metadata
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index:
- name: bge_micro
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 66.26865671641792
- type: ap
value: 28.174006539079688
- type: f1
value: 59.724963358211035
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 75.3691
- type: ap
value: 69.64182876373573
- type: f1
value: 75.2906345000088
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 35.806
- type: f1
value: 35.506516495961904
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.24
- type: map_at_10
value: 42.832
- type: map_at_100
value: 43.797000000000004
- type: map_at_1000
value: 43.804
- type: map_at_3
value: 38.134
- type: map_at_5
value: 40.744
- type: mrr_at_1
value: 27.951999999999998
- type: mrr_at_10
value: 43.111
- type: mrr_at_100
value: 44.083
- type: mrr_at_1000
value: 44.09
- type: mrr_at_3
value: 38.431
- type: mrr_at_5
value: 41.019
- type: ndcg_at_1
value: 27.24
- type: ndcg_at_10
value: 51.513
- type: ndcg_at_100
value: 55.762
- type: ndcg_at_1000
value: 55.938
- type: ndcg_at_3
value: 41.743
- type: ndcg_at_5
value: 46.454
- type: precision_at_1
value: 27.24
- type: precision_at_10
value: 7.93
- type: precision_at_100
value: 0.9820000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 17.402
- type: precision_at_5
value: 12.731
- type: recall_at_1
value: 27.24
- type: recall_at_10
value: 79.303
- type: recall_at_100
value: 98.151
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 52.205
- type: recall_at_5
value: 63.656
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 44.59766397469585
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 34.480143023109626
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 58.09326229984527
- type: mrr
value: 72.18429846546191
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 85.47582391622187
- type: cos_sim_spearman
value: 83.41635852964214
- type: euclidean_pearson
value: 84.21969728559216
- type: euclidean_spearman
value: 83.46575724558684
- type: manhattan_pearson
value: 83.83107014910223
- type: manhattan_spearman
value: 83.13321954800792
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 80.58116883116882
- type: f1
value: 80.53335622619781
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.13458676004344
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 29.720429607514898
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.051000000000002
- type: map_at_10
value: 36.291000000000004
- type: map_at_100
value: 37.632
- type: map_at_1000
value: 37.772
- type: map_at_3
value: 33.288000000000004
- type: map_at_5
value: 35.035
- type: mrr_at_1
value: 33.333
- type: mrr_at_10
value: 42.642
- type: mrr_at_100
value: 43.401
- type: mrr_at_1000
value: 43.463
- type: mrr_at_3
value: 40.272000000000006
- type: mrr_at_5
value: 41.753
- type: ndcg_at_1
value: 33.333
- type: ndcg_at_10
value: 42.291000000000004
- type: ndcg_at_100
value: 47.602
- type: ndcg_at_1000
value: 50.109
- type: ndcg_at_3
value: 38.033
- type: ndcg_at_5
value: 40.052
- type: precision_at_1
value: 33.333
- type: precision_at_10
value: 8.254999999999999
- type: precision_at_100
value: 1.353
- type: precision_at_1000
value: 0.185
- type: precision_at_3
value: 18.884
- type: precision_at_5
value: 13.447999999999999
- type: recall_at_1
value: 26.051000000000002
- type: recall_at_10
value: 53.107000000000006
- type: recall_at_100
value: 76.22
- type: recall_at_1000
value: 92.92399999999999
- type: recall_at_3
value: 40.073
- type: recall_at_5
value: 46.327
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.698999999999998
- type: map_at_10
value: 26.186
- type: map_at_100
value: 27.133000000000003
- type: map_at_1000
value: 27.256999999999998
- type: map_at_3
value: 24.264
- type: map_at_5
value: 25.307000000000002
- type: mrr_at_1
value: 24.712999999999997
- type: mrr_at_10
value: 30.703999999999997
- type: mrr_at_100
value: 31.445
- type: mrr_at_1000
value: 31.517
- type: mrr_at_3
value: 28.992
- type: mrr_at_5
value: 29.963
- type: ndcg_at_1
value: 24.712999999999997
- type: ndcg_at_10
value: 30.198000000000004
- type: ndcg_at_100
value: 34.412
- type: ndcg_at_1000
value: 37.174
- type: ndcg_at_3
value: 27.148
- type: ndcg_at_5
value: 28.464
- type: precision_at_1
value: 24.712999999999997
- type: precision_at_10
value: 5.489999999999999
- type: precision_at_100
value: 0.955
- type: precision_at_1000
value: 0.14400000000000002
- type: precision_at_3
value: 12.803
- type: precision_at_5
value: 8.981
- type: recall_at_1
value: 19.698999999999998
- type: recall_at_10
value: 37.595
- type: recall_at_100
value: 55.962
- type: recall_at_1000
value: 74.836
- type: recall_at_3
value: 28.538999999999998
- type: recall_at_5
value: 32.279
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 34.224
- type: map_at_10
value: 44.867000000000004
- type: map_at_100
value: 45.944
- type: map_at_1000
value: 46.013999999999996
- type: map_at_3
value: 42.009
- type: map_at_5
value: 43.684
- type: mrr_at_1
value: 39.436
- type: mrr_at_10
value: 48.301
- type: mrr_at_100
value: 49.055
