metadata
model-index:
- name: gte_tiny
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.76119402985076
- type: ap
value: 34.63659287952359
- type: f1
value: 65.88939512571113
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 86.61324999999998
- type: ap
value: 81.7476302802319
- type: f1
value: 86.5863470912001
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 42.61000000000001
- type: f1
value: 42.2217180000715
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.377999999999997
- type: map_at_10
value: 44.565
- type: map_at_100
value: 45.48
- type: map_at_1000
value: 45.487
- type: map_at_3
value: 39.841
- type: map_at_5
value: 42.284
- type: mrr_at_1
value: 29.445
- type: mrr_at_10
value: 44.956
- type: mrr_at_100
value: 45.877
- type: mrr_at_1000
value: 45.884
- type: mrr_at_3
value: 40.209
- type: mrr_at_5
value: 42.719
- type: ndcg_at_1
value: 28.377999999999997
- type: ndcg_at_10
value: 53.638
- type: ndcg_at_100
value: 57.354000000000006
- type: ndcg_at_1000
value: 57.513000000000005
- type: ndcg_at_3
value: 43.701
- type: ndcg_at_5
value: 48.114000000000004
- type: precision_at_1
value: 28.377999999999997
- type: precision_at_10
value: 8.272
- type: precision_at_100
value: 0.984
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 18.303
- type: precision_at_5
value: 13.129
- type: recall_at_1
value: 28.377999999999997
- type: recall_at_10
value: 82.717
- type: recall_at_100
value: 98.43499999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 54.908
- type: recall_at_5
value: 65.647
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 46.637318326729876
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 36.01134479855804
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 59.82917555338909
- type: mrr
value: 74.7888361254012
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 87.1657730995964
- type: cos_sim_spearman
value: 86.62787748941281
- type: euclidean_pearson
value: 85.48127914481798
- type: euclidean_spearman
value: 86.48148861167424
- type: manhattan_pearson
value: 85.07496934780823
- type: manhattan_spearman
value: 86.39473964708843
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 81.73051948051948
- type: f1
value: 81.66368364988331
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.18623707448217
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 32.12697757150375
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.160000000000004
- type: map_at_10
value: 40.474
- type: map_at_100
value: 41.905
- type: map_at_1000
value: 42.041000000000004
- type: map_at_3
value: 37.147000000000006
- type: map_at_5
value: 38.873999999999995
- type: mrr_at_1
value: 36.91
- type: mrr_at_10
value: 46.495999999999995
- type: mrr_at_100
value: 47.288000000000004
- type: mrr_at_1000
value: 47.339999999999996
- type: mrr_at_3
value: 43.777
- type: mrr_at_5
value: 45.257999999999996
- type: ndcg_at_1
value: 36.91
- type: ndcg_at_10
value: 46.722
- type: ndcg_at_100
value: 51.969
- type: ndcg_at_1000
value: 54.232
- type: ndcg_at_3
value: 41.783
- type: ndcg_at_5
value: 43.797000000000004
- type: precision_at_1
value: 36.91
- type: precision_at_10
value: 9.013
- type: precision_at_100
value: 1.455
- type: precision_at_1000
value: 0.193
- type: precision_at_3
value: 20.124
- type: precision_at_5
value: 14.363000000000001
- type: recall_at_1
value: 29.160000000000004
- type: recall_at_10
value: 58.521
- type: recall_at_100
value: 80.323
- type: recall_at_1000
value: 95.13000000000001
- type: recall_at_3
value: 44.205
- type: recall_at_5
value: 49.97
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.750000000000004
- type: map_at_10
value: 36.39
- type: map_at_100
value: 37.5
- type: map_at_1000
value: 37.625
- type: map_at_3
value: 33.853
- type: map_at_5
value: 35.397
- type: mrr_at_1
value: 34.14
- type: mrr_at_10
value: 41.841
- type: mrr_at_100
value: 42.469
- type: mrr_at_1000
value: 42.521
- type: mrr_at_3
value: 39.724
- type: mrr_at_5
value: 40.955999999999996
- type: ndcg_at_1
value: 34.14
- type: ndcg_at_10
value: 41.409
- type: ndcg_at_100
value: 45.668
- type: ndcg_at_1000
value: 47.916
- type: ndcg_at_3
value: 37.836
- type: ndcg_at_5
value: 39.650999999999996
- type: precision_at_1
value: 34.14
- type: precision_at_10
value: 7.739
- type: precision_at_100
value: 1.2630000000000001
- type: precision_at_1000
value: 0.173
- type: precision_at_3
value: 18.217
- type: precision_at_5
value: 12.854
- type: recall_at_1
value: 27.750000000000004
- type: recall_at_10
value: 49.882
- type: recall_at_100
value: 68.556
- type: recall_at_1000
value: 83.186
- type: recall_at_3
value: 39.047
- type: recall_at_5
value: 44.458
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.879
- type: map_at_10
value: 48.878
- type: map_at_100
value: 49.918
- type: map_at_1000
value: 49.978
- type: map_at_3
value: 45.867999999999995
- type: map_at_5
value: 47.637
- type: mrr_at_1
value: 42.696
- type: mrr_at_10
value: 52.342
- type: mrr_at_100
value: 53.044000000000004
- type: mrr_at_1000
