---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- mteb
model-index:
- name: b1ade_embed_kd
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification
config: default
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.81709145427287
- type: ap
value: 23.581082591688467
- type: f1
value: 62.54637626017967
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 80.300125
- type: ap
value: 74.26836190039964
- type: f1
value: 80.2158066692679
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification
config: default
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 43.084
- type: f1
value: 42.66774553366831
- task:
type: Retrieval
dataset:
type: mteb/arguana
name: MTEB ArguAna
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 29.232000000000003
- type: map_at_10
value: 45.777
- type: map_at_100
value: 46.634
- type: map_at_1000
value: 46.64
- type: map_at_20
value: 46.489000000000004
- type: map_at_3
value: 40.861
- type: map_at_5
value: 43.659
- type: mrr_at_1
value: 30.156
- type: mrr_at_10
value: 46.141
- type: mrr_at_100
value: 46.983999999999995
- type: mrr_at_1000
value: 46.989999999999995
- type: mrr_at_20
value: 46.839
- type: mrr_at_3
value: 41.157
- type: mrr_at_5
value: 44.013000000000005
- type: ndcg_at_1
value: 29.232000000000003
- type: ndcg_at_10
value: 54.832
- type: ndcg_at_100
value: 58.303000000000004
- type: ndcg_at_1000
value: 58.451
- type: ndcg_at_20
value: 57.328
- type: ndcg_at_3
value: 44.685
- type: ndcg_at_5
value: 49.756
- type: precision_at_1
value: 29.232000000000003
- type: precision_at_10
value: 8.371
- type: precision_at_100
value: 0.985
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.6690000000000005
- type: precision_at_3
value: 18.587
- type: precision_at_5
value: 13.627
- type: recall_at_1
value: 29.232000000000003
- type: recall_at_10
value: 83.71300000000001
- type: recall_at_100
value: 98.506
- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 93.38499999999999
- type: recall_at_3
value: 55.761
- type: recall_at_5
value: 68.137
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 45.801946024895756
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 37.639210206045206
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 57.589359041891576
- type: mrr
value: 70.88334872268389
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.63594177060354
- type: cos_sim_spearman
value: 84.75132870687939
- type: euclidean_pearson
value: 85.05646621990854
- type: euclidean_spearman
value: 84.68686940680522
- type: manhattan_pearson
value: 85.08705700579426
- type: manhattan_spearman
value: 84.83446313127413
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.1948051948052
- type: f1
value: 85.13229898343104
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 38.68616898014911
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 34.45376891835619
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-android
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 26.340000000000003
- type: map_at_10
value: 36.513
- type: map_at_100
value: 37.968
- type: map_at_1000
value: 38.107
- type: map_at_20
value: 37.355
- type: map_at_3
value: 33.153
- type: map_at_5
value: 34.899
- type: mrr_at_1
value: 33.763
- type: mrr_at_10
value: 42.778
- type: mrr_at_100
value: 43.667
- type: mrr_at_1000
value: 43.724000000000004
- type: mrr_at_20
value: 43.349
- type: mrr_at_3
value: 40.32
- type: mrr_at_5
value: 41.657
- type: ndcg_at_1
value: 33.763
- type: ndcg_at_10
value: 42.783
- type: ndcg_at_100
value: 48.209999999999994
- type: ndcg_at_1000
value: 50.678999999999995
- type: ndcg_at_20
value: 45.073
- type: ndcg_at_3
value: 37.841
- type: ndcg_at_5
value: 39.818999999999996
- type: precision_at_1
value: 33.763
- type: precision_at_10
value: 8.398
- type: precision_at_100
value: 1.396
- type: precision_at_1000
value: 0.188
- type: precision_at_20
value: 5.0569999999999995
- type: precision_at_3
value: 18.503
- type: precision_at_5
value: 13.219
- type: recall_at_1
value: 26.340000000000003
- type: recall_at_10
value: 54.603
- type: recall_at_100
value: 77.264
- type: recall_at_1000
value: 93.882
- type: recall_at_20
value: 63.101
- type: recall_at_3
value: 39.6
- type: recall_at_5
value: 45.651
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-english
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 24.313000000000002
- type: map_at_10
value: 33.225
- type: map_at_100
value: 34.293
- type: map_at_1000
value: 34.421
- type: map_at_20
value: 33.818
- type: map_at_3
value: 30.545
- type: map_at_5
value: 32.144
- type: mrr_at_1
value: 31.083
- type: mrr_at_10
value: 39.199
- type: mrr_at_100
value: 39.835
- type: mrr_at_1000
value: 39.892
- type: mrr_at_20
value: 39.57
- type: mrr_at_3
value: 36.879
- type: mrr_at_5
value: 38.245000000000005
- type: ndcg_at_1
value: 31.083
- type: ndcg_at_10
value: 38.553
- type: ndcg_at_100
value: 42.685
- type: ndcg_at_1000
value: 45.144
- type: ndcg_at_20
value: 40.116
- type: ndcg_at_3
value: 34.608
- type: ndcg_at_5
value: 36.551
- type: precision_at_1
value: 31.083
- type: precision_at_10
value: 7.28
- type: precision_at_100
value: 1.183
- type: precision_at_1000
value: 0.168
- type: precision_at_20
value: 4.322
- type: precision_at_3
value: 16.858
- type: precision_at_5
value: 12.127
- type: recall_at_1
value: 24.313000000000002
- type: recall_at_10
value: 48.117
- type: recall_at_100
value: 65.768
- type: recall_at_1000
value: 81.935
- type: recall_at_20
value: 53.689
- type: recall_at_3
value: 36.335
- type: recall_at_5
value: 41.803000000000004
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-gaming
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 33.013999999999996
- type: map_at_10
value: 44.567
- type: map_at_100
value: 45.664
- type: map_at_1000
value: 45.732
- type: map_at_20
value: 45.190000000000005
- type: map_at_3
value: 41.393
- type: map_at_5
value: 43.147000000000006
- type: mrr_at_1
