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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:
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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:
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- type: accuracy
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value: 77.77611940298506
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- type: ap
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value: 42.052710266606056
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- type: f1
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value: 72.12040628266567
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- task:
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type: Classification
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dataset:
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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:
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- type: accuracy
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value: 92.81012500000001
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- type: ap
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value: 89.4213700757244
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- type: f1
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value: 92.8039091197065
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- task:
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type: Classification
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dataset:
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type: mteb/amazon_reviews_multi
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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:
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- type: accuracy
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value: 46.711999999999996
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- type: f1
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value: 46.11544975436018
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- task:
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type: Retrieval
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dataset:
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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
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metrics:
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- type: map_at_1
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value: 23.186
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- type: map_at_10
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value: 36.632999999999996
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- type: map_at_100
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value: 37.842
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- type: map_at_1000
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value: 37.865
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- type: map_at_3
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value: 32.278
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- type: map_at_5
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value: 34.760999999999996
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- type: mrr_at_1
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value: 23.400000000000002
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- type: mrr_at_10
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value: 36.721
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- type: mrr_at_100
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value: 37.937
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- type: mrr_at_1000
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value: 37.96
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- type: mrr_at_3
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value: 32.302
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- type: mrr_at_5
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value: 34.894
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- type: ndcg_at_1
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value: 23.186
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- type: ndcg_at_10
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value: 44.49
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- type: ndcg_at_100
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value: 50.065000000000005
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- type: ndcg_at_1000
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value: 50.629999999999995
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- type: ndcg_at_3
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value: 35.461
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- type: ndcg_at_5
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value: 39.969
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- type: precision_at_1
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value: 23.186
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- type: precision_at_10
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value: 6.97
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- type: precision_at_100
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value: 0.951
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- type: precision_at_1000
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value: 0.099
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- type: precision_at_3
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value: 14.912
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- type: precision_at_5
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value: 11.152
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- type: recall_at_1
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value: 23.186
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- type: recall_at_10
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value: 69.70100000000001
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- type: recall_at_100
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value: 95.092
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- type: recall_at_1000
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value: 99.431
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- type: recall_at_3
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value: 44.737
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- type: recall_at_5
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value: 55.761
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- task:
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type: Clustering
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dataset:
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type: mteb/arxiv-clustering-p2p
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name: MTEB ArxivClusteringP2P
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config: default
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split: test
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revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
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metrics:
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- type: v_measure
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value: 46.10312401440185
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- task:
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type: Clustering
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dataset:
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type: mteb/arxiv-clustering-s2s
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name: MTEB ArxivClusteringS2S
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config: default
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split: test
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revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
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metrics:
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- type: v_measure
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value: 39.67275326095384
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- task:
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type: Reranking
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dataset:
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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:
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- type: map
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value: 58.97793816337376
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- type: mrr
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value: 72.76832431957087
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- task:
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type: STS
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dataset:
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type: mteb/biosses-sts
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name: MTEB BIOSSES
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config: default
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split: test
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
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metrics:
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- type: cos_sim_pearson
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value: 83.11646947018187
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- type: cos_sim_spearman
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value: 81.40064994975234
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- type: euclidean_pearson
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value: 82.37355689019232
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- type: euclidean_spearman
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value: 81.6777646977348
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- type: manhattan_pearson
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value: 82.61101422716945
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- type: manhattan_spearman
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value: 81.80427360442245
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- task:
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type: Classification
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dataset:
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type: mteb/banking77
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name: MTEB Banking77Classification
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config: default
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split: test
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
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metrics:
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- type: accuracy
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value: 83.52922077922076
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- type: f1
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value: 83.45298679360866
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- task:
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type: Clustering
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dataset:
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type: mteb/biorxiv-clustering-p2p
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name: MTEB BiorxivClusteringP2P
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config: default
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split: test
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
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metrics:
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- type: v_measure
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value: 37.495115019668496
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- task:
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type: Clustering
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dataset:
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type: mteb/biorxiv-clustering-s2s
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name: MTEB BiorxivClusteringS2S
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config: default
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split: test
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
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metrics:
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- type: v_measure
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value: 32.724792944166765
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- task:
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type: Retrieval
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dataset:
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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:
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- type: map_at_1
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value: 32.361000000000004
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- type: map_at_10
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value: 43.765
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- type: map_at_100
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value: 45.224
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- type: map_at_1000
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value: 45.35
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- type: map_at_3
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value: 40.353
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- type: map_at_5
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value: 42.195
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- type: mrr_at_1
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value: 40.629
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- type: mrr_at_10
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value: 50.458000000000006
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- type: mrr_at_100
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value: 51.06699999999999
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- type: mrr_at_1000
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value: 51.12
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- type: mrr_at_3
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value: 47.902
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- type: mrr_at_5
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value: 49.447
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- type: ndcg_at_1
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value: 40.629
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- type: ndcg_at_10
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value: 50.376
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- type: ndcg_at_100
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value: 55.065
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- type: ndcg_at_1000
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value: 57.196000000000005
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- type: ndcg_at_3
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value: 45.616
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- type: ndcg_at_5
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value: 47.646
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- type: precision_at_1
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value: 40.629
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- type: precision_at_10
