|
--- |
|
base_model: BeastyZ/e5-R-mistral-7b |
|
datasets: |
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- BeastyZ/E5-R |
|
language: |
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- en |
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library_name: transformers |
|
license: apache-2.0 |
|
tags: |
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- mteb |
|
- llama-cpp |
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- gguf-my-repo |
|
model-index: |
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- name: e5-R-mistral-7b |
|
results: |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB ArguAna |
|
type: mteb/arguana |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 33.57 |
|
- type: map_at_10 |
|
value: 49.952000000000005 |
|
- type: map_at_100 |
|
value: 50.673 |
|
- type: map_at_1000 |
|
value: 50.674 |
|
- type: map_at_3 |
|
value: 44.915 |
|
- type: map_at_5 |
|
value: 47.876999999999995 |
|
- type: mrr_at_1 |
|
value: 34.211000000000006 |
|
- type: mrr_at_10 |
|
value: 50.19 |
|
- type: mrr_at_100 |
|
value: 50.905 |
|
- type: mrr_at_1000 |
|
value: 50.906 |
|
- type: mrr_at_3 |
|
value: 45.128 |
|
- type: mrr_at_5 |
|
value: 48.097 |
|
- type: ndcg_at_1 |
|
value: 33.57 |
|
- type: ndcg_at_10 |
|
value: 58.994 |
|
- type: ndcg_at_100 |
|
value: 61.806000000000004 |
|
- type: ndcg_at_1000 |
|
value: 61.824999999999996 |
|
- type: ndcg_at_3 |
|
value: 48.681000000000004 |
|
- type: ndcg_at_5 |
|
value: 54.001 |
|
- type: precision_at_1 |
|
value: 33.57 |
|
- type: precision_at_10 |
|
value: 8.784 |
|
- type: precision_at_100 |
|
value: 0.9950000000000001 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 19.867 |
|
- type: precision_at_5 |
|
value: 14.495 |
|
- type: recall_at_1 |
|
value: 33.57 |
|
- type: recall_at_10 |
|
value: 87.83800000000001 |
|
- type: recall_at_100 |
|
value: 99.502 |
|
- type: recall_at_1000 |
|
value: 99.644 |
|
- type: recall_at_3 |
|
value: 59.602 |
|
- type: recall_at_5 |
|
value: 72.475 |
|
- type: main_score |
|
value: 58.994 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB CQADupstackRetrieval |
|
type: mteb/cqadupstack |
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config: default |
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split: test |
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revision: None |
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metrics: |
|
- type: map_at_1 |
|
value: 24.75 |
|
- type: map_at_10 |
|
value: 34.025 |
|
- type: map_at_100 |
|
value: 35.126000000000005 |
|
- type: map_at_1000 |
|
value: 35.219 |
|
- type: map_at_3 |
|
value: 31.607000000000003 |
|
- type: map_at_5 |
|
value: 32.962 |
|
- type: mrr_at_1 |
|
value: 27.357 |
|
- type: mrr_at_10 |
|
value: 36.370999999999995 |
|
- type: mrr_at_100 |
|
value: 37.364000000000004 |
|
- type: mrr_at_1000 |
|
value: 37.423 |
|
- type: mrr_at_3 |
|
value: 34.288000000000004 |
|
- type: mrr_at_5 |
|
value: 35.434 |
|
- type: ndcg_at_1 |
|
value: 27.357 |
|
- type: ndcg_at_10 |
|
value: 46.593999999999994 |
|
- type: ndcg_at_100 |
|
value: 44.317 |
|
- type: ndcg_at_1000 |
|
value: 46.475 |
|
- type: ndcg_at_3 |
|
value: 34.473 |
|
- type: ndcg_at_5 |