- type: mrr_at_1000
value: 49.099
- type: mrr_at_3
value: 45.956
- type: mrr_at_5
value: 47.445
- type: ndcg_at_1
value: 39.436
- type: ndcg_at_10
value: 50.214000000000006
- type: ndcg_at_100
value: 54.63
- type: ndcg_at_1000
value: 56.165
- type: ndcg_at_3
value: 45.272
- type: ndcg_at_5
value: 47.826
- type: precision_at_1
value: 39.436
- type: precision_at_10
value: 8.037999999999998
- type: precision_at_100
value: 1.118
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 20.125
- type: precision_at_5
value: 13.918
- type: recall_at_1
value: 34.224
- type: recall_at_10
value: 62.690999999999995
- type: recall_at_100
value: 81.951
- type: recall_at_1000
value: 92.93299999999999
- type: recall_at_3
value: 49.299
- type: recall_at_5
value: 55.533
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.375
- type: map_at_10
value: 28.366000000000003
- type: map_at_100
value: 29.363
- type: map_at_1000
value: 29.458000000000002
- type: map_at_3
value: 26.247
- type: map_at_5
value: 27.439000000000004
- type: mrr_at_1
value: 22.938
- type: mrr_at_10
value: 30.072
- type: mrr_at_100
value: 30.993
- type: mrr_at_1000
value: 31.070999999999998
- type: mrr_at_3
value: 28.004
- type: mrr_at_5
value: 29.179
- type: ndcg_at_1
value: 22.938
- type: ndcg_at_10
value: 32.516
- type: ndcg_at_100
value: 37.641999999999996
- type: ndcg_at_1000
value: 40.150999999999996
- type: ndcg_at_3
value: 28.341
- type: ndcg_at_5
value: 30.394
- type: precision_at_1
value: 22.938
- type: precision_at_10
value: 5.028
- type: precision_at_100
value: 0.8
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 12.052999999999999
- type: precision_at_5
value: 8.497
- type: recall_at_1
value: 21.375
- type: recall_at_10
value: 43.682
- type: recall_at_100
value: 67.619
- type: recall_at_1000
value: 86.64699999999999
- type: recall_at_3
value: 32.478
- type: recall_at_5
value: 37.347
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.95
- type: map_at_10
value: 21.417
- type: map_at_100
value: 22.525000000000002
- type: map_at_1000
value: 22.665
- type: map_at_3
value: 18.684
- type: map_at_5
value: 20.275000000000002
- type: mrr_at_1
value: 18.159
- type: mrr_at_10
value: 25.373
- type: mrr_at_100
value: 26.348
- type: mrr_at_1000
value: 26.432
- type: mrr_at_3
value: 22.698999999999998
- type: mrr_at_5
value: 24.254
- type: ndcg_at_1
value: 18.159
- type: ndcg_at_10
value: 26.043
- type: ndcg_at_100
value: 31.491999999999997
- type: ndcg_at_1000
value: 34.818
- type: ndcg_at_3
value: 21.05
- type: ndcg_at_5
value: 23.580000000000002
- type: precision_at_1
value: 18.159
- type: precision_at_10
value: 4.938
- type: precision_at_100
value: 0.872
- type: precision_at_1000
value: 0.129
- type: precision_at_3
value: 9.908999999999999
- type: precision_at_5
value: 7.611999999999999
- type: recall_at_1
value: 14.95
- type: recall_at_10
value: 36.285000000000004
- type: recall_at_100
value: 60.431999999999995
- type: recall_at_1000
value: 84.208
- type: recall_at_3
value: 23.006
- type: recall_at_5
value: 29.304999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.580000000000002
- type: map_at_10
value: 32.906
- type: map_at_100
value: 34.222
- type: map_at_1000
value: 34.346
- type: map_at_3
value: 29.891000000000002
- type: map_at_5
value: 31.679000000000002
- type: mrr_at_1
value: 28.778
- type: mrr_at_10
value: 37.783
- type: mrr_at_100
value: 38.746
- type: mrr_at_1000
value: 38.804
- type: mrr_at_3
value: 35.098
- type: mrr_at_5
value: 36.739
- type: ndcg_at_1
value: 28.778
- type: ndcg_at_10
value: 38.484
- type: ndcg_at_100
value: 44.322
- type: ndcg_at_1000
value: 46.772000000000006
- type: ndcg_at_3
value: 33.586
- type: ndcg_at_5
value: 36.098
- type: precision_at_1
value: 28.778
- type: precision_at_10
value: 7.151000000000001
- type: precision_at_100
value: 1.185
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 16.105
- type: precision_at_5
value: 11.704
- type: recall_at_1
value: 23.580000000000002
- type: recall_at_10
value: 50.151999999999994
- type: recall_at_100
value: 75.114
- type: recall_at_1000
value: 91.467
- type: recall_at_3
value: 36.552
- type: recall_at_5
value: 43.014
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.669999999999998
- type: map_at_10
value: 28.687
- type: map_at_100
value: 30.061
- type: map_at_1000
value: 30.197000000000003
- type: map_at_3
value: 26.134
- type: map_at_5
value: 27.508
- type: mrr_at_1
value: 26.256
- type: mrr_at_10
value: 34.105999999999995
- type: mrr_at_100
value: 35.137
- type: mrr_at_1000
value: 35.214
- type: mrr_at_3
value: 31.791999999999998
- type: mrr_at_5
value: 33.145
- type: ndcg_at_1
value: 26.256
- type: ndcg_at_10
value: 33.68
- type: ndcg_at_100
value: 39.7
- type: ndcg_at_1000
value: 42.625
- type: ndcg_at_3
value: 29.457
- type: ndcg_at_5
value: 31.355
- type: precision_at_1
value: 26.256
- type: precision_at_10
value: 6.2330000000000005
- type: precision_at_100
value: 1.08
- type: precision_at_1000
value: 0.149
- type: precision_at_3
value: 14.193
- type: precision_at_5
value: 10.113999999999999
