metadata
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: clip-ViT-B-32
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 57.999999999999986
- type: ap
value: 23.966099106216358
- type: f1
value: 52.8203944454417
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 62.366
- type: ap
value: 57.98090324593318
- type: f1
value: 61.62762218315074
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 28.584
- type: f1
value: 28.463306116150783
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.259
- type: map_at_10
value: 11.542
- type: map_at_100
value: 12.859000000000002
- type: map_at_1000
value: 12.966
- type: map_at_3
value: 9.128
- type: map_at_5
value: 10.262
- type: mrr_at_1
value: 6.259
- type: mrr_at_10
value: 11.536
- type: mrr_at_100
value: 12.859000000000002
- type: mrr_at_1000
value: 12.967
- type: mrr_at_3
value: 9.128
- type: mrr_at_5
value: 10.262
- type: ndcg_at_1
value: 6.259
- type: ndcg_at_10
value: 15.35
- type: ndcg_at_100
value: 22.107
- type: ndcg_at_1000
value: 25.355
- type: ndcg_at_3
value: 10.172
- type: ndcg_at_5
value: 12.22
- type: precision_at_1
value: 6.259
- type: precision_at_10
value: 2.795
- type: precision_at_100
value: 0.603
- type: precision_at_1000
value: 0.087
- type: precision_at_3
value: 4.41
- type: precision_at_5
value: 3.642
- type: recall_at_1
value: 6.259
- type: recall_at_10
value: 27.951999999999998
- type: recall_at_100
value: 60.313
- type: recall_at_1000
value: 86.771
- type: recall_at_3
value: 13.229
- type: recall_at_5
value: 18.208
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 30.95753257205936
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 26.586511396557583
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 51.090393666506415
- type: mrr
value: 65.19412566503979
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 69.9163188743249
- type: cos_sim_spearman
value: 64.1345938803495
- type: euclidean_pearson
value: 67.36703723549599
- type: euclidean_spearman
value: 63.067702100617005
- type: manhattan_pearson
value: 71.6901307580259
- type: manhattan_spearman
value: 67.04128661733944
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 73.22402597402598
- type: f1
value: 73.12739303105114
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 28.97385566120484
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 27.08579813861177
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.106999999999999
- type: map_at_10
value: 11.797
- type: map_at_100
value: 12.6
- type: map_at_1000
value: 12.711
- type: map_at_3
value: 10.369
- type: map_at_5
value: 10.881
- type: mrr_at_1
value: 9.299
- type: mrr_at_10
value: 15.076
- type: mrr_at_100
value: 15.842
- type: mrr_at_1000
value: 15.928
- type: mrr_at_3
value: 13.4
- type: mrr_at_5
value: 14.044
- type: ndcg_at_1
value: 9.299
- type: ndcg_at_10
value: 15.21
- type: ndcg_at_100
value: 19.374
- type: ndcg_at_1000
value: 22.527
- type: ndcg_at_3
value: 12.383
- type: ndcg_at_5
value: 13.096
- type: precision_at_1
value: 9.299
- type: precision_at_10
value: 3.1620000000000004
- type: precision_at_100
value: 0.662
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 6.3420000000000005
- type: precision_at_5
value: 4.492
- type: recall_at_1
value: 7.106999999999999
- type: recall_at_10
value: 22.544
- type: recall_at_100
value: 41.002
- type: recall_at_1000
value: 63.67699999999999
- type: recall_at_3
value: 14.316999999999998
- type: recall_at_5
value: 16.367
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.632000000000001
- type: map_at_10
value: 9.067
- type: map_at_100
value: 9.487
- type: map_at_1000
value: 9.563
- type: map_at_3
value: 8.344999999999999
- type: map_at_5
value: 8.742999999999999
- type: mrr_at_1
value: 8.599
- type: mrr_at_10
value: 11.332
- type: mrr_at_100
