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---
tags:
- mteb
- sentence-similarity
- sentence-transformers
- Sentence Transformers
model-index:
- name: gte-large
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 72.62686567164178
- type: ap
value: 34.46944126809772
- type: f1
value: 66.23684353950857
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 92.51805
- type: ap
value: 89.49842783330848
- type: f1
value: 92.51112169431808
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 49.074
- type: f1
value: 48.44785682572955
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.077
- type: map_at_10
value: 48.153
- type: map_at_100
value: 48.963
- type: map_at_1000
value: 48.966
- type: map_at_3
value: 43.184
- type: map_at_5
value: 46.072
- type: mrr_at_1
value: 33.073
- type: mrr_at_10
value: 48.54
- type: mrr_at_100
value: 49.335
- type: mrr_at_1000
value: 49.338
- type: mrr_at_3
value: 43.563
- type: mrr_at_5
value: 46.383
- type: ndcg_at_1
value: 32.077
- type: ndcg_at_10
value: 57.158
- type: ndcg_at_100
value: 60.324999999999996
- type: ndcg_at_1000
value: 60.402
- type: ndcg_at_3
value: 46.934
- type: ndcg_at_5
value: 52.158
- type: precision_at_1
value: 32.077
- type: precision_at_10
value: 8.591999999999999
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 19.275000000000002
- type: precision_at_5
value: 14.111
- type: recall_at_1
value: 32.077
- type: recall_at_10
value: 85.917
- type: recall_at_100
value: 99.075
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 57.824
- type: recall_at_5
value: 70.555
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.619246083417295
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 43.3574067664688
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 63.06359661829253
- type: mrr
value: 76.15596007562766
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 90.25407547368691
- type: cos_sim_spearman
value: 88.65081514968477
- type: euclidean_pearson
value: 88.14857116664494
- type: euclidean_spearman
value: 88.50683596540692
- type: manhattan_pearson
value: 87.9654797992225
- type: manhattan_spearman
value: 88.21164851646908
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 86.05844155844157
- type: f1
value: 86.01555597681825
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.10510519739522
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.84689960264385
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.800000000000004
- type: map_at_10
value: 44.857
- type: map_at_100
value: 46.512
- type: map_at_1000
value: 46.635
- type: map_at_3
value: 41.062
- type: map_at_5
value: 43.126
- type: mrr_at_1
value: 39.628
- type: mrr_at_10
value: 50.879
- type: mrr_at_100
value: 51.605000000000004
- type: mrr_at_1000
value: 51.641000000000005
- type: mrr_at_3
value: 48.14
- type: mrr_at_5
value: 49.835
- type: ndcg_at_1
value: 39.628
- type: ndcg_at_10
value: 51.819
- type: ndcg_at_100
value: 57.318999999999996
- type: ndcg_at_1000
value: 58.955999999999996
- type: ndcg_at_3
value: 46.409
- type: ndcg_at_5
value: 48.825
- type: precision_at_1
value: 39.628
- type: precision_at_10
value: 10.072000000000001
- type: precision_at_100
value: 1.625
- type: precision_at_1000
value: 0.21
- type: precision_at_3
value: 22.556
- type: precision_at_5
value: 16.309
- type: recall_at_1
value: 32.800000000000004
- type: recall_at_10
value: 65.078
- type: recall_at_100
value: 87.491
- type: recall_at_1000
value: 97.514
- type: recall_at_3
value: 49.561
- type: recall_at_5
value: 56.135999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.614
- type: map_at_10
value: 43.578
- type: map_at_100
value: 44.897
- type: map_at_1000
value: 45.023
- type: map_at_3
value: 40.282000000000004
- type: map_at_5
value: 42.117
- type: mrr_at_1
value: 40.510000000000005
- type: mrr_at_10
value: 49.428
- type: mrr_at_100
value: 50.068999999999996
- type: mrr_at_1000
value: 50.111000000000004
- type: mrr_at_3
value: 47.176
- type: mrr_at_5
value: 48.583999999999996
- type: ndcg_at_1
value: 40.510000000000005
- type: ndcg_at_10
value: 49.478
- type: ndcg_at_100
value: 53.852
- type: ndcg_at_1000
value: 55.782
- type: ndcg_at_3
value: 45.091
- type: ndcg_at_5
value: 47.19
- type: precision_at_1
value: 40.510000000000005
- type: precision_at_10
value: 9.363000000000001
- type: precision_at_100
value: 1.51
- type: precision_at_1000
value: 0.196
- type: precision_at_3
value: 21.741
- type: precision_at_5
value: 15.465000000000002
- type: recall_at_1
value: 32.614
- type: recall_at_10
value: 59.782000000000004
- type: recall_at_100
value: 78.012
- type: recall_at_1000
value: 90.319
- type: recall_at_3
value: 46.825
- type: recall_at_5
value: 52.688
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.266000000000005
- type: map_at_10
value: 53.756
- type: map_at_100
value: 54.809
- type: map_at_1000
value: 54.855
- type: map_at_3
value: 50.073
- type: map_at_5
value: 52.293
- type: mrr_at_1
value: 46.332
- type: mrr_at_10
value: 57.116
- type: mrr_at_100
value: 57.767
- type: mrr_at_1000
value: 57.791000000000004
- type: mrr_at_3
value: 54.461999999999996
- type: mrr_at_5
value: 56.092
- type: ndcg_at_1
value: 46.332
- type: ndcg_at_10