value: 37.806
- type: mrr_at_10
value: 47.841
- type: mrr_at_100
value: 48.597
- type: mrr_at_1000
value: 48.638
- type: mrr_at_20
value: 48.262
- type: mrr_at_3
value: 45.361000000000004
- type: mrr_at_5
value: 46.803
- type: ndcg_at_1
value: 37.806
- type: ndcg_at_10
value: 50.412
- type: ndcg_at_100
value: 55.019
- type: ndcg_at_1000
value: 56.52
- type: ndcg_at_20
value: 52.23100000000001
- type: ndcg_at_3
value: 44.944
- type: ndcg_at_5
value: 47.535
- type: precision_at_1
value: 37.806
- type: precision_at_10
value: 8.351
- type: precision_at_100
value: 1.163
- type: precision_at_1000
value: 0.134
- type: precision_at_20
value: 4.727
- type: precision_at_3
value: 20.376
- type: precision_at_5
value: 14.056
- type: recall_at_1
value: 33.013999999999996
- type: recall_at_10
value: 64.35600000000001
- type: recall_at_100
value: 84.748
- type: recall_at_1000
value: 95.525
- type: recall_at_20
value: 71.137
- type: recall_at_3
value: 49.726
- type: recall_at_5
value: 56.054
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-gis
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 18.476
- type: map_at_10
value: 24.715999999999998
- type: map_at_100
value: 25.72
- type: map_at_1000
value: 25.826999999999998
- type: map_at_20
value: 25.276
- type: map_at_3
value: 22.656000000000002
- type: map_at_5
value: 23.737
- type: mrr_at_1
value: 20.113
- type: mrr_at_10
value: 26.423999999999996
- type: mrr_at_100
value: 27.328000000000003
- type: mrr_at_1000
value: 27.418
- type: mrr_at_20
value: 26.936
- type: mrr_at_3
value: 24.275
- type: mrr_at_5
value: 25.501
- type: ndcg_at_1
value: 20.113
- type: ndcg_at_10
value: 28.626
- type: ndcg_at_100
value: 33.649
- type: ndcg_at_1000
value: 36.472
- type: ndcg_at_20
value: 30.581999999999997
- type: ndcg_at_3
value: 24.490000000000002
- type: ndcg_at_5
value: 26.394000000000002
- type: precision_at_1
value: 20.113
- type: precision_at_10
value: 4.52
- type: precision_at_100
value: 0.739
- type: precision_at_1000
value: 0.10200000000000001
- type: precision_at_20
value: 2.706
- type: precision_at_3
value: 10.433
- type: precision_at_5
value: 7.48
- type: recall_at_1
value: 18.476
- type: recall_at_10
value: 39.129000000000005
- type: recall_at_100
value: 62.44
- type: recall_at_1000
value: 83.95700000000001
- type: recall_at_20
value: 46.611999999999995
- type: recall_at_3
value: 27.772000000000002
- type: recall_at_5
value: 32.312000000000005
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-mathematica
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 10.126
- type: map_at_10
value: 15.916
- type: map_at_100
value: 17.049
- type: map_at_1000
value: 17.19
- type: map_at_20
value: 16.569
- type: map_at_3
value: 13.986
- type: map_at_5
value: 15.052999999999999
- type: mrr_at_1
value: 13.059999999999999
- type: mrr_at_10
value: 19.52
- type: mrr_at_100
value: 20.599999999999998
- type: mrr_at_1000
value: 20.693
- type: mrr_at_20
value: 20.177999999999997
- type: mrr_at_3
value: 17.496000000000002
- type: mrr_at_5
value: 18.541
- type: ndcg_at_1
value: 13.059999999999999
- type: ndcg_at_10
value: 19.987
- type: ndcg_at_100
value: 25.602000000000004
- type: ndcg_at_1000
value: 29.171999999999997
- type: ndcg_at_20
value: 22.31
- type: ndcg_at_3
value: 16.286
- type: ndcg_at_5
value: 17.931
- type: precision_at_1
value: 13.059999999999999
- type: precision_at_10
value: 3.9050000000000002
- type: precision_at_100
value: 0.771
- type: precision_at_1000
value: 0.123
- type: precision_at_20
value: 2.606
- type: precision_at_3
value: 8.167
- type: precision_at_5
value: 6.045
- type: recall_at_1
value: 10.126
- type: recall_at_10
value: 29.137
- type: recall_at_100
value: 53.824000000000005
- type: recall_at_1000
value: 79.373
- type: recall_at_20
value: 37.475
- type: recall_at_3
value: 18.791
- type: recall_at_5
value: 22.993
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-physics
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 25.281
- type: map_at_10
value: 34.875
- type: map_at_100
value: 36.268
- type: map_at_1000
value: 36.385
- type: map_at_20
value: 35.711999999999996
- type: map_at_3
value: 31.808999999999997
- type: map_at_5
value: 33.550999999999995
- type: mrr_at_1
value: 31.28
- type: mrr_at_10
value: 40.489000000000004
- type: mrr_at_100
value: 41.434
- type: mrr_at_1000
value: 41.491
- type: mrr_at_20
value: 41.088
- type: mrr_at_3
value: 38.033
- type: mrr_at_5
value: 39.621
- type: ndcg_at_1
value: 31.28
- type: ndcg_at_10
value: 40.716
- type: ndcg_at_100
value: 46.45
- type: ndcg_at_1000
value: 48.851
- type: ndcg_at_20
value: 43.216
- type: ndcg_at_3
value: 35.845
- type: ndcg_at_5
value: 38.251000000000005
- type: precision_at_1
value: 31.28
- type: precision_at_10
value: 7.623
- type: precision_at_100
value: 1.214
- type: precision_at_1000
value: 0.159
- type: precision_at_20
value: 4.625
- type: precision_at_3
value: 17.26
- type: precision_at_5
value: 12.435
- type: recall_at_1
value: 25.281
- type: recall_at_10
value: 52.476
- type: recall_at_100
value: 76.535
- type: recall_at_1000
value: 92.658
- type: recall_at_20
value: 61.211000000000006
- type: recall_at_3
value: 38.805
- type: recall_at_5
value: 45.053
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-programmers
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 20.092
- type: map_at_10
value: 27.805999999999997
- type: map_at_100
value: 29.137999999999998
- type: map_at_1000
value: 29.266
- type: map_at_20
value: 28.587
- type: map_at_3
value: 25.112000000000002
- type: map_at_5
value: 26.551000000000002
- type: mrr_at_1
value: 24.315
- type: mrr_at_10
value: 32.068000000000005
- type: mrr_at_100
value: 33.039
- type: mrr_at_1000
value: 33.114
- type: mrr_at_20
value: 32.66
- type: mrr_at_3
value: 29.49
- type: mrr_at_5
value: 30.906
- type: ndcg_at_1
value: 24.315
- type: ndcg_at_10
value: 32.9
- type: ndcg_at_100
value: 38.741
- type: ndcg_at_1000
value: 41.657