|
value: 36.561 |
|
- type: precision_at_1 |
|
value: 27.357 |
|
- type: precision_at_10 |
|
value: 6.081 |
|
- type: precision_at_100 |
|
value: 0.9299999999999999 |
|
- type: precision_at_1000 |
|
value: 0.124 |
|
- type: precision_at_3 |
|
value: 14.911 |
|
- type: precision_at_5 |
|
value: 10.24 |
|
- type: recall_at_1 |
|
value: 24.75 |
|
- type: recall_at_10 |
|
value: 51.856 |
|
- type: recall_at_100 |
|
value: 76.44300000000001 |
|
- type: recall_at_1000 |
|
value: 92.078 |
|
- type: recall_at_3 |
|
value: 39.427 |
|
- type: recall_at_5 |
|
value: 44.639 |
|
- type: main_score |
|
value: 46.593999999999994 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB ClimateFEVER |
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type: mteb/climate-fever |
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config: default |
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split: test |
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revision: None |
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metrics: |
|
- type: map_at_1 |
|
value: 16.436 |
|
- type: map_at_10 |
|
value: 29.693 |
|
- type: map_at_100 |
|
value: 32.179 |
|
- type: map_at_1000 |
|
value: 32.353 |
|
- type: map_at_3 |
|
value: 24.556 |
|
- type: map_at_5 |
|
value: 27.105 |
|
- type: mrr_at_1 |
|
value: 37.524 |
|
- type: mrr_at_10 |
|
value: 51.475 |
|
- type: mrr_at_100 |
|
value: 52.107000000000006 |
|
- type: mrr_at_1000 |
|
value: 52.123 |
|
- type: mrr_at_3 |
|
value: 48.35 |
|
- type: mrr_at_5 |
|
value: 50.249 |
|
- type: ndcg_at_1 |
|
value: 37.524 |
|
- type: ndcg_at_10 |
|
value: 40.258 |
|
- type: ndcg_at_100 |
|
value: 48.364000000000004 |
|
- type: ndcg_at_1000 |
|
value: 51.031000000000006 |
|
- type: ndcg_at_3 |
|
value: 33.359 |
|
- type: ndcg_at_5 |
|
value: 35.573 |
|
- type: precision_at_1 |
|
value: 37.524 |
|
- type: precision_at_10 |
|
value: 12.886000000000001 |
|
- type: precision_at_100 |
|
value: 2.169 |
|
- type: precision_at_1000 |
|
value: 0.268 |
|
- type: precision_at_3 |
|
value: 25.624000000000002 |
|
- type: precision_at_5 |
|
value: 19.453 |
|
- type: recall_at_1 |
|
value: 16.436 |
|
- type: recall_at_10 |
|
value: 47.77 |
|
- type: recall_at_100 |
|
value: 74.762 |
|
- type: recall_at_1000 |
|
value: 89.316 |
|
- type: recall_at_3 |
|
value: 30.508000000000003 |
|
- type: recall_at_5 |
|
value: 37.346000000000004 |
|
- type: main_score |
|
value: 40.258 |
|
- task: |
|
type: Retrieval |
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dataset: |
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name: MTEB DBPedia |
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type: mteb/dbpedia |
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config: default |
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split: test |
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revision: None |
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metrics: |
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- type: map_at_1 |
|
value: 10.147 |
|
- type: map_at_10 |
|
value: 24.631 |
|
- type: map_at_100 |
|
value: 35.657 |
|
- type: map_at_1000 |
|
value: 37.824999999999996 |
|
- type: map_at_3 |
|