value: 11.77
- type: mrr_at_1000
value: 11.843
- type: mrr_at_3
value: 10.478
- type: mrr_at_5
value: 10.959000000000001
- type: ndcg_at_1
value: 8.599
- type: ndcg_at_10
value: 10.843
- type: ndcg_at_100
value: 13.023000000000001
- type: ndcg_at_1000
value: 15.409
- type: ndcg_at_3
value: 9.673
- type: ndcg_at_5
value: 10.188
- type: precision_at_1
value: 8.599
- type: precision_at_10
value: 2.038
- type: precision_at_100
value: 0.383
- type: precision_at_1000
value: 0.074
- type: precision_at_3
value: 4.756
- type: precision_at_5
value: 3.3890000000000002
- type: recall_at_1
value: 6.632000000000001
- type: recall_at_10
value: 13.952
- type: recall_at_100
value: 23.966
- type: recall_at_1000
value: 41.411
- type: recall_at_3
value: 10.224
- type: recall_at_5
value: 11.799
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.153
- type: map_at_10
value: 15.751000000000001
- type: map_at_100
value: 16.464000000000002
- type: map_at_1000
value: 16.561
- type: map_at_3
value: 14.552000000000001
- type: map_at_5
value: 15.136
- type: mrr_at_1
value: 13.041
- type: mrr_at_10
value: 17.777
- type: mrr_at_100
value: 18.427
- type: mrr_at_1000
value: 18.504
- type: mrr_at_3
value: 16.479
- type: mrr_at_5
value: 17.175
- type: ndcg_at_1
value: 13.041
- type: ndcg_at_10
value: 18.581
- type: ndcg_at_100
value: 22.174
- type: ndcg_at_1000
value: 24.795
- type: ndcg_at_3
value: 16.185
- type: ndcg_at_5
value: 17.183
- type: precision_at_1
value: 13.041
- type: precision_at_10
value: 3.2230000000000003
- type: precision_at_100
value: 0.557
- type: precision_at_1000
value: 0.086
- type: precision_at_3
value: 7.544
- type: precision_at_5
value: 5.279
- type: recall_at_1
value: 11.153
- type: recall_at_10
value: 25.052999999999997
- type: recall_at_100
value: 41.521
- type: recall_at_1000
value: 61.138000000000005
- type: recall_at_3
value: 18.673000000000002
- type: recall_at_5
value: 20.964
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.303
- type: map_at_10
value: 7.649
- type: map_at_100
value: 7.983
- type: map_at_1000
value: 8.067
- type: map_at_3
value: 6.938
- type: map_at_5
value: 7.259
- type: mrr_at_1
value: 5.763
- type: mrr_at_10
value: 8.277
- type: mrr_at_100
value: 8.665000000000001
- type: mrr_at_1000
value: 8.747
- type: mrr_at_3
value: 7.457999999999999
- type: mrr_at_5
value: 7.808
- type: ndcg_at_1
value: 5.763
- type: ndcg_at_10
value: 9.1
- type: ndcg_at_100
value: 11.253
- type: ndcg_at_1000
value: 13.847999999999999
- type: ndcg_at_3
value: 7.521999999999999
- type: ndcg_at_5
value: 8.094
- type: precision_at_1
value: 5.763
- type: precision_at_10
value: 1.514
- type: precision_at_100
value: 0.28700000000000003
- type: precision_at_1000
value: 0.054
- type: precision_at_3
value: 3.277
- type: precision_at_5
value: 2.282
- type: recall_at_1
value: 5.303
- type: recall_at_10
value: 13.126
- type: recall_at_100
value: 23.855
- type: recall_at_1000
value: 44.417
- type: recall_at_3
value: 8.556
- type: recall_at_5
value: 10.006
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.153
- type: map_at_10
value: 3.447
- type: map_at_100
value: 3.73
- type: map_at_1000
value: 3.8219999999999996
- type: map_at_3
value: 3.0269999999999997
- type: map_at_5
value: 3.283
- type: mrr_at_1
value: 2.612
- type: mrr_at_10
value: 4.289
- type: mrr_at_100
value: 4.6080000000000005
- type: mrr_at_1000
value: 4.713
- type: mrr_at_3
value: 3.669
- type: mrr_at_5
value: 4.005
- type: ndcg_at_1
value: 2.612
- type: ndcg_at_10
value: 4.422000000000001
- type: ndcg_at_100
value: 6.15
- type: ndcg_at_1000
value: 9.25
- type: ndcg_at_3
value: 3.486
- type: ndcg_at_5
value: 3.95
- type: precision_at_1
value: 2.612
- type: precision_at_10
value: 0.8829999999999999
- type: precision_at_100
value: 0.211
