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---
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tags:
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- mteb
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- transformers.js
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- transformers
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model-index:
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- name: mxbai-angle-large-v1
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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: 75.044776119403
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- type: ap
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value: 37.7362433623053
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- type: f1
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value: 68.92736573359774
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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: 93.84025000000001
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- type: ap
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value: 90.93190875404055
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- type: f1
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value: 93.8297833897293
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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: 49.184
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- type: f1
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value: 48.74163227751588
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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: 41.252
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- type: map_at_10
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value: 57.778
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- type: map_at_100
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value: 58.233000000000004
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- type: map_at_1000
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value: 58.23700000000001
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- type: map_at_3
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value: 53.449999999999996
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- type: map_at_5
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value: 56.376000000000005
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- type: mrr_at_1
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value: 41.679
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- type: mrr_at_10
|
|
value: 57.92699999999999
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- type: mrr_at_100
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value: 58.389
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- type: mrr_at_1000
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value: 58.391999999999996
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- type: mrr_at_3
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value: 53.651
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- type: mrr_at_5
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value: 56.521
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- type: ndcg_at_1
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value: 41.252
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- type: ndcg_at_10
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value: 66.018
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- type: ndcg_at_100
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value: 67.774
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- type: ndcg_at_1000
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value: 67.84400000000001
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- type: ndcg_at_3
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value: 57.372
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- type: ndcg_at_5
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value: 62.646
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- type: precision_at_1
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value: 41.252
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- type: precision_at_10
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value: 9.189
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- type: precision_at_100
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value: 0.991
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- type: precision_at_1000
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value: 0.1
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- type: precision_at_3
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value: 22.902
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- type: precision_at_5
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value: 16.302
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- type: recall_at_1
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value: 41.252
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- type: recall_at_10
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value: 91.892
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- type: recall_at_100
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value: 99.14699999999999
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- type: recall_at_1000
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value: 99.644
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- type: recall_at_3
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value: 68.706
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- type: recall_at_5
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value: 81.50800000000001
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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: 48.97294504317859
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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: 42.98071077674629
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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: 65.16477858490782
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- type: mrr
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value: 78.23583080508287
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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: 89.6277629421789
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- type: cos_sim_spearman
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value: 88.4056288400568
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- type: euclidean_pearson
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value: 87.94871847578163
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- type: euclidean_spearman
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value: 88.4056288400568
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- type: manhattan_pearson
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value: 87.73271254229648
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- type: manhattan_spearman
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value: 87.91826833762677
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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: 87.81818181818181
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- type: f1
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value: 87.79879337316918
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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: 39.91773608582761
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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: 36.73059477462478
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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: 32.745999999999995
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- type: map_at_10
|
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value: 43.632
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- type: map_at_100
|
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value: 45.206
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- type: map_at_1000
|
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value: 45.341
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- type: map_at_3
|
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value: 39.956
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- type: map_at_5
|
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value: 42.031
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- type: mrr_at_1
|
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value: 39.485
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- type: mrr_at_10
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value: 49.537
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- type: mrr_at_100
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value: 50.249
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- type: mrr_at_1000
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value: 50.294000000000004