value: 16.423 |
|
- type: map_at_5 |
|
value: 19.666 |
|
- type: mrr_at_1 |
|
value: 76.5 |
|
- type: mrr_at_10 |
|
value: 82.793 |
|
- type: mrr_at_100 |
|
value: 83.015 |
|
- type: mrr_at_1000 |
|
value: 83.021 |
|
- type: mrr_at_3 |
|
value: 81.75 |
|
- type: mrr_at_5 |
|
value: 82.375 |
|
- type: ndcg_at_1 |
|
value: 64.75 |
|
- type: ndcg_at_10 |
|
value: 51.031000000000006 |
|
- type: ndcg_at_100 |
|
value: 56.005 |
|
- type: ndcg_at_1000 |
|
value: 63.068000000000005 |
|
- type: ndcg_at_3 |
|
value: 54.571999999999996 |
|
- type: ndcg_at_5 |
|
value: 52.66499999999999 |
|
- type: precision_at_1 |
|
value: 76.5 |
|
- type: precision_at_10 |
|
value: 42.15 |
|
- type: precision_at_100 |
|
value: 13.22 |
|
- type: precision_at_1000 |
|
value: 2.5989999999999998 |
|
- type: precision_at_3 |
|
value: 58.416999999999994 |
|
- type: precision_at_5 |
|
value: 52.2 |
|
- type: recall_at_1 |
|
value: 10.147 |
|
- type: recall_at_10 |
|
value: 30.786 |
|
- type: recall_at_100 |
|
value: 62.873000000000005 |
|
- type: recall_at_1000 |
|
value: 85.358 |
|
- type: recall_at_3 |
|
value: 17.665 |
|
- type: recall_at_5 |
|
value: 22.088 |
|
- type: main_score |
|
value: 51.031000000000006 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB FEVER |
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type: mteb/fever |
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config: default |
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split: test |
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revision: None |
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metrics: |
|
- type: map_at_1 |
|
value: 78.52900000000001 |
|
- type: map_at_10 |
|
value: 87.24199999999999 |
|
- type: map_at_100 |
|
value: 87.446 |
|
- type: map_at_1000 |
|
value: 87.457 |
|
- type: map_at_3 |
|
value: 86.193 |
|
- type: map_at_5 |
|
value: 86.898 |
|
- type: mrr_at_1 |
|
value: 84.518 |
|
- type: mrr_at_10 |
|
value: 90.686 |
|
- type: mrr_at_100 |
|
value: 90.73 |
|
- type: mrr_at_1000 |
|
value: 90.731 |
|
- type: mrr_at_3 |
|
value: 90.227 |
|
- type: mrr_at_5 |
|
value: 90.575 |
|
- type: ndcg_at_1 |
|
value: 84.518 |
|
- type: ndcg_at_10 |
|
value: 90.324 |
|
- type: ndcg_at_100 |
|
value: 90.96300000000001 |
|
- type: ndcg_at_1000 |
|
value: 91.134 |
|
- type: ndcg_at_3 |
|
value: 88.937 |
|
- type: ndcg_at_5 |
|
value: 89.788 |
|
- type: precision_at_1 |
|
value: 84.518 |
|
- type: precision_at_10 |
|
value: 10.872 |
|
- type: precision_at_100 |
|
value: 1.1440000000000001 |
|
- type: precision_at_1000 |
|
value: 0.117 |
|
- type: precision_at_3 |
|
value: 34.108 |
|
- type: precision_at_5 |
|
value: 21.154999999999998 |
|
- type: recall_at_1 |
|
value: 78.52900000000001 |
|
- type: recall_at_10 |
|
value: 96.123 |
|
- type: recall_at_100 |
|
value: 98.503 |
|
- type: recall_at_1000 |
|
value: 99.518 |
|
- type: recall_at_3 |
|
value: 92.444 |
|
- type: recall_at_5 |
|
value: 94.609 |
|
- type: main_score |
|
value: 90.324 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB FiQA2018 |
|
type: mteb/fiqa |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 29.38 |
|
- type: map_at_10 |
|
value: 50.28 |
|
- type: map_at_100 |
|
value: 52.532999999999994 |