- type: precision_at_1000
value: 0.059000000000000004
- type: precision_at_3
value: 1.6580000000000001
- type: precision_at_5
value: 1.294
- type: recall_at_1
value: 2.153
- type: recall_at_10
value: 6.607
- type: recall_at_100
value: 14.707
- type: recall_at_1000
value: 37.99
- type: recall_at_3
value: 4.122
- type: recall_at_5
value: 5.241
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.976999999999999
- type: map_at_10
value: 11.745
- type: map_at_100
value: 12.427000000000001
- type: map_at_1000
value: 12.528
- type: map_at_3
value: 10.478
- type: map_at_5
value: 11.224
- type: mrr_at_1
value: 9.432
- type: mrr_at_10
value: 14.021
- type: mrr_at_100
value: 14.734
- type: mrr_at_1000
value: 14.813
- type: mrr_at_3
value: 12.576
- type: mrr_at_5
value: 13.414000000000001
- type: ndcg_at_1
value: 9.432
- type: ndcg_at_10
value: 14.341000000000001
- type: ndcg_at_100
value: 18.168
- type: ndcg_at_1000
value: 21.129
- type: ndcg_at_3
value: 11.909
- type: ndcg_at_5
value: 13.139999999999999
- type: precision_at_1
value: 9.432
- type: precision_at_10
value: 2.6759999999999997
- type: precision_at_100
value: 0.563
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 5.679
- type: precision_at_5
value: 4.216
- type: recall_at_1
value: 7.976999999999999
- type: recall_at_10
value: 19.983999999999998
- type: recall_at_100
value: 37.181
- type: recall_at_1000
value: 58.714999999999996
- type: recall_at_3
value: 13.375
- type: recall_at_5
value: 16.54
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.682
- type: map_at_10
value: 7.817
- type: map_at_100
value: 8.3
- type: map_at_1000
value: 8.378
- type: map_at_3
value: 7.13
- type: map_at_5
value: 7.467
- type: mrr_at_1
value: 6.848999999999999
- type: mrr_at_10
value: 9.687999999999999
- type: mrr_at_100
value: 10.208
- type: mrr_at_1000
value: 10.281
- type: mrr_at_3
value: 8.770999999999999
- type: mrr_at_5
value: 9.256
- type: ndcg_at_1
value: 6.848999999999999
- type: ndcg_at_10
value: 9.519
- type: ndcg_at_100
value: 12.303
- type: ndcg_at_1000
value: 15.004999999999999
- type: ndcg_at_3
value: 8.077
- type: ndcg_at_5
value: 8.656
- type: precision_at_1
value: 6.848999999999999
- type: precision_at_10
value: 1.735
- type: precision_at_100
value: 0.363
- type: precision_at_1000
value: 0.073
- type: precision_at_3
value: 3.7289999999999996
- type: precision_at_5
value: 2.717
- type: recall_at_1
value: 5.682
- type: recall_at_10
value: 13.001
- type: recall_at_100
value: 25.916
- type: recall_at_1000
value: 46.303
- type: recall_at_3
value: 8.949
- type: recall_at_5
value: 10.413
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.441
- type: map_at_10
value: 7.997500000000002
- type: map_at_100
value: 8.47225
- type: map_at_1000
value: 8.557083333333333
- type: map_at_3
value: 7.17025
- type: map_at_5
value: 7.597833333333333
- type: mrr_at_1
value: 6.6329166666666675
- type: mrr_at_10
value: 9.596583333333333
- type: mrr_at_100
value: 10.094416666666667
- type: mrr_at_1000
value: 10.171583333333334
- type: mrr_at_3
value: 8.628416666666666
- type: mrr_at_5
value: 9.143416666666667
- type: ndcg_at_1
value: 6.6329166666666675
- type: ndcg_at_10
value: 9.81258333333333
- type: ndcg_at_100
value: 12.459416666666666
- type: ndcg_at_1000
value: 15.099416666666668
- type: ndcg_at_3
value: 8.177499999999998
- type: ndcg_at_5
value: 8.8765
- type: precision_at_1
value: 6.6329166666666675
- type: precision_at_10
value: 1.8355833333333336
- type: precision_at_100
value: 0.38033333333333336
- type: precision_at_1000
value: 0.07358333333333333
- type: precision_at_3
value: 3.912583333333333
- type: precision_at_5
value: 2.8570833333333336
- type: recall_at_1
value: 5.441
- type: recall_at_10
value: 13.79075
- type: recall_at_100
value: 26.12841666666667