value: 53.077
- type: mrr_at_3
value: 50.01
- type: mrr_at_5
value: 51.437
- type: ndcg_at_1
value: 42.696
- type: ndcg_at_10
value: 54.469
- type: ndcg_at_100
value: 58.664
- type: ndcg_at_1000
value: 59.951
- type: ndcg_at_3
value: 49.419999999999995
- type: ndcg_at_5
value: 52.007000000000005
- type: precision_at_1
value: 42.696
- type: precision_at_10
value: 8.734
- type: precision_at_100
value: 1.1769999999999998
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 22.027
- type: precision_at_5
value: 15.135000000000002
- type: recall_at_1
value: 36.879
- type: recall_at_10
value: 67.669
- type: recall_at_100
value: 85.822
- type: recall_at_1000
value: 95.092
- type: recall_at_3
value: 54.157999999999994
- type: recall_at_5
value: 60.436
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.942
- type: map_at_10
value: 31.741999999999997
- type: map_at_100
value: 32.721000000000004
- type: map_at_1000
value: 32.809
- type: map_at_3
value: 29.17
- type: map_at_5
value: 30.714000000000002
- type: mrr_at_1
value: 24.746000000000002
- type: mrr_at_10
value: 33.517
- type: mrr_at_100
value: 34.451
- type: mrr_at_1000
value: 34.522000000000006
- type: mrr_at_3
value: 31.148999999999997
- type: mrr_at_5
value: 32.606
- type: ndcg_at_1
value: 24.746000000000002
- type: ndcg_at_10
value: 36.553000000000004
- type: ndcg_at_100
value: 41.53
- type: ndcg_at_1000
value: 43.811
- type: ndcg_at_3
value: 31.674000000000003
- type: ndcg_at_5
value: 34.241
- type: precision_at_1
value: 24.746000000000002
- type: precision_at_10
value: 5.684
- type: precision_at_100
value: 0.859
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 13.597000000000001
- type: precision_at_5
value: 9.672
- type: recall_at_1
value: 22.942
- type: recall_at_10
value: 49.58
- type: recall_at_100
value: 72.614
- type: recall_at_1000
value: 89.89200000000001
- type: recall_at_3
value: 36.552
- type: recall_at_5
value: 42.702
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 15.345
- type: map_at_10
value: 22.428
- type: map_at_100
value: 23.756
- type: map_at_1000
value: 23.872
- type: map_at_3
value: 20.212
- type: map_at_5
value: 21.291
- type: mrr_at_1
value: 19.279
- type: mrr_at_10
value: 27.1
- type: mrr_at_100
value: 28.211000000000002
- type: mrr_at_1000
value: 28.279
- type: mrr_at_3
value: 24.813
- type: mrr_at_5
value: 25.889
- type: ndcg_at_1
value: 19.279
- type: ndcg_at_10
value: 27.36
- type: ndcg_at_100
value: 33.499
- type: ndcg_at_1000
value: 36.452
- type: ndcg_at_3
value: 23.233999999999998
- type: ndcg_at_5
value: 24.806
- type: precision_at_1
value: 19.279
- type: precision_at_10
value: 5.149
- type: precision_at_100
value: 0.938
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 11.360000000000001
- type: precision_at_5
value: 8.035
- type: recall_at_1
value: 15.345
- type: recall_at_10
value: 37.974999999999994
- type: recall_at_100
value: 64.472
- type: recall_at_1000
value: 85.97200000000001
- type: recall_at_3
value: 26.203
- type: recall_at_5
value: 30.485
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.362000000000002
- type: map_at_10
value: 36.406
- type: map_at_100
value: 37.726
- type: map_at_1000
value: 37.84
- type: map_at_3
value: 33.425
- type: map_at_5
value: 35.043
- type: mrr_at_1
value: 32.146
- type: mrr_at_10
value: 41.674
- type: mrr_at_100
value: 42.478
- type: mrr_at_1000
value: 42.524
- type: mrr_at_3
value: 38.948
- type: mrr_at_5
value: 40.415
- type: ndcg_at_1
value: 32.146
- type: ndcg_at_10
value: 42.374
- type: ndcg_at_100
value: 47.919
- type: ndcg_at_1000
value: 50.013
- type: ndcg_at_3
value: 37.29
- type: ndcg_at_5
value: 39.531
- type: precision_at_1
value: 32.146
- type: precision_at_10
value: 7.767
- type: precision_at_100
value: 1.236
- type: precision_at_1000
value: 0.16
- type: precision_at_3
value: 17.965999999999998
- type: precision_at_5
value: 12.742999999999999
- type: recall_at_1
value: 26.362000000000002
- type: recall_at_10
value: 54.98800000000001
- type: recall_at_100
value: 78.50200000000001
- type: recall_at_1000
value: 92.146
- type: recall_at_3
value: 40.486
- type: recall_at_5
value: 46.236
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.417
- type: map_at_10
value: 33.161
- type: map_at_100
value: 34.357
- type: map_at_1000
value: 34.473
- type: map_at_3
value: 30.245
- type: map_at_5
value: 31.541999999999998
- type: mrr_at_1
value: 29.909000000000002
- type: mrr_at_10
value: 38.211
- type: mrr_at_100
value: 39.056999999999995
- type: mrr_at_1000
value: 39.114
- type: mrr_at_3
value: 35.769
- type: mrr_at_5
value: 36.922
- type: ndcg_at_1
value: 29.909000000000002
- type: ndcg_at_10
value: 38.694
- type: ndcg_at_100
value: 44.057
- type: ndcg_at_1000
value: 46.6
- type: ndcg_at_3
value: 33.822
- type: ndcg_at_5
value: 35.454
- type: precision_at_1
value: 29.909000000000002
- type: precision_at_10
value: 7.180000000000001
- type: precision_at_100
value: 1.153
- type: precision_at_1000
value: 0.155
- type: precision_at_3
value: 16.134
- type: precision_at_5