value: 60.092
- type: ndcg_at_100
value: 64.034
- type: ndcg_at_1000
value: 64.937
- type: ndcg_at_3
value: 54.071000000000005
- type: ndcg_at_5
value: 57.254000000000005
- type: precision_at_1
value: 46.332
- type: precision_at_10
value: 9.799
- type: precision_at_100
value: 1.278
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 24.368000000000002
- type: precision_at_5
value: 16.89
- type: recall_at_1
value: 40.266000000000005
- type: recall_at_10
value: 75.41499999999999
- type: recall_at_100
value: 92.01700000000001
- type: recall_at_1000
value: 98.379
- type: recall_at_3
value: 59.476
- type: recall_at_5
value: 67.297
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.589
- type: map_at_10
value: 37.755
- type: map_at_100
value: 38.881
- type: map_at_1000
value: 38.954
- type: map_at_3
value: 34.759
- type: map_at_5
value: 36.544
- type: mrr_at_1
value: 30.734
- type: mrr_at_10
value: 39.742
- type: mrr_at_100
value: 40.774
- type: mrr_at_1000
value: 40.824
- type: mrr_at_3
value: 37.137
- type: mrr_at_5
value: 38.719
- type: ndcg_at_1
value: 30.734
- type: ndcg_at_10
value: 42.978
- type: ndcg_at_100
value: 48.309000000000005
- type: ndcg_at_1000
value: 50.068
- type: ndcg_at_3
value: 37.361
- type: ndcg_at_5
value: 40.268
- type: precision_at_1
value: 30.734
- type: precision_at_10
value: 6.565
- type: precision_at_100
value: 0.964
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 15.744
- type: precision_at_5
value: 11.096
- type: recall_at_1
value: 28.589
- type: recall_at_10
value: 57.126999999999995
- type: recall_at_100
value: 81.051
- type: recall_at_1000
value: 94.027
- type: recall_at_3
value: 42.045
- type: recall_at_5
value: 49.019
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.5
- type: map_at_10
value: 27.950999999999997
- type: map_at_100
value: 29.186
- type: map_at_1000
value: 29.298000000000002
- type: map_at_3
value: 25.141000000000002
- type: map_at_5
value: 26.848
- type: mrr_at_1
value: 22.637
- type: mrr_at_10
value: 32.572
- type: mrr_at_100
value: 33.472
- type: mrr_at_1000
value: 33.533
- type: mrr_at_3
value: 29.747
- type: mrr_at_5
value: 31.482
- type: ndcg_at_1
value: 22.637
- type: ndcg_at_10
value: 33.73
- type: ndcg_at_100
value: 39.568
- type: ndcg_at_1000
value: 42.201
- type: ndcg_at_3
value: 28.505999999999997
- type: ndcg_at_5
value: 31.255
- type: precision_at_1
value: 22.637
- type: precision_at_10
value: 6.281000000000001
- type: precision_at_100
value: 1.073
- type: precision_at_1000
value: 0.14300000000000002
- type: precision_at_3
value: 13.847000000000001
- type: precision_at_5
value: 10.224
- type: recall_at_1
value: 18.5
- type: recall_at_10
value: 46.744
- type: recall_at_100
value: 72.072
- type: recall_at_1000
value: 91.03999999999999
- type: recall_at_3
value: 32.551
- type: recall_at_5
value: 39.533
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.602
- type: map_at_10
value: 42.18
- type: map_at_100
value: 43.6
- type: map_at_1000
value: 43.704
- type: map_at_3
value: 38.413000000000004
- type: map_at_5
value: 40.626
- type: mrr_at_1
value: 37.344
- type: mrr_at_10
value: 47.638000000000005
- type: mrr_at_100
value: 48.485
- type: mrr_at_1000
value: 48.52
- type: mrr_at_3
value: 44.867000000000004
- type: mrr_at_5
value: 46.566
- type: ndcg_at_1
value: 37.344
- type: ndcg_at_10
value: 48.632
- type: ndcg_at_100
value: 54.215
- type: ndcg_at_1000
value: 55.981
- type: ndcg_at_3
value: 42.681999999999995
- type: ndcg_at_5
value: 45.732
- type: precision_at_1
value: 37.344
- type: precision_at_10
value: 8.932
- type: precision_at_100
value: 1.376
- type: precision_at_1000
value: 0.17099999999999999
- type: precision_at_3
value: 20.276
- type: precision_at_5
value: 14.726
- type: recall_at_1
value: 30.602
- type: recall_at_10
value: 62.273
- type: recall_at_100
value: 85.12100000000001
- type: recall_at_1000
value: 96.439
- type: recall_at_3
value: 45.848
- type: recall_at_5
value: 53.615
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.952
- type: map_at_10
value: 35.177
- type: map_at_100
value: 36.59
- type: map_at_1000
value: 36.703
- type: map_at_3
value: 31.261
- type: map_at_5
value: 33.222
- type: mrr_at_1
value: 29.337999999999997
- type: mrr_at_10
value: 40.152
- type: mrr_at_100
value: 40.963
- type: mrr_at_1000
value: 41.016999999999996
- type: mrr_at_3
value: 36.91
- type: mrr_at_5
value: 38.685
- type: ndcg_at_1
value: 29.337999999999997
- type: ndcg_at_10
value: 41.994
- type: ndcg_at_100
value: 47.587
- type: ndcg_at_1000
value: 49.791000000000004
- type: ndcg_at_3
value: 35.27
- type: ndcg_at_5
value: 38.042
- type: precision_at_1
value: 29.337999999999997
- type: precision_at_10
value: 8.276
- type: precision_at_100
value: 1.276
- type: precision_at_1000
value: 0.164
- type: precision_at_3
value: 17.161
- type: precision_at_5
value: 12.671
- type: recall_at_1
value: 23.952
- type: recall_at_10
value: 57.267
- type: recall_at_100
value: 80.886
- type: recall_at_1000
value: 95.611
- type: recall_at_3
value: 38.622
- type: recall_at_5
value: 45.811
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.092083333333335
- type: map_at_10
value: 37.2925
- type: map_at_100