- type: recall_at_1000
value: 46.1115
- type: recall_at_3
value: 9.212416666666666
- type: recall_at_5
value: 11.006499999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.973000000000001
- type: map_at_10
value: 6.583
- type: map_at_100
value: 7.013999999999999
- type: map_at_1000
value: 7.084
- type: map_at_3
value: 5.987
- type: map_at_5
value: 6.283999999999999
- type: mrr_at_1
value: 6.135
- type: mrr_at_10
value: 7.911
- type: mrr_at_100
value: 8.381
- type: mrr_at_1000
value: 8.451
- type: mrr_at_3
value: 7.234
- type: mrr_at_5
value: 7.595000000000001
- type: ndcg_at_1
value: 6.135
- type: ndcg_at_10
value: 7.8420000000000005
- type: ndcg_at_100
value: 10.335999999999999
- type: ndcg_at_1000
value: 12.742999999999999
- type: ndcg_at_3
value: 6.622
- type: ndcg_at_5
value: 7.156
- type: precision_at_1
value: 6.135
- type: precision_at_10
value: 1.3339999999999999
- type: precision_at_100
value: 0.293
- type: precision_at_1000
value: 0.053
- type: precision_at_3
value: 2.965
- type: precision_at_5
value: 2.086
- type: recall_at_1
value: 4.973000000000001
- type: recall_at_10
value: 10.497
- type: recall_at_100
value: 22.389
- type: recall_at_1000
value: 41.751
- type: recall_at_3
value: 7.248
- type: recall_at_5
value: 8.526
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.541
- type: map_at_10
value: 4.168
- type: map_at_100
value: 4.492
- type: map_at_1000
value: 4.553
- type: map_at_3
value: 3.62
- type: map_at_5
value: 3.927
- type: mrr_at_1
value: 3.131
- type: mrr_at_10
value: 5.037
- type: mrr_at_100
value: 5.428
- type: mrr_at_1000
value: 5.487
- type: mrr_at_3
value: 4.422000000000001
- type: mrr_at_5
value: 4.752
- type: ndcg_at_1
value: 3.131
- type: ndcg_at_10
value: 5.315
- type: ndcg_at_100
value: 7.207
- type: ndcg_at_1000
value: 9.271
- type: ndcg_at_3
value: 4.244
- type: ndcg_at_5
value: 4.742
- type: precision_at_1
value: 3.131
- type: precision_at_10
value: 1.0699999999999998
- type: precision_at_100
value: 0.247
- type: precision_at_1000
value: 0.053
- type: precision_at_3
value: 2.1340000000000003
- type: precision_at_5
value: 1.624
- type: recall_at_1
value: 2.541
- type: recall_at_10
value: 7.8740000000000006
- type: recall_at_100
value: 16.896
- type: recall_at_1000
value: 32.423
- type: recall_at_3
value: 4.925
- type: recall_at_5
value: 6.181
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.58
- type: map_at_10
value: 7.758
- type: map_at_100
value: 8.168000000000001
- type: map_at_1000
value: 8.239
- type: map_at_3
value: 6.895999999999999
- type: map_at_5
value: 7.412000000000001
- type: mrr_at_1
value: 6.81
- type: mrr_at_10
value: 9.295
- type: mrr_at_100
value: 9.763
- type: mrr_at_1000
value: 9.835
- type: mrr_at_3
value: 8.427
- type: mrr_at_5
value: 8.958
- type: ndcg_at_1
value: 6.81
- type: ndcg_at_10
value: 9.436
- type: ndcg_at_100
value: 11.955
- type: ndcg_at_1000
value: 14.387
- type: ndcg_at_3
value: 7.7410000000000005
- type: ndcg_at_5
value: 8.622
- type: precision_at_1
value: 6.81
- type: precision_at_10
value: 1.6230000000000002
- type: precision_at_100
value: 0.335
- type: precision_at_1000
value: 0.062
- type: precision_at_3
value: 3.576
- type: precision_at_5
value: 2.6870000000000003
- type: recall_at_1
value: 5.58
- type: recall_at_10
value: 13.232
- type: recall_at_100
value: 25.233
- type: recall_at_1000
value: 43.864999999999995
- type: recall_at_3
value: 8.549
- type: recall_at_5
value: 10.799
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.8739999999999997
- type: map_at_10
value: 6.491
- type: map_at_100
value: 7.065
- type: map_at_1000
value: 7.185
- type: map_at_3
value: 5.568
- type: map_at_5
value: 6.1080000000000005
- type: mrr_at_1
value: 5.335999999999999
- type: mrr_at_10
value: 8.288
- type: mrr_at_100
