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--- |
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tags: |
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- mteb |
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- Sentence Transformers |
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- sentence-similarity |
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- sentence-transformers |
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model-index: |
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- name: e5-base-v2 |
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results: |
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- task: |
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type: Classification |
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dataset: |
|
type: mteb/amazon_counterfactual |
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name: MTEB AmazonCounterfactualClassification (en) |
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config: en |
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split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
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metrics: |
|
- type: accuracy |
|
value: 77.77611940298506 |
|
- type: ap |
|
value: 42.052710266606056 |
|
- type: f1 |
|
value: 72.12040628266567 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
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name: MTEB AmazonPolarityClassification |
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config: default |
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split: test |
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revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
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metrics: |
|
- type: accuracy |
|
value: 92.81012500000001 |
|
- type: ap |
|
value: 89.4213700757244 |
|
- type: f1 |
|
value: 92.8039091197065 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
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config: en |
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split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
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metrics: |
|
- type: accuracy |
|
value: 46.711999999999996 |
|
- type: f1 |
|
value: 46.11544975436018 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: arguana |
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name: MTEB ArguAna |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.186 |
|
- type: map_at_10 |
|
value: 36.632999999999996 |
|
- type: map_at_100 |
|
value: 37.842 |
|
- type: map_at_1000 |
|
value: 37.865 |
|
- type: map_at_3 |
|
value: 32.278 |
|
- type: map_at_5 |
|
value: 34.760999999999996 |
|
- type: mrr_at_1 |
|
value: 23.400000000000002 |
|
- type: mrr_at_10 |
|
value: 36.721 |
|
- type: mrr_at_100 |
|
value: 37.937 |
|
- type: mrr_at_1000 |
|
value: 37.96 |
|
- type: mrr_at_3 |
|
value: 32.302 |
|
- type: mrr_at_5 |
|
value: 34.894 |
|
- type: ndcg_at_1 |
|
value: 23.186 |
|
- type: ndcg_at_10 |
|
value: 44.49 |
|
- type: ndcg_at_100 |
|
value: 50.065000000000005 |
|
- type: ndcg_at_1000 |
|
value: 50.629999999999995 |
|
- type: ndcg_at_3 |
|
value: 35.461 |
|
- type: ndcg_at_5 |
|
value: 39.969 |
|
- type: precision_at_1 |
|
value: 23.186 |
|
- type: precision_at_10 |
|
value: 6.97 |
|
- type: precision_at_100 |
|
value: 0.951 |
|
- type: precision_at_1000 |
|
value: 0.099 |
|
- type: precision_at_3 |
|
value: 14.912 |
|
- type: precision_at_5 |
|
value: 11.152 |
|
- type: recall_at_1 |
|
value: 23.186 |
|
- type: recall_at_10 |
|
value: 69.70100000000001 |
|
- type: recall_at_100 |
|
value: 95.092 |
|
- type: recall_at_1000 |
|
value: 99.431 |
|
- type: recall_at_3 |
|
value: 44.737 |
|
- type: recall_at_5 |
|
value: 55.761 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
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split: test |
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revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 46.10312401440185 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
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config: default |
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split: test |
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revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 39.67275326095384 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
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name: MTEB AskUbuntuDupQuestions |
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config: default |
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split: test |
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revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
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metrics: |
|
- type: map |
|
value: 58.97793816337376 |
|
- type: mrr |
|
value: 72.76832431957087 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
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split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.11646947018187 |
|
- type: cos_sim_spearman |
|
value: 81.40064994975234 |
|
- type: euclidean_pearson |
|
value: 82.37355689019232 |
|
- type: euclidean_spearman |
|
value: 81.6777646977348 |
|
- type: manhattan_pearson |
|
value: 82.61101422716945 |
|
- type: manhattan_spearman |
|
value: 81.80427360442245 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 83.52922077922076 |
|
- type: f1 |
|
value: 83.45298679360866 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
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config: default |
|
split: test |
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 37.495115019668496 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
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config: default |
|
split: test |
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 32.724792944166765 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
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name: MTEB CQADupstackAndroidRetrieval |
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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: 32.361000000000004 |
|
- type: map_at_10 |
|
value: 43.765 |
|
- type: map_at_100 |
|
value: 45.224 |
|
- type: map_at_1000 |
|
value: 45.35 |
|
- type: map_at_3 |
|
value: 40.353 |
|
- type: map_at_5 |
|
value: 42.195 |
|
- type: mrr_at_1 |
|
value: 40.629 |
|
- type: mrr_at_10 |
|
value: 50.458000000000006 |
|
- type: mrr_at_100 |
|
value: 51.06699999999999 |
|
- type: mrr_at_1000 |
|
value: 51.12 |
|
- type: mrr_at_3 |
|
value: 47.902 |
|
- type: mrr_at_5 |
|
value: 49.447 |
|
- type: ndcg_at_1 |
|
value: 40.629 |
|
- type: ndcg_at_10 |
|
value: 50.376 |
|
- type: ndcg_at_100 |
|
value: 55.065 |
|
- type: ndcg_at_1000 |
|
