value: 0.1
    - type: mrr_at_1
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      value: 77.7975
    - type: mrr_at_5
      value: 78.8039
    - type: mrr_at_10
      value: 79.313
    - type: mrr_at_20
      value: 79.37870000000001
    - type: mrr_at_100
      value: 79.4095
    - type: mrr_at_1000
      value: 79.4095
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    - type: main_score
      value: 83.828
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB CQADupstackAndroidRetrieval (default)
      revision: f46a197baaae43b4f621051089b82a364682dfeb
      split: test
      type: mteb/cqadupstack-android
    metrics:
    - type: ndcg_at_1
      value: 48.069
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      value: 55.233
    - type: ndcg_at_5
      value: 57.809999999999995
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      value: 65.664
    - type: recall_at_10
      value: 74.367
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    - type: recall_at_1000
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    - type: precision_at_1
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    - type: precision_at_10
      value: 11.888
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      value: 7.06
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      value: 1.775
    - type: precision_at_1000
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    - type: mrr_at_1
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    task:
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  - dataset:
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      name: MTEB CQADupstackEnglishRetrieval (default)
      revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
      split: test
      type: mteb/cqadupstack-english
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    task:
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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](https://huggingface.co/google/gemma-2-9b). 

We use the [BGE-EN-ICL training data](https://huggingface.co/datasets/cfli/bge-full-data).

# Research Team

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