value: 38.57041666666666
- type: map_at_1000
value: 38.68141666666667
- type: map_at_3
value: 34.080000000000005
- type: map_at_5
value: 35.89958333333333
- type: mrr_at_1
value: 31.94758333333333
- type: mrr_at_10
value: 41.51049999999999
- type: mrr_at_100
value: 42.36099999999999
- type: mrr_at_1000
value: 42.4125
- type: mrr_at_3
value: 38.849583333333335
- type: mrr_at_5
value: 40.448249999999994
- type: ndcg_at_1
value: 31.94758333333333
- type: ndcg_at_10
value: 43.17633333333333
- type: ndcg_at_100
value: 48.45241666666668
- type: ndcg_at_1000
value: 50.513999999999996
- type: ndcg_at_3
value: 37.75216666666667
- type: ndcg_at_5
value: 40.393833333333326
- type: precision_at_1
value: 31.94758333333333
- type: precision_at_10
value: 7.688916666666666
- type: precision_at_100
value: 1.2250833333333333
- type: precision_at_1000
value: 0.1595
- type: precision_at_3
value: 17.465999999999998
- type: precision_at_5
value: 12.548083333333333
- type: recall_at_1
value: 27.092083333333335
- type: recall_at_10
value: 56.286583333333326
- type: recall_at_100
value: 79.09033333333333
- type: recall_at_1000
value: 93.27483333333335
- type: recall_at_3
value: 41.35325
- type: recall_at_5
value: 48.072750000000006
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.825
- type: map_at_10
value: 33.723
- type: map_at_100
value: 34.74
- type: map_at_1000
value: 34.824
- type: map_at_3
value: 31.369000000000003
- type: map_at_5
value: 32.533
- type: mrr_at_1
value: 29.293999999999997
- type: mrr_at_10
value: 36.84
- type: mrr_at_100
value: 37.681
- type: mrr_at_1000
value: 37.742
- type: mrr_at_3
value: 34.79
- type: mrr_at_5
value: 35.872
- type: ndcg_at_1
value: 29.293999999999997
- type: ndcg_at_10
value: 38.385999999999996
- type: ndcg_at_100
value: 43.327
- type: ndcg_at_1000
value: 45.53
- type: ndcg_at_3
value: 33.985
- type: ndcg_at_5
value: 35.817
- type: precision_at_1
value: 29.293999999999997
- type: precision_at_10
value: 6.12
- type: precision_at_100
value: 0.9329999999999999
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 14.621999999999998
- type: precision_at_5
value: 10.030999999999999
- type: recall_at_1
value: 25.825
- type: recall_at_10
value: 49.647000000000006
- type: recall_at_100
value: 72.32300000000001
- type: recall_at_1000
value: 88.62400000000001
- type: recall_at_3
value: 37.366
- type: recall_at_5
value: 41.957
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.139
- type: map_at_10
value: 26.107000000000003
- type: map_at_100
value: 27.406999999999996
- type: map_at_1000
value: 27.535999999999998
- type: map_at_3
value: 23.445
- type: map_at_5
value: 24.916
- type: mrr_at_1
value: 21.817
- type: mrr_at_10
value: 29.99
- type: mrr_at_100
value: 31.052000000000003
- type: mrr_at_1000
value: 31.128
- type: mrr_at_3
value: 27.627000000000002
- type: mrr_at_5
value: 29.005
- type: ndcg_at_1
value: 21.817
- type: ndcg_at_10
value: 31.135
- type: ndcg_at_100
value: 37.108000000000004
- type: ndcg_at_1000
value: 39.965
- type: ndcg_at_3
value: 26.439
- type: ndcg_at_5
value: 28.655
- type: precision_at_1
value: 21.817
- type: precision_at_10
value: 5.757000000000001
- type: precision_at_100
value: 1.036
- type: precision_at_1000
value: 0.147
- type: precision_at_3
value: 12.537
- type: precision_at_5
value: 9.229
- type: recall_at_1
value: 18.139
- type: recall_at_10
value: 42.272999999999996
- type: recall_at_100
value: 68.657
- type: recall_at_1000
value: 88.93799999999999
- type: recall_at_3
value: 29.266
- type: recall_at_5
value: 34.892
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.755000000000003
- type: map_at_10
value: 37.384
- type: map_at_100
value: 38.56
- type: map_at_1000
value: 38.655
- type: map_at_3
value: 34.214
- type: map_at_5
value: 35.96
- type: mrr_at_1
value: 32.369
- type: mrr_at_10
value: 41.625
- type: mrr_at_100
value: 42.449
- type: mrr_at_1000
value: 42.502
- type: mrr_at_3
value: 38.899
- type: mrr_at_5
value: 40.489999999999995
- type: ndcg_at_1
value: 32.369
- type: ndcg_at_10
value: 43.287
- type: ndcg_at_100
value: 48.504999999999995
- type: ndcg_at_1000
value: 50.552
- type: ndcg_at_3
value: 37.549
- type: ndcg_at_5
value: 40.204
- type: precision_at_1
value: 32.369
- type: precision_at_10
value: 7.425
- type: precision_at_100
value: 1.134
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_3
value: 17.102
- type: precision_at_5
value: 12.107999999999999
- type: recall_at_1
value: 27.755000000000003
- type: recall_at_10
value: 57.071000000000005
- type: recall_at_100
value: 79.456
- type: recall_at_1000
value: 93.54299999999999
- type: recall_at_3
value: 41.298
- type: recall_at_5
value: 48.037
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.855
- type: map_at_10
value: 34.53
- type: map_at_100
value: 36.167
- type: map_at_1000
value: 36.394999999999996
- type: map_at_3
value: 31.037
- type: map_at_5
value: 33.119
- type: mrr_at_1
value: 30.631999999999998
- type: mrr_at_10
value: 39.763999999999996
- type: mrr_at_100
value: 40.77
- type: mrr_at_1000
value: 40.826
- type: mrr_at_3
value: 36.495
- type: mrr_at_5
value: 38.561
- type: ndcg_at_1
value: 30.631999999999998
- type: ndcg_at_10
value: 40.942