- type: recall_at_1
value: 20.669999999999998
- type: recall_at_10
value: 43.254999999999995
- type: recall_at_100
value: 69.118
- type: recall_at_1000
value: 89.408
- type: recall_at_3
value: 31.135
- type: recall_at_5
value: 36.574
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.488833333333336
- type: map_at_10
value: 29.025416666666665
- type: map_at_100
value: 30.141249999999992
- type: map_at_1000
value: 30.264083333333335
- type: map_at_3
value: 26.599333333333337
- type: map_at_5
value: 28.004666666666665
- type: mrr_at_1
value: 25.515
- type: mrr_at_10
value: 32.8235
- type: mrr_at_100
value: 33.69958333333333
- type: mrr_at_1000
value: 33.77191666666668
- type: mrr_at_3
value: 30.581000000000003
- type: mrr_at_5
value: 31.919666666666668
- type: ndcg_at_1
value: 25.515
- type: ndcg_at_10
value: 33.64241666666666
- type: ndcg_at_100
value: 38.75816666666667
- type: ndcg_at_1000
value: 41.472166666666666
- type: ndcg_at_3
value: 29.435083333333335
- type: ndcg_at_5
value: 31.519083333333338
- type: precision_at_1
value: 25.515
- type: precision_at_10
value: 5.89725
- type: precision_at_100
value: 0.9918333333333335
- type: precision_at_1000
value: 0.14075
- type: precision_at_3
value: 13.504000000000001
- type: precision_at_5
value: 9.6885
- type: recall_at_1
value: 21.488833333333336
- type: recall_at_10
value: 43.60808333333333
- type: recall_at_100
value: 66.5045
- type: recall_at_1000
value: 85.70024999999998
- type: recall_at_3
value: 31.922166666666662
- type: recall_at_5
value: 37.29758333333334
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.781
- type: map_at_10
value: 27.173000000000002
- type: map_at_100
value: 27.967
- type: map_at_1000
value: 28.061999999999998
- type: map_at_3
value: 24.973
- type: map_at_5
value: 26.279999999999998
- type: mrr_at_1
value: 23.773
- type: mrr_at_10
value: 29.849999999999998
- type: mrr_at_100
value: 30.595
- type: mrr_at_1000
value: 30.669
- type: mrr_at_3
value: 27.761000000000003
- type: mrr_at_5
value: 29.003
- type: ndcg_at_1
value: 23.773
- type: ndcg_at_10
value: 31.033
- type: ndcg_at_100
value: 35.174
- type: ndcg_at_1000
value: 37.72
- type: ndcg_at_3
value: 26.927
- type: ndcg_at_5
value: 29.047
- type: precision_at_1
value: 23.773
- type: precision_at_10
value: 4.8469999999999995
- type: precision_at_100
value: 0.75
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 11.452
- type: precision_at_5
value: 8.129
- type: recall_at_1
value: 20.781
- type: recall_at_10
value: 40.463
- type: recall_at_100
value: 59.483
- type: recall_at_1000
value: 78.396
- type: recall_at_3
value: 29.241
- type: recall_at_5
value: 34.544000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 15.074000000000002
- type: map_at_10
value: 20.757
- type: map_at_100
value: 21.72
- type: map_at_1000
value: 21.844
- type: map_at_3
value: 18.929000000000002
- type: map_at_5
value: 19.894000000000002
- type: mrr_at_1
value: 18.307000000000002
- type: mrr_at_10
value: 24.215
- type: mrr_at_100
value: 25.083
- type: mrr_at_1000
value: 25.168000000000003
- type: mrr_at_3
value: 22.316
- type: mrr_at_5
value: 23.36
- type: ndcg_at_1
value: 18.307000000000002
- type: ndcg_at_10
value: 24.651999999999997
- type: ndcg_at_100
value: 29.296
- type: ndcg_at_1000
value: 32.538
- type: ndcg_at_3
value: 21.243000000000002
- type: ndcg_at_5
value: 22.727
- type: precision_at_1
value: 18.307000000000002
- type: precision_at_10
value: 4.446
- type: precision_at_100
value: 0.792
- type: precision_at_1000
value: 0.124
- type: precision_at_3
value: 9.945
- type: precision_at_5
value: 7.123
- type: recall_at_1
value: 15.074000000000002
- type: recall_at_10
value: 33.031
- type: recall_at_100
value: 53.954
- type: recall_at_1000
value: 77.631
- type: recall_at_3
value: 23.253
- type: recall_at_5
value: 27.218999999999998
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.04
- type: map_at_10
value: 28.226000000000003
- type: map_at_100
value: 29.337999999999997
- type: map_at_1000
value: 29.448999999999998
- type: map_at_3
value: 25.759
- type: map_at_5
value: 27.226
- type: mrr_at_1
value: 24.067
- type: mrr_at_10
value: 31.646
- type: mrr_at_100
value: 32.592999999999996
- type: mrr_at_1000
value: 32.668
- type: mrr_at_3
value: 29.26
- type: mrr_at_5
value: 30.725
- type: ndcg_at_1
value: 24.067
- type: ndcg_at_10
value: 32.789
- type: ndcg_at_100
value: 38.253
- type: ndcg_at_1000
value: 40.961
- type: ndcg_at_3
value: 28.189999999999998
- type: ndcg_at_5
value: 30.557000000000002
- type: precision_at_1
value: 24.067
- type: precision_at_10
value: 5.532
- type: precision_at_100
value: 0.928
- type: precision_at_1000
value: 0.128
- type: precision_at_3
value: 12.5
- type: precision_at_5
value: 9.16
- type: recall_at_1
value: 21.04
- type: recall_at_10
value: 43.167
- type: recall_at_100
value: 67.569
- type: recall_at_1000
value: 86.817
- type: recall_at_3
value: 31.178
- type: recall_at_5
value: 36.730000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.439
- type: map_at_10
value: 28.531000000000002