|
- type: map_at_1000 |
|
value: 52.641000000000005 |
|
- type: map_at_3 |
|
value: 43.556 |
|
- type: map_at_5 |
|
value: 47.617 |
|
- type: mrr_at_1 |
|
value: 56.79 |
|
- type: mrr_at_10 |
|
value: 65.666 |
|
- type: mrr_at_100 |
|
value: 66.211 |
|
- type: mrr_at_1000 |
|
value: 66.226 |
|
- type: mrr_at_3 |
|
value: 63.452 |
|
- type: mrr_at_5 |
|
value: 64.895 |
|
- type: ndcg_at_1 |
|
value: 56.79 |
|
- type: ndcg_at_10 |
|
value: 58.68 |
|
- type: ndcg_at_100 |
|
value: 65.22 |
|
- type: ndcg_at_1000 |
|
value: 66.645 |
|
- type: ndcg_at_3 |
|
value: 53.981 |
|
- type: ndcg_at_5 |
|
value: 55.95 |
|
- type: precision_at_1 |
|
value: 56.79 |
|
- type: precision_at_10 |
|
value: 16.311999999999998 |
|
- type: precision_at_100 |
|
value: 2.316 |
|
- type: precision_at_1000 |
|
value: 0.258 |
|
- type: precision_at_3 |
|
value: 36.214 |
|
- type: precision_at_5 |
|
value: 27.067999999999998 |
|
- type: recall_at_1 |
|
value: 29.38 |
|
- type: recall_at_10 |
|
value: 66.503 |
|
- type: recall_at_100 |
|
value: 89.885 |
|
- type: recall_at_1000 |
|
value: 97.954 |
|
- type: recall_at_3 |
|
value: 48.866 |
|
- type: recall_at_5 |
|
value: 57.60999999999999 |
|
- type: main_score |
|
value: 58.68 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB HotpotQA |
|
type: mteb/hotpotqa |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 42.134 |
|
- type: map_at_10 |
|
value: 73.412 |
|
- type: map_at_100 |
|
value: 74.144 |
|
- type: map_at_1000 |
|
value: 74.181 |
|
- type: map_at_3 |
|
value: 70.016 |
|
- type: map_at_5 |
|
value: 72.174 |
|
- type: mrr_at_1 |
|
value: 84.267 |
|
- type: mrr_at_10 |
|
value: 89.18599999999999 |
|
- type: mrr_at_100 |
|
value: 89.29599999999999 |
|
- type: mrr_at_1000 |
|
value: 89.298 |
|
- type: mrr_at_3 |
|
value: 88.616 |
|
- type: mrr_at_5 |
|
value: 88.957 |
|
- type: ndcg_at_1 |
|
value: 84.267 |
|
- type: ndcg_at_10 |
|
value: 80.164 |
|
- type: ndcg_at_100 |
|
value: 82.52199999999999 |
|
- type: ndcg_at_1000 |
|
value: 83.176 |
|
- type: ndcg_at_3 |
|
value: 75.616 |
|
- type: ndcg_at_5 |
|
value: 78.184 |
|
- type: precision_at_1 |
|
value: 84.267 |
|
- type: precision_at_10 |
|
value: 16.916 |
|
- type: precision_at_100 |
|
value: 1.872 |
|
- type: precision_at_1000 |
|
value: 0.196 |
|
- type: precision_at_3 |
|
value: 49.71 |
|
- type: precision_at_5 |
|
value: 31.854 |
|
- type: recall_at_1 |
|
value: 42.134 |
|
- type: recall_at_10 |
|
value: 84.578 |
|
- type: recall_at_100 |
|
value: 93.606 |
|
- type: recall_at_1000 |
|
value: 97.86 |
|
- type: recall_at_3 |
|
value: 74.564 |
|
- type: recall_at_5 |
|
value: 79.635 |
|
- type: main_score |
|
value: 80.164 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB MSMARCO |
|
type: mteb/msmarco |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.276 |
|
- type: map_at_10 |
|
value: 35.493 |
|
- type: map_at_100 |
|
value: 36.656 |
|
- type: map_at_1000 |
|
value: 36.699 |
|
- type: map_at_3 |
|
value: 31.320999999999998 |
|
- type: map_at_5 |
|
value: 33.772999999999996 |
|
- type: mrr_at_1 |
|
value: 22.966 |
|
- type: mrr_at_10 |
|
value: 36.074 |
|
- type: mrr_at_100 |
|
value: 37.183 |
|
- type: mrr_at_1000 |
|