- type: ndcg_at_100
value: 47.07
- type: ndcg_at_1000
value: 49.363
- type: ndcg_at_3
value: 35.038000000000004
- type: ndcg_at_5
value: 38.161
- type: precision_at_1
value: 30.631999999999998
- type: precision_at_10
value: 7.983999999999999
- type: precision_at_100
value: 1.6070000000000002
- type: precision_at_1000
value: 0.246
- type: precision_at_3
value: 16.206
- type: precision_at_5
value: 12.253
- type: recall_at_1
value: 24.855
- type: recall_at_10
value: 53.291999999999994
- type: recall_at_100
value: 80.283
- type: recall_at_1000
value: 94.309
- type: recall_at_3
value: 37.257
- type: recall_at_5
value: 45.282
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.208
- type: map_at_10
value: 30.512
- type: map_at_100
value: 31.496000000000002
- type: map_at_1000
value: 31.595000000000002
- type: map_at_3
value: 27.904
- type: map_at_5
value: 29.491
- type: mrr_at_1
value: 22.736
- type: mrr_at_10
value: 32.379999999999995
- type: mrr_at_100
value: 33.245000000000005
- type: mrr_at_1000
value: 33.315
- type: mrr_at_3
value: 29.945
- type: mrr_at_5
value: 31.488
- type: ndcg_at_1
value: 22.736
- type: ndcg_at_10
value: 35.643
- type: ndcg_at_100
value: 40.535
- type: ndcg_at_1000
value: 43.042
- type: ndcg_at_3
value: 30.625000000000004
- type: ndcg_at_5
value: 33.323
- type: precision_at_1
value: 22.736
- type: precision_at_10
value: 5.6930000000000005
- type: precision_at_100
value: 0.889
- type: precision_at_1000
value: 0.122
- type: precision_at_3
value: 13.431999999999999
- type: precision_at_5
value: 9.575
- type: recall_at_1
value: 21.208
- type: recall_at_10
value: 49.47
- type: recall_at_100
value: 71.71499999999999
- type: recall_at_1000
value: 90.55499999999999
- type: recall_at_3
value: 36.124
- type: recall_at_5
value: 42.606
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.363
- type: map_at_10
value: 20.312
- type: map_at_100
value: 22.225
- type: map_at_1000
value: 22.411
- type: map_at_3
value: 16.68
- type: map_at_5
value: 18.608
- type: mrr_at_1
value: 25.537
- type: mrr_at_10
value: 37.933
- type: mrr_at_100
value: 38.875
- type: mrr_at_1000
value: 38.911
- type: mrr_at_3
value: 34.387
- type: mrr_at_5
value: 36.51
- type: ndcg_at_1
value: 25.537
- type: ndcg_at_10
value: 28.82
- type: ndcg_at_100
value: 36.341
- type: ndcg_at_1000
value: 39.615
- type: ndcg_at_3
value: 23.01
- type: ndcg_at_5
value: 25.269000000000002
- type: precision_at_1
value: 25.537
- type: precision_at_10
value: 9.153
- type: precision_at_100
value: 1.7319999999999998
- type: precision_at_1000
value: 0.234
- type: precision_at_3
value: 17.22
- type: precision_at_5
value: 13.629
- type: recall_at_1
value: 11.363
- type: recall_at_10
value: 35.382999999999996
- type: recall_at_100
value: 61.367000000000004
- type: recall_at_1000
value: 79.699
- type: recall_at_3
value: 21.495
- type: recall_at_5
value: 27.42
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.65
- type: map_at_10
value: 20.742
- type: map_at_100
value: 29.614
- type: map_at_1000
value: 31.373
- type: map_at_3
value: 14.667
- type: map_at_5
value: 17.186
- type: mrr_at_1
value: 69.75
- type: mrr_at_10
value: 76.762
- type: mrr_at_100
value: 77.171
- type: mrr_at_1000
value: 77.179
- type: mrr_at_3
value: 75.125
- type: mrr_at_5
value: 76.287
- type: ndcg_at_1
value: 57.62500000000001
- type: ndcg_at_10
value: 42.370999999999995
- type: ndcg_at_100
value: 47.897
- type: ndcg_at_1000
value: 55.393
- type: ndcg_at_3
value: 46.317
- type: ndcg_at_5
value: 43.906
- type: precision_at_1
value: 69.75
- type: precision_at_10
value: 33.95
- type: precision_at_100
value: 10.885
- type: precision_at_1000
value: 2.2239999999999998
- type: precision_at_3
value: 49.75
- type: precision_at_5
value: 42.3
- type: recall_at_1
value: 9.65
- type: recall_at_10
value: 26.117
- type: recall_at_100
value: 55.084
- type: recall_at_1000
value: 78.62400000000001
- type: recall_at_3
value: 15.823
- type: recall_at_5
value: 19.652
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 47.885
- type: f1
value: 42.99567641346983
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.97
- type: map_at_10
value: 80.34599999999999
- type: map_at_100
value: 80.571
- type: map_at_1000
value: 80.584
- type: map_at_3
value: 79.279
- type: map_at_5
value: 79.94
- type: mrr_at_1
value: 76.613
- type: mrr_at_10
value: 85.15700000000001
- type: mrr_at_100
value: 85.249
- type: mrr_at_1000
value: 85.252
- type: mrr_at_3
value: 84.33800000000001
- type: mrr_at_5
value: 84.89
- type: ndcg_at_1
value: 76.613
- type: ndcg_at_10
value: 84.53399999999999
- type: ndcg_at_100
value: 85.359
- type: ndcg_at_1000
value: 85.607
- type: ndcg_at_3
value: 82.76599999999999
- type: ndcg_at_5
value: 83.736
- type: precision_at_1
value: 76.613
- type: precision_at_10
value: 10.206
- type: precision_at_100
value: 1.083
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 31.913000000000004
- type: precision_at_5
value: 19.769000000000002
- type: recall_at_1
value: 70.97
- type: recall_at_10
value: 92.674
- type: recall_at_100
value: 95.985
- type: recall_at_1000
value: 97.57000000000001
- type: recall_at_3
value: 87.742
- type: recall_at_5
value: 90.28