value: 57.196000000000005 |
|
- type: ndcg_at_3 |
|
value: 45.616 |
|
- type: ndcg_at_5 |
|
value: 47.646 |
|
- type: precision_at_1 |
|
value: 40.629 |
|
- type: precision_at_10 |
|
value: 9.785 |
|
- type: precision_at_100 |
|
value: 1.562 |
|
- type: precision_at_1000 |
|
value: 0.2 |
|
- type: precision_at_3 |
|
value: 22.031 |
|
- type: precision_at_5 |
|
value: 15.737000000000002 |
|
- type: recall_at_1 |
|
value: 32.361000000000004 |
|
- type: recall_at_10 |
|
value: 62.214000000000006 |
|
- type: recall_at_100 |
|
value: 81.464 |
|
- type: recall_at_1000 |
|
value: 95.905 |
|
- type: recall_at_3 |
|
value: 47.5 |
|
- type: recall_at_5 |
|
value: 53.69500000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.971 |
|
- type: map_at_10 |
|
value: 37.444 |
|
- type: map_at_100 |
|
value: 38.607 |
|
- type: map_at_1000 |
|
value: 38.737 |
|
- type: map_at_3 |
|
value: 34.504000000000005 |
|
- type: map_at_5 |
|
value: 36.234 |
|
- type: mrr_at_1 |
|
value: 35.35 |
|
- type: mrr_at_10 |
|
value: 43.441 |
|
- type: mrr_at_100 |
|
value: 44.147999999999996 |
|
- type: mrr_at_1000 |
|
value: 44.196000000000005 |
|
- type: mrr_at_3 |
|
value: 41.285 |
|
- type: mrr_at_5 |
|
value: 42.552 |
|
- type: ndcg_at_1 |
|
value: 35.35 |
|
- type: ndcg_at_10 |
|
value: 42.903999999999996 |
|
- type: ndcg_at_100 |
|
value: 47.406 |
|
- type: ndcg_at_1000 |
|
value: 49.588 |
|
- type: ndcg_at_3 |
|
value: 38.778 |
|
- type: ndcg_at_5 |
|
value: 40.788000000000004 |
|
- type: precision_at_1 |
|
value: 35.35 |
|
- type: precision_at_10 |
|
value: 8.083 |
|
- type: precision_at_100 |
|
value: 1.313 |
|
- type: precision_at_1000 |
|
value: 0.18 |
|
- type: precision_at_3 |
|
value: 18.769 |
|
- type: precision_at_5 |
|
value: 13.439 |
|
- type: recall_at_1 |
|
value: 27.971 |
|
- type: recall_at_10 |
|
value: 52.492000000000004 |
|
- type: recall_at_100 |
|
value: 71.642 |
|
- type: recall_at_1000 |
|
value: 85.488 |
|
- type: recall_at_3 |
|
value: 40.1 |
|
- type: recall_at_5 |
|
value: 45.800000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 39.898 |
|
- type: map_at_10 |
|
value: 51.819 |
|
- type: map_at_100 |
|
value: 52.886 |
|
- type: map_at_1000 |
|
value: 52.941 |
|
- type: map_at_3 |
|
value: 48.619 |
|
- type: map_at_5 |
|
value: 50.493 |
|
- type: mrr_at_1 |
|
value: 45.391999999999996 |
|
- type: mrr_at_10 |
|
value: 55.230000000000004 |
|
- type: mrr_at_100 |
|
value: 55.887 |
|
- type: mrr_at_1000 |
|
value: 55.916 |
|
- type: mrr_at_3 |
|
value: 52.717000000000006 |
|
- type: mrr_at_5 |
|
value: 54.222 |
|
- type: ndcg_at_1 |
|
value: 45.391999999999996 |
|
- type: ndcg_at_10 |
|
value: 57.586999999999996 |
|
- type: ndcg_at_100 |
|
value: 61.745000000000005 |
|
- type: ndcg_at_1000 |
|
value: 62.83800000000001 |
|
- type: ndcg_at_3 |
|
value: 52.207 |
|
- type: ndcg_at_5 |
|
value: 54.925999999999995 |
|
- type: precision_at_1 |
|
value: 45.391999999999996 |
|
- type: precision_at_10 |
|
value: 9.21 |
|
- type: precision_at_100 |
|
value: 1.226 |
|
- type: precision_at_1000 |
|
value: 0.136 |
|
- type: precision_at_3 |
|
value: 23.177 |
|
- type: precision_at_5 |
|
value: 16.038 |
|
- type: recall_at_1 |
|
value: 39.898 |
|
- type: recall_at_10 |
|
value: 71.18900000000001 |
|
- type: recall_at_100 |
|
value: 89.082 |
|
- type: recall_at_1000 |
|
value: 96.865 |
|
- type: recall_at_3 |
|
value: 56.907 |
|
- type: recall_at_5 |
|
value: 63.397999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.706 |
|
- type: map_at_10 |
|
value: 30.818 |
|
- type: map_at_100 |
|
value: 32.038 |
|
- type: map_at_1000 |
|
value: 32.123000000000005 |
|
- type: map_at_3 |
|
value: 28.077 |
|
- type: map_at_5 |
|
value: 29.709999999999997 |
|
- type: mrr_at_1 |
|
value: 24.407 |
|
- type: mrr_at_10 |
|
value: 32.555 |
|
- type: mrr_at_100 |
|
value: 33.692 |
|
- type: mrr_at_1000 |
|
value: 33.751 |
|
- type: mrr_at_3 |
|
value: 29.848999999999997 |
|
- type: mrr_at_5 |
|
value: 31.509999999999998 |
|
- type: ndcg_at_1 |
|
value: 24.407 |
|
- type: ndcg_at_10 |
|
value: 35.624 |
|
- type: ndcg_at_100 |
|
value: 41.454 |
|
- type: ndcg_at_1000 |
|
value: 43.556 |
|
- type: ndcg_at_3 |
|
value: 30.217 |
|
- type: ndcg_at_5 |
|
value: 33.111000000000004 |
|
- type: precision_at_1 |
|
value: 24.407 |
|
- type: precision_at_10 |
|
value: 5.548 |
|
- type: precision_at_100 |
|
value: 0.8869999999999999 |
|
- type: precision_at_1000 |
|
value: 0.11100000000000002 |
|
- type: precision_at_3 |
|
value: 12.731 |
|
- type: precision_at_5 |
|
value: 9.22 |
|
- type: recall_at_1 |
|
value: 22.706 |
|
- type: recall_at_10 |
|
value: 48.772 |
|
- type: recall_at_100 |
|
value: 75.053 |
|
- type: recall_at_1000 |
|
value: 90.731 |
|
- type: recall_at_3 |
|
value: 34.421 |
|
- type: recall_at_5 |
|
value: 41.427 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 13.424 |
|
- type: map_at_10 |
|
value: 21.09 |
|
- type: map_at_100 |
|
value: 22.264999999999997 |
|
- type: map_at_1000 |
|
value: 22.402 |
|
- type: map_at_3 |
|
value: 18.312 |
|
- type: map_at_5 |
|
value: 19.874 |
|
- type: mrr_at_1 |
|
value: 16.915 |
|
- type: mrr_at_10 |
|
value: 25.258000000000003 |
|
- type: mrr_at_100 |
|
value: 26.228 |
|
- type: mrr_at_1000 |
|
value: 26.31 |
|
- type: mrr_at_3 |
|
value: 22.492 |
|
- type: mrr_at_5 |
|
value: 24.04 |
|
- type: ndcg_at_1 |
|
value: 16.915 |
|
- type: ndcg_at_10 |
|
value: 26.266000000000002 |
|
- type: ndcg_at_100 |
|
value: 32.08 |
|
- type: ndcg_at_1000 |
|
value: 35.086 |
|
- type: ndcg_at_3 |
|
value: 21.049 |
|
- type: ndcg_at_5 |
|
value: 23.508000000000003 |
|
- type: precision_at_1 |
|
value: 16.915 |
|
- type: precision_at_10 |
|
value: 5.1 |
|
- type: precision_at_100 |
|
value: 0.9329999999999999 |
|
- type: precision_at_1000 |
|
value: 0.131 |
|
- type: precision_at_3 |
|
value: 10.282 |
|
- type: precision_at_5 |
|
value: 7.836 |
|
- type: recall_at_1 |
|
value: 13.424 |
|
- type: recall_at_10 |
|
value: 38.179 |
|
- type: recall_at_100 |
|
value: 63.906 |
|
- type: recall_at_1000 |
|
value: 84.933 |
|
- type: recall_at_3 |
|
value: 23.878 |
|
- type: recall_at_5 |
|
value: 30.037999999999997 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.154 |
|
- type: map_at_10 |
|
value: 35.912 |
|
- type: map_at_100 |
|
value: 37.211 |
|
- type: map_at_1000 |
|
value: 37.327 |
|
- type: map_at_3 |
|
value: 32.684999999999995 |
|
- type: map_at_5 |
|
value: 34.562 |
|
- type: mrr_at_1 |
|
value: 32.435 |
|
- type: mrr_at_10 |
|
value: 41.411 |
|
- type: mrr_at_100 |
|
value: 42.297000000000004 |
|
- type: mrr_at_1000 |
|
value: 42.345 |
|
- type: mrr_at_3 |
|
value: 38.771 |
|
- type: mrr_at_5 |
|
value: 40.33 |
|
- type: ndcg_at_1 |
|
value: 32.435 |
|
- type: ndcg_at_10 |
|
value: 41.785 |
|
- type: ndcg_at_100 |
|
value: 47.469 |
|
- type: ndcg_at_1000 |
|
value: 49.685 |
|
- type: ndcg_at_3 |
|
value: 36.618 |