value: 37.219 |
|
- type: mrr_at_3 |
|
value: 31.984 |
|
- type: mrr_at_5 |
|
value: 34.419 |
|
- type: ndcg_at_1 |
|
value: 22.966 |
|
- type: ndcg_at_10 |
|
value: 42.895 |
|
- type: ndcg_at_100 |
|
value: 48.453 |
|
- type: ndcg_at_1000 |
|
value: 49.464999999999996 |
|
- type: ndcg_at_3 |
|
value: 34.410000000000004 |
|
- type: ndcg_at_5 |
|
value: 38.78 |
|
- type: precision_at_1 |
|
value: 22.966 |
|
- type: precision_at_10 |
|
value: 6.88 |
|
- type: precision_at_100 |
|
value: 0.966 |
|
- type: precision_at_1000 |
|
value: 0.105 |
|
- type: precision_at_3 |
|
value: 14.785 |
|
- type: precision_at_5 |
|
value: 11.074 |
|
- type: recall_at_1 |
|
value: 22.276 |
|
- type: recall_at_10 |
|
value: 65.756 |
|
- type: recall_at_100 |
|
value: 91.34100000000001 |
|
- type: recall_at_1000 |
|
value: 98.957 |
|
- type: recall_at_3 |
|
value: 42.67 |
|
- type: recall_at_5 |
|
value: 53.161 |
|
- type: main_score |
|
value: 42.895 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB NFCorpus |
|
type: mteb/nfcorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 7.188999999999999 |
|
- type: map_at_10 |
|
value: 16.176 |
|
- type: map_at_100 |
|
value: 20.504 |
|
- type: map_at_1000 |
|
value: 22.203999999999997 |
|
- type: map_at_3 |
|
value: 11.766 |
|
- type: map_at_5 |
|
value: 13.655999999999999 |
|
- type: mrr_at_1 |
|
value: 55.418 |
|
- type: mrr_at_10 |
|
value: 62.791 |
|
- type: mrr_at_100 |
|
value: 63.339 |
|
- type: mrr_at_1000 |
|
value: 63.369 |
|
- type: mrr_at_3 |
|
value: 60.99099999999999 |
|
- type: mrr_at_5 |
|
value: 62.059 |
|
- type: ndcg_at_1 |
|
value: 53.715 |
|
- type: ndcg_at_10 |
|
value: 41.377 |
|
- type: ndcg_at_100 |
|
value: 37.999 |
|
- type: ndcg_at_1000 |
|
value: 46.726 |
|
- type: ndcg_at_3 |
|
value: 47.262 |
|
- type: ndcg_at_5 |
|
value: 44.708999999999996 |
|
- type: precision_at_1 |
|
value: 55.108000000000004 |
|
- type: precision_at_10 |
|
value: 30.154999999999998 |
|
- type: precision_at_100 |
|
value: 9.582 |
|
- type: precision_at_1000 |
|
value: 2.2720000000000002 |
|
- type: precision_at_3 |
|
value: 43.55 |
|
- type: precision_at_5 |
|
value: 38.204 |
|
- type: recall_at_1 |
|
value: 7.188999999999999 |
|
- type: recall_at_10 |
|
value: 20.655 |
|
- type: recall_at_100 |
|
value: 38.068000000000005 |
|
- type: recall_at_1000 |
|
value: 70.208 |
|
- type: recall_at_3 |
|
value: 12.601 |
|
- type: recall_at_5 |
|
value: 15.573999999999998 |
|
- type: main_score |
|
value: 41.377 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB NQ |
|
type: mteb/nq |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 46.017 |
|
- type: map_at_10 |
|
value: 62.910999999999994 |
|
- type: map_at_100 |
|
value: 63.526 |
|
- type: map_at_1000 |
|
value: 63.536 |
|
- type: map_at_3 |
|
value: 59.077999999999996 |
|
- type: map_at_5 |
|
value: 61.521 |
|
- type: mrr_at_1 |
|
value: 51.68000000000001 |
|
- type: mrr_at_10 |
|
value: 65.149 |
|
- type: mrr_at_100 |
|
value: 65.542 |
|
- type: mrr_at_1000 |
|
value: 65.55 |
|
- type: mrr_at_3 |
|
value: 62.49 |
|
- type: mrr_at_5 |
|
value: 64.178 |
|
- type: ndcg_at_1 |
|
value: 51.651 |
|
- type: ndcg_at_10 |
|
value: 69.83500000000001 |
|
- type: ndcg_at_100 |