- type: ndcg_at_20
value: 35.338
- type: ndcg_at_3
value: 28.069
- type: ndcg_at_5
value: 30.169
- type: precision_at_1
value: 24.315
- type: precision_at_10
value: 6.2330000000000005
- type: precision_at_100
value: 1.072
- type: precision_at_1000
value: 0.15
- type: precision_at_20
value: 3.8580000000000005
- type: precision_at_3
value: 13.318
- type: precision_at_5
value: 9.748999999999999
- type: recall_at_1
value: 20.092
- type: recall_at_10
value: 43.832
- type: recall_at_100
value: 68.75099999999999
- type: recall_at_1000
value: 89.25
- type: recall_at_20
value: 52.445
- type: recall_at_3
value: 30.666
- type: recall_at_5
value: 35.873
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 19.317
- type: map_at_10
value: 26.653
- type: map_at_100
value: 28.011999999999997
- type: map_at_1000
value: 28.231
- type: map_at_20
value: 27.301
- type: map_at_3
value: 23.763
- type: map_at_5
value: 25.391000000000002
- type: mrr_at_1
value: 24.506
- type: mrr_at_10
value: 31.991999999999997
- type: mrr_at_100
value: 32.924
- type: mrr_at_1000
value: 32.993
- type: mrr_at_20
value: 32.521
- type: mrr_at_3
value: 29.48
- type: mrr_at_5
value: 30.982
- type: ndcg_at_1
value: 24.506
- type: ndcg_at_10
value: 32.202999999999996
- type: ndcg_at_100
value: 37.797
- type: ndcg_at_1000
value: 40.859
- type: ndcg_at_20
value: 34.098
- type: ndcg_at_3
value: 27.552
- type: ndcg_at_5
value: 29.781000000000002
- type: precision_at_1
value: 24.506
- type: precision_at_10
value: 6.462
- type: precision_at_100
value: 1.35
- type: precision_at_1000
value: 0.22499999999999998
- type: precision_at_20
value: 4.071000000000001
- type: precision_at_3
value: 13.241
- type: precision_at_5
value: 9.921000000000001
- type: recall_at_1
value: 19.317
- type: recall_at_10
value: 42.296
- type: recall_at_100
value: 68.2
- type: recall_at_1000
value: 88.565
- type: recall_at_20
value: 49.883
- type: recall_at_3
value: 28.608
- type: recall_at_5
value: 34.854
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-stats
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 18.0
- type: map_at_10
value: 24.444
- type: map_at_100
value: 25.205
- type: map_at_1000
value: 25.291000000000004
- type: map_at_20
value: 24.834
- type: map_at_3
value: 22.311
- type: map_at_5
value: 23.442
- type: mrr_at_1
value: 20.552
- type: mrr_at_10
value: 27.028999999999996
- type: mrr_at_100
value: 27.706999999999997
- type: mrr_at_1000
value: 27.775
- type: mrr_at_20
value: 27.366
- type: mrr_at_3
value: 25.051000000000002
- type: mrr_at_5
value: 26.063
- type: ndcg_at_1
value: 20.552
- type: ndcg_at_10
value: 28.519
- type: ndcg_at_100
value: 32.580999999999996
- type: ndcg_at_1000
value: 34.99
- type: ndcg_at_20
value: 29.848000000000003
- type: ndcg_at_3
value: 24.46
- type: ndcg_at_5
value: 26.273000000000003
- type: precision_at_1
value: 20.552
- type: precision_at_10
value: 4.801
- type: precision_at_100
value: 0.729
- type: precision_at_1000
value: 0.101
- type: precision_at_20
value: 2.715
- type: precision_at_3
value: 10.940999999999999
- type: precision_at_5
value: 7.761
- type: recall_at_1
value: 18.0
- type: recall_at_10
value: 38.425
- type: recall_at_100
value: 57.885
- type: recall_at_1000
value: 75.945
- type: recall_at_20
value: 43.472
- type: recall_at_3
value: 27.483
- type: recall_at_5
value: 31.866
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-tex
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 10.014000000000001
- type: map_at_10
value: 14.462
- type: map_at_100
value: 15.364
- type: map_at_1000
value: 15.482999999999999
- type: map_at_20
value: 14.931
- type: map_at_3
value: 12.842
- type: map_at_5
value: 13.697999999999999
- type: mrr_at_1
value: 12.526000000000002
- type: mrr_at_10
value: 17.433
- type: mrr_at_100
value: 18.296
- type: mrr_at_1000
value: 18.383
- type: mrr_at_20
value: 17.897
- type: mrr_at_3
value: 15.703
- type: mrr_at_5
value: 16.627
- type: ndcg_at_1
value: 12.526000000000002
- type: ndcg_at_10
value: 17.697
- type: ndcg_at_100
value: 22.33
- type: ndcg_at_1000
value: 25.587
- type: ndcg_at_20
value: 19.302
- type: ndcg_at_3
value: 14.606
- type: ndcg_at_5
value: 15.946
- type: precision_at_1
value: 12.526000000000002
- type: precision_at_10
value: 3.383
- type: precision_at_100
value: 0.6799999999999999
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_20
value: 2.147
- type: precision_at_3
value: 7.02
- type: precision_at_5
value: 5.196
- type: recall_at_1
value: 10.014000000000001
- type: recall_at_10
value: 24.623
- type: recall_at_100
value: 45.795
- type: recall_at_1000
value: 69.904
- type: recall_at_20
value: 30.534
- type: recall_at_3
value: 15.955
- type: recall_at_5
value: 19.394
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-unix
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 19.156000000000002
- type: map_at_10
value: 26.144000000000002
- type: map_at_100
value: 27.157999999999998
- type: map_at_1000
value: 27.288
- type: map_at_20
value: 26.689
- type: map_at_3
value: 24.125
- type: map_at_5
value: 25.369000000000003
- type: mrr_at_1
value: 22.854
- type: mrr_at_10
value: 29.874000000000002
- type: mrr_at_100
value: 30.738
- type: mrr_at_1000
value: 30.826999999999998
- type: mrr_at_20
value: 30.354
- type: mrr_at_3
value: 27.689999999999998
- type: mrr_at_5
value: 29.131
- type: ndcg_at_1
value: 22.854
- type: ndcg_at_10
value: 30.469
- type: ndcg_at_100
value: 35.475
- type: ndcg_at_1000
value: 38.59
- type: ndcg_at_20
value: 32.333
- type: ndcg_at_3
value: 26.674999999999997
- type: ndcg_at_5
value: 28.707
- type: precision_at_1
value: 22.854
- type: precision_at_10
value: 5.1209999999999996
- type: precision_at_100
value: 0.8500000000000001
- type: precision_at_1000
value: 0.123
- type: precision_at_20
value: 3.0460000000000003
- type: precision_at_3
value: 12.127
- type: precision_at_5
value: 8.75
- type: recall_at_1
value: 19.156000000000002
- type: recall_at_10