- type: map_at_100
value: 29.953999999999997
- type: map_at_1000
value: 30.171
- type: map_at_3
value: 26.546999999999997
- type: map_at_5
value: 27.71
- type: mrr_at_1
value: 26.087
- type: mrr_at_10
value: 32.635
- type: mrr_at_100
value: 33.629999999999995
- type: mrr_at_1000
value: 33.71
- type: mrr_at_3
value: 30.731
- type: mrr_at_5
value: 31.807999999999996
- type: ndcg_at_1
value: 26.087
- type: ndcg_at_10
value: 32.975
- type: ndcg_at_100
value: 38.853
- type: ndcg_at_1000
value: 42.158
- type: ndcg_at_3
value: 29.894
- type: ndcg_at_5
value: 31.397000000000002
- type: precision_at_1
value: 26.087
- type: precision_at_10
value: 6.2059999999999995
- type: precision_at_100
value: 1.298
- type: precision_at_1000
value: 0.22200000000000003
- type: precision_at_3
value: 14.097000000000001
- type: precision_at_5
value: 9.959999999999999
- type: recall_at_1
value: 21.439
- type: recall_at_10
value: 40.519
- type: recall_at_100
value: 68.073
- type: recall_at_1000
value: 89.513
- type: recall_at_3
value: 31.513
- type: recall_at_5
value: 35.702
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.983
- type: map_at_10
value: 24.898
- type: map_at_100
value: 25.836
- type: map_at_1000
value: 25.934
- type: map_at_3
value: 22.467000000000002
- type: map_at_5
value: 24.019
- type: mrr_at_1
value: 20.333000000000002
- type: mrr_at_10
value: 26.555
- type: mrr_at_100
value: 27.369
- type: mrr_at_1000
value: 27.448
- type: mrr_at_3
value: 24.091
- type: mrr_at_5
value: 25.662000000000003
- type: ndcg_at_1
value: 20.333000000000002
- type: ndcg_at_10
value: 28.834
- type: ndcg_at_100
value: 33.722
- type: ndcg_at_1000
value: 36.475
- type: ndcg_at_3
value: 24.08
- type: ndcg_at_5
value: 26.732
- type: precision_at_1
value: 20.333000000000002
- type: precision_at_10
value: 4.603
- type: precision_at_100
value: 0.771
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 9.982000000000001
- type: precision_at_5
value: 7.6160000000000005
- type: recall_at_1
value: 18.983
- type: recall_at_10
value: 39.35
- type: recall_at_100
value: 62.559
- type: recall_at_1000
value: 83.623
- type: recall_at_3
value: 26.799
- type: recall_at_5
value: 32.997
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.621
- type: map_at_10
value: 17.298
- type: map_at_100
value: 18.983
- type: map_at_1000
value: 19.182
- type: map_at_3
value: 14.552999999999999
- type: map_at_5
value: 15.912
- type: mrr_at_1
value: 23.453
- type: mrr_at_10
value: 33.932
- type: mrr_at_100
value: 34.891
- type: mrr_at_1000
value: 34.943000000000005
- type: mrr_at_3
value: 30.770999999999997
- type: mrr_at_5
value: 32.556000000000004
- type: ndcg_at_1
value: 23.453
- type: ndcg_at_10
value: 24.771
- type: ndcg_at_100
value: 31.738
- type: ndcg_at_1000
value: 35.419
- type: ndcg_at_3
value: 20.22
- type: ndcg_at_5
value: 21.698999999999998
- type: precision_at_1
value: 23.453
- type: precision_at_10
value: 7.785
- type: precision_at_100
value: 1.5270000000000001
- type: precision_at_1000
value: 0.22
- type: precision_at_3
value: 14.962
- type: precision_at_5
value: 11.401
- type: recall_at_1
value: 10.621
- type: recall_at_10
value: 29.726000000000003
- type: recall_at_100
value: 53.996
- type: recall_at_1000
value: 74.878
- type: recall_at_3
value: 18.572
- type: recall_at_5
value: 22.994999999999997
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.819
- type: map_at_10
value: 14.188
- type: map_at_100
value: 19.627
- type: map_at_1000
value: 20.757
- type: map_at_3
value: 10.352
- type: map_at_5
value: 12.096
- type: mrr_at_1
value: 54.25
- type: mrr_at_10
value: 63.798
- type: mrr_at_100
value: 64.25
- type: mrr_at_1000
value: 64.268
- type: mrr_at_3
value: 61.667
- type: mrr_at_5
value: 63.153999999999996
- type: ndcg_at_1
value: 39.5
- type: ndcg_at_10
value: 31.064999999999998
- type: ndcg_at_100
value: 34.701
- type: ndcg_at_1000
value: 41.687000000000005
- type: ndcg_at_3
value: 34.455999999999996
- type: ndcg_at_5
value: 32.919
- type: precision_at_1
value: 54.25
- type: precision_at_10
value: 25.4
- type: precision_at_100
value: 7.79
- type: precision_at_1000
value: 1.577
- type: precision_at_3
value: 39.333
- type: precision_at_5
value: 33.6
- type: recall_at_1
value: 6.819
- type: recall_at_10
value: 19.134
- type: recall_at_100
value: 41.191
- type: recall_at_1000
value: 64.699
- type: recall_at_3
value: 11.637
- type: recall_at_5
value: 14.807
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 42.474999999999994
- type: f1
value: 37.79154895614037
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 53.187
- type: map_at_10
value: 64.031
- type: map_at_100
value: 64.507
- type: map_at_1000
value: 64.526
- type: map_at_3
value: 61.926
- type: map_at_5
value: 63.278999999999996
- type: mrr_at_1
value: 57.396
- type: mrr_at_10
value: 68.296
- type: mrr_at_100
value: 68.679
- type: mrr_at_1000
value: 68.688
- type: mrr_at_3
value: 66.289
- type: mrr_at_5
value: 67.593
- type: ndcg_at_1
value: 57.396
- type: ndcg_at_10
value: 69.64
- type: ndcg_at_100
value: 71.75399999999999
- type: ndcg_at_1000