|
- type: ndcg_at_5 |
|
value: 39.101 |
|
- type: precision_at_1 |
|
value: 32.435 |
|
- type: precision_at_10 |
|
value: 7.642 |
|
- type: precision_at_100 |
|
value: 1.244 |
|
- type: precision_at_1000 |
|
value: 0.163 |
|
- type: precision_at_3 |
|
value: 17.485 |
|
- type: precision_at_5 |
|
value: 12.57 |
|
- type: recall_at_1 |
|
value: 26.154 |
|
- type: recall_at_10 |
|
value: 54.111 |
|
- type: recall_at_100 |
|
value: 78.348 |
|
- type: recall_at_1000 |
|
value: 92.996 |
|
- type: recall_at_3 |
|
value: 39.189 |
|
- type: recall_at_5 |
|
value: 45.852 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.308999999999997 |
|
- type: map_at_10 |
|
value: 35.524 |
|
- type: map_at_100 |
|
value: 36.774 |
|
- type: map_at_1000 |
|
value: 36.891 |
|
- type: map_at_3 |
|
value: 32.561 |
|
- type: map_at_5 |
|
value: 34.034 |
|
- type: mrr_at_1 |
|
value: 31.735000000000003 |
|
- type: mrr_at_10 |
|
value: 40.391 |
|
- type: mrr_at_100 |
|
value: 41.227000000000004 |
|
- type: mrr_at_1000 |
|
value: 41.288000000000004 |
|
- type: mrr_at_3 |
|
value: 37.938 |
|
- type: mrr_at_5 |
|
value: 39.193 |
|
- type: ndcg_at_1 |
|
value: 31.735000000000003 |
|
- type: ndcg_at_10 |
|
value: 41.166000000000004 |
|
- type: ndcg_at_100 |
|
value: 46.702 |
|
- type: ndcg_at_1000 |
|
value: 49.157000000000004 |
|
- type: ndcg_at_3 |
|
value: 36.274 |
|
- type: ndcg_at_5 |
|
value: 38.177 |
|
- type: precision_at_1 |
|
value: 31.735000000000003 |
|
- type: precision_at_10 |
|
value: 7.5569999999999995 |
|
- type: precision_at_100 |
|
value: 1.2109999999999999 |
|
- type: precision_at_1000 |
|
value: 0.16 |
|
- type: precision_at_3 |
|
value: 17.199 |
|
- type: precision_at_5 |
|
value: 12.123000000000001 |
|
- type: recall_at_1 |
|
value: 26.308999999999997 |
|
- type: recall_at_10 |
|
value: 53.083000000000006 |
|
- type: recall_at_100 |
|
value: 76.922 |
|
- type: recall_at_1000 |
|
value: 93.767 |
|
- type: recall_at_3 |
|
value: 39.262 |
|
- type: recall_at_5 |
|
value: 44.413000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.391250000000003 |
|
- type: map_at_10 |
|
value: 33.280166666666666 |
|
- type: map_at_100 |
|
value: 34.49566666666667 |
|
- type: map_at_1000 |
|
value: 34.61533333333333 |
|
- type: map_at_3 |
|
value: 30.52183333333333 |
|
- type: map_at_5 |
|
value: 32.06608333333333 |
|
- type: mrr_at_1 |
|
value: 29.105083333333337 |
|
- type: mrr_at_10 |
|
value: 37.44766666666666 |
|
- type: mrr_at_100 |
|
value: 38.32491666666667 |
|
- type: mrr_at_1000 |
|
value: 38.385666666666665 |
|
- type: mrr_at_3 |
|
value: 35.06883333333333 |
|
- type: mrr_at_5 |
|
value: 36.42066666666667 |
|
- type: ndcg_at_1 |
|
value: 29.105083333333337 |
|
- type: ndcg_at_10 |
|
value: 38.54358333333333 |
|
- type: ndcg_at_100 |
|
value: 43.833583333333344 |
|
- type: ndcg_at_1000 |
|
value: 46.215333333333334 |
|
- type: ndcg_at_3 |
|
value: 33.876 |
|
- type: ndcg_at_5 |
|
value: 36.05208333333333 |
|
- type: precision_at_1 |
|
value: 29.105083333333337 |
|
- type: precision_at_10 |
|
value: 6.823416666666665 |
|
- type: precision_at_100 |
|
value: 1.1270833333333334 |
|
- type: precision_at_1000 |
|
value: 0.15208333333333332 |
|
- type: precision_at_3 |
|
value: 15.696750000000002 |
|
- type: precision_at_5 |
|
value: 11.193499999999998 |
|
- type: recall_at_1 |
|
value: 24.391250000000003 |
|
- type: recall_at_10 |
|
value: 49.98808333333333 |
|
- type: recall_at_100 |
|
value: 73.31616666666666 |
|
- type: recall_at_1000 |
|
value: 89.96291666666667 |
|
- type: recall_at_3 |
|
value: 36.86666666666667 |
|
- type: recall_at_5 |
|
value: 42.54350000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.995 |
|
- type: map_at_10 |
|
value: 28.807 |
|
- type: map_at_100 |
|
value: 29.813000000000002 |
|
- type: map_at_1000 |
|
value: 29.903000000000002 |
|
- type: map_at_3 |
|
value: 26.636 |
|
- type: map_at_5 |
|
value: 27.912 |
|
- type: mrr_at_1 |
|
value: 24.847 |
|
- type: mrr_at_10 |
|
value: 31.494 |
|
- type: mrr_at_100 |
|
value: 32.381 |
|
- type: mrr_at_1000 |
|
value: 32.446999999999996 |
|
- type: mrr_at_3 |
|
value: 29.473 |
|
- type: mrr_at_5 |
|
value: 30.7 |
|
- type: ndcg_at_1 |
|
value: 24.847 |
|
- type: ndcg_at_10 |
|
value: 32.818999999999996 |
|
- type: ndcg_at_100 |
|
value: 37.835 |
|
- type: ndcg_at_1000 |
|
value: 40.226 |
|
- type: ndcg_at_3 |
|
value: 28.811999999999998 |
|
- type: ndcg_at_5 |
|
value: 30.875999999999998 |
|
- type: precision_at_1 |
|
value: 24.847 |
|
- type: precision_at_10 |
|
value: 5.244999999999999 |
|
- type: precision_at_100 |
|
value: 0.856 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 12.577 |
|
- type: precision_at_5 |
|
value: 8.895999999999999 |
|
- type: recall_at_1 |
|
value: 21.995 |
|
- type: recall_at_10 |
|
value: 42.479 |
|
- type: recall_at_100 |
|
value: 65.337 |
|
- type: recall_at_1000 |
|
value: 83.23700000000001 |
|
- type: recall_at_3 |
|
value: 31.573 |
|
- type: recall_at_5 |
|
value: 36.684 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 15.751000000000001 |
|
- type: map_at_10 |
|
value: 21.909 |
|
- type: map_at_100 |
|
value: 23.064 |
|
- type: map_at_1000 |
|
value: 23.205000000000002 |
|
- type: map_at_3 |
|
value: 20.138 |
|
- type: map_at_5 |
|
value: 20.973 |
|
- type: mrr_at_1 |
|
value: 19.305 |
|
- type: mrr_at_10 |
|
value: 25.647 |
|
- type: mrr_at_100 |
|
value: 26.659 |
|
- type: mrr_at_1000 |
|
value: 26.748 |
|
- type: mrr_at_3 |
|
value: 23.933 |
|
- type: mrr_at_5 |
|
value: 24.754 |
|
- type: ndcg_at_1 |
|
value: 19.305 |
|
- type: ndcg_at_10 |
|
value: 25.886 |
|
- type: ndcg_at_100 |
|
value: 31.56 |
|
- type: ndcg_at_1000 |
|
value: 34.799 |
|
- type: ndcg_at_3 |
|
value: 22.708000000000002 |
|
- type: ndcg_at_5 |
|
value: 23.838 |
|
- type: precision_at_1 |
|
value: 19.305 |
|
- type: precision_at_10 |
|
value: 4.677 |
|
- type: precision_at_100 |
|
value: 0.895 |
|
- type: precision_at_1000 |
|
value: 0.136 |
|
- type: precision_at_3 |
|
value: 10.771 |
|
- type: precision_at_5 |
|
value: 7.46 |
|
- type: recall_at_1 |
|
value: 15.751000000000001 |
|
- type: recall_at_10 |
|
value: 34.156 |
|
- type: recall_at_100 |
|
value: 59.899 |
|
- type: recall_at_1000 |
|
value: 83.08 |
|
- type: recall_at_3 |
|
value: 24.772 |
|
- type: recall_at_5 |
|
value: 28.009 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.34 |
|
- type: map_at_10 |
|
value: 32.383 |
|
- type: map_at_100 |
|
value: 33.629999999999995 |
|
- type: map_at_1000 |
|
value: 33.735 |
|
- type: map_at_3 |
|
value: 29.68 |
|
- type: map_at_5 |
|
value: 31.270999999999997 |
|
- type: mrr_at_1 |
|
value: 27.612 |
|
- type: mrr_at_10 |
|
value: 36.381 |
|
- type: mrr_at_100 |
|
value: 37.351 |
|
- type: mrr_at_1000 |
|
value: 37.411 |
|
- type: mrr_at_3 |
|
value: 33.893 |
|
- type: mrr_at_5 |
|
value: 35.353 |
|
- type: ndcg_at_1 |
|
value: 27.612 |