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- type: mrr_at_3
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value: 46.757
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- type: mrr_at_5
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value: 48.481
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- type: ndcg_at_1
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value: 39.485
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- type: ndcg_at_10
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value: 50.058
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- type: ndcg_at_100
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value: 55.586
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- type: ndcg_at_1000
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value: 57.511
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- type: ndcg_at_3
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value: 44.786
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- type: ndcg_at_5
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value: 47.339999999999996
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- type: precision_at_1
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value: 39.485
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- type: precision_at_10
|
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value: 9.557
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- type: precision_at_100
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value: 1.552
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- type: precision_at_1000
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value: 0.202
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- type: precision_at_3
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value: 21.412
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- type: precision_at_5
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value: 15.479000000000001
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- type: recall_at_1
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value: 32.745999999999995
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- type: recall_at_10
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value: 62.056
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- type: recall_at_100
|
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value: 85.088
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- type: recall_at_1000
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value: 96.952
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- type: recall_at_3
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value: 46.959
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- type: recall_at_5
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value: 54.06999999999999
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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: 31.898
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- type: map_at_10
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value: 42.142
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- type: map_at_100
|
|
value: 43.349
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- type: map_at_1000
|
|
value: 43.483
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- type: map_at_3
|
|
value: 39.18
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- type: map_at_5
|
|
value: 40.733000000000004
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- type: mrr_at_1
|
|
value: 39.617999999999995
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- type: mrr_at_10
|
|
value: 47.922
|
|
- type: mrr_at_100
|
|
value: 48.547000000000004
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- type: mrr_at_1000
|
|
value: 48.597
|
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- type: mrr_at_3
|
|
value: 45.86
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|
- type: mrr_at_5
|
|
value: 46.949000000000005
|
|
- type: ndcg_at_1
|
|
value: 39.617999999999995
|
|
- type: ndcg_at_10
|
|
value: 47.739
|
|
- type: ndcg_at_100
|
|
value: 51.934999999999995
|
|
- type: ndcg_at_1000
|
|
value: 54.007000000000005
|
|
- type: ndcg_at_3
|
|
value: 43.748
|
|
- type: ndcg_at_5
|
|
value: 45.345
|
|
- type: precision_at_1
|
|
value: 39.617999999999995
|
|
- type: precision_at_10
|
|
value: 8.962
|
|
- type: precision_at_100
|
|
value: 1.436
|
|
- type: precision_at_1000
|
|
value: 0.192
|
|
- type: precision_at_3
|
|
value: 21.083
|
|
- type: precision_at_5
|
|
value: 14.752
|
|
- type: recall_at_1
|
|
value: 31.898
|
|
- type: recall_at_10
|
|
value: 57.587999999999994
|
|
- type: recall_at_100
|
|
value: 75.323
|
|
- type: recall_at_1000
|
|
value: 88.304
|
|
- type: recall_at_3
|
|
value: 45.275
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- type: recall_at_5
|
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value: 49.99
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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: 40.458
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- type: map_at_10
|
|
value: 52.942
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- type: map_at_100
|
|
value: 53.974
|
|
- type: map_at_1000
|
|
value: 54.031
|
|
- type: map_at_3
|
|
value: 49.559999999999995
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- type: map_at_5
|
|
value: 51.408
|
|
- type: mrr_at_1
|
|
value: 46.27
|
|
- type: mrr_at_10
|
|
value: 56.31699999999999
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- type: mrr_at_100
|
|
value: 56.95099999999999
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|
- type: mrr_at_1000
|
|
value: 56.98
|
|
- type: mrr_at_3
|
|
value: 53.835
|
|
- type: mrr_at_5
|
|
value: 55.252
|
|
- type: ndcg_at_1
|
|
value: 46.27
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|
- type: ndcg_at_10
|
|
value: 58.964000000000006
|
|
- type: ndcg_at_100
|
|
value: 62.875
|
|
- type: ndcg_at_1000
|
|
value: 63.969
|
|
- type: ndcg_at_3
|
|
value: 53.297000000000004
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- type: ndcg_at_5
|
|
value: 55.938
|
|
- type: precision_at_1
|
|
value: 46.27
|
|
- type: precision_at_10
|
|
value: 9.549000000000001
|
|
- type: precision_at_100
|
|
value: 1.2409999999999999
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- type: precision_at_1000
|
|
value: 0.13799999999999998
|
|
- type: precision_at_3
|
|
value: 23.762
|
|
- type: precision_at_5
|
|
value: 16.262999999999998
|
|
- type: recall_at_1
|
|
value: 40.458
|
|
- type: recall_at_10
|
|
value: 73.446
|
|
- type: recall_at_100
|
|
value: 90.12400000000001
|
|
- type: recall_at_1000
|
|
value: 97.795
|
|
- type: recall_at_3
|
|
value: 58.123000000000005
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- type: recall_at_5
|
|
value: 64.68
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|
- task:
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type: Retrieval
|
|
dataset:
|
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type: BeIR/cqadupstack
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name: MTEB CQADupstackGisRetrieval
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config: default
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split: test
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revision: None
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metrics:
|
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- type: map_at_1
|
|
value: 27.443
|
|
- type: map_at_10
|
|
value: 36.081
|
|
- type: map_at_100
|
|
value: 37.163000000000004
|
|
- type: map_at_1000
|
|
value: 37.232
|
|
- type: map_at_3
|
|
value: 33.308
|
|
- type: map_at_5
|
|
value: 34.724
|
|
- type: mrr_at_1
|
|
value: 29.492
|
|
- type: mrr_at_10
|
|
value: 38.138
|
|
- type: mrr_at_100
|
|
value: 39.065
|
|
- type: mrr_at_1000
|
|
value: 39.119
|
|
- type: mrr_at_3
|
|
value: 35.593
|
|
- type: mrr_at_5
|
|
value: 36.785000000000004
|
|
- type: ndcg_at_1
|
|
value: 29.492
|
|
- type: ndcg_at_10
|
|
value: 41.134
|
|
- type: ndcg_at_100
|
|
value: 46.300999999999995
|
|
- type: ndcg_at_1000
|
|
value: 48.106
|
|
- type: ndcg_at_3
|
|
value: 35.77
|
|
- type: ndcg_at_5
|
|
value: 38.032
|
|
- type: precision_at_1
|
|
value: 29.492
|
|
- type: precision_at_10
|
|
value: 6.249
|
|
- type: precision_at_100
|
|
value: 0.9299999999999999
|
|
- type: precision_at_1000
|
|
value: 0.11199999999999999
|
|
- type: precision_at_3
|
|
value: 15.065999999999999
|
|
- type: precision_at_5
|
|
value: 10.373000000000001
|
|
- type: recall_at_1
|
|
value: 27.443
|
|
- type: recall_at_10
|
|
value: 54.80199999999999
|
|
- type: recall_at_100
|
|
value: 78.21900000000001
|
|
- type: recall_at_1000
|
|
value: 91.751
|
|
- type: recall_at_3
|
|
value: 40.211000000000006
|
|
- type: recall_at_5
|
|
value: 45.599000000000004
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackMathematicaRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 18.731
|
|
- type: map_at_10
|
|
value: 26.717999999999996
|
|
- type: map_at_100
|
|
value: 27.897
|
|
- type: map_at_1000
|
|
value: 28.029
|