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.494
- type: map_at_10
value: 36.491
- type: map_at_100
value: 38.550000000000004
- type: map_at_1000
value: 38.726
- type: map_at_3
value: 31.807000000000002
- type: map_at_5
value: 34.299
- type: mrr_at_1
value: 44.907000000000004
- type: mrr_at_10
value: 53.146
- type: mrr_at_100
value: 54.013999999999996
- type: mrr_at_1000
value: 54.044000000000004
- type: mrr_at_3
value: 50.952
- type: mrr_at_5
value: 52.124
- type: ndcg_at_1
value: 44.907000000000004
- type: ndcg_at_10
value: 44.499
- type: ndcg_at_100
value: 51.629000000000005
- type: ndcg_at_1000
value: 54.367
- type: ndcg_at_3
value: 40.900999999999996
- type: ndcg_at_5
value: 41.737
- type: precision_at_1
value: 44.907000000000004
- type: precision_at_10
value: 12.346
- type: precision_at_100
value: 1.974
- type: precision_at_1000
value: 0.246
- type: precision_at_3
value: 27.366
- type: precision_at_5
value: 19.846
- type: recall_at_1
value: 22.494
- type: recall_at_10
value: 51.156
- type: recall_at_100
value: 77.11200000000001
- type: recall_at_1000
value: 93.44
- type: recall_at_3
value: 36.574
- type: recall_at_5
value: 42.361
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.568999999999996
- type: map_at_10
value: 58.485
- type: map_at_100
value: 59.358999999999995
- type: map_at_1000
value: 59.429
- type: map_at_3
value: 55.217000000000006
- type: map_at_5
value: 57.236
- type: mrr_at_1
value: 77.137
- type: mrr_at_10
value: 82.829
- type: mrr_at_100
value: 83.04599999999999
- type: mrr_at_1000
value: 83.05399999999999
- type: mrr_at_3
value: 81.904
- type: mrr_at_5
value: 82.50800000000001
- type: ndcg_at_1
value: 77.137
- type: ndcg_at_10
value: 67.156
- type: ndcg_at_100
value: 70.298
- type: ndcg_at_1000
value: 71.65700000000001
- type: ndcg_at_3
value: 62.535
- type: ndcg_at_5
value: 65.095
- type: precision_at_1
value: 77.137
- type: precision_at_10
value: 13.911999999999999
- type: precision_at_100
value: 1.6389999999999998
- type: precision_at_1000
value: 0.182
- type: precision_at_3
value: 39.572
- type: precision_at_5
value: 25.766
- type: recall_at_1
value: 38.568999999999996
- type: recall_at_10
value: 69.56099999999999
- type: recall_at_100
value: 81.931
- type: recall_at_1000
value: 90.91799999999999
- type: recall_at_3
value: 59.358999999999995
- type: recall_at_5
value: 64.416
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 88.45600000000002
- type: ap
value: 84.09725115338568
- type: f1
value: 88.41874909080512
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.404999999999998
- type: map_at_10
value: 33.921
- type: map_at_100
value: 35.116
- type: map_at_1000
value: 35.164
- type: map_at_3
value: 30.043999999999997
- type: map_at_5
value: 32.327
- type: mrr_at_1
value: 21.977
- type: mrr_at_10
value: 34.505
- type: mrr_at_100
value: 35.638999999999996
- type: mrr_at_1000
value: 35.68
- type: mrr_at_3
value: 30.703999999999997
- type: mrr_at_5
value: 32.96
- type: ndcg_at_1
value: 21.963
- type: ndcg_at_10
value: 40.859
- type: ndcg_at_100
value: 46.614
- type: ndcg_at_1000
value: 47.789
- type: ndcg_at_3
value: 33.007999999999996
- type: ndcg_at_5
value: 37.084
- type: precision_at_1
value: 21.963
- type: precision_at_10
value: 6.493
- type: precision_at_100
value: 0.938
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.155000000000001
- type: precision_at_5
value: 10.544
- type: recall_at_1
value: 21.404999999999998
- type: recall_at_10
value: 62.175000000000004
- type: recall_at_100
value: 88.786
- type: recall_at_1000
value: 97.738
- type: recall_at_3
value: 40.925
- type: recall_at_5
value: 50.722
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.50661194710442
- type: f1
value: 93.30311193153668
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 73.24669402644778
- type: f1
value: 54.23122108002977
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.61936785474109
- type: f1
value: 70.52644941025565
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.76529926025555
- type: f1
value: 77.26872729322514
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.39450293021839
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.757796879839294
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.62512146657428
- type: mrr
value: 33.84624322066173
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.462
- type: map_at_10
value: 14.947
- type: map_at_100
value: 19.344
- type: map_at_1000
value: 20.933
- type: map_at_3
value: 10.761999999999999
- type: map_at_5
value: 12.744
- type: mrr_at_1
value: 47.988
- type: mrr_at_10
value: 57.365
- type: mrr_at_100
value: 57.931
- type: mrr_at_1000
value: 57.96
- type: mrr_at_3
value: 54.85
- type: mrr_at_5
value: 56.569
- type: ndcg_at_1
value: 46.129999999999995
- type: ndcg_at_10
value: 38.173
- type: ndcg_at_100
value: 35.983
- type: ndcg_at_1000
value: 44.507000000000005
- type: ndcg_at_3
value: 42.495
- type: ndcg_at_5
value: 41.019
- type: precision_at_1
value: 47.678
- type: precision_at_10
value: 28.731
- type: precision_at_100
value: 9.232
- type: precision_at_1000
value: 2.202
- type: precision_at_3
value: 39.628
- type: precision_at_5
value: 35.851
- type: recall_at_1