|
value: 72.18 |
|
- type: ndcg_at_1000 |
|
value: 72.393 |
|
- type: ndcg_at_3 |
|
value: 63.168 |
|
- type: ndcg_at_5 |
|
value: 66.958 |
|
- type: precision_at_1 |
|
value: 51.651 |
|
- type: precision_at_10 |
|
value: 10.626 |
|
- type: precision_at_100 |
|
value: 1.195 |
|
- type: precision_at_1000 |
|
value: 0.121 |
|
- type: precision_at_3 |
|
value: 28.012999999999998 |
|
- type: precision_at_5 |
|
value: 19.09 |
|
- type: recall_at_1 |
|
value: 46.017 |
|
- type: recall_at_10 |
|
value: 88.345 |
|
- type: recall_at_100 |
|
value: 98.129 |
|
- type: recall_at_1000 |
|
value: 99.696 |
|
- type: recall_at_3 |
|
value: 71.531 |
|
- type: recall_at_5 |
|
value: 80.108 |
|
- type: main_score |
|
value: 69.83500000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB QuoraRetrieval |
|
type: mteb/quora |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 72.473 |
|
- type: map_at_10 |
|
value: 86.72800000000001 |
|
- type: map_at_100 |
|
value: 87.323 |
|
- type: map_at_1000 |
|
value: 87.332 |
|
- type: map_at_3 |
|
value: 83.753 |
|
- type: map_at_5 |
|
value: 85.627 |
|
- type: mrr_at_1 |
|
value: 83.39 |
|
- type: mrr_at_10 |
|
value: 89.149 |
|
- type: mrr_at_100 |
|
value: 89.228 |
|
- type: mrr_at_1000 |
|
value: 89.229 |
|
- type: mrr_at_3 |
|
value: 88.335 |
|
- type: mrr_at_5 |
|
value: 88.895 |
|
- type: ndcg_at_1 |
|
value: 83.39 |
|
- type: ndcg_at_10 |
|
value: 90.109 |
|
- type: ndcg_at_100 |
|
value: 91.09 |
|
- type: ndcg_at_1000 |
|
value: 91.13900000000001 |
|
- type: ndcg_at_3 |
|
value: 87.483 |
|
- type: ndcg_at_5 |
|
value: 88.942 |
|
- type: precision_at_1 |
|
value: 83.39 |
|
- type: precision_at_10 |
|
value: 13.711 |
|
- type: precision_at_100 |
|
value: 1.549 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 38.342999999999996 |
|
- type: precision_at_5 |
|
value: 25.188 |
|
- type: recall_at_1 |
|
value: 72.473 |
|
- type: recall_at_10 |
|
value: 96.57 |
|
- type: recall_at_100 |
|
value: 99.792 |
|
- type: recall_at_1000 |
|
value: 99.99900000000001 |
|
- type: recall_at_3 |
|
value: 88.979 |
|
- type: recall_at_5 |
|
value: 93.163 |
|
- type: main_score |
|
value: 90.109 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB SCIDOCS |
|
type: mteb/scidocs |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.598 |
|
- type: map_at_10 |
|
value: 11.405999999999999 |
|
- type: map_at_100 |
|
value: 13.447999999999999 |
|
- type: map_at_1000 |
|
value: 13.758999999999999 |
|
- type: map_at_3 |
|
value: 8.332 |
|
- type: map_at_5 |
|
value: 9.709 |
|
- type: mrr_at_1 |
|
value: 22.6 |
|
- type: mrr_at_10 |
|
value: 32.978 |
|
- type: mrr_at_100 |
|
value: 34.149 |
|
- type: mrr_at_1000 |
|
value: 34.213 |
|
- type: mrr_at_3 |
|
value: 29.7 |
|
- type: mrr_at_5 |
|
value: 31.485000000000003 |
|
- type: ndcg_at_1 |
|
value: 22.6 |
|
- type: ndcg_at_10 |
|
value: 19.259999999999998 |
|
- type: ndcg_at_100 |
|
value: 27.21 |
|
- type: ndcg_at_1000 |
|
value: 32.7 |
|
- type: ndcg_at_3 |
|
value: 18.445 |
|
- type: ndcg_at_5 |
|
value: 15.812000000000001 |
|
- type: precision_at_1 |
|
value: 22.6 |
|
- type: precision_at_10 |
|
value: 9.959999999999999 |
|
- type: precision_at_100 |
|
value: 2.139 |
|