|
- type: map_at_3
|
|
value: 23.91
|
|
- type: map_at_5
|
|
value: 25.455
|
|
- type: mrr_at_1
|
|
value: 23.134
|
|
- type: mrr_at_10
|
|
value: 31.769
|
|
- type: mrr_at_100
|
|
value: 32.634
|
|
- type: mrr_at_1000
|
|
value: 32.707
|
|
- type: mrr_at_3
|
|
value: 28.938999999999997
|
|
- type: mrr_at_5
|
|
value: 30.531000000000002
|
|
- type: ndcg_at_1
|
|
value: 23.134
|
|
- type: ndcg_at_10
|
|
value: 32.249
|
|
- type: ndcg_at_100
|
|
value: 37.678
|
|
- type: ndcg_at_1000
|
|
value: 40.589999999999996
|
|
- type: ndcg_at_3
|
|
value: 26.985999999999997
|
|
- type: ndcg_at_5
|
|
value: 29.457
|
|
- type: precision_at_1
|
|
value: 23.134
|
|
- type: precision_at_10
|
|
value: 5.8709999999999996
|
|
- type: precision_at_100
|
|
value: 0.988
|
|
- type: precision_at_1000
|
|
value: 0.13799999999999998
|
|
- type: precision_at_3
|
|
value: 12.852
|
|
- type: precision_at_5
|
|
value: 9.428
|
|
- type: recall_at_1
|
|
value: 18.731
|
|
- type: recall_at_10
|
|
value: 44.419
|
|
- type: recall_at_100
|
|
value: 67.851
|
|
- type: recall_at_1000
|
|
value: 88.103
|
|
- type: recall_at_3
|
|
value: 29.919
|
|
- type: recall_at_5
|
|
value: 36.230000000000004
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackPhysicsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 30.324
|
|
- type: map_at_10
|
|
value: 41.265
|
|
- type: map_at_100
|
|
value: 42.559000000000005
|
|
- type: map_at_1000
|
|
value: 42.669000000000004
|
|
- type: map_at_3
|
|
value: 38.138
|
|
- type: map_at_5
|
|
value: 39.881
|
|
- type: mrr_at_1
|
|
value: 36.67
|
|
- type: mrr_at_10
|
|
value: 46.774
|
|
- type: mrr_at_100
|
|
value: 47.554
|
|
- type: mrr_at_1000
|
|
value: 47.593
|
|
- type: mrr_at_3
|
|
value: 44.338
|
|
- type: mrr_at_5
|
|
value: 45.723
|
|
- type: ndcg_at_1
|
|
value: 36.67
|
|
- type: ndcg_at_10
|
|
value: 47.367
|
|
- type: ndcg_at_100
|
|
value: 52.623
|
|
- type: ndcg_at_1000
|
|
value: 54.59
|
|
- type: ndcg_at_3
|
|
value: 42.323
|
|
- type: ndcg_at_5
|
|
value: 44.727
|
|
- type: precision_at_1
|
|
value: 36.67
|
|
- type: precision_at_10
|
|
value: 8.518
|
|
- type: precision_at_100
|
|
value: 1.2890000000000001
|
|
- type: precision_at_1000
|
|
value: 0.163
|
|
- type: precision_at_3
|
|
value: 19.955000000000002
|
|
- type: precision_at_5
|
|
value: 14.11
|
|
- type: recall_at_1
|
|
value: 30.324
|
|
- type: recall_at_10
|
|
value: 59.845000000000006
|
|
- type: recall_at_100
|
|
value: 81.77499999999999
|
|
- type: recall_at_1000
|
|
value: 94.463
|
|
- type: recall_at_3
|
|
value: 46.019
|
|
- type: recall_at_5
|
|
value: 52.163000000000004
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackProgrammersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 24.229
|
|
- type: map_at_10
|
|
value: 35.004000000000005
|
|
- type: map_at_100
|
|
value: 36.409000000000006
|
|
- type: map_at_1000
|
|
value: 36.521
|
|
- type: map_at_3
|
|
value: 31.793
|
|
- type: map_at_5
|
|
value: 33.432
|
|
- type: mrr_at_1
|
|
value: 30.365
|
|
- type: mrr_at_10
|
|
value: 40.502
|
|
- type: mrr_at_100
|
|
value: 41.372
|
|
- type: mrr_at_1000
|
|
value: 41.435
|
|
- type: mrr_at_3
|
|
value: 37.804
|
|
- type: mrr_at_5
|
|
value: 39.226
|
|
- type: ndcg_at_1
|
|
value: 30.365
|
|
- type: ndcg_at_10
|
|
value: 41.305
|
|
- type: ndcg_at_100
|
|
value: 47.028999999999996
|
|
- type: ndcg_at_1000
|
|
value: 49.375
|
|
- type: ndcg_at_3
|
|
value: 35.85
|
|
- type: ndcg_at_5
|
|
value: 38.12
|
|
- type: precision_at_1
|
|
value: 30.365
|
|
- type: precision_at_10
|
|
value: 7.808
|
|
- type: precision_at_100
|
|
value: 1.228
|
|
- type: precision_at_1000
|
|
value: 0.161
|
|
- type: precision_at_3
|
|
value: 17.352
|
|
- type: precision_at_5
|
|
value: 12.42
|
|
- type: recall_at_1
|
|
value: 24.229
|
|
- type: recall_at_10
|
|
value: 54.673
|
|
- type: recall_at_100
|
|
value: 78.766
|
|
- type: recall_at_1000
|
|
value: 94.625
|
|
- type: recall_at_3
|
|
value: 39.602
|
|
- type: recall_at_5
|
|
value: 45.558
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 26.695
|
|
- type: map_at_10
|
|
value: 36.0895
|
|
- type: map_at_100
|
|
value: 37.309416666666664
|
|
- type: map_at_1000
|
|
value: 37.42558333333334
|
|
- type: map_at_3
|
|
value: 33.19616666666666
|
|
- type: map_at_5
|
|
value: 34.78641666666667
|
|
- type: mrr_at_1
|
|
value: 31.486083333333337
|
|
- type: mrr_at_10
|
|
value: 40.34774999999999
|
|
- type: mrr_at_100
|
|
value: 41.17533333333333
|
|
- type: mrr_at_1000
|
|
value: 41.231583333333326
|
|
- type: mrr_at_3
|
|
value: 37.90075
|
|
- type: mrr_at_5
|
|
value: 39.266999999999996
|
|
- type: ndcg_at_1
|
|
value: 31.486083333333337
|
|
- type: ndcg_at_10
|
|
value: 41.60433333333334
|
|
- type: ndcg_at_100
|
|
value: 46.74525
|
|
- type: ndcg_at_1000
|
|
value: 48.96166666666667
|
|
- type: ndcg_at_3
|
|
value: 36.68825
|
|
- type: ndcg_at_5
|
|
value: 38.966499999999996
|
|
- type: precision_at_1
|
|
value: 31.486083333333337
|
|
- type: precision_at_10
|
|
value: 7.29675
|
|
- type: precision_at_100
|
|
value: 1.1621666666666666
|
|
- type: precision_at_1000
|
|
value: 0.1545
|
|
- type: precision_at_3
|
|
value: 16.8815
|
|
- type: precision_at_5
|
|
value: 11.974583333333333
|
|
- type: recall_at_1
|
|
value: 26.695
|
|
- type: recall_at_10
|
|
value: 53.651916666666665
|
|
- type: recall_at_100
|
|
value: 76.12083333333332
|
|
- type: recall_at_1000
|
|
value: 91.31191666666668
|
|
- type: recall_at_3
|
|
value: 40.03575
|
|
- type: recall_at_5
|
|
value: 45.876666666666665
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackStatsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 25.668000000000003
|
|
- type: map_at_10
|
|
value: 32.486
|
|
- type: map_at_100
|
|
value: 33.371
|
|
- type: map_at_1000
|
|
value: 33.458
|
|
- type: map_at_3
|
|
value: 30.261
|
|
- type: map_at_5
|
|
value: 31.418000000000003
|
|
- type: mrr_at_1
|
|
value: 28.988000000000003
|
|
- type: mrr_at_10
|
|
value: 35.414
|
|
- type: mrr_at_100
|
|
value: 36.149
|
|
- type: mrr_at_1000
|
|
value: 36.215
|
|
- type: mrr_at_3
|
|
value: 33.333
|
|
- type: mrr_at_5
|
|
value: 34.43
|
|
- type: ndcg_at_1
|
|
value: 28.988000000000003
|
|
- type: ndcg_at_10
|
|
value: 36.732
|
|
- type: ndcg_at_100
|
|
value: 41.331
|
|
- type: ndcg_at_1000
|
|
value: 43.575
|
|
- type: ndcg_at_3
|
|
value: 32.413
|
|
- type: ndcg_at_5
|
|
value: 34.316
|
|
- type: precision_at_1
|
|
value: 28.988000000000003
|
|
- type: precision_at_10
|
|
value: 5.7059999999999995
|
|
- type: precision_at_100
|
|
value: 0.882
|
|
- type: precision_at_1000
|
|
value: 0.11299999999999999
|
|
- type: precision_at_3
|
|
value: 13.65
|
|
- type: precision_at_5
|
|
value: 9.417
|
|
- type: recall_at_1
|
|
value: 25.668000000000003
|
|
- type: recall_at_10
|
|
value: 47.147
|
|
- type: recall_at_100
|
|
value: 68.504
|
|
- type: recall_at_1000
|
|
value: 85.272
|
|
- type: recall_at_3
|
|
value: 35.19
|
|
- type: recall_at_5
|
|
value: 39.925
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackTexRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 17.256
|
|
- type: map_at_10
|
|
value: 24.58
|
|
- type: map_at_100
|
|
value: 25.773000000000003
|
|
- type: map_at_1000
|
|
value: 25.899
|
|
- type: map_at_3
|
|
value: 22.236
|
|
- type: map_at_5
|
|
value: 23.507
|
|
- type: mrr_at_1
|
|
value: 20.957
|
|
- type: mrr_at_10
|
|
value: 28.416000000000004
|
|
- type: mrr_at_100
|
|
value: 29.447000000000003
|
|
- type: mrr_at_1000
|
|
value: 29.524
|
|
- type: mrr_at_3
|
|
value: 26.245
|
|
- type: mrr_at_5
|
|
value: 27.451999999999998
|
|
- type: ndcg_at_1
|
|
value: 20.957
|
|
- type: ndcg_at_10
|
|
value: 29.285
|
|
- type: ndcg_at_100
|
|
value: 35.003
|
|
- type: ndcg_at_1000
|
|
value: 37.881
|
|
- type: ndcg_at_3
|
|
value: 25.063000000000002
|
|
- type: ndcg_at_5
|
|
value: 26.983
|
|
- type: precision_at_1
|
|
value: 20.957
|
|
- type: precision_at_10
|
|
value: 5.344
|
|
- type: precision_at_100
|
|
value: 0.958
|
|
- type: precision_at_1000
|
|
value: 0.13799999999999998
|
|
- type: precision_at_3
|
|
value: 11.918
|
|
- type: precision_at_5
|
|
value: 8.596
|
|
- type: recall_at_1
|
|
value: 17.256
|
|
- type: recall_at_10
|
|
value: 39.644
|
|
- type: recall_at_100
|
|
value: 65.279
|
|
- type: recall_at_1000
|
|
value: 85.693
|
|
- type: recall_at_3
|
|
value: 27.825
|
|
- type: recall_at_5
|
|
value: 32.792
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackUnixRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 26.700000000000003
|
|
- type: map_at_10
|
|
value: 36.205999999999996
|
|
- type: map_at_100
|
|
value: 37.316
|
|
- type: map_at_1000
|
|
value: 37.425000000000004
|
|
- type: map_at_3
|
|
value: 33.166000000000004
|
|
- type: map_at_5
|
|
value: 35.032999999999994
|
|
- type: mrr_at_1
|
|
value: 31.436999999999998
|
|
- type: mrr_at_10
|
|
value: 40.61
|
|
- type: mrr_at_100
|
|
value: 41.415
|
|
- type: mrr_at_1000
|
|
value: 41.48
|
|
- type: mrr_at_3
|
|
value: 37.966
|
|
- type: mrr_at_5
|
|
value: 39.599000000000004
|
|
- type: ndcg_at_1
|
|
value: 31.436999999999998
|
|
- type: ndcg_at_10
|
|
value: 41.771
|
|
- type: ndcg_at_100
|
|