value: 40.009
- type: recall_at_100
value: 62.419999999999995
- type: recall_at_1000
value: 84.585
- type: recall_at_20
value: 46.912
- type: recall_at_3
value: 29.733999999999998
- type: recall_at_5
value: 34.741
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-webmasters
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 19.317
- type: map_at_10
value: 26.653
- type: map_at_100
value: 28.011999999999997
- type: map_at_1000
value: 28.231
- type: map_at_20
value: 27.301
- type: map_at_3
value: 23.763
- type: map_at_5
value: 25.391000000000002
- type: mrr_at_1
value: 24.506
- type: mrr_at_10
value: 31.991999999999997
- type: mrr_at_100
value: 32.924
- type: mrr_at_1000
value: 32.993
- type: mrr_at_20
value: 32.521
- type: mrr_at_3
value: 29.48
- type: mrr_at_5
value: 30.982
- type: ndcg_at_1
value: 24.506
- type: ndcg_at_10
value: 32.202999999999996
- type: ndcg_at_100
value: 37.797
- type: ndcg_at_1000
value: 40.859
- type: ndcg_at_20
value: 34.098
- type: ndcg_at_3
value: 27.552
- type: ndcg_at_5
value: 29.781000000000002
- type: precision_at_1
value: 24.506
- type: precision_at_10
value: 6.462
- type: precision_at_100
value: 1.35
- type: precision_at_1000
value: 0.22499999999999998
- type: precision_at_20
value: 4.071000000000001
- type: precision_at_3
value: 13.241
- type: precision_at_5
value: 9.921000000000001
- type: recall_at_1
value: 19.317
- type: recall_at_10
value: 42.296
- type: recall_at_100
value: 68.2
- type: recall_at_1000
value: 88.565
- type: recall_at_20
value: 49.883
- type: recall_at_3
value: 28.608
- type: recall_at_5
value: 34.854
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-wordpress
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 12.822
- type: map_at_10
value: 18.055
- type: map_at_100
value: 18.942
- type: map_at_1000
value: 19.057
- type: map_at_20
value: 18.544
- type: map_at_3
value: 15.964
- type: map_at_5
value: 16.833000000000002
- type: mrr_at_1
value: 14.048
- type: mrr_at_10
value: 19.489
- type: mrr_at_100
value: 20.392
- type: mrr_at_1000
value: 20.49
- type: mrr_at_20
value: 19.979
- type: mrr_at_3
value: 17.344
- type: mrr_at_5
value: 18.287
- type: ndcg_at_1
value: 14.048
- type: ndcg_at_10
value: 21.737000000000002
- type: ndcg_at_100
value: 26.383000000000003
- type: ndcg_at_1000
value: 29.555
- type: ndcg_at_20
value: 23.463
- type: ndcg_at_3
value: 17.29
- type: ndcg_at_5
value: 18.829
- type: precision_at_1
value: 14.048
- type: precision_at_10
value: 3.6229999999999998
- type: precision_at_100
value: 0.641
- type: precision_at_1000
value: 0.099
- type: precision_at_20
value: 2.1999999999999997
- type: precision_at_3
value: 7.2090000000000005
- type: precision_at_5
value: 5.213
- type: recall_at_1
value: 12.822
- type: recall_at_10
value: 32.123000000000005
- type: recall_at_100
value: 53.657999999999994
- type: recall_at_1000
value: 77.72200000000001
- type: recall_at_20
value: 38.66
- type: recall_at_3
value: 19.814999999999998
- type: recall_at_5
value: 23.432
- task:
type: Retrieval
dataset:
type: mteb/climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 13.119
- type: map_at_10
value: 22.999
- type: map_at_100
value: 25.108000000000004
- type: map_at_1000
value: 25.306
- type: map_at_20
value: 24.141000000000002
- type: map_at_3
value: 19.223000000000003
- type: map_at_5
value: 21.181
- type: mrr_at_1
value: 30.554
- type: mrr_at_10
value: 42.553000000000004
- type: mrr_at_100
value: 43.498
- type: mrr_at_1000
value: 43.527
- type: mrr_at_20
value: 43.193
- type: mrr_at_3
value: 39.283
- type: mrr_at_5
value: 41.143
- type: ndcg_at_1
value: 30.554
- type: ndcg_at_10
value: 31.946
- type: ndcg_at_100
value: 39.934999999999995
- type: ndcg_at_1000
value: 43.256
- type: ndcg_at_20
value: 35.101
- type: ndcg_at_3
value: 26.489
- type: ndcg_at_5
value: 28.272000000000002
- type: precision_at_1
value: 30.554
- type: precision_at_10
value: 10.039
- type: precision_at_100
value: 1.864
- type: precision_at_1000
value: 0.248
- type: precision_at_20
value: 6.371
- type: precision_at_3
value: 20.174
- type: precision_at_5
value: 15.296000000000001
- type: recall_at_1
value: 13.119
- type: recall_at_10
value: 37.822
- type: recall_at_100
value: 65.312
- type: recall_at_1000
value: 83.817
- type: recall_at_20
value: 46.760000000000005
- type: recall_at_3
value: 23.858999999999998
- type: recall_at_5
value: 29.609999999999996
- task:
type: Retrieval
dataset:
type: mteb/dbpedia
name: MTEB DBPedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 8.176
- type: map_at_10
value: 19.594
- type: map_at_100
value: 28.081
- type: map_at_1000
value: 29.864
- type: map_at_20
value: 22.983999999999998
- type: map_at_3
value: 13.923
- type: map_at_5
value: 16.597
- type: mrr_at_1
value: 66.75
- type: mrr_at_10
value: 75.82600000000001
- type: mrr_at_100
value: 76.145
- type: mrr_at_1000
value: 76.14999999999999
- type: mrr_at_20
value: 76.074
- type: mrr_at_3
value: 74.333
- type: mrr_at_5
value: 75.25800000000001
- type: ndcg_at_1
value: 54.50000000000001
- type: ndcg_at_10
value: 41.806
- type: ndcg_at_100
value: 47.067
- type: ndcg_at_1000
value: 54.397
- type: ndcg_at_20
value: 41.727
- type: ndcg_at_3
value: 46.92
- type: ndcg_at_5
value: 44.381
- type: precision_at_1
value: 66.75
- type: precision_at_10
value: 33.35
- type: precision_at_100
value: 10.92
- type: precision_at_1000
value: 2.222
- type: precision_at_20
value: 25.862000000000002
- type: precision_at_3
value: 51.417
- type: precision_at_5
value: 43.65
- type: recall_at_1
value: 8.176
- type: recall_at_10
value: 26.029000000000003
- type: recall_at_100
value: 53.872
- type: recall_at_1000
value: 76.895
- type: recall_at_20
value: 34.192
- type: recall_at_3
value: 15.789
- type: recall_at_5
value: 20.255000000000003
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 48.22
- type: f1
value: 43.59074485488622
- task:
type: Retrieval
dataset:
type: mteb/fever
name: MTEB FEVER