- type: precision_at_1000 |
|
value: 0.345 |
|
- type: precision_at_3 |
|
value: 17.299999999999997 |
|
- type: precision_at_5 |
|
value: 13.719999999999999 |
|
- type: recall_at_1 |
|
value: 4.598 |
|
- type: recall_at_10 |
|
value: 20.186999999999998 |
|
- type: recall_at_100 |
|
value: 43.362 |
|
- type: recall_at_1000 |
|
value: 70.11800000000001 |
|
- type: recall_at_3 |
|
value: 10.543 |
|
- type: recall_at_5 |
|
value: 13.923 |
|
- type: main_score |
|
value: 19.259999999999998 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB SciFact |
|
type: mteb/scifact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 65.467 |
|
- type: map_at_10 |
|
value: 74.935 |
|
- type: map_at_100 |
|
value: 75.395 |
|
- type: map_at_1000 |
|
value: 75.412 |
|
- type: map_at_3 |
|
value: 72.436 |
|
- type: map_at_5 |
|
value: 73.978 |
|
- type: mrr_at_1 |
|
value: 68.667 |
|
- type: mrr_at_10 |
|
value: 76.236 |
|
- type: mrr_at_100 |
|
value: 76.537 |
|
- type: mrr_at_1000 |
|
value: 76.55499999999999 |
|
- type: mrr_at_3 |
|
value: 74.722 |
|
- type: mrr_at_5 |
|
value: 75.639 |
|
- type: ndcg_at_1 |
|
value: 68.667 |
|
- type: ndcg_at_10 |
|
value: 78.92099999999999 |
|
- type: ndcg_at_100 |
|
value: 80.645 |
|
- type: ndcg_at_1000 |
|
value: 81.045 |
|
- type: ndcg_at_3 |
|
value: 75.19500000000001 |
|
- type: ndcg_at_5 |
|
value: 77.114 |
|
- type: precision_at_1 |
|
value: 68.667 |
|
- type: precision_at_10 |
|
value: 10.133000000000001 |
|
- type: precision_at_100 |
|
value: 1.0999999999999999 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 28.889 |
|
- type: precision_at_5 |
|
value: 18.8 |
|
- type: recall_at_1 |
|
value: 65.467 |
|
- type: recall_at_10 |
|
value: 89.517 |
|
- type: recall_at_100 |
|
value: 97 |
|
- type: recall_at_1000 |
|
value: 100 |
|
- type: recall_at_3 |
|
value: 79.72200000000001 |
|
- type: recall_at_5 |
|
value: 84.511 |
|
- type: main_score |
|
value: 78.92099999999999 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB TRECCOVID |
|
type: mteb/trec-covid |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.244 |
|
- type: map_at_10 |
|
value: 2.183 |
|
- type: map_at_100 |
|
value: 13.712 |
|
- type: map_at_1000 |
|
value: 33.147 |
|
- type: map_at_3 |
|
value: 0.7270000000000001 |
|
- type: map_at_5 |
|
value: 1.199 |
|
- type: mrr_at_1 |
|
value: 94 |
|
- type: mrr_at_10 |
|
value: 97 |
|
- type: mrr_at_100 |
|
value: 97 |
|
- type: mrr_at_1000 |
|
value: 97 |
|
- type: mrr_at_3 |
|
value: 97 |
|
- type: mrr_at_5 |
|
value: 97 |
|
- type: ndcg_at_1 |
|
value: 92 |
|
- type: ndcg_at_10 |
|
value: 84.399 |
|
- type: ndcg_at_100 |
|
value: 66.771 |
|
- type: ndcg_at_1000 |
|
value: 59.092 |
|
- type: ndcg_at_3 |
|
value: 89.173 |
|
- type: ndcg_at_5 |
|
value: 88.52600000000001 |
|
- type: precision_at_1 |
|
value: 94 |
|
- type: precision_at_10 |
|
value: 86.8 |
|
- type: precision_at_100 |
|
value: 68.24 |
|
- type: precision_at_1000 |
|
value: 26.003999999999998 |
|
- type: precision_at_3 |
|
value: 92.667 |
|
- type: precision_at_5 |
|
value: 92.4 |
|
- type: recall_at_1 |
|
value: 0.244 |
|
- type: recall_at_10 |
|
value: 2.302 |
|
- type: recall_at_100 |
|
value: 16.622 |
|
- type: recall_at_1000 |