value: 46.784
|
|
- type: ndcg_at_1000
|
|
value: 49.183
|
|
- type: ndcg_at_3
|
|
value: 36.437000000000005
|
|
- type: ndcg_at_5
|
|
value: 39.291
|
|
- type: precision_at_1
|
|
value: 31.436999999999998
|
|
- type: precision_at_10
|
|
value: 6.987
|
|
- type: precision_at_100
|
|
value: 1.072
|
|
- type: precision_at_1000
|
|
value: 0.13899999999999998
|
|
- type: precision_at_3
|
|
value: 16.448999999999998
|
|
- type: precision_at_5
|
|
value: 11.866
|
|
- type: recall_at_1
|
|
value: 26.700000000000003
|
|
- type: recall_at_10
|
|
value: 54.301
|
|
- type: recall_at_100
|
|
value: 75.871
|
|
- type: recall_at_1000
|
|
value: 92.529
|
|
- type: recall_at_3
|
|
value: 40.201
|
|
- type: recall_at_5
|
|
value: 47.208
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWebmastersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 24.296
|
|
- type: map_at_10
|
|
value: 33.116
|
|
- type: map_at_100
|
|
value: 34.81
|
|
- type: map_at_1000
|
|
value: 35.032000000000004
|
|
- type: map_at_3
|
|
value: 30.105999999999998
|
|
- type: map_at_5
|
|
value: 31.839000000000002
|
|
- type: mrr_at_1
|
|
value: 29.051
|
|
- type: mrr_at_10
|
|
value: 37.803
|
|
- type: mrr_at_100
|
|
value: 38.856
|
|
- type: mrr_at_1000
|
|
value: 38.903999999999996
|
|
- type: mrr_at_3
|
|
value: 35.211
|
|
- type: mrr_at_5
|
|
value: 36.545
|
|
- type: ndcg_at_1
|
|
value: 29.051
|
|
- type: ndcg_at_10
|
|
value: 39.007
|
|
- type: ndcg_at_100
|
|
value: 45.321
|
|
- type: ndcg_at_1000
|
|
value: 47.665
|
|
- type: ndcg_at_3
|
|
value: 34.1
|
|
- type: ndcg_at_5
|
|
value: 36.437000000000005
|
|
- type: precision_at_1
|
|
value: 29.051
|
|
- type: precision_at_10
|
|
value: 7.668
|
|
- type: precision_at_100
|
|
value: 1.542
|
|
- type: precision_at_1000
|
|
value: 0.24
|
|
- type: precision_at_3
|
|
value: 16.14
|
|
- type: precision_at_5
|
|
value: 11.897
|
|
- type: recall_at_1
|
|
value: 24.296
|
|
- type: recall_at_10
|
|
value: 49.85
|
|
- type: recall_at_100
|
|
value: 78.457
|
|
- type: recall_at_1000
|
|
value: 92.618
|
|
- type: recall_at_3
|
|
value: 36.138999999999996
|
|
- type: recall_at_5
|
|
value: 42.223
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWordpressRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 20.591
|
|
- type: map_at_10
|
|
value: 28.902
|
|
- type: map_at_100
|
|
value: 29.886000000000003
|
|
- type: map_at_1000
|
|
value: 29.987000000000002
|
|
- type: map_at_3
|
|
value: 26.740000000000002
|
|
- type: map_at_5
|
|
value: 27.976
|
|
- type: mrr_at_1
|
|
value: 22.366
|
|
- type: mrr_at_10
|
|
value: 30.971
|
|
- type: mrr_at_100
|
|
value: 31.865
|
|
- type: mrr_at_1000
|
|
value: 31.930999999999997
|
|
- type: mrr_at_3
|
|
value: 28.927999999999997
|
|
- type: mrr_at_5
|
|
value: 30.231
|
|
- type: ndcg_at_1
|
|
value: 22.366
|
|
- type: ndcg_at_10
|
|
value: 33.641
|
|
- type: ndcg_at_100
|
|
value: 38.477
|
|
- type: ndcg_at_1000
|
|
value: 41.088
|
|
- type: ndcg_at_3
|
|
value: 29.486
|
|
- type: ndcg_at_5
|
|
value: 31.612000000000002
|
|
- type: precision_at_1
|
|
value: 22.366
|
|
- type: precision_at_10
|
|
value: 5.3420000000000005
|
|
- type: precision_at_100
|
|
value: 0.828
|
|
- type: precision_at_1000
|
|
value: 0.11800000000000001
|
|
- type: precision_at_3
|
|
value: 12.939
|
|
- type: precision_at_5
|
|
value: 9.094
|
|
- type: recall_at_1
|
|
value: 20.591
|
|
- type: recall_at_10
|
|
value: 46.052
|
|
- type: recall_at_100
|
|
value: 68.193
|
|
- type: recall_at_1000
|
|
value: 87.638
|
|
- type: recall_at_3
|
|
value: 34.966
|
|
- type: recall_at_5
|
|
value: 40.082
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: climate-fever
|
|
name: MTEB ClimateFEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 15.091
|
|
- type: map_at_10
|
|
value: 26.38
|
|
- type: map_at_100
|
|
value: 28.421999999999997
|
|
- type: map_at_1000
|
|
value: 28.621999999999996
|
|
- type: map_at_3
|
|
value: 21.597
|
|
- type: map_at_5
|
|
value: 24.12
|
|
- type: mrr_at_1
|
|
value: 34.266999999999996
|
|
- type: mrr_at_10
|
|
value: 46.864
|
|
- type: mrr_at_100
|
|
value: 47.617
|
|
- type: mrr_at_1000
|
|
value: 47.644
|
|
- type: mrr_at_3
|
|
value: 43.312
|
|
- type: mrr_at_5
|
|
value: 45.501000000000005
|
|
- type: ndcg_at_1
|
|
value: 34.266999999999996
|
|
- type: ndcg_at_10
|
|
value: 36.095
|
|
- type: ndcg_at_100
|
|
value: 43.447
|
|
- type: ndcg_at_1000
|
|
value: 46.661
|
|
- type: ndcg_at_3
|
|
value: 29.337999999999997
|
|
- type: ndcg_at_5
|
|
value: 31.824
|
|
- type: precision_at_1
|
|
value: 34.266999999999996
|
|
- type: precision_at_10
|
|
value: 11.472
|
|
- type: precision_at_100
|
|
value: 1.944
|
|
- type: precision_at_1000
|
|
value: 0.255
|
|
- type: precision_at_3
|
|
value: 21.933
|
|
- type: precision_at_5
|
|
value: 17.224999999999998
|
|
- type: recall_at_1
|
|
value: 15.091
|
|
- type: recall_at_10
|
|
value: 43.022
|
|
- type: recall_at_100
|
|
value: 68.075
|
|
- type: recall_at_1000
|
|
value: 85.76
|
|
- type: recall_at_3
|
|
value: 26.564
|
|
- type: recall_at_5
|
|
value: 33.594
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: dbpedia-entity
|
|
name: MTEB DBPedia
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 9.252
|
|
- type: map_at_10
|
|
value: 20.923
|
|
- type: map_at_100
|
|
value: 30.741000000000003
|
|
- type: map_at_1000
|
|
value: 32.542
|
|
- type: map_at_3
|
|
value: 14.442
|
|
- type: map_at_5
|
|
value: 17.399
|
|
- type: mrr_at_1
|
|
value: 70.25
|
|
- type: mrr_at_10
|
|
value: 78.17
|
|
- type: mrr_at_100
|
|
value: 78.444
|
|
- type: mrr_at_1000
|
|
value: 78.45100000000001
|
|
- type: mrr_at_3
|
|
value: 76.958
|
|
- type: mrr_at_5
|
|
value: 77.571
|
|
- type: ndcg_at_1
|
|
value: 58.375
|
|
- type: ndcg_at_10
|
|
value: 44.509
|
|
- type: ndcg_at_100
|
|
value: 49.897999999999996
|
|
- type: ndcg_at_1000
|
|
value: 57.269999999999996
|
|
- type: ndcg_at_3
|
|
value: 48.64
|
|
- type: ndcg_at_5
|
|
value: 46.697
|
|
- type: precision_at_1
|
|
value: 70.25
|
|
- type: precision_at_10
|
|
value: 36.05
|
|
- type: precision_at_100
|
|
value: 11.848
|
|
- type: precision_at_1000
|
|
value: 2.213
|
|
- type: precision_at_3
|
|
value: 52.917
|
|
- type: precision_at_5
|
|
value: 45.7
|
|
- type: recall_at_1
|
|
value: 9.252
|
|
- type: recall_at_10
|
|
value: 27.006999999999998
|
|
- type: recall_at_100
|
|
value: 57.008
|
|
- type: recall_at_1000
|
|
value: 80.697
|
|
- type: recall_at_3
|
|
value: 15.798000000000002
|
|
- type: recall_at_5
|
|
value: 20.4
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/emotion
|
|
name: MTEB EmotionClassification
|
|
config: default
|
|
split: test
|
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
|
metrics:
|
|
- type: accuracy
|
|
value: 50.88
|
|
- type: f1
|
|
value: 45.545495028653384
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fever
|
|
name: MTEB FEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 75.424
|
|
- type: map_at_10
|
|
value: 83.435
|
|
- type: map_at_100
|
|
value: 83.66900000000001
|
|
- type: map_at_1000
|
|
value: 83.685
|
|
- type: map_at_3
|
|
value: 82.39800000000001
|
|
- type: map_at_5
|
|
value: 83.07
|
|
- type: mrr_at_1
|
|
value: 81.113
|
|
- type: mrr_at_10
|
|
value: 87.77199999999999
|
|
- type: mrr_at_100
|
|
value: 87.862
|
|
- type: mrr_at_1000
|
|
value: 87.86500000000001
|
|
- type: mrr_at_3
|
|
value: 87.17099999999999
|
|
- type: mrr_at_5
|
|
value: 87.616
|
|
- type: ndcg_at_1
|
|
value: 81.113
|
|
- type: ndcg_at_10
|
|
value: 86.909
|
|
- type: ndcg_at_100
|
|
value: 87.746
|
|
- type: ndcg_at_1000
|
|
value: 88.017
|
|
- type: ndcg_at_3
|
|
value: 85.368
|
|
- type: ndcg_at_5
|
|
value: 86.28099999999999
|
|
- type: precision_at_1
|
|
value: 81.113
|
|
- type: precision_at_10
|
|
value: 10.363
|
|
- type: precision_at_100
|
|
value: 1.102
|
|
- type: precision_at_1000
|
|
value: 0.11399999999999999
|
|
- type: precision_at_3
|
|
value: 32.507999999999996
|
|
- type: precision_at_5
|
|
value: 20.138
|
|
- type: recall_at_1
|
|
value: 75.424
|
|
- type: recall_at_10
|
|
value: 93.258
|
|
- type: recall_at_100
|
|
value: 96.545
|
|
- type: recall_at_1000
|
|
value: 98.284
|
|
- type: recall_at_3
|
|
value: 89.083
|
|
- type: recall_at_5
|
|
value: 91.445
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fiqa
|
|
name: MTEB FiQA2018
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 22.532
|
|
- type: map_at_10
|
|
value: 37.141999999999996
|
|
- type: map_at_100
|
|
value: 39.162
|
|
- type: map_at_1000
|
|
value: 39.322
|
|
- type: map_at_3
|
|
value: 32.885
|
|
- type: map_at_5
|
|
value: 35.093999999999994
|
|
- type: mrr_at_1
|
|
value: 44.29
|
|
- type: mrr_at_10
|
|
value: 53.516
|
|
- type: mrr_at_100
|
|
value: 54.24
|
|
- type: mrr_at_1000
|
|
value: 54.273
|
|
- type: mrr_at_3
|
|
value: 51.286
|
|
- type: mrr_at_5
|
|
value: 52.413
|
|
- type: ndcg_at_1
|
|
value: 44.29
|
|
- type: ndcg_at_10
|
|
value: 45.268
|
|
- type: ndcg_at_100
|
|
value: 52.125
|
|
- type: ndcg_at_1000
|
|
value: 54.778000000000006
|
|
- type: ndcg_at_3
|
|
value: 41.829
|
|
- type: ndcg_at_5
|
|
value: 42.525
|
|
- type: precision_at_1
|
|
value: 44.29
|
|