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 40.872
- type: map_at_10
value: 55.178000000000004
- type: map_at_100
value: 55.859
- type: map_at_1000
value: 55.881
- type: map_at_20
value: 55.66
- type: map_at_3
value: 51.4
- type: map_at_5
value: 53.754000000000005
- type: mrr_at_1
value: 43.744
- type: mrr_at_10
value: 58.36900000000001
- type: mrr_at_100
value: 58.911
- type: mrr_at_1000
value: 58.916999999999994
- type: mrr_at_20
value: 58.779
- type: mrr_at_3
value: 54.653
- type: mrr_at_5
value: 56.987
- type: ndcg_at_1
value: 43.744
- type: ndcg_at_10
value: 62.936
- type: ndcg_at_100
value: 65.666
- type: ndcg_at_1000
value: 66.08699999999999
- type: ndcg_at_20
value: 64.548
- type: ndcg_at_3
value: 55.543
- type: ndcg_at_5
value: 59.646
- type: precision_at_1
value: 43.744
- type: precision_at_10
value: 9.191
- type: precision_at_100
value: 1.072
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 4.967
- type: precision_at_3
value: 23.157
- type: precision_at_5
value: 16.115
- type: recall_at_1
value: 40.872
- type: recall_at_10
value: 83.818
- type: recall_at_100
value: 95.14200000000001
- type: recall_at_1000
value: 97.897
- type: recall_at_20
value: 89.864
- type: recall_at_3
value: 64.19200000000001
- type: recall_at_5
value: 74.029
- task:
type: Retrieval
dataset:
type: mteb/fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 14.804999999999998
- type: map_at_10
value: 22.86
- type: map_at_100
value: 24.823999999999998
- type: map_at_1000
value: 25.041000000000004
- type: map_at_20
value: 23.881
- type: map_at_3
value: 20.09
- type: map_at_5
value: 21.39
- type: mrr_at_1
value: 29.938
- type: mrr_at_10
value: 37.041000000000004
- type: mrr_at_100
value: 38.196000000000005
- type: mrr_at_1000
value: 38.256
- type: mrr_at_20
value: 37.693
- type: mrr_at_3
value: 34.721999999999994
- type: mrr_at_5
value: 35.787
- type: ndcg_at_1
value: 29.938
- type: ndcg_at_10
value: 29.358
- type: ndcg_at_100
value: 37.544
- type: ndcg_at_1000
value: 41.499
- type: ndcg_at_20
value: 32.354
- type: ndcg_at_3
value: 26.434
- type: ndcg_at_5
value: 26.93
- type: precision_at_1
value: 29.938
- type: precision_at_10
value: 8.117
- type: precision_at_100
value: 1.611
- type: precision_at_1000
value: 0.232
- type: precision_at_20
value: 5.255
- type: precision_at_3
value: 17.49
- type: precision_at_5
value: 12.747
- type: recall_at_1
value: 14.804999999999998
- type: recall_at_10
value: 34.776
- type: recall_at_100
value: 66.279
- type: recall_at_1000
value: 89.96600000000001
- type: recall_at_20
value: 44.31
- type: recall_at_3
value: 23.623
- type: recall_at_5
value: 27.194000000000003
- task:
type: Retrieval
dataset:
type: mteb/hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 38.555
- type: map_at_10
value: 54.20700000000001
- type: map_at_100
value: 55.177
- type: map_at_1000
value: 55.254999999999995
- type: map_at_20
value: 54.788000000000004
- type: map_at_3
value: 51.034
- type: map_at_5
value: 52.998
- type: mrr_at_1
value: 77.11
- type: mrr_at_10
value: 82.93199999999999
- type: mrr_at_100
value: 83.14200000000001
- type: mrr_at_1000
value: 83.15
- type: mrr_at_20
value: 83.062
- type: mrr_at_3
value: 81.95599999999999
- type: mrr_at_5
value: 82.586
- type: ndcg_at_1
value: 77.11
- type: ndcg_at_10
value: 63.853
- type: ndcg_at_100
value: 67.18499999999999
- type: ndcg_at_1000
value: 68.676
- type: ndcg_at_20
value: 65.279
- type: ndcg_at_3
value: 59.301
- type: ndcg_at_5
value: 61.822
- type: precision_at_1
value: 77.11
- type: precision_at_10
value: 13.044
- type: precision_at_100
value: 1.5630000000000002
- type: precision_at_1000
value: 0.17600000000000002
- type: precision_at_20
value: 6.979
- type: precision_at_3
value: 36.759
- type: precision_at_5
value: 24.054000000000002
- type: recall_at_1
value: 38.555
- type: recall_at_10
value: 65.21900000000001
- type: recall_at_100
value: 78.16300000000001
- type: recall_at_1000
value: 88.02799999999999
- type: recall_at_20
value: 69.791
- type: recall_at_3
value: 55.138
- type: recall_at_5
value: 60.135000000000005
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 69.8728
- type: ap
value: 63.98214492125858
- type: f1
value: 69.59975497754624
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification
config: default
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.76288189694483
- type: f1
value: 94.52150972672682
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification
config: default
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 76.83994528043777
- type: f1
value: 57.95571154189732
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification
config: default
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 46.1163416274378
- type: f1
value: 45.425692244093064
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification
config: default
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
- type: accuracy
value: 45.57834566240753
- type: f1
value: 43.84840097785479
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 32.86396397182615
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 34.018965727588565
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
metrics:
- type: map
value: 31.286618059824573
- type: mrr
value: 32.481830769278965
- task:
type: Retrieval
dataset:
type: mteb/nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 4.236
- type: map_at_10
value: 9.352
- type: map_at_100
value: 12.382
- type: map_at_1000
value: 13.828999999999999
- type: map_at_20
value: 10.619
- type: map_at_3
value: 6.814000000000001
- type: map_at_5
value: 7.887
- type: mrr_at_1
value: 37.152
- type: mrr_at_10
value: 47.055
- type: mrr_at_100
value: 47.82
- type: mrr_at_1000
value: 47.86
- type: mrr_at_20
value: 47.605
- type: mrr_at_3
value: 44.118
- type: mrr_at_5
value: 46.115
- type: ndcg_at_1
value: 34.365
- type: ndcg_at_10
value: 28.473
- type: ndcg_at_100
value: 27.311999999999998
- type: ndcg_at_1000