|
value: 55.175 |
|
- type: recall_at_3 |
|
value: 0.748 |
|
- type: recall_at_5 |
|
value: 1.247 |
|
- type: main_score |
|
value: 84.399 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB Touche2020 |
|
type: mteb/touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 2.707 |
|
- type: map_at_10 |
|
value: 10.917 |
|
- type: map_at_100 |
|
value: 16.308 |
|
- type: map_at_1000 |
|
value: 17.953 |
|
- type: map_at_3 |
|
value: 5.65 |
|
- type: map_at_5 |
|
value: 7.379 |
|
- type: mrr_at_1 |
|
value: 34.694 |
|
- type: mrr_at_10 |
|
value: 49.745 |
|
- type: mrr_at_100 |
|
value: 50.309000000000005 |
|
- type: mrr_at_1000 |
|
value: 50.32 |
|
- type: mrr_at_3 |
|
value: 44.897999999999996 |
|
- type: mrr_at_5 |
|
value: 48.061 |
|
- type: ndcg_at_1 |
|
value: 33.672999999999995 |
|
- type: ndcg_at_10 |
|
value: 26.894000000000002 |
|
- type: ndcg_at_100 |
|
value: 37.423 |
|
- type: ndcg_at_1000 |
|
value: 49.376999999999995 |
|
- type: ndcg_at_3 |
|
value: 30.456 |
|
- type: ndcg_at_5 |
|
value: 27.772000000000002 |
|
- type: precision_at_1 |
|
value: 34.694 |
|
- type: precision_at_10 |
|
value: 23.878 |
|
- type: precision_at_100 |
|
value: 7.489999999999999 |
|
- type: precision_at_1000 |
|
value: 1.555 |
|
- type: precision_at_3 |
|
value: 31.293 |
|
- type: precision_at_5 |
|
value: 26.939 |
|
- type: recall_at_1 |
|
value: 2.707 |
|
- type: recall_at_10 |
|
value: 18.104 |
|
- type: recall_at_100 |
|
value: 46.93 |
|
- type: recall_at_1000 |
|
value: 83.512 |
|
- type: recall_at_3 |
|
value: 6.622999999999999 |
|
- type: recall_at_5 |
|
value: 10.051 |
|
- type: main_score |
|
value: 26.894000000000002 |
|
--- |
|
|
|
# VenkatNDivi77/e5-R-mistral-7b-Q4_K_M-GGUF |
|
This model was converted to GGUF format from [`BeastyZ/e5-R-mistral-7b`](https://huggingface.co/BeastyZ/e5-R-mistral-7b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. |
|
Refer to the [original model card](https://huggingface.co/BeastyZ/e5-R-mistral-7b) for more details on the model. |
|
|
|
## Use with llama.cpp |
|
Install llama.cpp through brew (works on Mac and Linux) |
|
|
|
```bash |
|
brew install llama.cpp |
|
|
|
``` |
|
Invoke the llama.cpp server or the CLI. |
|
|
|
### CLI: |
|
```bash |
|
llama-cli --hf-repo VenkatNDivi77/e5-R-mistral-7b-Q4_K_M-GGUF --hf-file e5-r-mistral-7b-q4_k_m.gguf -p "The meaning to life and the universe is" |
|
``` |
|
|
|
### Server: |
|
```bash |
|
llama-server --hf-repo VenkatNDivi77/e5-R-mistral-7b-Q4_K_M-GGUF --hf-file e5-r-mistral-7b-q4_k_m.gguf -c 2048 |
|
``` |
|
|
|
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. |
|
|
|
Step 1: Clone llama.cpp from GitHub. |
|
``` |
|
git clone https://github.com/ggerganov/llama.cpp |
|
``` |
|
|
|
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). |
|
``` |
|
cd llama.cpp && LLAMA_CURL=1 make |
|
``` |
|
|
|
Step 3: Run inference through the main binary. |
|
``` |
|
./llama-cli --hf-repo VenkatNDivi77/e5-R-mistral-7b-Q4_K_M-GGUF --hf-file e5-r-mistral-7b-q4_k_m.gguf -p "The meaning to life and the universe is" |
|
``` |
|
or |
|
``` |
|
./llama-server --hf-repo VenkatNDivi77/e5-R-mistral-7b-Q4_K_M-GGUF --hf-file e5-r-mistral-7b-q4_k_m.gguf -c 2048 |
|
``` |
|
|