value: 11.256
- type: recall_at_1
value: 24.417
- type: recall_at_10
value: 50.260000000000005
- type: recall_at_100
value: 73.55699999999999
- type: recall_at_1000
value: 91.216
- type: recall_at_3
value: 35.971
- type: recall_at_5
value: 40.793
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.266916666666663
- type: map_at_10
value: 32.75025
- type: map_at_100
value: 33.91341666666667
- type: map_at_1000
value: 34.031749999999995
- type: map_at_3
value: 30.166416666666674
- type: map_at_5
value: 31.577000000000005
- type: mrr_at_1
value: 28.828166666666664
- type: mrr_at_10
value: 36.80991666666667
- type: mrr_at_100
value: 37.67075
- type: mrr_at_1000
value: 37.733
- type: mrr_at_3
value: 34.513416666666664
- type: mrr_at_5
value: 35.788
- type: ndcg_at_1
value: 28.828166666666664
- type: ndcg_at_10
value: 37.796
- type: ndcg_at_100
value: 42.94783333333333
- type: ndcg_at_1000
value: 45.38908333333333
- type: ndcg_at_3
value: 33.374750000000006
- type: ndcg_at_5
value: 35.379666666666665
- type: precision_at_1
value: 28.828166666666664
- type: precision_at_10
value: 6.615749999999999
- type: precision_at_100
value: 1.0848333333333333
- type: precision_at_1000
value: 0.1484166666666667
- type: precision_at_3
value: 15.347833333333332
- type: precision_at_5
value: 10.848916666666666
- type: recall_at_1
value: 24.266916666666663
- type: recall_at_10
value: 48.73458333333333
- type: recall_at_100
value: 71.56341666666667
- type: recall_at_1000
value: 88.63091666666668
- type: recall_at_3
value: 36.31208333333333
- type: recall_at_5
value: 41.55633333333333
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.497
- type: map_at_10
value: 30.249
- type: map_at_100
value: 30.947000000000003
- type: map_at_1000
value: 31.049
- type: map_at_3
value: 28.188000000000002
- type: map_at_5
value: 29.332
- type: mrr_at_1
value: 26.687
- type: mrr_at_10
value: 33.182
- type: mrr_at_100
value: 33.794999999999995
- type: mrr_at_1000
value: 33.873
- type: mrr_at_3
value: 31.263
- type: mrr_at_5
value: 32.428000000000004
- type: ndcg_at_1
value: 26.687
- type: ndcg_at_10
value: 34.252
- type: ndcg_at_100
value: 38.083
- type: ndcg_at_1000
value: 40.682
- type: ndcg_at_3
value: 30.464999999999996
- type: ndcg_at_5
value: 32.282
- type: precision_at_1
value: 26.687
- type: precision_at_10
value: 5.2909999999999995
- type: precision_at_100
value: 0.788
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 13.037
- type: precision_at_5
value: 9.049
- type: recall_at_1
value: 23.497
- type: recall_at_10
value: 43.813
- type: recall_at_100
value: 61.88399999999999
- type: recall_at_1000
value: 80.926
- type: recall_at_3
value: 33.332
- type: recall_at_5
value: 37.862
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.073
- type: map_at_10
value: 22.705000000000002
- type: map_at_100
value: 23.703
- type: map_at_1000
value: 23.833
- type: map_at_3
value: 20.593
- type: map_at_5
value: 21.7
- type: mrr_at_1
value: 19.683
- type: mrr_at_10
value: 26.39
- type: mrr_at_100
value: 27.264
- type: mrr_at_1000
value: 27.349
- type: mrr_at_3
value: 24.409
- type: mrr_at_5
value: 25.474000000000004
- type: ndcg_at_1
value: 19.683
- type: ndcg_at_10
value: 27.014
- type: ndcg_at_100
value: 31.948
- type: ndcg_at_1000
value: 35.125
- type: ndcg_at_3
value: 23.225
- type: ndcg_at_5
value: 24.866
- type: precision_at_1
value: 19.683
- type: precision_at_10
value: 4.948
- type: precision_at_100
value: 0.876
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 10.943
- type: precision_at_5
value: 7.86
- type: recall_at_1
value: 16.073
- type: recall_at_10
value: 36.283
- type: recall_at_100
value: 58.745999999999995
- type: recall_at_1000
value: 81.711
- type: recall_at_3
value: 25.637
- type: recall_at_5
value: 29.919
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.776
- type: map_at_10
value: 33.317
- type: map_at_100
value: 34.437
- type: map_at_1000
value: 34.54
- type: map_at_3
value: 30.706
- type: map_at_5
value: 32.202999999999996
- type: mrr_at_1
value: 30.224
- type: mrr_at_10
value: 37.34
- type: mrr_at_100
value: 38.268
- type: mrr_at_1000
value: 38.335
- type: mrr_at_3
value: 35.075
- type: mrr_at_5
value: 36.348
- type: ndcg_at_1
value: 30.224
- type: ndcg_at_10
value: 38.083
- type: ndcg_at_100
value: 43.413000000000004
- type: ndcg_at_1000
value: 45.856
- type: ndcg_at_3
value: 33.437
- type: ndcg_at_5
value: 35.661
- type: precision_at_1
value: 30.224
- type: precision_at_10
value: 6.1850000000000005
- type: precision_at_100
value: 1.0030000000000001
- type: precision_at_1000
value: 0.132
- type: precision_at_3
value: 14.646
- type: precision_at_5
value: 10.428999999999998
- type: recall_at_1
value: 25.776
- type: recall_at_10
value: 48.787000000000006
- type: recall_at_100
value: 72.04899999999999
- type: recall_at_1000
value: 89.339
- type: recall_at_3
value: 36.192
- type: recall_at_5
value: 41.665
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.156
- type: map_at_10
value: 30.886000000000003
- type: map_at_100