value: 36.671
- type: ndcg_at_20
value: 27.137
- type: ndcg_at_3
value: 31.939
- type: ndcg_at_5
value: 30.428
- type: precision_at_1
value: 36.223
- type: precision_at_10
value: 21.858
- type: precision_at_100
value: 7.417999999999999
- type: precision_at_1000
value: 2.0709999999999997
- type: precision_at_20
value: 16.502
- type: precision_at_3
value: 30.857
- type: precision_at_5
value: 26.997
- type: recall_at_1
value: 4.236
- type: recall_at_10
value: 13.489
- type: recall_at_100
value: 29.580000000000002
- type: recall_at_1000
value: 62.726000000000006
- type: recall_at_20
value: 18.346999999999998
- type: recall_at_3
value: 7.811
- type: recall_at_5
value: 10.086
- task:
type: Retrieval
dataset:
type: mteb/nq
name: MTEB NQ
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 21.123
- type: map_at_10
value: 34.429
- type: map_at_100
value: 35.803000000000004
- type: map_at_1000
value: 35.853
- type: map_at_20
value: 35.308
- type: map_at_3
value: 30.095
- type: map_at_5
value: 32.435
- type: mrr_at_1
value: 23.841
- type: mrr_at_10
value: 36.864999999999995
- type: mrr_at_100
value: 37.935
- type: mrr_at_1000
value: 37.97
- type: mrr_at_20
value: 37.566
- type: mrr_at_3
value: 32.918
- type: mrr_at_5
value: 35.11
- type: ndcg_at_1
value: 23.841
- type: ndcg_at_10
value: 42.043
- type: ndcg_at_100
value: 48.015
- type: ndcg_at_1000
value: 49.152
- type: ndcg_at_20
value: 44.936
- type: ndcg_at_3
value: 33.513999999999996
- type: ndcg_at_5
value: 37.541999999999994
- type: precision_at_1
value: 23.841
- type: precision_at_10
value: 7.454
- type: precision_at_100
value: 1.081
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_20
value: 4.413
- type: precision_at_3
value: 15.672
- type: precision_at_5
value: 11.657
- type: recall_at_1
value: 21.123
- type: recall_at_10
value: 63.096
- type: recall_at_100
value: 89.27199999999999
- type: recall_at_1000
value: 97.69
- type: recall_at_20
value: 73.873
- type: recall_at_3
value: 40.588
- type: recall_at_5
value: 49.928
- task:
type: Retrieval
dataset:
type: mteb/quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 70.255
- type: map_at_10
value: 84.387
- type: map_at_100
value: 85.027
- type: map_at_1000
value: 85.043
- type: map_at_20
value: 84.809
- type: map_at_3
value: 81.5
- type: map_at_5
value: 83.286
- type: mrr_at_1
value: 80.85
- type: mrr_at_10
value: 87.25699999999999
- type: mrr_at_100
value: 87.363
- type: mrr_at_1000
value: 87.363
- type: mrr_at_20
value: 87.336
- type: mrr_at_3
value: 86.357
- type: mrr_at_5
value: 86.939
- type: ndcg_at_1
value: 80.86
- type: ndcg_at_10
value: 88.151
- type: ndcg_at_100
value: 89.381
- type: ndcg_at_1000
value: 89.47800000000001
- type: ndcg_at_20
value: 88.82100000000001
- type: ndcg_at_3
value: 85.394
- type: ndcg_at_5
value: 86.855
- type: precision_at_1
value: 80.86
- type: precision_at_10
value: 13.397
- type: precision_at_100
value: 1.5310000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_20
value: 7.106999999999999
- type: precision_at_3
value: 37.46
- type: precision_at_5
value: 24.568
- type: recall_at_1
value: 70.255
- type: recall_at_10
value: 95.405
- type: recall_at_100
value: 99.56
- type: recall_at_1000
value: 99.98599999999999
- type: recall_at_20
value: 97.544
- type: recall_at_3
value: 87.414
- type: recall_at_5
value: 91.598
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 54.7557403999403
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 56.2773308957202
- task:
type: Retrieval
dataset:
type: mteb/scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 4.123
- type: map_at_10
value: 9.940999999999999
- type: map_at_100
value: 11.928999999999998
- type: map_at_1000
value: 12.257
- type: map_at_20
value: 10.866000000000001
- type: map_at_3
value: 7.091
- type: map_at_5
value: 8.393
- type: mrr_at_1
value: 20.3
- type: mrr_at_10
value: 30.068
- type: mrr_at_100
value: 31.296000000000003
- type: mrr_at_1000
value: 31.36
- type: mrr_at_20
value: 30.756
- type: mrr_at_3
value: 26.667
- type: mrr_at_5
value: 28.616999999999997
- type: ndcg_at_1
value: 20.3
- type: ndcg_at_10
value: 17.305
- type: ndcg_at_100
value: 25.529000000000003
- type: ndcg_at_1000
value: 31.41
- type: ndcg_at_20
value: 19.967
- type: ndcg_at_3
value: 16.022
- type: ndcg_at_5
value: 14.12
- type: precision_at_1
value: 20.3
- type: precision_at_10
value: 9.06
- type: precision_at_100
value: 2.103
- type: precision_at_1000
value: 0.35200000000000004
- type: precision_at_20
value: 6.075
- type: precision_at_3
value: 14.832999999999998
- type: precision_at_5
value: 12.36
- type: recall_at_1
value: 4.123
- type: recall_at_10
value: 18.383
- type: recall_at_100
value: 42.67
- type: recall_at_1000
value: 71.44800000000001
- type: recall_at_20
value: 24.64
- type: recall_at_3
value: 9.043
- type: recall_at_5
value: 12.543000000000001
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 84.37101718384514
- type: cos_sim_spearman
value: 80.73657031880697
- type: euclidean_pearson
value: 81.42351850520845
- type: euclidean_spearman
value: 80.81452496851979
- type: manhattan_pearson
value: 81.47676252115669
- type: manhattan_spearman
value: 80.87566944708885
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.79559176971591
- type: cos_sim_spearman
value: 75.41866597445552
- type: euclidean_pearson
value: 83.20287101163838
- type: euclidean_spearman
value: 75.54564777571143
- type: manhattan_pearson
value: 83.24622548900163
- type: manhattan_spearman
value: 75.63826258190343
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.63322096299294
- type: cos_sim_spearman
value: 85.48272638914783
- type: euclidean_pearson
value: 85.57327707819331
- type: euclidean_spearman
value: 85.90735298172922
- type: manhattan_pearson
value: 85.5744191274933
- type: manhattan_spearman
value: 85.90828008488766
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.05530140566407
- type: cos_sim_spearman
value: 78.85454907951474