- type: precision_at_10
|
|
value: 12.5
|
|
- type: precision_at_100
|
|
value: 1.9720000000000002
|
|
- type: precision_at_1000
|
|
value: 0.245
|
|
- type: precision_at_3
|
|
value: 28.035
|
|
- type: precision_at_5
|
|
value: 20.093
|
|
- type: recall_at_1
|
|
value: 22.532
|
|
- type: recall_at_10
|
|
value: 52.419000000000004
|
|
- type: recall_at_100
|
|
value: 77.43299999999999
|
|
- type: recall_at_1000
|
|
value: 93.379
|
|
- type: recall_at_3
|
|
value: 38.629000000000005
|
|
- type: recall_at_5
|
|
value: 43.858000000000004
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: hotpotqa
|
|
name: MTEB HotpotQA
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 39.359
|
|
- type: map_at_10
|
|
value: 63.966
|
|
- type: map_at_100
|
|
value: 64.87
|
|
- type: map_at_1000
|
|
value: 64.92599999999999
|
|
- type: map_at_3
|
|
value: 60.409
|
|
- type: map_at_5
|
|
value: 62.627
|
|
- type: mrr_at_1
|
|
value: 78.717
|
|
- type: mrr_at_10
|
|
value: 84.468
|
|
- type: mrr_at_100
|
|
value: 84.655
|
|
- type: mrr_at_1000
|
|
value: 84.661
|
|
- type: mrr_at_3
|
|
value: 83.554
|
|
- type: mrr_at_5
|
|
value: 84.133
|
|
- type: ndcg_at_1
|
|
value: 78.717
|
|
- type: ndcg_at_10
|
|
value: 72.03399999999999
|
|
- type: ndcg_at_100
|
|
value: 75.158
|
|
- type: ndcg_at_1000
|
|
value: 76.197
|
|
- type: ndcg_at_3
|
|
value: 67.049
|
|
- type: ndcg_at_5
|
|
value: 69.808
|
|
- type: precision_at_1
|
|
value: 78.717
|
|
- type: precision_at_10
|
|
value: 15.201
|
|
- type: precision_at_100
|
|
value: 1.764
|
|
- type: precision_at_1000
|
|
value: 0.19
|
|
- type: precision_at_3
|
|
value: 43.313
|
|
- type: precision_at_5
|
|
value: 28.165000000000003
|
|
- type: recall_at_1
|
|
value: 39.359
|
|
- type: recall_at_10
|
|
value: 76.003
|
|
- type: recall_at_100
|
|
value: 88.197
|
|
- type: recall_at_1000
|
|
value: 95.003
|
|
- type: recall_at_3
|
|
value: 64.97
|
|
- type: recall_at_5
|
|
value: 70.41199999999999
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/imdb
|
|
name: MTEB ImdbClassification
|
|
config: default
|
|
split: test
|
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 92.83200000000001
|
|
- type: ap
|
|
value: 89.33560571859861
|
|
- type: f1
|
|
value: 92.82322915005167
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: msmarco
|
|
name: MTEB MSMARCO
|
|
config: default
|
|
split: dev
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 21.983
|
|
- type: map_at_10
|
|
value: 34.259
|
|
- type: map_at_100
|
|
value: 35.432
|
|
- type: map_at_1000
|
|
value: 35.482
|
|
- type: map_at_3
|
|
value: 30.275999999999996
|
|
- type: map_at_5
|
|
value: 32.566
|
|
- type: mrr_at_1
|
|
value: 22.579
|
|
- type: mrr_at_10
|
|
value: 34.882999999999996
|
|
- type: mrr_at_100
|
|
value: 35.984
|
|
- type: mrr_at_1000
|
|
value: 36.028
|
|
- type: mrr_at_3
|
|
value: 30.964999999999996
|
|
- type: mrr_at_5
|
|
value: 33.245000000000005
|
|
- type: ndcg_at_1
|
|
value: 22.564
|
|
- type: ndcg_at_10
|
|
value: 41.258
|
|
- type: ndcg_at_100
|
|
value: 46.824
|
|
- type: ndcg_at_1000
|
|
value: 48.037
|
|
- type: ndcg_at_3
|
|
value: 33.17
|
|
- type: ndcg_at_5
|
|
value: 37.263000000000005
|
|
- type: precision_at_1
|
|
value: 22.564
|
|
- type: precision_at_10
|
|
value: 6.572
|
|
- type: precision_at_100
|
|
value: 0.935
|
|
- type: precision_at_1000
|
|
value: 0.104
|
|
- type: precision_at_3
|
|
value: 14.130999999999998
|
|
- type: precision_at_5
|
|
value: 10.544
|
|
- type: recall_at_1
|
|
value: 21.983
|
|
- type: recall_at_10
|
|
value: 62.775000000000006
|
|
- type: recall_at_100
|
|
value: 88.389
|
|
- type: recall_at_1000
|
|
value: 97.603
|
|
- type: recall_at_3
|
|
value: 40.878
|
|
- type: recall_at_5
|
|
value: 50.690000000000005
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 93.95120839033288
|
|
- type: f1
|
|
value: 93.73824125055208
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 76.78978568171455
|
|
- type: f1
|
|
value: 57.50180552858304
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 76.24411566913248
|
|
- type: f1
|
|
value: 74.37851403532832
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 79.94620040349699
|
|
- type: f1
|
|
value: 80.21293397970435
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-p2p
|
|
name: MTEB MedrxivClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
|
metrics:
|
|
- type: v_measure
|
|
value: 33.44403096245675
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-s2s
|
|
name: MTEB MedrxivClusteringS2S
|
|
config: default
|
|
split: test
|
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
|
metrics:
|
|
- type: v_measure
|
|
value: 31.659594631336812
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/mind_small
|
|
name: MTEB MindSmallReranking
|
|
config: default
|
|
split: test
|
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
|
|
metrics:
|
|
- type: map
|
|
value: 32.53833075108798
|
|
- type: mrr
|
|
value: 33.78840823218308
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nfcorpus
|
|
name: MTEB NFCorpus
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 7.185999999999999
|
|
- type: map_at_10
|
|
value: 15.193999999999999
|
|
- type: map_at_100
|
|
value: 19.538
|
|
- type: map_at_1000
|
|
value: 21.178
|
|
- type: map_at_3
|
|
value: 11.208
|
|
- type: map_at_5
|
|
value: 12.745999999999999
|
|
- type: mrr_at_1
|
|
value: 48.916
|
|
- type: mrr_at_10
|
|
value: 58.141
|
|
- type: mrr_at_100
|
|
value: 58.656
|
|
- type: mrr_at_1000
|
|
value: 58.684999999999995
|
|
- type: mrr_at_3
|
|
value: 55.521
|
|
- type: mrr_at_5
|
|
value: 57.239
|
|
- type: ndcg_at_1
|
|
value: 47.059
|
|
- type: ndcg_at_10
|
|
value: 38.644
|
|
- type: ndcg_at_100
|
|
value: 36.272999999999996
|
|
- type: ndcg_at_1000
|
|
value: 44.996
|
|
- type: ndcg_at_3
|
|
value: 43.293
|
|
- type: ndcg_at_5
|
|
value: 40.819
|
|
- type: precision_at_1
|
|
value: 48.916
|
|
- type: precision_at_10
|
|
value: 28.607
|
|
- type: precision_at_100
|
|
value: 9.195
|
|
- type: precision_at_1000
|
|
value: 2.225
|
|
- type: precision_at_3
|
|
value: 40.454
|
|
- type: precision_at_5
|
|
value: 34.985
|
|
- type: recall_at_1
|
|
value: 7.185999999999999
|
|
- type: recall_at_10
|
|
value: 19.654
|
|
- type: recall_at_100
|
|
value: 37.224000000000004
|
|
- type: recall_at_1000
|
|
value: 68.663
|
|
- type: recall_at_3
|
|
value: 12.158
|
|
- type: recall_at_5
|
|
value: 14.674999999999999
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nq
|
|
name: MTEB NQ
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 31.552000000000003
|
|
- type: map_at_10
|
|
value: 47.75
|
|
- type: map_at_100
|
|
value: 48.728
|
|
- type: map_at_1000
|
|
value: 48.754
|
|
- type: map_at_3
|
|
value: 43.156
|
|
- type: map_at_5
|
|
value: 45.883
|
|
- type: mrr_at_1
|
|
value: 35.66
|
|
- type: mrr_at_10
|
|
value: 50.269
|
|
- type: mrr_at_100
|
|
value: 50.974
|
|
- type: mrr_at_1000
|
|
value: 50.991
|
|
- type: mrr_at_3
|
|
value: 46.519
|
|
- type: mrr_at_5
|
|
value: 48.764
|
|
- type: ndcg_at_1
|
|
value: 35.632000000000005
|
|
- type: ndcg_at_10
|
|
value: 55.786
|
|
- type: ndcg_at_100
|
|
value: 59.748999999999995
|
|
- type: ndcg_at_1000
|
|
value: 60.339
|
|
- type: ndcg_at_3
|
|
value: 47.292
|
|
- type: ndcg_at_5
|
|
value: 51.766999999999996
|
|
- type: precision_at_1
|
|
value: 35.632000000000005
|
|
- type: precision_at_10
|
|
value: 9.267
|
|
- type: precision_at_100
|
|
value: 1.149
|
|
- type: precision_at_1000
|
|
value: 0.12
|
|
- type: precision_at_3
|
|
value: 21.601
|
|
- type: precision_at_5
|
|
value: 15.539
|
|
- type: recall_at_1
|
|
value: 31.552000000000003
|
|
- type: recall_at_10
|
|
value: 77.62400000000001
|
|
- type: recall_at_100
|
|
value: 94.527
|
|
- type: recall_at_1000
|
|
value: 98.919
|
|
- type: recall_at_3
|
|
value: 55.898
|
|
- type: recall_at_5
|
|
value: 66.121
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: quora
|
|
name: MTEB QuoraRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 71.414
|
|
- type: map_at_10
|
|
value: 85.37400000000001
|
|
- type: map_at_100
|
|
value: 86.01100000000001
|
|
- type: map_at_1000
|
|
value: 86.027
|
|
- type: map_at_3
|
|
value: 82.562
|
|
- type: map_at_5
|
|
value: 84.284
|
|
- type: mrr_at_1
|
|
value: 82.24000000000001
|
|
- type: mrr_at_10
|
|
value: 88.225
|
|
- type: mrr_at_100
|
|
value: 88.324
|
|
- type: mrr_at_1000
|
|
value: 88.325
|
|
- type: mrr_at_3
|
|
value: 87.348
|
|
- type: mrr_at_5
|
|
value: 87.938
|
|
- type: ndcg_at_1
|
|
value: 82.24000000000001
|
|
- type: ndcg_at_10
|
|
value: 88.97699999999999
|
|
- type: ndcg_at_100
|
|
value: 90.16
|
|
- type: ndcg_at_1000
|
|
value: 90.236
|
|
- type: ndcg_at_3
|
|
value: 86.371
|
|
- type: ndcg_at_5
|
|
value: 87.746
|
|
- type: precision_at_1
|
|
value: 82.24000000000001
|
|
- type: precision_at_10
|
|
value: 13.481000000000002
|
|
- type: precision_at_100
|
|
value: 1.534
|
|
- type: precision_at_1000
|
|
value: 0.157
|
|
- type: precision_at_3
|
|
value: 37.86
|
|
- type: precision_at_5
|
|
value: 24.738
|
|
- type: recall_at_1
|
|
value: 71.414
|
|
- type: recall_at_10
|
|
value: 95.735
|
|
- type: recall_at_100
|