|
- type: ndcg_at_10 |
|
value: 37.714999999999996 |
|
- type: ndcg_at_100 |
|
value: 43.525000000000006 |
|
- type: ndcg_at_1000 |
|
value: 45.812999999999995 |
|
- type: ndcg_at_3 |
|
value: 32.796 |
|
- type: ndcg_at_5 |
|
value: 35.243 |
|
- type: precision_at_1 |
|
value: 27.612 |
|
- type: precision_at_10 |
|
value: 6.465 |
|
- type: precision_at_100 |
|
value: 1.0619999999999998 |
|
- type: precision_at_1000 |
|
value: 0.13699999999999998 |
|
- type: precision_at_3 |
|
value: 15.049999999999999 |
|
- type: precision_at_5 |
|
value: 10.764999999999999 |
|
- type: recall_at_1 |
|
value: 23.34 |
|
- type: recall_at_10 |
|
value: 49.856 |
|
- type: recall_at_100 |
|
value: 75.334 |
|
- type: recall_at_1000 |
|
value: 91.156 |
|
- type: recall_at_3 |
|
value: 36.497 |
|
- type: recall_at_5 |
|
value: 42.769 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.097 |
|
- type: map_at_10 |
|
value: 34.599999999999994 |
|
- type: map_at_100 |
|
value: 36.174 |
|
- type: map_at_1000 |
|
value: 36.398 |
|
- type: map_at_3 |
|
value: 31.781 |
|
- type: map_at_5 |
|
value: 33.22 |
|
- type: mrr_at_1 |
|
value: 31.225 |
|
- type: mrr_at_10 |
|
value: 39.873 |
|
- type: mrr_at_100 |
|
value: 40.853 |
|
- type: mrr_at_1000 |
|
value: 40.904 |
|
- type: mrr_at_3 |
|
value: 37.681 |
|
- type: mrr_at_5 |
|
value: 38.669 |
|
- type: ndcg_at_1 |
|
value: 31.225 |
|
- type: ndcg_at_10 |
|
value: 40.586 |
|
- type: ndcg_at_100 |
|
value: 46.226 |
|
- type: ndcg_at_1000 |
|
value: 48.788 |
|
- type: ndcg_at_3 |
|
value: 36.258 |
|
- type: ndcg_at_5 |
|
value: 37.848 |
|
- type: precision_at_1 |
|
value: 31.225 |
|
- type: precision_at_10 |
|
value: 7.707999999999999 |
|
- type: precision_at_100 |
|
value: 1.536 |
|
- type: precision_at_1000 |
|
value: 0.242 |
|
- type: precision_at_3 |
|
value: 17.26 |
|
- type: precision_at_5 |
|
value: 12.253 |
|
- type: recall_at_1 |
|
value: 25.097 |
|
- type: recall_at_10 |
|
value: 51.602000000000004 |
|
- type: recall_at_100 |
|
value: 76.854 |
|
- type: recall_at_1000 |
|
value: 93.303 |
|
- type: recall_at_3 |
|
value: 38.68 |
|
- type: recall_at_5 |
|
value: 43.258 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.689 |
|
- type: map_at_10 |
|
value: 25.291000000000004 |
|
- type: map_at_100 |
|
value: 26.262 |
|
- type: map_at_1000 |
|
value: 26.372 |
|
- type: map_at_3 |
|
value: 22.916 |
|
- type: map_at_5 |
|
value: 24.315 |
|
- type: mrr_at_1 |
|
value: 19.409000000000002 |
|
- type: mrr_at_10 |
|
value: 27.233 |
|
- type: mrr_at_100 |
|
value: 28.109 |
|
- type: mrr_at_1000 |
|
value: 28.192 |
|
- type: mrr_at_3 |
|
value: 24.892 |
|
- type: mrr_at_5 |
|
value: 26.278000000000002 |
|
- type: ndcg_at_1 |
|
value: 19.409000000000002 |
|
- type: ndcg_at_10 |
|
value: 29.809 |
|
- type: ndcg_at_100 |
|
value: 34.936 |
|
- type: ndcg_at_1000 |
|
value: 37.852000000000004 |
|
- type: ndcg_at_3 |
|
value: 25.179000000000002 |
|
- type: ndcg_at_5 |
|
value: 27.563 |
|
- type: precision_at_1 |
|
value: 19.409000000000002 |
|
- type: precision_at_10 |
|
value: 4.861 |
|
- type: precision_at_100 |
|
value: 0.8 |
|
- type: precision_at_1000 |
|
value: 0.116 |
|
- type: precision_at_3 |
|
value: 11.029 |
|
- type: precision_at_5 |
|
value: 7.985 |
|
- type: recall_at_1 |
|
value: 17.689 |
|
- type: recall_at_10 |
|
value: 41.724 |
|
- type: recall_at_100 |
|
value: 65.95299999999999 |
|
- type: recall_at_1000 |
|
value: 88.094 |
|
- type: recall_at_3 |
|
value: 29.621 |
|
- type: recall_at_5 |
|
value: 35.179 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 10.581 |
|
- type: map_at_10 |
|
value: 18.944 |
|
- type: map_at_100 |
|
value: 20.812 |
|
- type: map_at_1000 |
|
value: 21.002000000000002 |
|
- type: map_at_3 |
|
value: 15.661 |
|
- type: map_at_5 |
|
value: 17.502000000000002 |
|
- type: mrr_at_1 |
|
value: 23.388 |
|
- type: mrr_at_10 |
|
value: 34.263 |
|
- type: mrr_at_100 |
|
value: 35.364000000000004 |
|
- type: mrr_at_1000 |
|
value: 35.409 |
|
- type: mrr_at_3 |
|
value: 30.586000000000002 |
|
- type: mrr_at_5 |
|
value: 32.928000000000004 |
|
- type: ndcg_at_1 |
|
value: 23.388 |
|
- type: ndcg_at_10 |
|
value: 26.56 |
|
- type: ndcg_at_100 |
|
value: 34.248 |
|
- type: ndcg_at_1000 |
|
value: 37.779 |
|
- type: ndcg_at_3 |
|
value: 21.179000000000002 |
|
- type: ndcg_at_5 |
|
value: 23.504 |
|
- type: precision_at_1 |
|
value: 23.388 |
|
- type: precision_at_10 |
|
value: 8.476 |
|
- type: precision_at_100 |
|
value: 1.672 |
|
- type: precision_at_1000 |
|
value: 0.233 |
|
- type: precision_at_3 |
|
value: 15.852 |
|
- type: precision_at_5 |
|
value: 12.73 |
|
- type: recall_at_1 |
|
value: 10.581 |
|
- type: recall_at_10 |
|
value: 32.512 |
|
- type: recall_at_100 |
|
value: 59.313 |
|
- type: recall_at_1000 |
|
value: 79.25 |
|
- type: recall_at_3 |
|
value: 19.912 |
|
- type: recall_at_5 |
|
value: 25.832 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 9.35 |
|
- type: map_at_10 |
|
value: 20.134 |
|
- type: map_at_100 |
|
value: 28.975 |
|
- type: map_at_1000 |
|
value: 30.709999999999997 |
|
- type: map_at_3 |
|
value: 14.513000000000002 |
|
- type: map_at_5 |
|
value: 16.671 |
|
- type: mrr_at_1 |
|
value: 69.75 |
|
- type: mrr_at_10 |
|
value: 77.67699999999999 |
|
- type: mrr_at_100 |
|
value: 77.97500000000001 |
|
- type: mrr_at_1000 |
|
value: 77.985 |
|
- type: mrr_at_3 |
|
value: 76.292 |
|
- type: mrr_at_5 |
|
value: 77.179 |
|
- type: ndcg_at_1 |
|
value: 56.49999999999999 |
|
- type: ndcg_at_10 |
|
value: 42.226 |
|
- type: ndcg_at_100 |
|
value: 47.562 |
|
- type: ndcg_at_1000 |
|
value: 54.923 |
|
- type: ndcg_at_3 |
|
value: 46.564 |
|
- type: ndcg_at_5 |
|
value: 43.830000000000005 |
|
- type: precision_at_1 |
|
value: 69.75 |
|
- type: precision_at_10 |
|
value: 33.525 |
|
- type: precision_at_100 |
|
value: 11.035 |
|
- type: precision_at_1000 |
|
value: 2.206 |
|
- type: precision_at_3 |
|
value: 49.75 |
|
- type: precision_at_5 |
|
value: 42 |
|
- type: recall_at_1 |
|
value: 9.35 |
|
- type: recall_at_10 |
|
value: 25.793 |
|
- type: recall_at_100 |
|
value: 54.186 |
|
- type: recall_at_1000 |
|
value: 77.81 |
|
- type: recall_at_3 |
|
value: 15.770000000000001 |
|
- type: recall_at_5 |
|
value: 19.09 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 46.945 |
|
- type: f1 |
|
value: 42.07407842992542 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 71.04599999999999 |
|
- type: map_at_10 |
|
value: 80.718 |
|
- type: map_at_100 |
|
value: 80.961 |
|
- type: map_at_1000 |
|
value: 80.974 |
|
- type: map_at_3 |
|
value: 79.49199999999999 |
|
- type: map_at_5 |
|
value: 80.32000000000001 |
|
- type: mrr_at_1 |
|
value: 76.388 |
|
- type: mrr_at_10 |
|
value: 85.214 |
|
- type: mrr_at_100 |
|
value: 85.302 |
|
- type: mrr_at_1000 |
|
value: 85.302 |
|
- type: mrr_at_3 |
|
value: 84.373 |
|
- type: mrr_at_5 |
|
value: 84.979 |