value: 32.551
- type: map_at_1000
value: 32.769
- type: map_at_3
value: 28.584
- type: map_at_5
value: 29.959999999999997
- type: mrr_at_1
value: 28.260999999999996
- type: mrr_at_10
value: 35.555
- type: mrr_at_100
value: 36.687
- type: mrr_at_1000
value: 36.742999999999995
- type: mrr_at_3
value: 33.531
- type: mrr_at_5
value: 34.717
- type: ndcg_at_1
value: 28.260999999999996
- type: ndcg_at_10
value: 36.036
- type: ndcg_at_100
value: 42.675000000000004
- type: ndcg_at_1000
value: 45.303
- type: ndcg_at_3
value: 32.449
- type: ndcg_at_5
value: 34.293
- type: precision_at_1
value: 28.260999999999996
- type: precision_at_10
value: 6.837999999999999
- type: precision_at_100
value: 1.4569999999999999
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 15.217
- type: precision_at_5
value: 11.028
- type: recall_at_1
value: 23.156
- type: recall_at_10
value: 45.251999999999995
- type: recall_at_100
value: 75.339
- type: recall_at_1000
value: 91.56
- type: recall_at_3
value: 34.701
- type: recall_at_5
value: 39.922999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.846
- type: map_at_10
value: 26.367
- type: map_at_100
value: 27.439999999999998
- type: map_at_1000
value: 27.552
- type: map_at_3
value: 24.006
- type: map_at_5
value: 25.230999999999998
- type: mrr_at_1
value: 21.257
- type: mrr_at_10
value: 28.071
- type: mrr_at_100
value: 29.037000000000003
- type: mrr_at_1000
value: 29.119
- type: mrr_at_3
value: 25.692999999999998
- type: mrr_at_5
value: 27.006000000000004
- type: ndcg_at_1
value: 21.257
- type: ndcg_at_10
value: 30.586000000000002
- type: ndcg_at_100
value: 35.949
- type: ndcg_at_1000
value: 38.728
- type: ndcg_at_3
value: 25.862000000000002
- type: ndcg_at_5
value: 27.967
- type: precision_at_1
value: 21.257
- type: precision_at_10
value: 4.861
- type: precision_at_100
value: 0.8130000000000001
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 10.906
- type: precision_at_5
value: 7.763000000000001
- type: recall_at_1
value: 19.846
- type: recall_at_10
value: 41.805
- type: recall_at_100
value: 66.89699999999999
- type: recall_at_1000
value: 87.401
- type: recall_at_3
value: 29.261
- type: recall_at_5
value: 34.227000000000004
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.333
- type: map_at_10
value: 17.14
- type: map_at_100
value: 18.878
- type: map_at_1000
value: 19.067
- type: map_at_3
value: 14.123
- type: map_at_5
value: 15.699
- type: mrr_at_1
value: 23.192
- type: mrr_at_10
value: 33.553
- type: mrr_at_100
value: 34.553
- type: mrr_at_1000
value: 34.603
- type: mrr_at_3
value: 29.848000000000003
- type: mrr_at_5
value: 32.18
- type: ndcg_at_1
value: 23.192
- type: ndcg_at_10
value: 24.707
- type: ndcg_at_100
value: 31.701
- type: ndcg_at_1000
value: 35.260999999999996
- type: ndcg_at_3
value: 19.492
- type: ndcg_at_5
value: 21.543
- type: precision_at_1
value: 23.192
- type: precision_at_10
value: 7.824000000000001
- type: precision_at_100
value: 1.52
- type: precision_at_1000
value: 0.218
- type: precision_at_3
value: 14.180000000000001
- type: precision_at_5
value: 11.530999999999999
- type: recall_at_1
value: 10.333
- type: recall_at_10
value: 30.142999999999997
- type: recall_at_100
value: 54.298
- type: recall_at_1000
value: 74.337
- type: recall_at_3
value: 17.602999999999998
- type: recall_at_5
value: 22.938
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.03
- type: map_at_10
value: 17.345
- type: map_at_100
value: 23.462
- type: map_at_1000
value: 24.77
- type: map_at_3
value: 12.714
- type: map_at_5
value: 14.722
- type: mrr_at_1
value: 61
- type: mrr_at_10
value: 69.245
- type: mrr_at_100
value: 69.715
- type: mrr_at_1000
value: 69.719
- type: mrr_at_3
value: 67.583
- type: mrr_at_5
value: 68.521
- type: ndcg_at_1
value: 47.625
- type: ndcg_at_10
value: 35.973
- type: ndcg_at_100
value: 39.875
- type: ndcg_at_1000
value: 46.922000000000004
- type: ndcg_at_3
value: 40.574
- type: ndcg_at_5
value: 38.18
- type: precision_at_1
value: 61
- type: precision_at_10
value: 29.049999999999997
- type: precision_at_100
value: 8.828
- type: precision_at_1000
value: 1.8290000000000002
- type: precision_at_3
value: 45.333
- type: precision_at_5
value: 37.9
- type: recall_at_1
value: 8.03
- type: recall_at_10
value: 22.334
- type: recall_at_100
value: 45.919
- type: recall_at_1000
value: 68.822
- type: recall_at_3
value: 14.038999999999998
- type: recall_at_5
value: 17.118
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 44.714999999999996
- type: f1
value: 39.83929362259356
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 52.242999999999995
- type: map_at_10
value: 64.087
- type: map_at_100
value: 64.549
- type: map_at_1000
value: 64.567
- type: map_at_3
value: 61.667
- type: map_at_5
value: 63.266
- type: mrr_at_1
value: 56.271
- type: mrr_at_10
value: 68.146
- type: mrr_at_100
value: 68.524
- type: mrr_at_1000
value: 68.53200000000001
- type: mrr_at_3
value: 65.869
- type: mrr_at_5
value: 67.37100000000001
- type: ndcg_at_1
value: 56.271
- type: ndcg_at_10