|
value: 99.696
|
|
- type: recall_at_1000
|
|
value: 99.979
|
|
- type: recall_at_3
|
|
value: 88.105
|
|
- type: recall_at_5
|
|
value: 92.17999999999999
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering
|
|
name: MTEB RedditClustering
|
|
config: default
|
|
split: test
|
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
|
metrics:
|
|
- type: v_measure
|
|
value: 60.22146692057259
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering-p2p
|
|
name: MTEB RedditClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
|
metrics:
|
|
- type: v_measure
|
|
value: 65.29273320614578
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scidocs
|
|
name: MTEB SCIDOCS
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 5.023
|
|
- type: map_at_10
|
|
value: 14.161000000000001
|
|
- type: map_at_100
|
|
value: 16.68
|
|
- type: map_at_1000
|
|
value: 17.072000000000003
|
|
- type: map_at_3
|
|
value: 9.763
|
|
- type: map_at_5
|
|
value: 11.977
|
|
- type: mrr_at_1
|
|
value: 24.8
|
|
- type: mrr_at_10
|
|
value: 37.602999999999994
|
|
- type: mrr_at_100
|
|
value: 38.618
|
|
- type: mrr_at_1000
|
|
value: 38.659
|
|
- type: mrr_at_3
|
|
value: 34.117
|
|
- type: mrr_at_5
|
|
value: 36.082
|
|
- type: ndcg_at_1
|
|
value: 24.8
|
|
- type: ndcg_at_10
|
|
value: 23.316
|
|
- type: ndcg_at_100
|
|
value: 32.613
|
|
- type: ndcg_at_1000
|
|
value: 38.609
|
|
- type: ndcg_at_3
|
|
value: 21.697
|
|
- type: ndcg_at_5
|
|
value: 19.241
|
|
- type: precision_at_1
|
|
value: 24.8
|
|
- type: precision_at_10
|
|
value: 12.36
|
|
- type: precision_at_100
|
|
value: 2.593
|
|
- type: precision_at_1000
|
|
value: 0.402
|
|
- type: precision_at_3
|
|
value: 20.767
|
|
- type: precision_at_5
|
|
value: 17.34
|
|
- type: recall_at_1
|
|
value: 5.023
|
|
- type: recall_at_10
|
|
value: 25.069999999999997
|
|
- type: recall_at_100
|
|
value: 52.563
|
|
- type: recall_at_1000
|
|
value: 81.525
|
|
- type: recall_at_3
|
|
value: 12.613
|
|
- type: recall_at_5
|
|
value: 17.583
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sickr-sts
|
|
name: MTEB SICK-R
|
|
config: default
|
|
split: test
|
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 87.71506247604255
|
|
- type: cos_sim_spearman
|
|
value: 82.91813463738802
|
|
- type: euclidean_pearson
|
|
value: 85.5154616194479
|
|
- type: euclidean_spearman
|
|
value: 82.91815254466314
|
|
- type: manhattan_pearson
|
|
value: 85.5280917850374
|
|
- type: manhattan_spearman
|
|
value: 82.92276537286398
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts12-sts
|
|
name: MTEB STS12
|
|
config: default
|
|
split: test
|
|
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 87.43772054228462
|
|
- type: cos_sim_spearman
|
|
value: 78.75750601716682
|
|
- type: euclidean_pearson
|
|
value: 85.76074482955764
|
|
- type: euclidean_spearman
|
|
value: 78.75651057223058
|
|
- type: manhattan_pearson
|
|
value: 85.73390291701668
|
|
- type: manhattan_spearman
|
|
value: 78.72699385957797
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts13-sts
|
|
name: MTEB STS13
|
|
config: default
|
|
split: test
|
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 89.58144067172472
|
|
- type: cos_sim_spearman
|
|
value: 90.3524512966946
|
|
- type: euclidean_pearson
|
|
value: 89.71365391594237
|
|
- type: euclidean_spearman
|
|
value: 90.35239632843408
|
|
- type: manhattan_pearson
|
|
value: 89.66905421746478
|
|
- type: manhattan_spearman
|
|
value: 90.31508211683513
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts14-sts
|
|
name: MTEB STS14
|
|
config: default
|
|
split: test
|
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 87.77692637102102
|
|
- type: cos_sim_spearman
|
|
value: 85.45710562643485
|
|
- type: euclidean_pearson
|
|
value: 87.42456979928723
|
|
- type: euclidean_spearman
|
|
value: 85.45709386240908
|
|
- type: manhattan_pearson
|
|
value: 87.40754529526272
|
|
- type: manhattan_spearman
|
|
value: 85.44834854173303
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts15-sts
|
|
name: MTEB STS15
|
|
config: default
|
|
split: test
|
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 88.28491331695997
|
|
- type: cos_sim_spearman
|
|
value: 89.62037029566964
|
|
- type: euclidean_pearson
|
|
value: 89.02479391362826
|
|
- type: euclidean_spearman
|
|
value: 89.62036733618466
|
|
- type: manhattan_pearson
|
|
value: 89.00394756040342
|
|
- type: manhattan_spearman
|
|
value: 89.60867744215236
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts16-sts
|
|
name: MTEB STS16
|
|
config: default
|
|
split: test
|
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 85.08911381280191
|
|
- type: cos_sim_spearman
|
|
value: 86.5791780765767
|
|
- type: euclidean_pearson
|
|
value: 86.16063473577861
|
|
- type: euclidean_spearman
|
|
value: 86.57917745378766
|
|
- type: manhattan_pearson
|
|
value: 86.13677924604175
|
|
- type: manhattan_spearman
|
|
value: 86.56115615768685
|
|
- 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: 89.58029496205235
|
|
- type: cos_sim_spearman
|
|
value: 89.49551253826998
|
|
- type: euclidean_pearson
|
|
value: 90.13714840963748
|
|
- type: euclidean_spearman
|
|
value: 89.49551253826998
|
|
- type: manhattan_pearson
|
|
value: 90.13039633601363
|
|
- type: manhattan_spearman
|
|
value: 89.4513453745516
|
|
- 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: 69.01546399666435
|
|
- type: cos_sim_spearman
|
|
value: 69.33824484595624
|
|
- type: euclidean_pearson
|
|
value: 70.76511642998874
|
|
- type: euclidean_spearman
|
|
value: 69.33824484595624
|
|
- type: manhattan_pearson
|
|
value: 70.84320785047453
|
|
- type: manhattan_spearman
|
|
value: 69.54233632223537
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/stsbenchmark-sts
|
|
name: MTEB STSBenchmark
|
|
config: default
|
|
split: test
|
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 87.26389196390119
|
|
- type: cos_sim_spearman
|
|
value: 89.09721478341385
|
|
- type: euclidean_pearson
|
|
value: 88.97208685922517
|
|
- type: euclidean_spearman
|
|
value: 89.09720927308881
|
|
- type: manhattan_pearson
|
|
value: 88.97513670502573
|
|
- type: manhattan_spearman
|
|
value: 89.07647853984004
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/scidocs-reranking
|
|
name: MTEB SciDocsRR
|
|
config: default
|
|
split: test
|
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
|
metrics:
|
|
- type: map
|
|
value: 87.53075025771936
|
|
- type: mrr
|
|
value: 96.24327651288436
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scifact
|
|
name: MTEB SciFact
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 60.428000000000004
|
|
- type: map_at_10
|
|
value: 70.088
|
|
- type: map_at_100
|
|
value: 70.589
|
|
- type: map_at_1000
|
|
value: 70.614
|
|
- type: map_at_3
|
|
value: 67.191
|
|
- type: map_at_5
|
|
value: 68.515
|
|
- type: mrr_at_1
|
|
value: 63.333
|
|
- type: mrr_at_10
|
|
value: 71.13000000000001
|
|
- type: mrr_at_100
|
|
value: 71.545
|
|
- type: mrr_at_1000
|
|
value: 71.569
|
|
- type: mrr_at_3
|
|
value: 68.944
|
|
- type: mrr_at_5
|
|
value: 70.078
|
|
- type: ndcg_at_1
|
|
value: 63.333
|
|
- type: ndcg_at_10
|
|
value: 74.72800000000001
|
|
- type: ndcg_at_100
|
|
value: 76.64999999999999
|
|
- type: ndcg_at_1000
|
|
value: 77.176
|
|
- type: ndcg_at_3
|
|
value: 69.659
|
|
- type: ndcg_at_5
|
|
value: 71.626
|
|
- type: precision_at_1
|
|
value: 63.333
|
|
- type: precision_at_10
|
|
value: 10
|
|
- type: precision_at_100
|
|
value: 1.09
|
|
- type: precision_at_1000
|
|
value: 0.11299999999999999
|
|
- type: precision_at_3
|
|
value: 27.111
|
|
- type: precision_at_5
|
|
value: 17.666999999999998
|
|
- type: recall_at_1
|
|
value: 60.428000000000004
|
|
- type: recall_at_10
|
|
value: 87.98899999999999
|
|
- type: recall_at_100
|
|
value: 96.167
|
|
- type: recall_at_1000
|
|
value: 100
|
|
- type: recall_at_3
|
|
value: 74.006
|
|
- type: recall_at_5
|
|
value: 79.05
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/sprintduplicatequestions-pairclassification
|
|
name: MTEB SprintDuplicateQuestions
|
|
config: default
|
|
split: test
|
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 99.87326732673267
|
|
- type: cos_sim_ap
|
|
value: 96.81770773701805
|
|
- type: cos_sim_f1
|
|
value: 93.6318407960199
|
|
- type: cos_sim_precision
|
|
value: 93.16831683168317
|
|
- type: cos_sim_recall
|
|
value: 94.1
|
|
- type: dot_accuracy
|
|
value: 99.87326732673267
|
|
- type: dot_ap
|
|
value: 96.8174218946665
|
|
- type: dot_f1
|
|
value: 93.6318407960199
|
|
- type: dot_precision
|
|
value: 93.16831683168317
|
|
- type: dot_recall
|
|
value: 94.1
|
|
- type: euclidean_accuracy
|
|
value: 99.87326732673267
|
|
- type: euclidean_ap
|
|
value: 96.81770773701807
|
|
- type: euclidean_f1
|
|
value: 93.6318407960199
|
|
- type: euclidean_precision
|
|
value: 93.16831683168317
|
|
- type: euclidean_recall
|
|
value: 94.1
|
|
- type: manhattan_accuracy
|
|
value: 99.87227722772278
|
|
- type: manhattan_ap
|
|
value: 96.83164126821747
|
|
- type: manhattan_f1
|
|
value: 93.54677338669335
|
|
- type: manhattan_precision
|
|
value: 93.5935935935936