|
- type: ndcg_at_1 |
|
value: 76.388 |
|
- type: ndcg_at_10 |
|
value: 84.987 |
|
- type: ndcg_at_100 |
|
value: 85.835 |
|
- type: ndcg_at_1000 |
|
value: 86.04899999999999 |
|
- type: ndcg_at_3 |
|
value: 83.04 |
|
- type: ndcg_at_5 |
|
value: 84.22500000000001 |
|
- type: precision_at_1 |
|
value: 76.388 |
|
- type: precision_at_10 |
|
value: 10.35 |
|
- type: precision_at_100 |
|
value: 1.099 |
|
- type: precision_at_1000 |
|
value: 0.11399999999999999 |
|
- type: precision_at_3 |
|
value: 32.108 |
|
- type: precision_at_5 |
|
value: 20.033 |
|
- type: recall_at_1 |
|
value: 71.04599999999999 |
|
- type: recall_at_10 |
|
value: 93.547 |
|
- type: recall_at_100 |
|
value: 96.887 |
|
- type: recall_at_1000 |
|
value: 98.158 |
|
- type: recall_at_3 |
|
value: 88.346 |
|
- type: recall_at_5 |
|
value: 91.321 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.8 |
|
- type: map_at_10 |
|
value: 31.979999999999997 |
|
- type: map_at_100 |
|
value: 33.876 |
|
- type: map_at_1000 |
|
value: 34.056999999999995 |
|
- type: map_at_3 |
|
value: 28.067999999999998 |
|
- type: map_at_5 |
|
value: 30.066 |
|
- type: mrr_at_1 |
|
value: 38.735 |
|
- type: mrr_at_10 |
|
value: 47.749 |
|
- type: mrr_at_100 |
|
value: 48.605 |
|
- type: mrr_at_1000 |
|
value: 48.644999999999996 |
|
- type: mrr_at_3 |
|
value: 45.165 |
|
- type: mrr_at_5 |
|
value: 46.646 |
|
- type: ndcg_at_1 |
|
value: 38.735 |
|
- type: ndcg_at_10 |
|
value: 39.883 |
|
- type: ndcg_at_100 |
|
value: 46.983000000000004 |
|
- type: ndcg_at_1000 |
|
value: 50.043000000000006 |
|
- type: ndcg_at_3 |
|
value: 35.943000000000005 |
|
- type: ndcg_at_5 |
|
value: 37.119 |
|
- type: precision_at_1 |
|
value: 38.735 |
|
- type: precision_at_10 |
|
value: 10.940999999999999 |
|
- type: precision_at_100 |
|
value: 1.836 |
|
- type: precision_at_1000 |
|
value: 0.23900000000000002 |
|
- type: precision_at_3 |
|
value: 23.817 |
|
- type: precision_at_5 |
|
value: 17.346 |
|
- type: recall_at_1 |
|
value: 19.8 |
|
- type: recall_at_10 |
|
value: 47.082 |
|
- type: recall_at_100 |
|
value: 73.247 |
|
- type: recall_at_1000 |
|
value: 91.633 |
|
- type: recall_at_3 |
|
value: 33.201 |
|
- type: recall_at_5 |
|
value: 38.81 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 38.102999999999994 |
|
- type: map_at_10 |
|
value: 60.547 |
|
- type: map_at_100 |
|
value: 61.466 |
|
- type: map_at_1000 |
|
value: 61.526 |
|
- type: map_at_3 |
|
value: 56.973 |
|
- type: map_at_5 |
|
value: 59.244 |
|
- type: mrr_at_1 |
|
value: 76.205 |
|
- type: mrr_at_10 |
|
value: 82.816 |
|
- type: mrr_at_100 |
|
value: 83.002 |
|
- type: mrr_at_1000 |
|
value: 83.009 |
|
- type: mrr_at_3 |
|
value: 81.747 |
|
- type: mrr_at_5 |
|
value: 82.467 |
|
- type: ndcg_at_1 |
|
value: 76.205 |
|
- type: ndcg_at_10 |
|
value: 69.15 |
|
- type: ndcg_at_100 |
|
value: 72.297 |
|
- type: ndcg_at_1000 |
|
value: 73.443 |
|
- type: ndcg_at_3 |
|
value: 64.07000000000001 |
|
- type: ndcg_at_5 |
|
value: 66.96600000000001 |
|
- type: precision_at_1 |
|
value: 76.205 |
|
- type: precision_at_10 |
|
value: 14.601 |
|
- type: precision_at_100 |
|
value: 1.7049999999999998 |
|
- type: precision_at_1000 |
|
value: 0.186 |
|
- type: precision_at_3 |
|
value: 41.202 |
|
- type: precision_at_5 |
|
value: 27.006000000000004 |
|
- type: recall_at_1 |
|
value: 38.102999999999994 |
|
- type: recall_at_10 |
|
value: 73.005 |
|
- type: recall_at_100 |
|
value: 85.253 |
|
- type: recall_at_1000 |
|
value: 92.795 |
|
- type: recall_at_3 |
|
value: 61.803 |
|
- type: recall_at_5 |
|
value: 67.515 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 86.15 |
|
- type: ap |
|
value: 80.36282825265391 |
|
- type: f1 |
|
value: 86.07368510726472 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.6 |
|
- type: map_at_10 |
|
value: 34.887 |
|
- type: map_at_100 |
|
value: 36.069 |
|
- type: map_at_1000 |
|
value: 36.115 |
|
- type: map_at_3 |
|
value: 31.067 |
|
- type: map_at_5 |
|
value: 33.300000000000004 |
|
- type: mrr_at_1 |
|
value: 23.238 |
|
- type: mrr_at_10 |
|
value: 35.47 |
|
- type: mrr_at_100 |
|
value: 36.599 |
|
- type: mrr_at_1000 |
|
value: 36.64 |
|
- type: mrr_at_3 |
|
value: 31.735999999999997 |
|
- type: mrr_at_5 |
|
value: 33.939 |
|
- type: ndcg_at_1 |
|
value: 23.252 |
|
- type: ndcg_at_10 |
|
value: 41.765 |
|
- type: ndcg_at_100 |
|
value: 47.402 |
|
- type: ndcg_at_1000 |
|
value: 48.562 |
|
- type: ndcg_at_3 |
|
value: 34.016999999999996 |
|
- type: ndcg_at_5 |
|
value: 38.016 |
|
- type: precision_at_1 |
|
value: 23.252 |
|
- type: precision_at_10 |
|
value: 6.569 |
|
- type: precision_at_100 |
|
value: 0.938 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 14.479000000000001 |
|
- type: precision_at_5 |
|
value: 10.722 |
|
- type: recall_at_1 |
|
value: 22.6 |
|
- type: recall_at_10 |
|
value: 62.919000000000004 |
|
- type: recall_at_100 |
|
value: 88.82 |
|
- type: recall_at_1000 |
|
value: 97.71600000000001 |
|
- type: recall_at_3 |
|
value: 41.896 |
|
- type: recall_at_5 |
|
value: 51.537 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 93.69357045143639 |
|
- type: f1 |
|
value: 93.55489858177597 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 75.31235750114 |
|
- type: f1 |
|
value: 57.891491963121155 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 73.04303967720243 |
|
- type: f1 |
|
value: 70.51516022297616 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 77.65299260255549 |
|
- type: f1 |
|
value: 77.49059766538576 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 31.458906115906597 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 28.9851513122443 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 31.2916268497217 |
|
- type: mrr |
|
value: 32.328276715593816 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 6.3740000000000006 |
|
- type: map_at_10 |
|
value: 13.089999999999998 |
|
- type: map_at_100 |
|
value: 16.512 |
|
- type: map_at_1000 |
|
value: 18.014 |
|
- type: map_at_3 |
|
value: 9.671000000000001 |
|
- type: map_at_5 |
|
value: 11.199 |
|
- type: mrr_at_1 |
|
value: 46.749 |
|
- type: mrr_at_10 |
|
value: 55.367 |
|
- type: mrr_at_100 |
|
value: 56.021 |
|
- type: mrr_at_1000 |
|
value: 56.058 |
|
- type: mrr_at_3 |
|
value: 53.30200000000001 |
|
- type: mrr_at_5 |
|
value: 54.773 |
|
- type: ndcg_at_1 |
|
value: 45.046 |
|
- type: ndcg_at_10 |
|
value: 35.388999999999996 |
|
- type: ndcg_at_100 |
|
value: 32.175 |
|
- type: ndcg_at_1000 |
|
value: 41.018 |
|
- type: ndcg_at_3 |
|
value: 40.244 |
|
- type: ndcg_at_5 |
|
value: 38.267 |
|
- type: precision_at_1 |
|
value: 46.749 |
|
- type: precision_at_10 |
|
value: 26.563 |
|
- type: precision_at_100 |
|
value: 8.074 |
|
- type: precision_at_1000 |
|
value: 2.099 |
|
- type: precision_at_3 |
|
value: 37.358000000000004 |
|
- type: precision_at_5 |
|
value: 33.003 |
|