value: 70.109
- type: ndcg_at_100
value: 72.09
- type: ndcg_at_1000
value: 72.479
- type: ndcg_at_3
value: 65.559
- type: ndcg_at_5
value: 68.242
- type: precision_at_1
value: 56.271
- type: precision_at_10
value: 9.286999999999999
- type: precision_at_100
value: 1.039
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 26.308
- type: precision_at_5
value: 17.291
- type: recall_at_1
value: 52.242999999999995
- type: recall_at_10
value: 84.71
- type: recall_at_100
value: 93.309
- type: recall_at_1000
value: 96.013
- type: recall_at_3
value: 72.554
- type: recall_at_5
value: 79.069
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.346
- type: map_at_10
value: 24.552
- type: map_at_100
value: 26.161
- type: map_at_1000
value: 26.345000000000002
- type: map_at_3
value: 21.208
- type: map_at_5
value: 22.959
- type: mrr_at_1
value: 29.166999999999998
- type: mrr_at_10
value: 38.182
- type: mrr_at_100
value: 39.22
- type: mrr_at_1000
value: 39.263
- type: mrr_at_3
value: 35.983
- type: mrr_at_5
value: 37.14
- type: ndcg_at_1
value: 29.166999999999998
- type: ndcg_at_10
value: 31.421
- type: ndcg_at_100
value: 38.129999999999995
- type: ndcg_at_1000
value: 41.569
- type: ndcg_at_3
value: 28.172000000000004
- type: ndcg_at_5
value: 29.029
- type: precision_at_1
value: 29.166999999999998
- type: precision_at_10
value: 8.997
- type: precision_at_100
value: 1.5709999999999997
- type: precision_at_1000
value: 0.22
- type: precision_at_3
value: 19.187
- type: precision_at_5
value: 13.980999999999998
- type: recall_at_1
value: 14.346
- type: recall_at_10
value: 37.963
- type: recall_at_100
value: 63.43299999999999
- type: recall_at_1000
value: 84.057
- type: recall_at_3
value: 26.119999999999997
- type: recall_at_5
value: 30.988
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.059
- type: map_at_10
value: 46.421
- type: map_at_100
value: 47.323
- type: map_at_1000
value: 47.403
- type: map_at_3
value: 43.553999999999995
- type: map_at_5
value: 45.283
- type: mrr_at_1
value: 66.117
- type: mrr_at_10
value: 73.10900000000001
- type: mrr_at_100
value: 73.444
- type: mrr_at_1000
value: 73.46000000000001
- type: mrr_at_3
value: 71.70400000000001
- type: mrr_at_5
value: 72.58099999999999
- type: ndcg_at_1
value: 66.117
- type: ndcg_at_10
value: 55.696999999999996
- type: ndcg_at_100
value: 59.167
- type: ndcg_at_1000
value: 60.809000000000005
- type: ndcg_at_3
value: 51.243
- type: ndcg_at_5
value: 53.627
- type: precision_at_1
value: 66.117
- type: precision_at_10
value: 11.538
- type: precision_at_100
value: 1.429
- type: precision_at_1000
value: 0.165
- type: precision_at_3
value: 31.861
- type: precision_at_5
value: 20.997
- type: recall_at_1
value: 33.059
- type: recall_at_10
value: 57.691
- type: recall_at_100
value: 71.458
- type: recall_at_1000
value: 82.35
- type: recall_at_3
value: 47.792
- type: recall_at_5
value: 52.492000000000004
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 80.544
- type: ap
value: 74.69592367984956
- type: f1
value: 80.51138138449883
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 17.095
- type: map_at_10
value: 28.038999999999998
- type: map_at_100
value: 29.246
- type: map_at_1000
value: 29.311
- type: map_at_3
value: 24.253
- type: map_at_5
value: 26.442
- type: mrr_at_1
value: 17.535999999999998
- type: mrr_at_10
value: 28.53
- type: mrr_at_100
value: 29.697000000000003
- type: mrr_at_1000
value: 29.755
- type: mrr_at_3
value: 24.779999999999998
- type: mrr_at_5
value: 26.942
- type: ndcg_at_1
value: 17.549999999999997
- type: ndcg_at_10
value: 34.514
- type: ndcg_at_100
value: 40.497
- type: ndcg_at_1000
value: 42.17
- type: ndcg_at_3
value: 26.764
- type: ndcg_at_5
value: 30.678
- type: precision_at_1
value: 17.549999999999997
- type: precision_at_10
value: 5.692
- type: precision_at_100
value: 0.8699999999999999
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 11.562
- type: precision_at_5
value: 8.917
- type: recall_at_1
value: 17.095
- type: recall_at_10
value: 54.642
- type: recall_at_100
value: 82.652
- type: recall_at_1000
value: 95.555
- type: recall_at_3
value: 33.504
- type: recall_at_5
value: 42.925000000000004
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 91.75558595531236
- type: f1
value: 91.25979279648296
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 69.90424076607387
- type: f1
value: 52.067408707562244
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.13449899125757
- type: f1
value: 67.62456762910598
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.862138533961
- type: f1
value: 74.66457222091381
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.10761942610792
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.673172170578408
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.058704977250315
- type: mrr
value: 33.24327760839221
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.163
- type: map_at_10
value: 11.652999999999999
- type: map_at_100
value: 14.849
- type: map_at_1000
value: 16.253999999999998
- type: map_at_3