value: 8.886
- type: mrr_at_1000
value: 8.976
- type: mrr_at_3
value: 7.115
- type: mrr_at_5
value: 7.846
- type: ndcg_at_1
value: 5.335999999999999
- type: ndcg_at_10
value: 8.463
- type: ndcg_at_100
value: 11.456
- type: ndcg_at_1000
value: 14.662
- type: ndcg_at_3
value: 6.7589999999999995
- type: ndcg_at_5
value: 7.5969999999999995
- type: precision_at_1
value: 5.335999999999999
- type: precision_at_10
value: 1.9369999999999998
- type: precision_at_100
value: 0.498
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 3.689
- type: precision_at_5
value: 2.9250000000000003
- type: recall_at_1
value: 3.8739999999999997
- type: recall_at_10
value: 12.281
- type: recall_at_100
value: 26.368000000000002
- type: recall_at_1000
value: 50.422
- type: recall_at_3
value: 7.353
- type: recall_at_5
value: 9.66
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.317
- type: map_at_10
value: 3.697
- type: map_at_100
value: 3.9370000000000003
- type: map_at_1000
value: 3.994
- type: map_at_3
value: 3.1329999999999996
- type: map_at_5
value: 3.45
- type: mrr_at_1
value: 2.588
- type: mrr_at_10
value: 4.168
- type: mrr_at_100
value: 4.421
- type: mrr_at_1000
value: 4.481
- type: mrr_at_3
value: 3.512
- type: mrr_at_5
value: 3.909
- type: ndcg_at_1
value: 2.588
- type: ndcg_at_10
value: 4.679
- type: ndcg_at_100
value: 6.114
- type: ndcg_at_1000
value: 8.167
- type: ndcg_at_3
value: 3.5290000000000004
- type: ndcg_at_5
value: 4.093999999999999
- type: precision_at_1
value: 2.588
- type: precision_at_10
value: 0.832
- type: precision_at_100
value: 0.165
- type: precision_at_1000
value: 0.037
- type: precision_at_3
value: 1.6019999999999999
- type: precision_at_5
value: 1.294
- type: recall_at_1
value: 2.317
- type: recall_at_10
value: 7.338
- type: recall_at_100
value: 14.507
- type: recall_at_1000
value: 31.226
- type: recall_at_3
value: 4.258
- type: recall_at_5
value: 5.582
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 33.535
- type: f1
value: 29.64261331714107
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 57.03359999999999
- type: ap
value: 54.289515246345985
- type: f1
value: 56.404319444675686
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 86.70770633834928
- type: f1
value: 86.3521440956975
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 62.35750113999089
- type: f1
value: 41.01929492285308
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.34902488231339
- type: f1
value: 59.90320313789715
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.51513113651649
- type: f1
value: 72.02695487206958
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 27.274796122083107
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 26.79725352760558
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 26.13036834909186
- type: mrr
value: 26.44693141383913
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 42.20822777687787
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 50.46829369249206
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 68.56822847816088
- type: cos_sim_spearman
value: 67.89762106712074
- type: euclidean_pearson
value: 72.85990051290051
- type: euclidean_spearman
value: 70.57485701927138
- type: manhattan_pearson
value: 75.55042864114424
- type: manhattan_spearman
value: 71.93915751894929
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 75.78267692127127
- type: cos_sim_spearman
value: 72.29619737860627
- type: euclidean_pearson
value: 70.1450545025718
- type: euclidean_spearman
value: 67.45917489688871
- type: manhattan_pearson
value: 71.38506807589515
- type: manhattan_spearman
value: 67.2756870294321
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 58.319097196559234
- type: cos_sim_spearman
value: 64.92943196450905
- type: euclidean_pearson