- type: recall_at_1 |
|
value: 6.3740000000000006 |
|
- type: recall_at_10 |
|
value: 16.805999999999997 |
|
- type: recall_at_100 |
|
value: 31.871 |
|
- type: recall_at_1000 |
|
value: 64.098 |
|
- type: recall_at_3 |
|
value: 10.383000000000001 |
|
- type: recall_at_5 |
|
value: 13.166 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 34.847 |
|
- type: map_at_10 |
|
value: 50.532 |
|
- type: map_at_100 |
|
value: 51.504000000000005 |
|
- type: map_at_1000 |
|
value: 51.528 |
|
- type: map_at_3 |
|
value: 46.219 |
|
- type: map_at_5 |
|
value: 48.868 |
|
- type: mrr_at_1 |
|
value: 39.137 |
|
- type: mrr_at_10 |
|
value: 53.157 |
|
- type: mrr_at_100 |
|
value: 53.839999999999996 |
|
- type: mrr_at_1000 |
|
value: 53.857 |
|
- type: mrr_at_3 |
|
value: 49.667 |
|
- type: mrr_at_5 |
|
value: 51.847 |
|
- type: ndcg_at_1 |
|
value: 39.108 |
|
- type: ndcg_at_10 |
|
value: 58.221000000000004 |
|
- type: ndcg_at_100 |
|
value: 62.021 |
|
- type: ndcg_at_1000 |
|
value: 62.57 |
|
- type: ndcg_at_3 |
|
value: 50.27199999999999 |
|
- type: ndcg_at_5 |
|
value: 54.623999999999995 |
|
- type: precision_at_1 |
|
value: 39.108 |
|
- type: precision_at_10 |
|
value: 9.397 |
|
- type: precision_at_100 |
|
value: 1.1520000000000001 |
|
- type: precision_at_1000 |
|
value: 0.12 |
|
- type: precision_at_3 |
|
value: 22.644000000000002 |
|
- type: precision_at_5 |
|
value: 16.141 |
|
- type: recall_at_1 |
|
value: 34.847 |
|
- type: recall_at_10 |
|
value: 78.945 |
|
- type: recall_at_100 |
|
value: 94.793 |
|
- type: recall_at_1000 |
|
value: 98.904 |
|
- type: recall_at_3 |
|
value: 58.56 |
|
- type: recall_at_5 |
|
value: 68.535 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 68.728 |
|
- type: map_at_10 |
|
value: 82.537 |
|
- type: map_at_100 |
|
value: 83.218 |
|
- type: map_at_1000 |
|
value: 83.238 |
|
- type: map_at_3 |
|
value: 79.586 |
|
- type: map_at_5 |
|
value: 81.416 |
|
- type: mrr_at_1 |
|
value: 79.17999999999999 |
|
- type: mrr_at_10 |
|
value: 85.79299999999999 |
|
- type: mrr_at_100 |
|
value: 85.937 |
|
- type: mrr_at_1000 |
|
value: 85.938 |
|
- type: mrr_at_3 |
|
value: 84.748 |
|
- type: mrr_at_5 |
|
value: 85.431 |
|
- type: ndcg_at_1 |
|
value: 79.17 |
|
- type: ndcg_at_10 |
|
value: 86.555 |
|
- type: ndcg_at_100 |
|
value: 88.005 |
|
- type: ndcg_at_1000 |
|
value: 88.146 |
|
- type: ndcg_at_3 |
|
value: 83.557 |
|
- type: ndcg_at_5 |
|
value: 85.152 |
|
- type: precision_at_1 |
|
value: 79.17 |
|
- type: precision_at_10 |
|
value: 13.163 |
|
- type: precision_at_100 |
|
value: 1.52 |
|
- type: precision_at_1000 |
|
value: 0.156 |
|
- type: precision_at_3 |
|
value: 36.53 |
|
- type: precision_at_5 |
|
value: 24.046 |
|
- type: recall_at_1 |
|
value: 68.728 |
|
- type: recall_at_10 |
|
value: 94.217 |
|
- type: recall_at_100 |
|
value: 99.295 |
|
- type: recall_at_1000 |
|
value: 99.964 |
|
- type: recall_at_3 |
|
value: 85.646 |
|
- type: recall_at_5 |
|
value: 90.113 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 56.15680266226348 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 63.4318549229047 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.353 |
|
- type: map_at_10 |
|
value: 10.956000000000001 |
|
- type: map_at_100 |
|
value: 12.873999999999999 |
|
- type: map_at_1000 |
|
value: 13.177 |
|
- type: map_at_3 |
|
value: 7.854 |
|
- type: map_at_5 |
|
value: 9.327 |
|
- type: mrr_at_1 |
|
value: 21.4 |
|
- type: mrr_at_10 |
|
value: 31.948999999999998 |
|
- type: mrr_at_100 |
|
value: 33.039 |
|
- type: mrr_at_1000 |
|
value: 33.106 |
|
- type: mrr_at_3 |
|
value: 28.449999999999996 |
|
- type: mrr_at_5 |
|
value: 30.535 |
|
- type: ndcg_at_1 |
|
value: 21.4 |
|
- type: ndcg_at_10 |
|
value: 18.694 |
|
- type: ndcg_at_100 |
|
value: 26.275 |
|
- type: ndcg_at_1000 |
|
value: 31.836 |
|
- type: ndcg_at_3 |
|
value: 17.559 |
|
- type: ndcg_at_5 |
|
value: 15.372 |
|
- type: precision_at_1 |
|
value: 21.4 |
|
- type: precision_at_10 |
|
value: 9.790000000000001 |
|
- type: precision_at_100 |
|
value: 2.0709999999999997 |
|
- type: precision_at_1000 |
|
value: 0.34099999999999997 |
|
- type: precision_at_3 |
|
value: 16.467000000000002 |
|
- type: precision_at_5 |
|
value: 13.54 |
|
- type: recall_at_1 |
|
value: 4.353 |
|
- type: recall_at_10 |
|
value: 19.892000000000003 |
|
- type: recall_at_100 |
|
value: 42.067 |
|
- type: recall_at_1000 |
|
value: 69.268 |
|
- type: recall_at_3 |
|
value: 10.042 |
|
- type: recall_at_5 |
|
value: 13.741999999999999 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.75433886279843 |
|
- type: cos_sim_spearman |
|
value: 78.29727771767095 |
|
- type: euclidean_pearson |
|
value: 80.83057828506621 |
|
- type: euclidean_spearman |
|
value: 78.35203149750356 |
|
- type: manhattan_pearson |
|
value: 80.7403553891142 |
|
- type: manhattan_spearman |
|
value: 78.33670488531051 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.59999465280839 |
|
- type: cos_sim_spearman |
|
value: 75.79279003980383 |
|
- type: euclidean_pearson |
|
value: 82.29895375956758 |
|
- type: euclidean_spearman |
|
value: 77.33856514102094 |
|
- type: manhattan_pearson |
|
value: 82.22694214534756 |
|
- type: manhattan_spearman |
|
value: 77.3028993008695 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.09296929691297 |
|
- type: cos_sim_spearman |
|
value: 83.58056936846941 |
|
- type: euclidean_pearson |
|
value: 83.84067483060005 |
|
- type: euclidean_spearman |
|
value: 84.45155680480985 |
|
- type: manhattan_pearson |
|
value: 83.82353052971942 |
|
- type: manhattan_spearman |
|
value: 84.43030567861112 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.74616852320915 |
|
- type: cos_sim_spearman |
|
value: 79.948683747966 |
|
- type: euclidean_pearson |
|
value: 81.55702283757084 |
|
- type: euclidean_spearman |
|
value: 80.1721505114231 |
|
- type: manhattan_pearson |
|
value: 81.52251518619441 |
|
- type: manhattan_spearman |
|
value: 80.1469800135577 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.97170104226318 |
|
- type: cos_sim_spearman |
|
value: 88.82021731518206 |
|
- type: euclidean_pearson |
|
value: 87.92950547187615 |
|
- type: euclidean_spearman |
|
value: 88.67043634645866 |
|
- type: manhattan_pearson |
|
value: 87.90668112827639 |
|
- type: manhattan_spearman |
|
value: 88.64471082785317 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.02790375770599 |
|
- type: cos_sim_spearman |
|
value: 84.46308496590792 |
|
- type: euclidean_pearson |
|
value: 84.29430000414911 |
|
- type: euclidean_spearman |
|
value: 84.77298303589936 |
|
- type: manhattan_pearson |
|
value: 84.23919291368665 |
|
- type: manhattan_spearman |
|
value: 84.75272234871308 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts17-crosslingual-sts |
|
name: MTEB STS17 (en-en) |
|
config: en-en |
|
split: test |
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.62885108477064 |