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value: 9.785
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- type: precision_at_100
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value: 1.562
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- type: precision_at_1000
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value: 0.2
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- type: precision_at_3
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value: 22.031
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- type: precision_at_5
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value: 15.737000000000002
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- type: recall_at_1
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value: 32.361000000000004
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- type: recall_at_10
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value: 62.214000000000006
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- type: recall_at_100
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value: 81.464
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- type: recall_at_1000
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value: 95.905
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- type: recall_at_3
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value: 47.5
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- type: recall_at_5
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value: 53.69500000000001
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- task:
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type: Retrieval
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dataset:
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type: BeIR/cqadupstack
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name: MTEB CQADupstackEnglishRetrieval
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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
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value: 27.971
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- type: map_at_10
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value: 37.444
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- type: map_at_100
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value: 38.607
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- type: map_at_1000
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value: 38.737
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- type: map_at_3
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value: 34.504000000000005
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- type: map_at_5
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value: 36.234
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- type: mrr_at_1
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value: 35.35
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- type: mrr_at_10
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value: 43.441
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- type: mrr_at_100
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value: 44.147999999999996
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- type: mrr_at_1000
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value: 44.196000000000005
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- type: mrr_at_3
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value: 41.285
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- type: mrr_at_5
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value: 42.552
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- type: ndcg_at_1
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value: 35.35
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- type: ndcg_at_10
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value: 42.903999999999996
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- type: ndcg_at_100
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value: 47.406
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- type: ndcg_at_1000
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value: 49.588
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- type: ndcg_at_3
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value: 38.778
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- type: ndcg_at_5
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value: 40.788000000000004
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- type: precision_at_1
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value: 35.35
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- type: precision_at_10
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value: 8.083
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- type: precision_at_100
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value: 1.313
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- type: precision_at_1000
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value: 0.18
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- type: precision_at_3
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value: 18.769
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- type: precision_at_5
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value: 13.439
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- type: recall_at_1
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value: 27.971
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- type: recall_at_10
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value: 52.492000000000004
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- type: recall_at_100
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value: 71.642
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- type: recall_at_1000
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value: 85.488
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- type: recall_at_3
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value: 40.1
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- type: recall_at_5
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value: 45.800000000000004
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- task:
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type: Retrieval
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dataset:
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type: BeIR/cqadupstack
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name: MTEB CQADupstackGamingRetrieval
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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
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value: 39.898
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- type: map_at_10
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value: 51.819
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- type: map_at_100
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value: 52.886
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- type: map_at_1000
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|
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value: 52.941
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- type: map_at_3
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|
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value: 48.619
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- type: map_at_5
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|
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value: 50.493
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- type: mrr_at_1
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|
|
value: 45.391999999999996
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- type: mrr_at_10
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|
|
value: 55.230000000000004
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|
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- type: mrr_at_100
|
|
|
value: 55.887
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|
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- type: mrr_at_1000
|
|
|
value: 55.916
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|
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- type: mrr_at_3
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|
|
value: 52.717000000000006
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|
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- type: mrr_at_5
|
|
|
value: 54.222
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|
|
- type: ndcg_at_1
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|
|
value: 45.391999999999996
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|
|
- type: ndcg_at_10
|
|
|
value: 57.586999999999996
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|
|
- type: ndcg_at_100
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|
|
value: 61.745000000000005
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|
|
- type: ndcg_at_1000
|
|
|
value: 62.83800000000001
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|
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- type: ndcg_at_3
|
|
|
value: 52.207
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|
|
- type: ndcg_at_5
|
|
|
value: 54.925999999999995
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|
|
- type: precision_at_1
|
|
|
value: 45.391999999999996
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|
|
- type: precision_at_10
|
|
|
value: 9.21
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|
|
- type: precision_at_100
|
|
|
value: 1.226
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|
|
- type: precision_at_1000
|
|
|
value: 0.136
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|
|
- type: precision_at_3
|
|
|
value: 23.177
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|
|
- type: precision_at_5
|
|
|
value: 16.038
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|
|
- type: recall_at_1
|
|
|
value: 39.898
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|
|
- type: recall_at_10
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|
|
value: 71.18900000000001
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|
|
- type: recall_at_100
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|
|
value: 89.082
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|
|
- type: recall_at_1000
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|
|
value: 96.865
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|
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- type: recall_at_3
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|
|
value: 56.907
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|
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- type: recall_at_5
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|
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value: 63.397999999999996
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|
|
- task:
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type: Retrieval
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|
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dataset:
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type: BeIR/cqadupstack
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name: MTEB CQADupstackGisRetrieval
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|
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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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|
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- type: map_at_1
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|
|
value: 22.706
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|
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- type: map_at_10
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|
|
value: 30.818
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|
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- type: map_at_100
|
|
|
value: 32.038
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|
|
- type: map_at_1000
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|
|
value: 32.123000000000005
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|
|
- type: map_at_3
|
|
|
value: 28.077
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|
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- type: map_at_5
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|
|
value: 29.709999999999997
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|
|
- type: mrr_at_1
|
|
|
value: 24.407
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|
|
- type: mrr_at_10
|
|
|
value: 32.555
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|
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- type: mrr_at_100
|
|
|
value: 33.692
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|
|
- type: mrr_at_1000
|
|
|
value: 33.751
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|
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- type: mrr_at_3
|
|
|
value: 29.848999999999997
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|
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- type: mrr_at_5
|
|
|
value: 31.509999999999998
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|
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- type: ndcg_at_1
|
|
|
value: 24.407
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|
|
- 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.
|
|
|
|
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Here are some rules of thumb:
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- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
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- Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval.
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- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
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**2. Why are my reproduced results slightly different from reported in the model card?**
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Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
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**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
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This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
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For text embedding tasks like text retrieval or semantic similarity,
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what matters is the relative order of the scores instead of the absolute values,
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so this should not be an issue.
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## Citation
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If you find our paper or models helpful, please consider cite as follows:
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```
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@article{wang2022text,
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title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
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author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
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journal={arXiv preprint arXiv:2212.03533},
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year={2022}
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}
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```
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## Limitations
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This model only works for English texts. Long texts will be truncated to at most 512 tokens.
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