value: 8.616999999999999
- type: map_at_5
value: 10.100000000000001
- type: mrr_at_1
value: 44.272
- type: mrr_at_10
value: 52.25
- type: mrr_at_100
value: 52.761
- type: mrr_at_1000
value: 52.811
- type: mrr_at_3
value: 50.31
- type: mrr_at_5
value: 51.347
- type: ndcg_at_1
value: 42.105
- type: ndcg_at_10
value: 32.044
- type: ndcg_at_100
value: 29.763
- type: ndcg_at_1000
value: 38.585
- type: ndcg_at_3
value: 36.868
- type: ndcg_at_5
value: 35.154999999999994
- type: precision_at_1
value: 43.653
- type: precision_at_10
value: 23.622
- type: precision_at_100
value: 7.7490000000000006
- type: precision_at_1000
value: 2.054
- type: precision_at_3
value: 34.262
- type: precision_at_5
value: 30.154999999999998
- type: recall_at_1
value: 5.163
- type: recall_at_10
value: 15.478
- type: recall_at_100
value: 30.424
- type: recall_at_1000
value: 62.67
- type: recall_at_3
value: 9.615
- type: recall_at_5
value: 12.369
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.618000000000002
- type: map_at_10
value: 35.465
- type: map_at_100
value: 36.712
- type: map_at_1000
value: 36.757
- type: map_at_3
value: 31.189
- type: map_at_5
value: 33.537
- type: mrr_at_1
value: 24.305
- type: mrr_at_10
value: 37.653
- type: mrr_at_100
value: 38.662
- type: mrr_at_1000
value: 38.694
- type: mrr_at_3
value: 33.889
- type: mrr_at_5
value: 35.979
- type: ndcg_at_1
value: 24.305
- type: ndcg_at_10
value: 43.028
- type: ndcg_at_100
value: 48.653999999999996
- type: ndcg_at_1000
value: 49.733
- type: ndcg_at_3
value: 34.768
- type: ndcg_at_5
value: 38.753
- type: precision_at_1
value: 24.305
- type: precision_at_10
value: 7.59
- type: precision_at_100
value: 1.076
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 16.271
- type: precision_at_5
value: 12.068
- type: recall_at_1
value: 21.618000000000002
- type: recall_at_10
value: 63.977
- type: recall_at_100
value: 89.03999999999999
- type: recall_at_1000
value: 97.10600000000001
- type: recall_at_3
value: 42.422
- type: recall_at_5
value: 51.629000000000005
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 69.405
- type: map_at_10
value: 83.05
- type: map_at_100
value: 83.684
- type: map_at_1000
value: 83.70400000000001
- type: map_at_3
value: 80.08800000000001
- type: map_at_5
value: 81.937
- type: mrr_at_1
value: 79.85
- type: mrr_at_10
value: 86.369
- type: mrr_at_100
value: 86.48599999999999
- type: mrr_at_1000
value: 86.48700000000001
- type: mrr_at_3
value: 85.315
- type: mrr_at_5
value: 86.044
- type: ndcg_at_1
value: 79.86999999999999
- type: ndcg_at_10
value: 87.04499999999999
- type: ndcg_at_100
value: 88.373
- type: ndcg_at_1000
value: 88.531
- type: ndcg_at_3
value: 84.04
- type: ndcg_at_5
value: 85.684
- type: precision_at_1
value: 79.86999999999999
- type: precision_at_10
value: 13.183
- type: precision_at_100
value: 1.51
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.67
- type: precision_at_5
value: 24.12
- type: recall_at_1
value: 69.405
- type: recall_at_10
value: 94.634
- type: recall_at_100
value: 99.214
- type: recall_at_1000
value: 99.958
- type: recall_at_3
value: 85.992
- type: recall_at_5
value: 90.656
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 50.191676323145465
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 56.4874020363744
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.228
- type: map_at_10
value: 11.245
- type: map_at_100
value: 13.353000000000002
- type: map_at_1000
value: 13.665
- type: map_at_3
value: 7.779999999999999
- type: map_at_5
value: 9.405
- type: mrr_at_1
value: 20.9
- type: mrr_at_10
value: 31.657999999999998
- type: mrr_at_100
value: 32.769999999999996
- type: mrr_at_1000
value: 32.833
- type: mrr_at_3
value: 28.333000000000002
- type: mrr_at_5
value: 30.043
- type: ndcg_at_1
value: 20.9
- type: ndcg_at_10
value: 19.073
- type: ndcg_at_100
value: 27.055
- type: ndcg_at_1000
value: 32.641
- type: ndcg_at_3
value: 17.483999999999998
- type: ndcg_at_5
value: 15.42
- type: precision_at_1
value: 20.9
- type: precision_at_10
value: 10.17
- type: precision_at_100
value: 2.162
- type: precision_at_1000
value: 0.35100000000000003
- type: precision_at_3
value: 16.467000000000002
- type: precision_at_5
value: 13.68
- type: recall_at_1
value: 4.228
- type: recall_at_10
value: 20.573
- type: recall_at_100
value: 43.887
- type: recall_at_1000
value: 71.22
- type: recall_at_3
value: 10.023
- type: recall_at_5
value: 13.873
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.77965135067481
- type: cos_sim_spearman
value: 75.85121335808076
- type: euclidean_pearson
value: 80.09115175262697
- type: euclidean_spearman
value: 75.72249155647123
- type: manhattan_pearson
value: 79.89723577351782
- type: manhattan_spearman
value: 75.49855259442387
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 80.46084116030949
- type: cos_sim_spearman
value: 72.57579204392951
- type: euclidean_pearson
value: 76.39020830763684
- type: euclidean_spearman
value: 72.3718627025895
- type: manhattan_pearson
value: 76.6148833027359