value: 66.58719740666398
- type: euclidean_spearman
value: 67.53564380155727
- type: manhattan_pearson
value: 68.40736205376945
- type: manhattan_spearman
value: 68.83617823881784
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 53.31328133696752
- type: cos_sim_spearman
value: 54.95348091071938
- type: euclidean_pearson
value: 62.387046499499476
- type: euclidean_spearman
value: 61.1353898211832
- type: manhattan_pearson
value: 65.6417443455959
- type: manhattan_spearman
value: 63.242670107784384
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 60.528757851414014
- type: cos_sim_spearman
value: 64.23576213334218
- type: euclidean_pearson
value: 72.97957845156205
- type: euclidean_spearman
value: 73.65719038687413
- type: manhattan_pearson
value: 74.78225875672878
- type: manhattan_spearman
value: 75.49116886100272
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 61.987373739107696
- type: cos_sim_spearman
value: 70.192277875975
- type: euclidean_pearson
value: 72.63709361494375
- type: euclidean_spearman
value: 73.11242796462018
- type: manhattan_pearson
value: 73.72926634930128
- type: manhattan_spearman
value: 73.98477033865957
- 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: 70.04069064325459
- type: cos_sim_spearman
value: 74.38400000348688
- type: euclidean_pearson
value: 82.08401389635375
- type: euclidean_spearman
value: 81.95480539585296
- type: manhattan_pearson
value: 84.99052315893229
- type: manhattan_spearman
value: 84.66072647748268
- 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: 48.154362861306986
- type: cos_sim_spearman
value: 48.58749841932341
- type: euclidean_pearson
value: 50.41642902043279
- type: euclidean_spearman
value: 51.371094727414935
- type: manhattan_pearson
value: 53.06081362594791
- type: manhattan_spearman
value: 52.92177971301313
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 59.11445268439452
- type: cos_sim_spearman
value: 61.46376153396639
- type: euclidean_pearson
value: 70.4367704900615
- type: euclidean_spearman
value: 69.71716383694748
- type: manhattan_pearson
value: 72.72973072359753
- type: manhattan_spearman
value: 71.48785771698903
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 69.56970649232905
- type: mrr
value: 89.47439089595952
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.6009900990099
- type: cos_sim_ap
value: 85.25193332879603
- type: cos_sim_f1
value: 78.88563049853373
- type: cos_sim_precision
value: 77.151051625239
- type: cos_sim_recall
value: 80.7
- type: dot_accuracy
value: 99.01287128712872
- type: dot_ap
value: 7.20643686800152
- type: dot_f1
value: 14.143920595533496
- type: dot_precision
value: 9.405940594059405
- type: dot_recall
value: 28.499999999999996
- type: euclidean_accuracy
value: 99.590099009901
- type: euclidean_ap
value: 83.37987878104964
- type: euclidean_f1
value: 78.22990844354018
- type: euclidean_precision
value: 79.60662525879917
- type: euclidean_recall
value: 76.9
- type: manhattan_accuracy
value: 99.609900990099
- type: manhattan_ap
value: 85.6481020725528
- type: manhattan_f1
value: 79.23790913531998
- type: manhattan_precision
value: 77.45940783190068
- type: manhattan_recall
value: 81.10000000000001
- type: max_accuracy
value: 99.609900990099
- type: max_ap
value: 85.6481020725528
- type: max_f1
value: 79.23790913531998
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 51.49824324480644
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 29.27365407025942
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 37.62142967031895
- type: mrr
value: 37.80931690858162
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 27.279280594311935
- type: cos_sim_spearman
value: 28.055012324260563
- type: dot_pearson
value: 19.315154386546453
- type: dot_spearman
value: 19.17304603866006