value: 72.179
- type: ndcg_at_3
value: 65.66199999999999
- type: ndcg_at_5
value: 67.932
- type: precision_at_1
value: 57.396
- type: precision_at_10
value: 9.073
- type: precision_at_100
value: 1.024
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 26.133
- type: precision_at_5
value: 16.943
- type: recall_at_1
value: 53.187
- type: recall_at_10
value: 82.839
- type: recall_at_100
value: 92.231
- type: recall_at_1000
value: 95.249
- type: recall_at_3
value: 72.077
- type: recall_at_5
value: 77.667
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.957
- type: map_at_10
value: 18.427
- type: map_at_100
value: 19.885
- type: map_at_1000
value: 20.088
- type: map_at_3
value: 15.709000000000001
- type: map_at_5
value: 17.153
- type: mrr_at_1
value: 22.377
- type: mrr_at_10
value: 30.076999999999998
- type: mrr_at_100
value: 31.233
- type: mrr_at_1000
value: 31.311
- type: mrr_at_3
value: 27.521
- type: mrr_at_5
value: 29.025000000000002
- type: ndcg_at_1
value: 22.377
- type: ndcg_at_10
value: 24.367
- type: ndcg_at_100
value: 31.04
- type: ndcg_at_1000
value: 35.106
- type: ndcg_at_3
value: 21.051000000000002
- type: ndcg_at_5
value: 22.231
- type: precision_at_1
value: 22.377
- type: precision_at_10
value: 7.005999999999999
- type: precision_at_100
value: 1.3599999999999999
- type: precision_at_1000
value: 0.208
- type: precision_at_3
value: 13.991999999999999
- type: precision_at_5
value: 10.833
- type: recall_at_1
value: 10.957
- type: recall_at_10
value: 30.274
- type: recall_at_100
value: 55.982
- type: recall_at_1000
value: 80.757
- type: recall_at_3
value: 19.55
- type: recall_at_5
value: 24.105999999999998
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.526999999999997
- type: map_at_10
value: 40.714
- type: map_at_100
value: 41.655
- type: map_at_1000
value: 41.744
- type: map_at_3
value: 38.171
- type: map_at_5
value: 39.646
- type: mrr_at_1
value: 59.055
- type: mrr_at_10
value: 66.411
- type: mrr_at_100
value: 66.85900000000001
- type: mrr_at_1000
value: 66.88300000000001
- type: mrr_at_3
value: 64.846
- type: mrr_at_5
value: 65.824
- type: ndcg_at_1
value: 59.055
- type: ndcg_at_10
value: 49.732
- type: ndcg_at_100
value: 53.441
- type: ndcg_at_1000
value: 55.354000000000006
- type: ndcg_at_3
value: 45.551
- type: ndcg_at_5
value: 47.719
- type: precision_at_1
value: 59.055
- type: precision_at_10
value: 10.366
- type: precision_at_100
value: 1.328
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 28.322999999999997
- type: precision_at_5
value: 18.709
- type: recall_at_1
value: 29.526999999999997
- type: recall_at_10
value: 51.83
- type: recall_at_100
value: 66.42099999999999
- type: recall_at_1000
value: 79.176
- type: recall_at_3
value: 42.485
- type: recall_at_5
value: 46.772000000000006
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 70.69959999999999
- type: ap
value: 64.95539314492567
- type: f1
value: 70.5554935943308
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 13.153
- type: map_at_10
value: 22.277
- type: map_at_100
value: 23.462
- type: map_at_1000
value: 23.546
- type: map_at_3
value: 19.026
- type: map_at_5
value: 20.825
- type: mrr_at_1
value: 13.539000000000001
- type: mrr_at_10
value: 22.753
- type: mrr_at_100
value: 23.906
- type: mrr_at_1000
value: 23.982999999999997
- type: mrr_at_3
value: 19.484
- type: mrr_at_5
value: 21.306
- type: ndcg_at_1
value: 13.553
- type: ndcg_at_10
value: 27.848
- type: ndcg_at_100
value: 33.900999999999996
- type: ndcg_at_1000
value: 36.155
- type: ndcg_at_3
value: 21.116
- type: ndcg_at_5
value: 24.349999999999998
- type: precision_at_1
value: 13.553
- type: precision_at_10
value: 4.695
- type: precision_at_100
value: 0.7779999999999999
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 9.207
- type: precision_at_5
value: 7.155
- type: recall_at_1
value: 13.153
- type: recall_at_10
value: 45.205
- type: recall_at_100
value: 73.978
- type: recall_at_1000
value: 91.541
- type: recall_at_3
value: 26.735
- type: recall_at_5
value: 34.493
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.2530779753762
- type: f1
value: 89.59402328284126
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 67.95029639762883
- type: f1
value: 48.99988836758662
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.77740416946874
- type: f1
value: 66.21341120969817
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.03631472763955
- type: f1
value: 72.5779336237941
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.98182669158824
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 29.259462874407582
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.29342377286548
- type: mrr
value: 32.32805799117226
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.692
- type: map_at_10
value: 10.559000000000001
- type: map_at_100
value: 13.665
- type: map_at_1000
value: 15.082
- type: map_at_3
value: 7.68
- type: map_at_5
value: 8.844000000000001
- type: mrr_at_1
value: 38.7
- type: mrr_at_10
value: 47.864000000000004
- type: mrr_at_100
value: 48.583999999999996