- type: manhattan_spearman
value: 72.57570008442319
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 80.43678068337017
- type: cos_sim_spearman
value: 82.38941154076062
- type: euclidean_pearson
value: 81.59260573633661
- type: euclidean_spearman
value: 82.31144262574114
- type: manhattan_pearson
value: 81.43266909137056
- type: manhattan_spearman
value: 82.14704293004861
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 80.73713431763163
- type: cos_sim_spearman
value: 77.97860512809388
- type: euclidean_pearson
value: 80.35755041527027
- type: euclidean_spearman
value: 78.021703511412
- type: manhattan_pearson
value: 80.24440317109162
- type: manhattan_spearman
value: 77.93165415697575
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 85.15111852351204
- type: cos_sim_spearman
value: 86.54032447238258
- type: euclidean_pearson
value: 86.14157021537433
- type: euclidean_spearman
value: 86.67537291929713
- type: manhattan_pearson
value: 86.081041854808
- type: manhattan_spearman
value: 86.61561701560558
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 81.34532445104026
- type: cos_sim_spearman
value: 83.31325001474116
- type: euclidean_pearson
value: 82.81892375201032
- type: euclidean_spearman
value: 83.4521695148055
- type: manhattan_pearson
value: 82.72503790526163
- type: manhattan_spearman
value: 83.37833652941349
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.25463453839801
- type: cos_sim_spearman
value: 88.27655263515948
- type: euclidean_pearson
value: 88.0248334411439
- type: euclidean_spearman
value: 88.18141448876868
- type: manhattan_pearson
value: 87.8080451127279
- type: manhattan_spearman
value: 88.01028114423058
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 63.57551045355218
- type: cos_sim_spearman
value: 66.67614095126629
- type: euclidean_pearson
value: 66.0787243112528
- type: euclidean_spearman
value: 66.83660560636939
- type: manhattan_pearson
value: 66.74684019662031
- type: manhattan_spearman
value: 67.11761598074368
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 83.70881496766829
- type: cos_sim_spearman
value: 84.37803542941634
- type: euclidean_pearson
value: 84.84501245857096
- type: euclidean_spearman
value: 84.47088079741476
- type: manhattan_pearson
value: 84.77244090794765
- type: manhattan_spearman
value: 84.43307343706205
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 81.53946254759089
- type: mrr
value: 94.68259953554072
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 51.817
- type: map_at_10
value: 62.339999999999996
- type: map_at_100
value: 62.88
- type: map_at_1000
value: 62.909000000000006
- type: map_at_3
value: 59.004
- type: map_at_5
value: 60.906000000000006
- type: mrr_at_1
value: 54.333
- type: mrr_at_10
value: 63.649
- type: mrr_at_100
value: 64.01
- type: mrr_at_1000
value: 64.039
- type: mrr_at_3
value: 61.056
- type: mrr_at_5
value: 62.639
- type: ndcg_at_1
value: 54.333
- type: ndcg_at_10
value: 67.509
- type: ndcg_at_100
value: 69.69999999999999
- type: ndcg_at_1000
value: 70.613
- type: ndcg_at_3
value: 61.729
- type: ndcg_at_5
value: 64.696
- type: precision_at_1
value: 54.333
- type: precision_at_10
value: 9.2
- type: precision_at_100
value: 1.043
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 24
- type: precision_at_5
value: 16.2
- type: recall_at_1
value: 51.817
- type: recall_at_10
value: 82.056
- type: recall_at_100
value: 91.667
- type: recall_at_1000
value: 99
- type: recall_at_3
value: 66.717
- type: recall_at_5
value: 74.17200000000001
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.82475247524752
- type: cos_sim_ap
value: 95.4781199603258
- type: cos_sim_f1
value: 91.16186693147964
- type: cos_sim_precision
value: 90.53254437869822
- type: cos_sim_recall
value: 91.8
- type: dot_accuracy
value: 99.75049504950495
- type: dot_ap
value: 93.05183539809457
- type: dot_f1
value: 87.31117824773412
- type: dot_precision
value: 87.93103448275862
- type: dot_recall
value: 86.7
- type: euclidean_accuracy
value: 99.82475247524752
- type: euclidean_ap
value: 95.38547978154382
- type: euclidean_f1
value: 91.16325511732403
- type: euclidean_precision
value: 91.02691924227318
- type: euclidean_recall
value: 91.3
- type: manhattan_accuracy
value: 99.82574257425742
- type: manhattan_ap
value: 95.47237521890308
- type: manhattan_f1
value: 91.27849355797821
- type: manhattan_precision
value: 90.47151277013754
- type: manhattan_recall
value: 92.10000000000001
- type: max_accuracy
value: 99.82574257425742
- type: max_ap
value: 95.4781199603258
- type: max_f1
value: 91.27849355797821
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 57.542169376331245
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.74399302634387
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.65076347632749
- type: mrr
value: 50.418099057804945
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.73997756592847
- type: cos_sim_spearman
value: 29.465208011593308
- type: dot_pearson
value: 24.83735342474541
- type: dot_spearman