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 63.2888
- type: ap
value: 11.062527367094436
- type: f1
value: 48.6893658037416
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 49.275608375778155
- type: f1
value: 49.487704374827324
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 37.31132794113957
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 80.008344757704
- type: cos_sim_ap
value: 50.955726976655036
- type: cos_sim_f1
value: 49.800796812749006
- type: cos_sim_precision
value: 42.8898208158597
- type: cos_sim_recall
value: 59.36675461741425
- type: dot_accuracy
value: 77.42743041068128
- type: dot_ap
value: 19.216239898966027
- type: dot_f1
value: 36.95323548056761
- type: dot_precision
value: 22.665550038882575
- type: dot_recall
value: 99.9736147757256
- type: euclidean_accuracy
value: 81.12296596530965
- type: euclidean_ap
value: 55.99371814327642
- type: euclidean_f1
value: 54.55376528396755
- type: euclidean_precision
value: 48.11529933481153
- type: euclidean_recall
value: 62.98153034300792
- type: manhattan_accuracy
value: 81.3673481552125
- type: manhattan_ap
value: 57.126538198748456
- type: manhattan_f1
value: 55.38567651454189
- type: manhattan_precision
value: 49.073130983907106
- type: manhattan_recall
value: 63.562005277044854
- type: max_accuracy
value: 81.3673481552125
- type: max_ap
value: 57.126538198748456
- type: max_f1
value: 55.38567651454189
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 84.02607986960065
- type: cos_sim_ap
value: 74.07757228336027
- type: cos_sim_f1
value: 66.0694778239021
- type: cos_sim_precision
value: 62.67790520934089
- type: cos_sim_recall
value: 69.84909146904835
- type: dot_accuracy
value: 74.79722125198897
- type: dot_ap
value: 25.478024888904727
- type: dot_f1
value: 40.76642277589147
- type: dot_precision
value: 25.705095989546688
- type: dot_recall
value: 98.45241761626117
- type: euclidean_accuracy
value: 85.51053673303062
- type: euclidean_ap
value: 78.24178926488659
- type: euclidean_f1
value: 70.50944224857267
- type: euclidean_precision
value: 67.19447544642857
- type: euclidean_recall
value: 74.16846319679703
- type: manhattan_accuracy
value: 85.72398804672643
- type: manhattan_ap
value: 78.90411073933831
- type: manhattan_f1
value: 70.90586145648314
- type: manhattan_precision
value: 65.8224508640021
- type: manhattan_recall
value: 76.84016014782877
- type: max_accuracy
value: 85.72398804672643
- type: max_ap
value: 78.90411073933831
- type: max_f1
value: 70.90586145648314
clip-ViT-B-32
This is the Image & Text model CLIP, which maps text and images to a shared vector space. For applications of the models, have a look in our documentation SBERT.net - Image Search
Usage
After installing sentence-transformers (pip install sentence-transformers), the usage of this model is easy:
from sentence_transformers import SentenceTransformer, util
from PIL import Image
#Load CLIP model
model = SentenceTransformer('clip-ViT-B-32')
#Encode an image:
img_emb = model.encode(Image.open('two_dogs_in_snow.jpg'))
#Encode text descriptions
text_emb = model.encode(['Two dogs in the snow', 'A cat on a table', 'A picture of London at night'])
#Compute cosine similarities
cos_scores = util.cos_sim(img_emb, text_emb)
print(cos_scores)
See our SBERT.net - Image Search documentation for more examples how the model can be used for image search, zero-shot image classification, image clustering and image deduplication.
Performance
In the following table we find the zero-shot ImageNet validation set accuracy:
| Model | Top 1 Performance |
|---|---|
| clip-ViT-B-32 | 63.3 |
| clip-ViT-B-16 | 68.1 |
| clip-ViT-L-14 | 75.4 |
For a multilingual version of the CLIP model for 50+ languages have a look at: clip-ViT-B-32-multilingual-v1