- type: mrr_at_1000
value: 48.636
- type: mrr_at_3
value: 45.975
- type: mrr_at_5
value: 47.074
- type: ndcg_at_1
value: 36.378
- type: ndcg_at_10
value: 30.038999999999998
- type: ndcg_at_100
value: 28.226000000000003
- type: ndcg_at_1000
value: 36.958
- type: ndcg_at_3
value: 33.469
- type: ndcg_at_5
value: 32.096999999999994
- type: precision_at_1
value: 38.080000000000005
- type: precision_at_10
value: 22.941
- type: precision_at_100
value: 7.632
- type: precision_at_1000
value: 2.0420000000000003
- type: precision_at_3
value: 31.579
- type: precision_at_5
value: 28.235
- type: recall_at_1
value: 4.692
- type: recall_at_10
value: 14.496
- type: recall_at_100
value: 29.69
- type: recall_at_1000
value: 61.229
- type: recall_at_3
value: 8.871
- type: recall_at_5
value: 10.825999999999999
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.120000000000001
- type: map_at_10
value: 24.092
- type: map_at_100
value: 25.485999999999997
- type: map_at_1000
value: 25.557999999999996
- type: map_at_3
value: 20.076
- type: map_at_5
value: 22.368
- type: mrr_at_1
value: 15.093
- type: mrr_at_10
value: 26.142
- type: mrr_at_100
value: 27.301
- type: mrr_at_1000
value: 27.357
- type: mrr_at_3
value: 22.364
- type: mrr_at_5
value: 24.564
- type: ndcg_at_1
value: 15.093
- type: ndcg_at_10
value: 30.734
- type: ndcg_at_100
value: 37.147999999999996
- type: ndcg_at_1000
value: 38.997
- type: ndcg_at_3
value: 22.82
- type: ndcg_at_5
value: 26.806
- type: precision_at_1
value: 15.093
- type: precision_at_10
value: 5.863
- type: precision_at_100
value: 0.942
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 11.047
- type: precision_at_5
value: 8.863999999999999
- type: recall_at_1
value: 13.120000000000001
- type: recall_at_10
value: 49.189
- type: recall_at_100
value: 78.032
- type: recall_at_1000
value: 92.034
- type: recall_at_3
value: 28.483000000000004
- type: recall_at_5
value: 37.756
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 67.765
- type: map_at_10
value: 81.069
- type: map_at_100
value: 81.757
- type: map_at_1000
value: 81.782
- type: map_at_3
value: 78.148
- type: map_at_5
value: 79.95400000000001
- type: mrr_at_1
value: 77.8
- type: mrr_at_10
value: 84.639
- type: mrr_at_100
value: 84.789
- type: mrr_at_1000
value: 84.79100000000001
- type: mrr_at_3
value: 83.467
- type: mrr_at_5
value: 84.251
- type: ndcg_at_1
value: 77.82
- type: ndcg_at_10
value: 85.286
- type: ndcg_at_100
value: 86.86500000000001
- type: ndcg_at_1000
value: 87.062
- type: ndcg_at_3
value: 82.116
- type: ndcg_at_5
value: 83.811
- type: precision_at_1
value: 77.82
- type: precision_at_10
value: 12.867999999999999
- type: precision_at_100
value: 1.498
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 35.723
- type: precision_at_5
value: 23.52
- type: recall_at_1
value: 67.765
- type: recall_at_10
value: 93.381
- type: recall_at_100
value: 98.901
- type: recall_at_1000
value: 99.864
- type: recall_at_3
value: 84.301
- type: recall_at_5
value: 89.049
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 45.27190981742137
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 54.47444004585028
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.213
- type: map_at_10
value: 10.166
- type: map_at_100
value: 11.987
- type: map_at_1000
value: 12.285
- type: map_at_3
value: 7.538
- type: map_at_5
value: 8.606
- type: mrr_at_1
value: 20.8
- type: mrr_at_10
value: 30.066
- type: mrr_at_100
value: 31.290000000000003
- type: mrr_at_1000
value: 31.357000000000003
- type: mrr_at_3
value: 27.083000000000002
- type: mrr_at_5
value: 28.748
- type: ndcg_at_1
value: 20.8
- type: ndcg_at_10
value: 17.258000000000003
- type: ndcg_at_100
value: 24.801000000000002
- type: ndcg_at_1000
value: 30.348999999999997
- type: ndcg_at_3
value: 16.719
- type: ndcg_at_5
value: 14.145
- type: precision_at_1
value: 20.8
- type: precision_at_10
value: 8.88
- type: precision_at_100
value: 1.9789999999999999
- type: precision_at_1000
value: 0.332
- type: precision_at_3
value: 15.5
- type: precision_at_5
value: 12.1
- type: recall_at_1
value: 4.213
- type: recall_at_10
value: 17.983
- type: recall_at_100
value: 40.167
- type: recall_at_1000
value: 67.43
- type: recall_at_3
value: 9.433
- type: recall_at_5
value: 12.267999999999999
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 80.36742239848913
- type: cos_sim_spearman
value: 72.39470010828755
- type: euclidean_pearson
value: 77.26919895870947
- type: euclidean_spearman
value: 72.26534999077315
- type: manhattan_pearson
value: 77.04066349814258
- type: manhattan_spearman
value: 72.0072248699278
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 80.26991474037257
- type: cos_sim_spearman
value: 71.90287122017716
- type: euclidean_pearson
value: 76.68006075912453
- type: euclidean_spearman
value: 71.69301858764365
- type: manhattan_pearson
value: 76.72277285842371
- type: manhattan_spearman
value: 71.73265239703795
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 79.74371413317881
- type: cos_sim_spearman
value: 80.9279612820358
- type: euclidean_pearson