|
- type: cos_sim_spearman |
|
value: 87.58456196391622 |
|
- type: euclidean_pearson |
|
value: 88.2602775281007 |
|
- type: euclidean_spearman |
|
value: 87.51556278299846 |
|
- type: manhattan_pearson |
|
value: 88.11224053672842 |
|
- type: manhattan_spearman |
|
value: 87.4336094383095 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (en) |
|
config: en |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 63.98187965128411 |
|
- type: cos_sim_spearman |
|
value: 64.0653163219731 |
|
- type: euclidean_pearson |
|
value: 62.30616725924099 |
|
- type: euclidean_spearman |
|
value: 61.556971332295916 |
|
- type: manhattan_pearson |
|
value: 62.07642330128549 |
|
- type: manhattan_spearman |
|
value: 61.155494129828 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.6089703921826 |
|
- type: cos_sim_spearman |
|
value: 86.52303197250791 |
|
- type: euclidean_pearson |
|
value: 85.95801955963246 |
|
- type: euclidean_spearman |
|
value: 86.25242424112962 |
|
- type: manhattan_pearson |
|
value: 85.88829100470312 |
|
- type: manhattan_spearman |
|
value: 86.18742955805165 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 83.02282098487036 |
|
- type: mrr |
|
value: 95.05126409538174 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 55.928 |
|
- type: map_at_10 |
|
value: 67.308 |
|
- type: map_at_100 |
|
value: 67.89500000000001 |
|
- type: map_at_1000 |
|
value: 67.91199999999999 |
|
- type: map_at_3 |
|
value: 65.091 |
|
- type: map_at_5 |
|
value: 66.412 |
|
- type: mrr_at_1 |
|
value: 58.667 |
|
- type: mrr_at_10 |
|
value: 68.401 |
|
- type: mrr_at_100 |
|
value: 68.804 |
|
- type: mrr_at_1000 |
|
value: 68.819 |
|
- type: mrr_at_3 |
|
value: 66.72200000000001 |
|
- type: mrr_at_5 |
|
value: 67.72200000000001 |
|
- type: ndcg_at_1 |
|
value: 58.667 |
|
- type: ndcg_at_10 |
|
value: 71.944 |
|
- type: ndcg_at_100 |
|
value: 74.464 |
|
- type: ndcg_at_1000 |
|
value: 74.82799999999999 |
|
- type: ndcg_at_3 |
|
value: 68.257 |
|
- type: ndcg_at_5 |
|
value: 70.10300000000001 |
|
- type: precision_at_1 |
|
value: 58.667 |
|
- type: precision_at_10 |
|
value: 9.533 |
|
- type: precision_at_100 |
|
value: 1.09 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 27.222 |
|
- type: precision_at_5 |
|
value: 17.533 |
|
- type: recall_at_1 |
|
value: 55.928 |
|
- type: recall_at_10 |
|
value: 84.65 |
|
- type: recall_at_100 |
|
value: 96.267 |
|
- type: recall_at_1000 |
|
value: 99 |
|
- type: recall_at_3 |
|
value: 74.656 |
|
- type: recall_at_5 |
|
value: 79.489 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.79009900990098 |
|
- type: cos_sim_ap |
|
value: 94.5795129511524 |
|
- type: cos_sim_f1 |
|
value: 89.34673366834171 |
|
- type: cos_sim_precision |
|
value: 89.79797979797979 |
|
- type: cos_sim_recall |
|
value: 88.9 |
|
- type: dot_accuracy |
|
value: 99.53465346534654 |
|
- type: dot_ap |
|
value: 81.56492504352725 |
|
- type: dot_f1 |
|
value: 76.33816908454227 |
|
- type: dot_precision |
|
value: 76.37637637637637 |
|
- type: dot_recall |
|
value: 76.3 |
|
- type: euclidean_accuracy |
|
value: 99.78514851485149 |
|
- type: euclidean_ap |
|
value: 94.59134620408962 |
|
- type: euclidean_f1 |
|
value: 88.96484375 |
|
- type: euclidean_precision |
|
value: 86.92748091603053 |
|
- type: euclidean_recall |
|
value: 91.10000000000001 |
|
- type: manhattan_accuracy |
|
value: 99.78415841584159 |
|
- type: manhattan_ap |
|
value: 94.5190197328845 |
|
- type: manhattan_f1 |
|
value: 88.84462151394423 |
|
- type: manhattan_precision |
|
value: 88.4920634920635 |
|
- type: manhattan_recall |
|
value: 89.2 |
|
- type: max_accuracy |
|
value: 99.79009900990098 |
|
- type: max_ap |
|
value: 94.59134620408962 |
|
- type: max_f1 |
|
value: 89.34673366834171 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 65.1487505617497 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 32.502518166001856 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 50.33775480236701 |
|
- type: mrr |
|
value: 51.17302223919871 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.561111309808208 |
|
- type: cos_sim_spearman |
|
value: 30.2839254379273 |
|
- type: dot_pearson |
|
value: 29.560242291401973 |
|
- type: dot_spearman |
|
value: 30.51527274679116 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.215 |
|
- type: map_at_10 |
|
value: 1.752 |
|
- type: map_at_100 |
|
value: 9.258 |
|
- type: map_at_1000 |
|
value: 23.438 |
|
- type: map_at_3 |
|
value: 0.6 |
|
- type: map_at_5 |
|
value: 0.968 |
|
- type: mrr_at_1 |
|
value: 84 |
|
- type: mrr_at_10 |
|
value: 91.333 |
|
- type: mrr_at_100 |
|
value: 91.333 |
|
- type: mrr_at_1000 |
|
value: 91.333 |
|
- type: mrr_at_3 |
|
value: 91.333 |
|
- type: mrr_at_5 |
|
value: 91.333 |
|
- type: ndcg_at_1 |
|
value: 75 |
|
- type: ndcg_at_10 |
|
value: 69.596 |
|
- type: ndcg_at_100 |
|
value: 51.970000000000006 |
|
- type: ndcg_at_1000 |
|
value: 48.864999999999995 |
|
- type: ndcg_at_3 |
|
value: 73.92699999999999 |
|
- type: ndcg_at_5 |
|
value: 73.175 |
|
- type: precision_at_1 |
|
value: 84 |
|
- type: precision_at_10 |
|
value: 74 |
|
- type: precision_at_100 |
|
value: 53.2 |
|
- type: precision_at_1000 |
|
value: 21.836 |
|
- type: precision_at_3 |
|
value: 79.333 |
|
- type: precision_at_5 |
|
value: 78.4 |
|
- type: recall_at_1 |
|
value: 0.215 |
|
- type: recall_at_10 |
|
value: 1.9609999999999999 |
|
- type: recall_at_100 |
|
value: 12.809999999999999 |
|
- type: recall_at_1000 |
|
value: 46.418 |
|
- type: recall_at_3 |
|
value: 0.6479999999999999 |
|
- type: recall_at_5 |
|
value: 1.057 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 3.066 |
|
- type: map_at_10 |
|
value: 10.508000000000001 |
|
- type: map_at_100 |
|
value: 16.258 |
|
- type: map_at_1000 |
|
value: 17.705000000000002 |
|
- type: map_at_3 |
|
value: 6.157 |
|
- type: map_at_5 |
|
value: 7.510999999999999 |
|
- type: mrr_at_1 |
|
value: 34.694 |
|
- type: mrr_at_10 |
|
value: 48.786 |
|
- type: mrr_at_100 |
|
value: 49.619 |
|
- type: mrr_at_1000 |
|
value: 49.619 |
|
- type: mrr_at_3 |
|
value: 45.918 |
|
- type: mrr_at_5 |
|
value: 46.837 |
|
- type: ndcg_at_1 |
|
value: 31.633 |
|
- type: ndcg_at_10 |
|
value: 26.401999999999997 |
|
- type: ndcg_at_100 |
|
value: 37.139 |
|
- type: ndcg_at_1000 |
|
value: 48.012 |
|
- type: ndcg_at_3 |
|
value: 31.875999999999998 |
|
- type: ndcg_at_5 |
|
value: 27.383000000000003 |
|
- type: precision_at_1 |
|
value: 34.694 |
|
- type: precision_at_10 |
|
value: 22.857 |
|
- type: precision_at_100 |
|
value: 7.611999999999999 |
|
- type: precision_at_1000 |
|
value: 1.492 |
|
- type: precision_at_3 |
|
value: 33.333 |
|
- type: precision_at_5 |
|
value: 26.122 |
|
- type: recall_at_1 |
|
value: 3.066 |
|
- type: recall_at_10 |
|
value: 16.239 |
|
- type: recall_at_100 |
|
value: 47.29 |
|
- type: recall_at_1000 |