value: 26.005180528584855
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.208
- type: map_at_10
value: 1.434
- type: map_at_100
value: 7.829
- type: map_at_1000
value: 19.807
- type: map_at_3
value: 0.549
- type: map_at_5
value: 0.8330000000000001
- type: mrr_at_1
value: 78
- type: mrr_at_10
value: 85.35199999999999
- type: mrr_at_100
value: 85.673
- type: mrr_at_1000
value: 85.673
- type: mrr_at_3
value: 84.667
- type: mrr_at_5
value: 85.06700000000001
- type: ndcg_at_1
value: 72
- type: ndcg_at_10
value: 59.214999999999996
- type: ndcg_at_100
value: 44.681
- type: ndcg_at_1000
value: 43.035000000000004
- type: ndcg_at_3
value: 66.53099999999999
- type: ndcg_at_5
value: 63.23
- type: precision_at_1
value: 78
- type: precision_at_10
value: 62.4
- type: precision_at_100
value: 45.76
- type: precision_at_1000
value: 19.05
- type: precision_at_3
value: 71.333
- type: precision_at_5
value: 67.2
- type: recall_at_1
value: 0.208
- type: recall_at_10
value: 1.6580000000000001
- type: recall_at_100
value: 11.324
- type: recall_at_1000
value: 41.537
- type: recall_at_3
value: 0.579
- type: recall_at_5
value: 0.8959999999999999
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.442
- type: map_at_10
value: 8.863
- type: map_at_100
value: 14.606
- type: map_at_1000
value: 16.258
- type: map_at_3
value: 4.396
- type: map_at_5
value: 6.199000000000001
- type: mrr_at_1
value: 30.612000000000002
- type: mrr_at_10
value: 43.492
- type: mrr_at_100
value: 44.557
- type: mrr_at_1000
value: 44.557
- type: mrr_at_3
value: 40.816
- type: mrr_at_5
value: 42.143
- type: ndcg_at_1
value: 25.509999999999998
- type: ndcg_at_10
value: 22.076
- type: ndcg_at_100
value: 34.098
- type: ndcg_at_1000
value: 46.265
- type: ndcg_at_3
value: 24.19
- type: ndcg_at_5
value: 23.474
- type: precision_at_1
value: 30.612000000000002
- type: precision_at_10
value: 19.796
- type: precision_at_100
value: 7.286
- type: precision_at_1000
value: 1.5310000000000001
- type: precision_at_3
value: 25.85
- type: precision_at_5
value: 24.490000000000002
- type: recall_at_1
value: 2.442
- type: recall_at_10
value: 15.012
- type: recall_at_100
value: 45.865
- type: recall_at_1000
value: 82.958
- type: recall_at_3
value: 5.731
- type: recall_at_5
value: 9.301
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.974
- type: ap
value: 14.534996211286682
- type: f1
value: 54.785946183399005
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 58.56819468024901
- type: f1
value: 58.92391487111204
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 43.273202335218194
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.37742146986946
- type: cos_sim_ap
value: 68.1684129575579
- type: cos_sim_f1
value: 64.93475108748189
- type: cos_sim_precision
value: 59.89745876058849
- type: cos_sim_recall
value: 70.89709762532982
- type: dot_accuracy
value: 80.49710913750968
- type: dot_ap
value: 54.699790073944186
- type: dot_f1
value: 54.45130013221684
- type: dot_precision
value: 46.74612183125236
- type: dot_recall
value: 65.19788918205805
- type: euclidean_accuracy
value: 84.5085533766466
- type: euclidean_ap
value: 68.38835695236224
- type: euclidean_f1
value: 65.3391121002694
- type: euclidean_precision
value: 58.75289656625237
- type: euclidean_recall
value: 73.58839050131925
- type: manhattan_accuracy
value: 84.40126363473803
- type: manhattan_ap
value: 68.09539181555348
- type: manhattan_f1
value: 64.99028182701653
- type: manhattan_precision
value: 60.22062134173795
- type: manhattan_recall
value: 70.58047493403694
- type: max_accuracy
value: 84.5085533766466
- type: max_ap
value: 68.38835695236224
- type: max_f1
value: 65.3391121002694
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.34167733923235
- type: cos_sim_ap
value: 84.84136381147736
- type: cos_sim_f1
value: 77.01434980904001
- type: cos_sim_precision
value: 74.27937915742794
- type: cos_sim_recall
value: 79.95842315983985
- type: dot_accuracy
value: 85.06422944075756
- type: dot_ap
value: 76.49446747522325
- type: dot_f1
value: 71.11606520830432
- type: dot_precision
value: 64.93638676844785
- type: dot_recall
value: 78.59562673236834
- type: euclidean_accuracy
value: 88.45810532852097
- type: euclidean_ap
value: 84.91526721863501
- type: euclidean_f1
value: 77.04399001750662
- type: euclidean_precision
value: 74.62298867162133
- type: euclidean_recall
value: 79.62734832152756
- type: manhattan_accuracy
value: 88.46004579500912
- type: manhattan_ap
value: 84.81590026238194
- type: manhattan_f1
value: 76.97804626491822
- type: manhattan_precision
value: 73.79237288135593
- type: manhattan_recall
value: 80.45118570988605
- type: max_accuracy
value: 88.46004579500912
- type: max_ap
value: 84.91526721863501
- type: max_f1
value: 77.04399001750662
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
{gte-tiny}
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 thenlper/gte-small, with comparable (slightly worse) performance at around half the size.
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})
)