value: 6.462
- type: recall_at_10
value: 18.968
- type: recall_at_100
value: 37.131
- type: recall_at_1000
value: 67.956
- type: recall_at_3
value: 11.905000000000001
- type: recall_at_5
value: 15.097
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.335
- type: map_at_10
value: 46.611999999999995
- type: map_at_100
value: 47.632000000000005
- type: map_at_1000
value: 47.661
- type: map_at_3
value: 41.876999999999995
- type: map_at_5
value: 44.799
- type: mrr_at_1
value: 34.125
- type: mrr_at_10
value: 49.01
- type: mrr_at_100
value: 49.75
- type: mrr_at_1000
value: 49.768
- type: mrr_at_3
value: 45.153
- type: mrr_at_5
value: 47.589999999999996
- type: ndcg_at_1
value: 34.125
- type: ndcg_at_10
value: 54.777
- type: ndcg_at_100
value: 58.914
- type: ndcg_at_1000
value: 59.521
- type: ndcg_at_3
value: 46.015
- type: ndcg_at_5
value: 50.861000000000004
- type: precision_at_1
value: 34.125
- type: precision_at_10
value: 9.166
- type: precision_at_100
value: 1.149
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 21.147
- type: precision_at_5
value: 15.469
- type: recall_at_1
value: 30.335
- type: recall_at_10
value: 77.194
- type: recall_at_100
value: 94.812
- type: recall_at_1000
value: 99.247
- type: recall_at_3
value: 54.681000000000004
- type: recall_at_5
value: 65.86800000000001
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.62
- type: map_at_10
value: 84.536
- type: map_at_100
value: 85.167
- type: map_at_1000
value: 85.184
- type: map_at_3
value: 81.607
- type: map_at_5
value: 83.423
- type: mrr_at_1
value: 81.36
- type: mrr_at_10
value: 87.506
- type: mrr_at_100
value: 87.601
- type: mrr_at_1000
value: 87.601
- type: mrr_at_3
value: 86.503
- type: mrr_at_5
value: 87.179
- type: ndcg_at_1
value: 81.36
- type: ndcg_at_10
value: 88.319
- type: ndcg_at_100
value: 89.517
- type: ndcg_at_1000
value: 89.60900000000001
- type: ndcg_at_3
value: 85.423
- type: ndcg_at_5
value: 86.976
- type: precision_at_1
value: 81.36
- type: precision_at_10
value: 13.415
- type: precision_at_100
value: 1.529
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.342999999999996
- type: precision_at_5
value: 24.534
- type: recall_at_1
value: 70.62
- type: recall_at_10
value: 95.57600000000001
- type: recall_at_100
value: 99.624
- type: recall_at_1000
value: 99.991
- type: recall_at_3
value: 87.22
- type: recall_at_5
value: 91.654
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 60.826438478212744
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 64.24027467551447
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.997999999999999
- type: map_at_10
value: 14.267
- type: map_at_100
value: 16.843
- type: map_at_1000
value: 17.229
- type: map_at_3
value: 9.834
- type: map_at_5
value: 11.92
- type: mrr_at_1
value: 24.7
- type: mrr_at_10
value: 37.685
- type: mrr_at_100
value: 38.704
- type: mrr_at_1000
value: 38.747
- type: mrr_at_3
value: 34.150000000000006
- type: mrr_at_5
value: 36.075
- type: ndcg_at_1
value: 24.7
- type: ndcg_at_10
value: 23.44
- type: ndcg_at_100
value: 32.617000000000004
- type: ndcg_at_1000
value: 38.628
- type: ndcg_at_3
value: 21.747
- type: ndcg_at_5
value: 19.076
- type: precision_at_1
value: 24.7
- type: precision_at_10
value: 12.47
- type: precision_at_100
value: 2.564
- type: precision_at_1000
value: 0.4
- type: precision_at_3
value: 20.767
- type: precision_at_5
value: 17.06
- type: recall_at_1
value: 4.997999999999999
- type: recall_at_10
value: 25.3
- type: recall_at_100
value: 52.048
- type: recall_at_1000
value: 81.093
- type: recall_at_3
value: 12.642999999999999
- type: recall_at_5
value: 17.312
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 85.44942006292234
- type: cos_sim_spearman
value: 79.80930790660699
- type: euclidean_pearson
value: 82.93400777494863
- type: euclidean_spearman
value: 80.04664991110705
- type: manhattan_pearson
value: 82.93551681854949
- type: manhattan_spearman
value: 80.03156736837379
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 85.63574059135726
- type: cos_sim_spearman
value: 76.80552915288186
- type: euclidean_pearson
value: 82.46368529820518
- type: euclidean_spearman
value: 76.60338474719275
- type: manhattan_pearson
value: 82.4558617035968
- type: manhattan_spearman
value: 76.57936082895705
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 86.24116811084211
- type: cos_sim_spearman
value: 88.10998662068769
- type: euclidean_pearson
value: 87.04961732352689
- type: euclidean_spearman
value: 88.12543945864087
- type: manhattan_pearson
value: 86.9905224528854
- type: manhattan_spearman
value: 88.07827944705546
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 84.74847296555048
- type: cos_sim_spearman
value: 82.66200957916445
- type: euclidean_pearson
value: 84.48132256004965
- type: euclidean_spearman
value: 82.67915286000596
- type: manhattan_pearson
value: 84.44950477268334
- type: manhattan_spearman
value: 82.63327639173352
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.23056258027053
- type: cos_sim_spearman
value: 88.92791680286955
- type: euclidean_pearson
value: 88.13819235461933
- type: euclidean_spearman
value: 88.87294661361716
- type: manhattan_pearson