- type: euclidean_pearson
value: 81.4307311680376
- type: euclidean_spearman
value: 78.99131623529348
- type: manhattan_pearson
value: 81.46870892683134
- type: manhattan_spearman
value: 79.05473823658481
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 83.66620817683379
- type: cos_sim_spearman
value: 85.23347998035328
- type: euclidean_pearson
value: 84.59001637865366
- type: euclidean_spearman
value: 85.0081410316597
- type: manhattan_pearson
value: 84.59742325369818
- type: manhattan_spearman
value: 85.01721329704324
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 79.86344730144208
- type: cos_sim_spearman
value: 82.15966778685441
- type: euclidean_pearson
value: 81.85580574642779
- type: euclidean_spearman
value: 82.06482873417123
- type: manhattan_pearson
value: 81.82971046102377
- type: manhattan_spearman
value: 82.04185436355144
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17
config: default
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cos_sim_pearson
value: 31.440481026661672
- type: cos_sim_spearman
value: 31.592743544965913
- type: euclidean_pearson
value: 31.15111049327518
- type: euclidean_spearman
value: 30.555124184361464
- type: manhattan_pearson
value: 31.724139249295654
- type: manhattan_spearman
value: 30.483389245793504
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22
config: default
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cos_sim_pearson
value: 34.51489724275415
- type: cos_sim_spearman
value: 47.06532141601629
- type: euclidean_pearson
value: 33.28904737503036
- type: euclidean_spearman
value: 45.111172981641865
- type: manhattan_pearson
value: 33.36374172942392
- type: manhattan_spearman
value: 45.100940945158534
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 82.09996292950329
- type: cos_sim_spearman
value: 82.69376206796092
- type: euclidean_pearson
value: 82.83254956369134
- type: euclidean_spearman
value: 82.34202999843637
- type: manhattan_pearson
value: 82.8048494319632
- type: manhattan_spearman
value: 82.34713123336984
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 82.1402269601644
- type: mrr
value: 94.84447197682492
- task:
type: Retrieval
dataset:
type: mteb/scifact
name: MTEB SciFact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 49.138999999999996
- type: map_at_10
value: 60.288
- type: map_at_100
value: 61.082
- type: map_at_1000
value: 61.11
- type: map_at_20
value: 60.831999999999994
- type: map_at_3
value: 57.106
- type: map_at_5
value: 58.857000000000006
- type: mrr_at_1
value: 51.333
- type: mrr_at_10
value: 61.364
- type: mrr_at_100
value: 62.029999999999994
- type: mrr_at_1000
value: 62.056
- type: mrr_at_20
value: 61.85000000000001
- type: mrr_at_3
value: 58.721999999999994
- type: mrr_at_5
value: 60.221999999999994
- type: ndcg_at_1
value: 51.333
- type: ndcg_at_10
value: 65.71900000000001
- type: ndcg_at_100
value: 69.036
- type: ndcg_at_1000
value: 69.626
- type: ndcg_at_20
value: 67.571
- type: ndcg_at_3
value: 60.019
- type: ndcg_at_5
value: 62.733000000000004
- type: precision_at_1
value: 51.333
- type: precision_at_10
value: 9.067
- type: precision_at_100
value: 1.083
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 4.95
- type: precision_at_3
value: 23.889
- type: precision_at_5
value: 16.0
- type: recall_at_1
value: 49.138999999999996
- type: recall_at_10
value: 81.256
- type: recall_at_100
value: 95.6
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 88.289
- type: recall_at_3
value: 66.078
- type: recall_at_5
value: 72.661
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.73762376237623
- type: cos_sim_ap
value: 93.02149432690442
- type: cos_sim_f1
value: 86.59079663532904
- type: cos_sim_precision
value: 85.70029382957884
- type: cos_sim_recall
value: 87.5
- type: dot_accuracy
value: 99.73267326732673
- type: dot_ap
value: 92.38661051842968
- type: dot_f1
value: 85.92283628779978
- type: dot_precision
value: 89.76034858387798
- type: dot_recall
value: 82.39999999999999
- type: euclidean_accuracy
value: 99.73960396039604
- type: euclidean_ap
value: 92.99557708360517
- type: euclidean_f1
value: 86.49183572488866
- type: euclidean_precision
value: 85.60235063663075
- type: euclidean_recall
value: 87.4
- type: manhattan_accuracy
value: 99.74059405940594
- type: manhattan_ap
value: 93.24237279644005
- type: manhattan_f1
value: 86.77727501256913
- type: manhattan_precision
value: 87.25985844287159
- type: manhattan_recall
value: 86.3
- type: max_accuracy
value: 99.74059405940594
- type: max_ap
value: 93.24237279644005
- type: max_f1
value: 86.77727501256913
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 63.94924261127149
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.22297034902405
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 46.12948438780115
- type: mrr
value: 46.77186783804431
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.02235612863601
- type: cos_sim_spearman
value: 30.567504287706598
- type: dot_pearson
value: 28.943978981614897
- type: dot_spearman
value: 29.905635915797358
- task:
type: Retrieval
dataset:
type: mteb/trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.173
- type: map_at_10
value: 1.124
- type: map_at_100
value: 5.645
- type: map_at_1000
value: 14.965
- type: map_at_20
value: 1.876
- type: map_at_3
value: 0.45599999999999996
- type: map_at_5
value: 0.699
- type: mrr_at_1
value: 70.0
- type: mrr_at_10
value: 81.786
- type: mrr_at_100
value: 81.786
- type: mrr_at_1000
value: 81.786
- type: mrr_at_20
value: 81.786
- type: mrr_at_3
value: 80.0
- type: mrr_at_5
value: 81.5
- type: ndcg_at_1
value: 65.0
- type: ndcg_at_10
value: 53.88699999999999
- type: ndcg_at_100
value: 38.028
- type: ndcg_at_1000
value: 37.183
- type: ndcg_at_20
value: 49.286
- type: ndcg_at_3
value: 63.05
- type: ndcg_at_5
value: 59.49100000000001
- type: precision_at_1
value: 70.0
- type: precision_at_10
value: 55.400000000000006
- type: precision_at_100