|
|
- type: manhattan_recall
|
|
value: 93.5
|
|
- type: max_accuracy
|
|
value: 99.87326732673267
|
|
- type: max_ap
|
|
value: 96.83164126821747
|
|
- type: max_f1
|
|
value: 93.6318407960199
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering
|
|
name: MTEB StackExchangeClustering
|
|
config: default
|
|
split: test
|
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
|
metrics:
|
|
- type: v_measure
|
|
value: 65.6212042420246
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering-p2p
|
|
name: MTEB StackExchangeClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
|
metrics:
|
|
- type: v_measure
|
|
value: 35.779230635982564
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/stackoverflowdupquestions-reranking
|
|
name: MTEB StackOverflowDupQuestions
|
|
config: default
|
|
split: test
|
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
|
metrics:
|
|
- type: map
|
|
value: 55.217701909036286
|
|
- type: mrr
|
|
value: 56.17658995416349
|
|
- task:
|
|
type: Summarization
|
|
dataset:
|
|
type: mteb/summeval
|
|
name: MTEB SummEval
|
|
config: default
|
|
split: test
|
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 30.954206018888453
|
|
- type: cos_sim_spearman
|
|
value: 32.71062599450096
|
|
- type: dot_pearson
|
|
value: 30.95420929056943
|
|
- type: dot_spearman
|
|
value: 32.71062599450096
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: trec-covid
|
|
name: MTEB TRECCOVID
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 0.22699999999999998
|
|
- type: map_at_10
|
|
value: 1.924
|
|
- type: map_at_100
|
|
value: 10.525
|
|
- type: map_at_1000
|
|
value: 24.973
|
|
- type: map_at_3
|
|
value: 0.638
|
|
- type: map_at_5
|
|
value: 1.0659999999999998
|
|
- type: mrr_at_1
|
|
value: 84
|
|
- type: mrr_at_10
|
|
value: 91.067
|
|
- type: mrr_at_100
|
|
value: 91.067
|
|
- type: mrr_at_1000
|
|
value: 91.067
|
|
- type: mrr_at_3
|
|
value: 90.667
|
|
- type: mrr_at_5
|
|
value: 91.067
|
|
- type: ndcg_at_1
|
|
value: 81
|
|
- type: ndcg_at_10
|
|
value: 75.566
|
|
- type: ndcg_at_100
|
|
value: 56.387
|
|
- type: ndcg_at_1000
|
|
value: 49.834
|
|
- type: ndcg_at_3
|
|
value: 80.899
|
|
- type: ndcg_at_5
|
|
value: 80.75099999999999
|
|
- type: precision_at_1
|
|
value: 84
|
|
- type: precision_at_10
|
|
value: 79
|
|
- type: precision_at_100
|
|
value: 57.56
|
|
- type: precision_at_1000
|
|
value: 21.8
|
|
- type: precision_at_3
|
|
value: 84.667
|
|
- type: precision_at_5
|
|
value: 85.2
|
|
- type: recall_at_1
|
|
value: 0.22699999999999998
|
|
- type: recall_at_10
|
|
value: 2.136
|
|
- type: recall_at_100
|
|
value: 13.861
|
|
- type: recall_at_1000
|
|
value: 46.299
|
|
- type: recall_at_3
|
|
value: 0.6649999999999999
|
|
- type: recall_at_5
|
|
value: 1.145
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: webis-touche2020
|
|
name: MTEB Touche2020
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 2.752
|
|
- type: map_at_10
|
|
value: 9.951
|
|
- type: map_at_100
|
|
value: 16.794999999999998
|
|
- type: map_at_1000
|
|
value: 18.251
|
|
- type: map_at_3
|
|
value: 5.288
|
|
- type: map_at_5
|
|
value: 6.954000000000001
|
|
- type: mrr_at_1
|
|
value: 38.775999999999996
|
|
- type: mrr_at_10
|
|
value: 50.458000000000006
|
|
- type: mrr_at_100
|
|
value: 51.324999999999996
|
|
- type: mrr_at_1000
|
|
value: 51.339999999999996
|
|
- type: mrr_at_3
|
|
value: 46.939
|
|
- type: mrr_at_5
|
|
value: 47.857
|
|
- type: ndcg_at_1
|
|
value: 36.735
|
|
- type: ndcg_at_10
|
|
value: 25.198999999999998
|
|
- type: ndcg_at_100
|
|
value: 37.938
|
|
- type: ndcg_at_1000
|
|
value: 49.145
|
|
- type: ndcg_at_3
|
|
value: 29.348000000000003
|
|
- type: ndcg_at_5
|
|
value: 25.804
|
|
- type: precision_at_1
|
|
value: 38.775999999999996
|
|
- type: precision_at_10
|
|
value: 22.041
|
|
- type: precision_at_100
|
|
value: 7.939
|
|
- type: precision_at_1000
|
|
value: 1.555
|
|
- type: precision_at_3
|
|
value: 29.932
|
|
- type: precision_at_5
|
|
value: 24.490000000000002
|
|
- type: recall_at_1
|
|
value: 2.752
|
|
- type: recall_at_10
|
|
value: 16.197
|
|
- type: recall_at_100
|
|
value: 49.166
|
|
- type: recall_at_1000
|
|
value: 84.18900000000001
|
|
- type: recall_at_3
|
|
value: 6.438000000000001
|
|
- type: recall_at_5
|
|
value: 9.093
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/toxic_conversations_50k
|
|
name: MTEB ToxicConversationsClassification
|
|
config: default
|
|
split: test
|
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
|
metrics:
|
|
- type: accuracy
|
|
value: 71.47980000000001
|
|
- type: ap
|
|
value: 14.605194452178754
|
|
- type: f1
|
|
value: 55.07362924988948
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/tweet_sentiment_extraction
|
|
name: MTEB TweetSentimentExtractionClassification
|
|
config: default
|
|
split: test
|
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
|
metrics:
|
|
- type: accuracy
|
|
value: 59.708545557441994
|
|
- type: f1
|
|
value: 60.04751270975683
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/twentynewsgroups-clustering
|
|
name: MTEB TwentyNewsgroupsClustering
|
|
config: default
|
|
split: test
|
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
|
metrics:
|
|
- type: v_measure
|
|
value: 53.21105960597211
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twittersemeval2015-pairclassification
|
|
name: MTEB TwitterSemEval2015
|
|
config: default
|
|
split: test
|
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 87.58419264469214
|
|
- type: cos_sim_ap
|
|
value: 78.55300004517404
|
|
- type: cos_sim_f1
|
|
value: 71.49673530889001
|
|
- type: cos_sim_precision
|
|
value: 68.20795400095831
|
|
- type: cos_sim_recall
|
|
value: 75.11873350923483
|
|
- type: dot_accuracy
|
|
value: 87.58419264469214
|
|
- type: dot_ap
|
|
value: 78.55297659559511
|
|
- type: dot_f1
|
|
value: 71.49673530889001
|
|
- type: dot_precision
|
|
value: 68.20795400095831
|
|
- type: dot_recall
|
|
value: 75.11873350923483
|
|
- type: euclidean_accuracy
|
|
value: 87.58419264469214
|
|
- type: euclidean_ap
|
|
value: 78.55300477331477
|
|
- type: euclidean_f1
|
|
value: 71.49673530889001
|
|
- type: euclidean_precision
|
|
value: 68.20795400095831
|
|
- type: euclidean_recall
|
|
value: 75.11873350923483
|
|
- type: manhattan_accuracy
|
|
value: 87.5663110210407
|
|
- type: manhattan_ap
|
|
value: 78.49982050876562
|
|
- type: manhattan_f1
|
|
value: 71.35488740722104
|
|
- type: manhattan_precision
|
|
value: 68.18946862226497
|
|
- type: manhattan_recall
|
|
value: 74.82849604221636
|
|
- type: max_accuracy
|
|
value: 87.58419264469214
|
|
- type: max_ap
|
|
value: 78.55300477331477
|
|
- type: max_f1
|
|
value: 71.49673530889001
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twitterurlcorpus-pairclassification
|
|
name: MTEB TwitterURLCorpus
|
|
config: default
|
|
split: test
|
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 89.09069740365584
|
|
- type: cos_sim_ap
|
|
value: 86.22749303724757
|
|
- type: cos_sim_f1
|
|
value: 78.36863452005407
|
|
- type: cos_sim_precision
|
|
value: 76.49560117302053
|
|
- type: cos_sim_recall
|
|
value: 80.33569448721897
|
|
- type: dot_accuracy
|
|
value: 89.09069740365584
|
|
- type: dot_ap
|
|
value: 86.22750233655673
|
|
- type: dot_f1
|
|
value: 78.36863452005407
|
|
- type: dot_precision
|
|
value: 76.49560117302053
|
|
- type: dot_recall
|
|
value: 80.33569448721897
|
|
- type: euclidean_accuracy
|
|
value: 89.09069740365584
|
|
- type: euclidean_ap
|
|
value: 86.22749355597347
|
|
- type: euclidean_f1
|
|
value: 78.36863452005407
|
|
- type: euclidean_precision
|
|
value: 76.49560117302053
|
|
- type: euclidean_recall
|
|
value: 80.33569448721897
|
|
- type: manhattan_accuracy
|
|
value: 89.08293553770326
|
|
- type: manhattan_ap
|
|
value: 86.21913616084771
|
|
- type: manhattan_f1
|
|
value: 78.3907031479847
|
|
- type: manhattan_precision
|
|
value: 75.0352013517319
|
|
- type: manhattan_recall
|
|
value: 82.06036341238065
|
|
- type: max_accuracy
|
|
value: 89.09069740365584
|
|
- type: max_ap
|
|
value: 86.22750233655673
|
|
- type: max_f1
|
|
value: 78.3907031479847
|
|
license: apache-2.0
|
|
language:
|
|
- en
|
|
library_name: sentence-transformers
|
|
pipeline_tag: feature-extraction
|
|
---
|
|
|
|
<br><br>
|
|
|
|
<p align="center">
|
|
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</p>
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<p align="center">
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<b>The crispy sentence embedding family from <a href="https://mixedbread.ai"><b>Mixedbread</b></a>.</b>
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</p>
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# mixedbread-ai/mxbai-embed-large-v1
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Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt `Represent this sentence for searching relevant passages:` for query if you want to use it for retrieval. Besides that you don't need any prompt. Our model also supports [Matryoshka Representation Learning and binary quantization](https://www.mixedbread.ai/blog/binary-mrl).