value: 88.14212133687899
- type: manhattan_spearman
value: 88.88551854529777
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.64179522732887
- type: cos_sim_spearman
value: 84.25028809903114
- type: euclidean_pearson
value: 83.40175015236979
- type: euclidean_spearman
value: 84.23369296429406
- type: manhattan_pearson
value: 83.43768174261321
- type: manhattan_spearman
value: 84.27855229214734
- 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: 88.20378955494732
- type: cos_sim_spearman
value: 88.46863559173111
- type: euclidean_pearson
value: 88.8249295811663
- type: euclidean_spearman
value: 88.6312737724905
- type: manhattan_pearson
value: 88.87744466378827
- type: manhattan_spearman
value: 88.82908423767314
- 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: 69.91342028796086
- type: cos_sim_spearman
value: 69.71495021867864
- type: euclidean_pearson
value: 70.65334330405646
- type: euclidean_spearman
value: 69.4321253472211
- type: manhattan_pearson
value: 70.59743494727465
- type: manhattan_spearman
value: 69.11695509297482
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.42451709766952
- type: cos_sim_spearman
value: 86.07166710670508
- type: euclidean_pearson
value: 86.12711421258899
- type: euclidean_spearman
value: 86.05232086925126
- type: manhattan_pearson
value: 86.15591089932126
- type: manhattan_spearman
value: 86.0890128623439
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.1976344717285
- type: mrr
value: 96.3703145075694
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 59.511
- type: map_at_10
value: 69.724
- type: map_at_100
value: 70.208
- type: map_at_1000
value: 70.22800000000001
- type: map_at_3
value: 66.986
- type: map_at_5
value: 68.529
- type: mrr_at_1
value: 62.333000000000006
- type: mrr_at_10
value: 70.55
- type: mrr_at_100
value: 70.985
- type: mrr_at_1000
value: 71.004
- type: mrr_at_3
value: 68.611
- type: mrr_at_5
value: 69.728
- type: ndcg_at_1
value: 62.333000000000006
- type: ndcg_at_10
value: 74.265
- type: ndcg_at_100
value: 76.361
- type: ndcg_at_1000
value: 76.82900000000001
- type: ndcg_at_3
value: 69.772
- type: ndcg_at_5
value: 71.94800000000001
- type: precision_at_1
value: 62.333000000000006
- type: precision_at_10
value: 9.9
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 27.444000000000003
- type: precision_at_5
value: 18
- type: recall_at_1
value: 59.511
- type: recall_at_10
value: 87.156
- type: recall_at_100
value: 96.5
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 75.2
- type: recall_at_5
value: 80.661
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.81683168316832
- type: cos_sim_ap
value: 95.74716566563774
- type: cos_sim_f1
value: 90.64238745574103
- type: cos_sim_precision
value: 91.7093142272262
- type: cos_sim_recall
value: 89.60000000000001
- type: dot_accuracy
value: 99.69405940594059
- type: dot_ap
value: 91.09013507754594
- type: dot_f1
value: 84.54227113556779
- type: dot_precision
value: 84.58458458458459
- type: dot_recall
value: 84.5
- type: euclidean_accuracy
value: 99.81782178217821
- type: euclidean_ap
value: 95.6324301072609
- type: euclidean_f1
value: 90.58341862845445
- type: euclidean_precision
value: 92.76729559748428
- type: euclidean_recall
value: 88.5
- type: manhattan_accuracy
value: 99.81980198019802
- type: manhattan_ap
value: 95.68510494437183
- type: manhattan_f1
value: 90.58945191313342
- type: manhattan_precision
value: 93.79014989293361
- type: manhattan_recall
value: 87.6
- type: max_accuracy
value: 99.81980198019802
- type: max_ap
value: 95.74716566563774
- type: max_f1
value: 90.64238745574103
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 67.63761899427078
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.572473369697235
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 53.63000245208579
- type: mrr
value: 54.504193722943725
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.300791939416545
- type: cos_sim_spearman
value: 31.662904057924123
- type: dot_pearson
value: 26.21198530758316
- type: dot_spearman
value: 27.006921548904263
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.197
- type: map_at_10
value: 1.752
- type: map_at_100
value: 10.795
- type: map_at_1000
value: 27.18
- type: map_at_3
value: 0.5890000000000001
- type: map_at_5
value: 0.938
- type: mrr_at_1
value: 74
- type: mrr_at_10
value: 85.833
- type: mrr_at_100
value: 85.833
- type: mrr_at_1000
value: 85.833
- type: mrr_at_3
value: 85.333
- type: mrr_at_5
value: 85.833
- type: ndcg_at_1
value: 69
- type: ndcg_at_10
value: 70.22
- type: ndcg_at_100
value: 55.785
- type: ndcg_at_1000
value: 52.93600000000001
- type: ndcg_at_3
value: 72.084
- type: ndcg_at_5
value: 71.184
- type: precision_at_1
value: 74
- type: precision_at_10
value: 75.2
- type: precision_at_100
value: 57.3
- type: precision_at_1000
value: 23.302
- type: precision_at_3
value: 77.333
- type: precision_at_5
value: 75.6
- type: recall_at_1
value: 0.197
- type: recall_at_10
value: 2.019
- type: recall_at_100
value: 14.257
- type: recall_at_1000
value: 50.922
- type: recall_at_3
value: 0.642
- type: recall_at_5
value: 1.043
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.803
- type: map_at_10