value: 80.6417435294782
- type: euclidean_spearman
value: 81.17460969254459
- type: manhattan_pearson
value: 80.51820155178402
- type: manhattan_spearman
value: 81.08028700017084
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 80.37085777051112
- type: cos_sim_spearman
value: 76.60308382518285
- type: euclidean_pearson
value: 79.59684787227351
- type: euclidean_spearman
value: 76.8769048249242
- type: manhattan_pearson
value: 79.55617632538295
- type: manhattan_spearman
value: 76.90186497973124
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 83.99513105301321
- type: cos_sim_spearman
value: 84.92034548133665
- type: euclidean_pearson
value: 84.70872540095195
- type: euclidean_spearman
value: 85.14591726040749
- type: manhattan_pearson
value: 84.65707417430595
- type: manhattan_spearman
value: 85.10407163865375
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 79.40758449150897
- type: cos_sim_spearman
value: 80.71692246880549
- type: euclidean_pearson
value: 80.51658552062683
- type: euclidean_spearman
value: 80.87118389043233
- type: manhattan_pearson
value: 80.41534690825016
- type: manhattan_spearman
value: 80.73925282537256
- 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: 84.93617076910748
- type: cos_sim_spearman
value: 85.61118538966805
- type: euclidean_pearson
value: 85.56187558635287
- type: euclidean_spearman
value: 85.21910090757267
- type: manhattan_pearson
value: 85.29916699037645
- type: manhattan_spearman
value: 84.96820527868671
- 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: 64.22294088543077
- type: cos_sim_spearman
value: 65.89748502901078
- type: euclidean_pearson
value: 66.15637850660805
- type: euclidean_spearman
value: 65.86095841381278
- type: manhattan_pearson
value: 66.80966197857856
- type: manhattan_spearman
value: 66.48325202219692
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 81.75298158703048
- type: cos_sim_spearman
value: 81.32168373072322
- type: euclidean_pearson
value: 82.3251793712207
- type: euclidean_spearman
value: 81.31655163330606
- type: manhattan_pearson
value: 82.14136865023298
- type: manhattan_spearman
value: 81.13410964028606
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 78.77937068780793
- type: mrr
value: 93.334709952357
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 50.705999999999996
- type: map_at_10
value: 60.699999999999996
- type: map_at_100
value: 61.256
- type: map_at_1000
value: 61.285000000000004
- type: map_at_3
value: 57.633
- type: map_at_5
value: 59.648
- type: mrr_at_1
value: 53
- type: mrr_at_10
value: 61.717999999999996
- type: mrr_at_100
value: 62.165000000000006
- type: mrr_at_1000
value: 62.190999999999995
- type: mrr_at_3
value: 59.389
- type: mrr_at_5
value: 60.922
- type: ndcg_at_1
value: 53
- type: ndcg_at_10
value: 65.413
- type: ndcg_at_100
value: 68.089
- type: ndcg_at_1000
value: 69.01899999999999
- type: ndcg_at_3
value: 60.327
- type: ndcg_at_5
value: 63.263999999999996
- type: precision_at_1
value: 53
- type: precision_at_10
value: 8.933
- type: precision_at_100
value: 1.04
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 23.778
- type: precision_at_5
value: 16.2
- type: recall_at_1
value: 50.705999999999996
- type: recall_at_10
value: 78.633
- type: recall_at_100
value: 91.333
- type: recall_at_1000
value: 99
- type: recall_at_3
value: 65.328
- type: recall_at_5
value: 72.583
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.82178217821782
- type: cos_sim_ap
value: 95.30078788098801
- type: cos_sim_f1
value: 91.11549851924975
- type: cos_sim_precision
value: 89.96101364522417
- type: cos_sim_recall
value: 92.30000000000001
- type: dot_accuracy
value: 99.74851485148515
- type: dot_ap
value: 93.12383012680787
- type: dot_f1
value: 87.17171717171716
- type: dot_precision
value: 88.06122448979592
- type: dot_recall
value: 86.3
- type: euclidean_accuracy
value: 99.82673267326733
- type: euclidean_ap
value: 95.29507269622621
- type: euclidean_f1
value: 91.3151364764268
- type: euclidean_precision
value: 90.64039408866995
- type: euclidean_recall
value: 92
- type: manhattan_accuracy
value: 99.82178217821782
- type: manhattan_ap
value: 95.34300712110257
- type: manhattan_f1
value: 91.05367793240556
- type: manhattan_precision
value: 90.51383399209486
- type: manhattan_recall
value: 91.60000000000001
- type: max_accuracy
value: 99.82673267326733
- type: max_ap
value: 95.34300712110257
- type: max_f1
value: 91.3151364764268
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 53.10993894014712
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 34.67216071080345
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 48.96344255085851
- type: mrr
value: 49.816123419064596
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.580410074992177
- type: cos_sim_spearman
value: 31.155995112739966
- type: dot_pearson
value: 31.112094423048998
- type: dot_spearman
value: 31.29974829801922
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.17700000000000002
- type: map_at_10
value: 1.22
- type: map_at_100