|
value: 81.137 |
|
- type: recall_at_3 |
|
value: 7.069 |
|
- type: recall_at_5 |
|
value: 9.483 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 72.1126 |
|
- type: ap |
|
value: 14.710862719285753 |
|
- type: f1 |
|
value: 55.437808972378846 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 60.39049235993209 |
|
- type: f1 |
|
value: 60.69810537250234 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 48.15576640316866 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 86.52917684925792 |
|
- type: cos_sim_ap |
|
value: 75.97497873817315 |
|
- type: cos_sim_f1 |
|
value: 70.01151926276718 |
|
- type: cos_sim_precision |
|
value: 67.98409147402435 |
|
- type: cos_sim_recall |
|
value: 72.16358839050132 |
|
- type: dot_accuracy |
|
value: 82.47004828038385 |
|
- type: dot_ap |
|
value: 62.48739894974198 |
|
- type: dot_f1 |
|
value: 59.13107511045656 |
|
- type: dot_precision |
|
value: 55.27765029830197 |
|
- type: dot_recall |
|
value: 63.562005277044854 |
|
- type: euclidean_accuracy |
|
value: 86.46361089586935 |
|
- type: euclidean_ap |
|
value: 75.59282886839452 |
|
- type: euclidean_f1 |
|
value: 69.6465443945099 |
|
- type: euclidean_precision |
|
value: 64.52847175331982 |
|
- type: euclidean_recall |
|
value: 75.64643799472296 |
|
- type: manhattan_accuracy |
|
value: 86.43380818978363 |
|
- type: manhattan_ap |
|
value: 75.5742420974403 |
|
- type: manhattan_f1 |
|
value: 69.8636926889715 |
|
- type: manhattan_precision |
|
value: 65.8644859813084 |
|
- type: manhattan_recall |
|
value: 74.37994722955145 |
|
- type: max_accuracy |
|
value: 86.52917684925792 |
|
- type: max_ap |
|
value: 75.97497873817315 |
|
- type: max_f1 |
|
value: 70.01151926276718 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 89.29056545193464 |
|
- type: cos_sim_ap |
|
value: 86.63028865482376 |
|
- type: cos_sim_f1 |
|
value: 79.18166458532285 |
|
- type: cos_sim_precision |
|
value: 75.70585756426465 |
|
- type: cos_sim_recall |
|
value: 82.99199260856174 |
|
- type: dot_accuracy |
|
value: 85.23305002522606 |
|
- type: dot_ap |
|
value: 76.0482687263196 |
|
- type: dot_f1 |
|
value: 70.80484330484332 |
|
- type: dot_precision |
|
value: 65.86933474688577 |
|
- type: dot_recall |
|
value: 76.53988296889437 |
|
- type: euclidean_accuracy |
|
value: 89.26145845461248 |
|
- type: euclidean_ap |
|
value: 86.54073288416006 |
|
- type: euclidean_f1 |
|
value: 78.9721371479794 |
|
- type: euclidean_precision |
|
value: 76.68649354417525 |
|
- type: euclidean_recall |
|
value: 81.39821373575609 |
|
- type: manhattan_accuracy |
|
value: 89.22847052431405 |
|
- type: manhattan_ap |
|
value: 86.51250729037905 |
|
- type: manhattan_f1 |
|
value: 78.94601825044894 |
|
- type: manhattan_precision |
|
value: 75.32694594027555 |
|
- type: manhattan_recall |
|
value: 82.93039728980598 |
|
- type: max_accuracy |
|
value: 89.29056545193464 |
|
- type: max_ap |
|
value: 86.63028865482376 |
|
- type: max_f1 |
|
value: 79.18166458532285 |
|
language: |
|
- en |
|
license: mit |
|
--- |
|
|
|
# E5-base-v2 |
|
|
|
[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf). |
|
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 |
|
|
|
This model has 12 layers and the embedding size is 768. |
|
|
|
## Usage |
|
|
|
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. |
|
|
|
```python |
|
import torch.nn.functional as F |
|
|
|
from torch import Tensor |
|
from transformers import AutoTokenizer, AutoModel |
|
|
|
|
|
def average_pool(last_hidden_states: Tensor, |
|
attention_mask: Tensor) -> Tensor: |
|
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) |
|
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
|
|
|
|
|
# Each input text should start with "query: " or "passage: ". |
|
# For tasks other than retrieval, you can simply use the "query: " prefix. |
|
input_texts = ['query: how much protein should a female eat', |
|
'query: summit define', |
|
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", |
|
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."] |
|
|
|
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-base-v2') |
|
model = AutoModel.from_pretrained('intfloat/e5-base-v2') |
|
|
|
# Tokenize the input texts |
|
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') |
|
|
|
outputs = model(**batch_dict) |
|
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) |
|
|
|
# normalize embeddings |
|
embeddings = F.normalize(embeddings, p=2, dim=1) |
|
scores = (embeddings[:2] @ embeddings[2:].T) * 100 |
|
print(scores.tolist()) |
|
``` |
|
|
|
## Training Details |
|
|
|
Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf). |
|
|
|
## Benchmark Evaluation |
|
|
|
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results |
|
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). |
|
|
|
## Support for Sentence Transformers |
|
|
|
Below is an example for usage with sentence_transformers. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
model = SentenceTransformer('intfloat/e5-base-v2') |
|
input_texts = [ |
|
'query: how much protein should a female eat', |
|
'query: summit define', |
|
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", |
|
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." |
|
] |
|
embeddings = model.encode(input_texts, normalize_embeddings=True) |
|
``` |
|
|
|
Package requirements |
|
|
|
`pip install sentence_transformers~=2.2.2` |
|
|
|
Contributors: [michaelfeil](https://huggingface.co/michaelfeil) |
|
|
|
## FAQ |
|
|
|
**1. Do I need to add the prefix "query: " and "passage: " to input texts?** |
|
|
|
Yes, this is how the model is trained, otherwise you will see a performance degradation. |
|
|
|
Here are some rules of thumb: |
|
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. |
|
|
|
- Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval. |
|
|
|
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. |
|
|
|
**2. Why are my reproduced results slightly different from reported in the model card?** |
|
|
|
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. |
|
|
|
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** |
|
|
|
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. |
|
|
|
For text embedding tasks like text retrieval or semantic similarity, |
|
what matters is the relative order of the scores instead of the absolute values, |
|
so this should not be an issue. |
|
|
|
## Citation |
|
|
|
If you find our paper or models helpful, please consider cite as follows: |
|
|
|
``` |
|
@article{wang2022text, |
|
title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, |
|
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, |
|
journal={arXiv preprint arXiv:2212.03533}, |
|
year={2022} |
|
} |
|
``` |
|
|
|
## Limitations |
|
|
|
This model only works for English texts. Long texts will be truncated to at most 512 tokens. |
|
|