value: 38.800000000000004
- type: precision_at_1000
value: 17.082
- type: precision_at_20
value: 50.7
- type: precision_at_3
value: 66.667
- type: precision_at_5
value: 62.4
- type: recall_at_1
value: 0.173
- type: recall_at_10
value: 1.353
- type: recall_at_100
value: 8.887
- type: recall_at_1000
value: 36.012
- type: recall_at_20
value: 2.476
- type: recall_at_3
value: 0.508
- type: recall_at_5
value: 0.795
- task:
type: Retrieval
dataset:
type: mteb/touche2020
name: MTEB Touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 2.614
- type: map_at_10
value: 6.651999999999999
- type: map_at_100
value: 11.59
- type: map_at_1000
value: 13.044
- type: map_at_20
value: 8.702
- type: map_at_3
value: 4.159
- type: map_at_5
value: 5.327
- type: mrr_at_1
value: 30.612000000000002
- type: mrr_at_10
value: 42.664
- type: mrr_at_100
value: 43.957
- type: mrr_at_1000
value: 43.957
- type: mrr_at_20
value: 43.193
- type: mrr_at_3
value: 40.476
- type: mrr_at_5
value: 42.007
- type: ndcg_at_1
value: 27.551
- type: ndcg_at_10
value: 18.098
- type: ndcg_at_100
value: 30.019000000000002
- type: ndcg_at_1000
value: 42.179
- type: ndcg_at_20
value: 19.552
- type: ndcg_at_3
value: 21.22
- type: ndcg_at_5
value: 19.774
- type: precision_at_1
value: 30.612000000000002
- type: precision_at_10
value: 15.101999999999999
- type: precision_at_100
value: 6.510000000000001
- type: precision_at_1000
value: 1.4569999999999999
- type: precision_at_20
value: 12.449
- type: precision_at_3
value: 22.448999999999998
- type: precision_at_5
value: 19.592000000000002
- type: recall_at_1
value: 2.614
- type: recall_at_10
value: 11.068
- type: recall_at_100
value: 42.317
- type: recall_at_1000
value: 79.063
- type: recall_at_20
value: 18.589
- type: recall_at_3
value: 5.06
- type: recall_at_5
value: 7.356
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 75.0146484375
- type: ap
value: 16.80191476928431
- type: f1
value: 58.08037205204817
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.80249009620826
- type: f1
value: 62.24155926661914
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 47.074846780747094
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.21785778148656
- type: cos_sim_ap
value: 71.06584074764645
- type: cos_sim_f1
value: 65.81720166625826
- type: cos_sim_precision
value: 61.43641354071363
- type: cos_sim_recall
value: 70.87071240105541
- type: dot_accuracy
value: 84.30589497526375
- type: dot_ap
value: 68.85872202019365
- type: dot_f1
value: 64.20295157946092
- type: dot_precision
value: 59.69607620775687
- type: dot_recall
value: 69.44591029023746
- type: euclidean_accuracy
value: 85.21189724026942
- type: euclidean_ap
value: 71.18847194129523
- type: euclidean_f1
value: 66.00049962528105
- type: euclidean_precision
value: 62.66603415559773
- type: euclidean_recall
value: 69.70976253298153
- type: manhattan_accuracy
value: 85.25958157000656
- type: manhattan_ap
value: 71.12967638566641
- type: manhattan_f1
value: 65.77477594492791
- type: manhattan_precision
value: 64.77359938603223
- type: manhattan_recall
value: 66.80738786279683
- type: max_accuracy
value: 85.25958157000656
- type: max_ap
value: 71.18847194129523
- type: max_f1
value: 66.00049962528105
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.22330888345559
- type: cos_sim_ap
value: 84.40304506741951
- type: cos_sim_f1
value: 76.46823520855303
- type: cos_sim_precision
value: 72.45537867824409
- type: cos_sim_recall
value: 80.95164767477672
- type: dot_accuracy
value: 87.9400007761866
- type: dot_ap
value: 83.63499141834609
- type: dot_f1
value: 75.98620939938304
- type: dot_precision
value: 71.86792064254823
- type: dot_recall
value: 80.60517400677548
- type: euclidean_accuracy
value: 88.21166608452671
- type: euclidean_ap
value: 84.40463988450605
- type: euclidean_f1
value: 76.52312831312177
- type: euclidean_precision
value: 72.40621135083138
- type: euclidean_recall
value: 81.13643363104404
- type: manhattan_accuracy
value: 88.24659448131331
- type: manhattan_ap
value: 84.42287495905447
- type: manhattan_f1
value: 76.54849595413475
- type: manhattan_precision
value: 72.39036442248302
- type: manhattan_recall
value: 81.21342777948875
- type: max_accuracy
value: 88.24659448131331
- type: max_ap
value: 84.42287495905447
- type: max_f1
value: 76.54849595413475
---
# b1ade-embed-kd
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
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](https://www.SBERT.net), 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.
```python
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
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was distilled with teacher model as
and student model as b1ade-embed
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 275105 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 5000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 5e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Results:
Good agreement with teacher model, at least on STS:
Teacher:
```
2024-05-20 16:29:07 - Teacher Performance:
2024-05-20 16:29:07 - EmbeddingSimilarityEvaluator: Evaluating the model on the sts-dev dataset:
2024-05-20 16:29:12 - Cosine-Similarity : Pearson: 0.8561 Spearman: 0.8597
2024-05-20 16:29:12 - Manhattan-Distance: Pearson: 0.8569 Spearman: 0.8567
2024-05-20 16:29:12 - Euclidean-Distance: Pearson: 0.8575 Spearman: 0.8571
2024-05-20 16:29:12 - Dot-Product-Similarity: Pearson: 0.8624 Spearman: 0.8662
```
Student:
```
2024-05-20 16:29:12 - Student Performance:
2024-05-20 16:29:12 - EmbeddingSimilarityEvaluator: Evaluating the model on the sts-dev dataset:
2024-05-20 16:29:17 - Cosine-Similarity : Pearson: 0.8561 Spearman: 0.8597
2024-05-20 16:29:17 - Manhattan-Distance: Pearson: 0.8569 Spearman: 0.8567
2024-05-20 16:29:17 - Euclidean-Distance: Pearson: 0.8575 Spearman: 0.8571
2024-05-20 16:29:17 - Dot-Product-Similarity: Pearson: 0.8624 Spearman: 0.8662
```