value: 6.2170000000000005
- type: map_at_1000
value: 15.406
- type: map_at_3
value: 0.483
- type: map_at_5
value: 0.729
- type: mrr_at_1
value: 64
- type: mrr_at_10
value: 76.333
- type: mrr_at_100
value: 76.47
- type: mrr_at_1000
value: 76.47
- type: mrr_at_3
value: 75
- type: mrr_at_5
value: 76
- type: ndcg_at_1
value: 59
- type: ndcg_at_10
value: 52.62
- type: ndcg_at_100
value: 39.932
- type: ndcg_at_1000
value: 37.317
- type: ndcg_at_3
value: 57.123000000000005
- type: ndcg_at_5
value: 56.376000000000005
- type: precision_at_1
value: 64
- type: precision_at_10
value: 55.800000000000004
- type: precision_at_100
value: 41.04
- type: precision_at_1000
value: 17.124
- type: precision_at_3
value: 63.333
- type: precision_at_5
value: 62
- type: recall_at_1
value: 0.17700000000000002
- type: recall_at_10
value: 1.46
- type: recall_at_100
value: 9.472999999999999
- type: recall_at_1000
value: 35.661
- type: recall_at_3
value: 0.527
- type: recall_at_5
value: 0.8250000000000001
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.539
- type: map_at_10
value: 7.178
- type: map_at_100
value: 12.543000000000001
- type: map_at_1000
value: 14.126
- type: map_at_3
value: 3.09
- type: map_at_5
value: 5.008
- type: mrr_at_1
value: 18.367
- type: mrr_at_10
value: 32.933
- type: mrr_at_100
value: 34.176
- type: mrr_at_1000
value: 34.176
- type: mrr_at_3
value: 27.551
- type: mrr_at_5
value: 30.714000000000002
- type: ndcg_at_1
value: 15.306000000000001
- type: ndcg_at_10
value: 18.343
- type: ndcg_at_100
value: 30.076000000000004
- type: ndcg_at_1000
value: 42.266999999999996
- type: ndcg_at_3
value: 17.233999999999998
- type: ndcg_at_5
value: 18.677
- type: precision_at_1
value: 18.367
- type: precision_at_10
value: 18.367
- type: precision_at_100
value: 6.837
- type: precision_at_1000
value: 1.467
- type: precision_at_3
value: 19.048000000000002
- type: precision_at_5
value: 21.224
- type: recall_at_1
value: 1.539
- type: recall_at_10
value: 13.289000000000001
- type: recall_at_100
value: 42.480000000000004
- type: recall_at_1000
value: 79.463
- type: recall_at_3
value: 4.202999999999999
- type: recall_at_5
value: 7.9030000000000005
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.2056
- type: ap
value: 13.564165903349778
- type: f1
value: 53.303385089202656
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 56.71477079796264
- type: f1
value: 57.01563439439609
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 39.373040570976514
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 83.44757703999524
- type: cos_sim_ap
value: 65.78689843625949
- type: cos_sim_f1
value: 62.25549384206713
- type: cos_sim_precision
value: 57.39091718610864
- type: cos_sim_recall
value: 68.02110817941951
- type: dot_accuracy
value: 81.3971508612982
- type: dot_ap
value: 58.42933051967154
- type: dot_f1
value: 57.85580214198962
- type: dot_precision
value: 49.74368710841086
- type: dot_recall
value: 69.12928759894459
- type: euclidean_accuracy
value: 83.54294569946951
- type: euclidean_ap
value: 66.10612585693795
- type: euclidean_f1
value: 62.66666666666667
- type: euclidean_precision
value: 58.88631090487239
- type: euclidean_recall
value: 66.96569920844327
- type: manhattan_accuracy
value: 83.43565595756095
- type: manhattan_ap
value: 65.88532290329134
- type: manhattan_f1
value: 62.58408721874276
- type: manhattan_precision
value: 55.836092715231786
- type: manhattan_recall
value: 71.18733509234828
- type: max_accuracy
value: 83.54294569946951
- type: max_ap
value: 66.10612585693795
- type: max_f1
value: 62.66666666666667
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.02344083517679
- type: cos_sim_ap
value: 84.21589190889944
- type: cos_sim_f1
value: 76.36723039754007
- type: cos_sim_precision
value: 72.79134682484299
- type: cos_sim_recall
value: 80.31259624268556
- type: dot_accuracy
value: 87.43353902278108
- type: dot_ap
value: 82.08962394120071
- type: dot_f1
value: 74.97709923664122
- type: dot_precision
value: 74.34150772025431
- type: dot_recall
value: 75.62365260240222
- type: euclidean_accuracy
value: 87.97686963946133
- type: euclidean_ap
value: 84.20578083922416
- type: euclidean_f1
value: 76.4299182903834
- type: euclidean_precision
value: 73.51874244256348
- type: euclidean_recall
value: 79.58115183246073
- type: manhattan_accuracy
value: 88.00209570380719
- type: manhattan_ap
value: 84.14700304263556
- type: manhattan_f1
value: 76.36429345861944
- type: manhattan_precision
value: 71.95886119057349
- type: manhattan_recall
value: 81.34431783184478
- type: max_accuracy
value: 88.02344083517679
- type: max_ap
value: 84.21589190889944
- type: max_f1
value: 76.4299182903834
bge-micro
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is distilled from bge-small-en-v1.5, with 1/4 the non-embedding parameters. It has 1/2 the parameters of the smallest commonly-used embedding model, all-MiniLM-L6-v2, with similar performance.
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
Evaluation Results
For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)