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## Quickstart
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Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt `Represent this sentence for searching relevant passages:` for query if you want to use it for retrieval. Besides that you don't need any prompt.
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### sentence-transformers
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```
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python -m pip install -U sentence-transformers
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```
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```python
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from sentence_transformers import SentenceTransformer
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from sentence_transformers.util import cos_sim
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from sentence_transformers.quantization import quantize_embeddings
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# 1. Specify preffered dimensions
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dimensions = 512
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# 2. load model
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model = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1", truncate_dim=dimensions)
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# For retrieval you need to pass this prompt.
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query = 'Represent this sentence for searching relevant passages: A man is eating a piece of bread'
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docs = [
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query,
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"A man is eating food.",
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"A man is eating pasta.",
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"The girl is carrying a baby.",
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"A man is riding a horse.",
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]
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# 2. Encode
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embeddings = model.encode(docs)
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# Optional: Quantize the embeddings
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binary_embeddings = quantize_embeddings(embeddings, precision="ubinary")
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similarities = cos_sim(embeddings[0], embeddings[1:])
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print('similarities:', similarities)
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```
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### Transformers
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```python
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from typing import Dict
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import torch
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import numpy as np
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from transformers import AutoModel, AutoTokenizer
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from sentence_transformers.util import cos_sim
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# For retrieval you need to pass this prompt. Please find our more in our blog post.
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def transform_query(query: str) -> str:
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""" For retrieval, add the prompt for query (not for documents).
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"""
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return f'Represent this sentence for searching relevant passages: {query}'
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# The model works really well with cls pooling (default) but also with mean pooling.
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def pooling(outputs: torch.Tensor, inputs: Dict, strategy: str = 'cls') -> np.ndarray:
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if strategy == 'cls':
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outputs = outputs[:, 0]
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elif strategy == 'mean':
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outputs = torch.sum(
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outputs * inputs["attention_mask"][:, :, None], dim=1) / torch.sum(inputs["attention_mask"], dim=1, keepdim=True)
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else:
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raise NotImplementedError
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return outputs.detach().cpu().numpy()
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# 1. load model
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model_id = 'mixedbread-ai/mxbai-embed-large-v1'
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModel.from_pretrained(model_id).cuda()
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docs = [
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transform_query('A man is eating a piece of bread'),
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"A man is eating food.",
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"A man is eating pasta.",
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"The girl is carrying a baby.",
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"A man is riding a horse.",
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]
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# 2. encode
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inputs = tokenizer(docs, padding=True, return_tensors='pt')
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for k, v in inputs.items():
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inputs[k] = v.cuda()
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outputs = model(**inputs).last_hidden_state
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embeddings = pooling(outputs, inputs, 'cls')
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similarities = cos_sim(embeddings[0], embeddings[1:])
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print('similarities:', similarities)
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```
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### Transformers.js
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If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:
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```bash
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npm i @xenova/transformers
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```
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You can then use the model to compute embeddings like this:
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```js
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import { pipeline, cos_sim } from '@xenova/transformers';
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// Create a feature extraction pipeline
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const extractor = await pipeline('feature-extraction', 'mixedbread-ai/mxbai-embed-large-v1', {
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quantized: false, // Comment out this line to use the quantized version
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});
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// Generate sentence embeddings
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const docs = [
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'Represent this sentence for searching relevant passages: A man is eating a piece of bread',
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'A man is eating food.',
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'A man is eating pasta.',
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'The girl is carrying a baby.',
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'A man is riding a horse.',
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]
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const output = await extractor(docs, { pooling: 'cls' });
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// Compute similarity scores
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const [source_embeddings, ...document_embeddings ] = output.tolist();
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const similarities = document_embeddings.map(x => cos_sim(source_embeddings, x));
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console.log(similarities); // [0.7919578577247139, 0.6369278664248345, 0.16512018371357193, 0.3620778366720027]
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```
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### Using API
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You can use the model via our API as follows:
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```python
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from mixedbread_ai.client import MixedbreadAI, EncodingFormat
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from sklearn.metrics.pairwise import cosine_similarity
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import os
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mxbai = MixedbreadAI(api_key="{MIXEDBREAD_API_KEY}")
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english_sentences = [
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'What is the capital of Australia?',
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'Canberra is the capital of Australia.'
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]
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res = mxbai.embeddings(
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input=english_sentences,
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model="mixedbread-ai/mxbai-embed-large-v1",
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normalized=True,
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encoding_format=[EncodingFormat.FLOAT, EncodingFormat.UBINARY, EncodingFormat.INT_8],
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dimensions=512
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)
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encoded_embeddings = res.data[0].embedding
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print(res.dimensions, encoded_embeddings.ubinary, encoded_embeddings.float_, encoded_embeddings.int_8)
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```
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The API comes with native int8 and binary quantization support! Check out the [docs](https://mixedbread.ai/docs) for more information.
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## Evaluation
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As of March 2024, our model archives SOTA performance for Bert-large sized models on the [MTEB](https://huggingface.co/spaces/mteb/leaderboard). It ourperforms commercial models like OpenAIs text-embedding-3-large and matches the performance of model 20x it's size like the [echo-mistral-7b](https://huggingface.co/jspringer/echo-mistral-7b-instruct-lasttoken). Our model was trained with no overlap of the MTEB data, which indicates that our model generalizes well across several domains, tasks and text length. We know there are some limitations with this model, which will be fixed in v2.
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| Model | Avg (56 datasets) | Classification (12 datasets) | Clustering (11 datasets) | PairClassification (3 datasets) | Reranking (4 datasets) | Retrieval (15 datasets) | STS (10 datasets) | Summarization (1 dataset) |
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| --------------------------------------------------------------------------------------------- | ----------------- | ---------------------------- | ------------------------ | ------------------------------- | ---------------------- | ----------------------- | ----------------- | ------------------------- |
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| **mxbai-embed-large-v1** | **64.68** | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85.00 | 32.71 |
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| [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 |
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| [mxbai-embed-2d-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-2d-large-v1) | 63.25 | 74.14 | 46.07 | 85.89 | 58.94 | 51.42 | 84.9 | 31.55 |
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| [nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) | 62.39 | 74.12 | 43.91 | 85.15 | 55.69 | 52.81 | 82.06 | 30.08 |
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| [jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) | 60.38 | 73.45 | 41.73 | 85.38 | 56.98 | 47.87 | 80.7 | 31.6 |
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| *Proprietary Models* | | | | | | | | |
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| [OpenAI text-embedding-3-large](https://openai.com/blog/new-embedding-models-and-api-updates) | 64.58 | 75.45 | 49.01 | 85.72 | 59.16 | 55.44 | 81.73 | 29.92 |
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| [Cohere embed-english-v3.0](https://txt.cohere.com/introducing-embed-v3/) | 64.47 | 76.49 | 47.43 | 85.84 | 58.01 | 55.00 | 82.62 | 30.18 |
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| [OpenAI text-embedding-ada-002](https://openai.com/blog/new-and-improved-embedding-model) | 60.99 | 70.93 | 45.90 | 84.89 | 56.32 | 49.25 | 80.97 | 30.80 |
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Please find more information in our [blog post](https://mixedbread.ai/blog/mxbai-embed-large-v1).
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## Matryoshka and Binary Quantization
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Embeddings in their commonly used form (float arrays) have a high memory footprint when used at scale. Two approaches to solve this problem are Matryoshka Representation Learning (MRL) and (Binary) Quantization. While MRL reduces the number of dimensions of an embedding, binary quantization transforms the value of each dimension from a float32 into a lower precision (int8 or even binary). <b> The model supports both approaches! </b>
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You can also take it one step further, and combine both MRL and quantization. This combination of binary quantization and MRL allows you to reduce the memory usage of your embeddings significantly. This leads to much lower costs when using a vector database in particular. You can read more about the technology and its advantages in our [blog post](https://www.mixedbread.ai/blog/binary-mrl).
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## Community
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Please join our [Discord Community](https://discord.gg/jDfMHzAVfU) and share your feedback and thoughts! We are here to help and also always happy to chat.
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## License
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Apache 2.0
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## Citation
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|
|
|
```bibtex
|
|
@online{emb2024mxbai,
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title={Open Source Strikes Bread - New Fluffy Embeddings Model},
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author={Sean Lee and Aamir Shakir and Darius Koenig and Julius Lipp},
|
|
year={2024},
|
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url={https://www.mixedbread.ai/blog/mxbai-embed-large-v1},
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}
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|
|
@article{li2023angle,
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title={AnglE-optimized Text Embeddings},
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author={Li, Xianming and Li, Jing},
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journal={arXiv preprint arXiv:2309.12871},
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year={2023}
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}
|
|
```
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|