value: 10.407
- type: map_at_100
value: 16.948
- type: map_at_1000
value: 18.424
- type: map_at_3
value: 5.405
- type: map_at_5
value: 6.908
- type: mrr_at_1
value: 36.735
- type: mrr_at_10
value: 50.221000000000004
- type: mrr_at_100
value: 51.388
- type: mrr_at_1000
value: 51.402
- type: mrr_at_3
value: 47.278999999999996
- type: mrr_at_5
value: 49.626
- type: ndcg_at_1
value: 34.694
- type: ndcg_at_10
value: 25.507
- type: ndcg_at_100
value: 38.296
- type: ndcg_at_1000
value: 49.492000000000004
- type: ndcg_at_3
value: 29.006999999999998
- type: ndcg_at_5
value: 25.979000000000003
- type: precision_at_1
value: 36.735
- type: precision_at_10
value: 22.041
- type: precision_at_100
value: 8.02
- type: precision_at_1000
value: 1.567
- type: precision_at_3
value: 28.571
- type: precision_at_5
value: 24.490000000000002
- type: recall_at_1
value: 2.803
- type: recall_at_10
value: 16.378
- type: recall_at_100
value: 50.489
- type: recall_at_1000
value: 85.013
- type: recall_at_3
value: 6.505
- type: recall_at_5
value: 9.243
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.55579999999999
- type: ap
value: 14.206982753316227
- type: f1
value: 54.372142814964285
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 56.57611771363893
- type: f1
value: 56.924172639063144
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 52.82304915719759
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.92716218632653
- type: cos_sim_ap
value: 73.73359122546046
- type: cos_sim_f1
value: 68.42559487116262
- type: cos_sim_precision
value: 64.22124508215691
- type: cos_sim_recall
value: 73.21899736147758
- type: dot_accuracy
value: 80.38981939560112
- type: dot_ap
value: 54.61060862444974
- type: dot_f1
value: 53.45710627400769
- type: dot_precision
value: 44.87638839125761
- type: dot_recall
value: 66.09498680738787
- type: euclidean_accuracy
value: 86.02849138701794
- type: euclidean_ap
value: 73.95673761922404
- type: euclidean_f1
value: 68.6783042394015
- type: euclidean_precision
value: 65.1063829787234
- type: euclidean_recall
value: 72.66490765171504
- type: manhattan_accuracy
value: 85.9808070572808
- type: manhattan_ap
value: 73.9050720058029
- type: manhattan_f1
value: 68.57560618983794
- type: manhattan_precision
value: 63.70839936608558
- type: manhattan_recall
value: 74.24802110817942
- type: max_accuracy
value: 86.02849138701794
- type: max_ap
value: 73.95673761922404
- type: max_f1
value: 68.6783042394015
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.72783017037295
- type: cos_sim_ap
value: 85.52705223340233
- type: cos_sim_f1
value: 77.91659078492079
- type: cos_sim_precision
value: 73.93378032764221
- type: cos_sim_recall
value: 82.35294117647058
- type: dot_accuracy
value: 85.41739434159972
- type: dot_ap
value: 77.17734818118443
- type: dot_f1
value: 71.63473589973144
- type: dot_precision
value: 66.96123719622415
- type: dot_recall
value: 77.00954727440714
- type: euclidean_accuracy
value: 88.68125897465751
- type: euclidean_ap
value: 85.47712213906692
- type: euclidean_f1
value: 77.81419950830664
- type: euclidean_precision
value: 75.37162649733006
- type: euclidean_recall
value: 80.42038805050817
- type: manhattan_accuracy
value: 88.67349710870494
- type: manhattan_ap
value: 85.46506475241955
- type: manhattan_f1
value: 77.87259084890393
- type: manhattan_precision
value: 74.54929577464789
- type: manhattan_recall
value: 81.50600554357868
- type: max_accuracy
value: 88.72783017037295
- type: max_ap
value: 85.52705223340233
- type: max_f1
value: 77.91659078492079
language:
- en
license: mit
---
# gte-large
General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281)
The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc.
## Metrics
We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 |
| [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 |
| [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 |
| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 |
| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 |
| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 |
| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 |
## Usage
Code example
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
input_texts = [
"what is the capital of China?",
"how to implement quick sort in python?",
"Beijing",
"sorting algorithms"
]
tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-large")
model = AutoModel.from_pretrained("thenlper/gte-large")
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
```
Use with sentence-transformers:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = ['That is a happy person', 'That is a very happy person']
model = SentenceTransformer('thenlper/gte-large')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```
### Limitation
This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
### Citation
If you find our paper or models helpful, please consider citing them as follows:
```
@misc{li2023general,
title={Towards General Text Embeddings with Multi-stage Contrastive Learning},
author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
year={2023},
eprint={2308.03281},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|