metadata
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
language: en
license: apache-2.0
datasets:
- s2orc
- flax-sentence-embeddings/stackexchange_xml
- MS Marco
- gooaq
- yahoo_answers_topics
- code_search_net
- search_qa
- eli5
- snli
- multi_nli
- wikihow
- natural_questions
- trivia_qa
- embedding-data/sentence-compression
- embedding-data/flickr30k-captions
- embedding-data/altlex
- embedding-data/simple-wiki
- embedding-data/QQP
- embedding-data/SPECTER
- embedding-data/PAQ_pairs
- embedding-data/WikiAnswers
model-index:
- name: all-MiniLM-L12-v2
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
metrics:
- type: accuracy
value: 65.28358208955224
- type: ap
value: 28.02247873560022
- type: f1
value: 59.09977445939425
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (de)
config: de
split: test
metrics:
- type: accuracy
value: 57.09850107066381
- type: ap
value: 73.38224986285773
- type: f1
value: 55.183322516223434
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en-ext)
config: en-ext
split: test
metrics:
- type: accuracy
value: 67.24137931034483
- type: ap
value: 17.93337056203553
- type: f1
value: 55.200711090858846
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (ja)
config: ja
split: test
metrics:
- type: accuracy
value: 59.91434689507494
- type: ap
value: 13.610920446878454
- type: f1
value: 48.70464699796398
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
metrics:
- type: accuracy
value: 62.984899999999996
- type: ap
value: 58.19701547898307
- type: f1
value: 62.704020410756144
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
metrics:
- type: accuracy
value: 30.792
- type: f1
value: 30.254565315575437
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (de)
config: de
split: test
metrics:
- type: accuracy
value: 25.907999999999998
- type: f1
value: 25.538149526380543
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (es)
config: es
split: test
metrics:
- type: accuracy
value: 27.634000000000004
- type: f1
value: 27.287076320171728
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
metrics:
- type: accuracy
value: 27.540000000000003
- type: f1
value: 27.21486019130574
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (ja)
config: ja
split: test
metrics:
- type: accuracy
value: 23.566000000000003
- type: f1
value: 23.3492650771905
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
metrics:
- type: accuracy
value: 22.99
- type: f1
value: 22.47175043426865
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
metrics:
- type: map_at_1
value: 23.257
- type: map_at_10
value: 38.083
- type: map_at_100
value: 39.263999999999996
- type: map_at_1000
value: 39.273
- type: map_at_3
value: 32.574999999999996
- type: map_at_5
value: 35.669000000000004
- type: mrr_at_1
value: 23.613
- type: mrr_at_10
value: 38.243
- type: mrr_at_100
value: 39.410000000000004
- type: mrr_at_1000
value: 39.419
- type: mrr_at_3
value: 32.883
- type: mrr_at_5
value: 35.766999999999996
- type: ndcg_at_1
value: 23.257
- type: ndcg_at_10
value: 47.128
- type: ndcg_at_100
value: 52.093
- type: ndcg_at_1000
value: 52.315999999999995
- type: ndcg_at_3
value: 35.794
- type: ndcg_at_5
value: 41.364000000000004
- type: precision_at_1
value: 23.257
- type: precision_at_10
value: 7.632
- type: precision_at_100
value: 0.979
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 15.055
- type: precision_at_5
value: 11.735
- type: recall_at_1
value: 23.257
- type: recall_at_10
value: 76.31599999999999
- type: recall_at_100
value: 97.866
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 45.164
- type: recall_at_5
value: 58.677
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
metrics:
- type: v_measure
value: 46.06982724111873
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
metrics:
- type: v_measure
value: 37.501829188148264
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
metrics:
- type: map
value: 64.06160552465775
- type: mrr
value: 77.40029899309677
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
metrics:
- type: cos_sim_pearson
value: 86.73300462416691
- type: cos_sim_spearman
value: 83.56756679430214
- type: euclidean_pearson
value: 84.35153960397948
- type: euclidean_spearman
value: 83.56756679430214
- type: manhattan_pearson
value: 84.10087673223914
- type: manhattan_spearman
value: 83.58383222516198
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
metrics:
- type: accuracy
value: 80.40259740259741
- type: f1
value: 79.7932665380276
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
metrics:
- type: v_measure
value: 36.985834019439366
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
metrics:
- type: v_measure
value: 33.207831360185644
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
metrics:
- type: map_at_1
value: 34.975
- type: map_at_10
value: 47.227999999999994
- type: map_at_100
value: 48.91
- type: map_at_1000
value: 49.016
- type: map_at_3
value: 43.334
- type: map_at_5
value: 45.353
- type: mrr_at_1
value: 43.348
- type: mrr_at_10
value: 53.744
- type: mrr_at_100
value: 54.432
- type: mrr_at_1000
value: 54.458
- type: mrr_at_3
value: 51.359
- type: mrr_at_5
value: 52.825
- type: ndcg_at_1
value: 43.348
- type: ndcg_at_10
value: 54.118
- type: ndcg_at_100
value: 59.496
- type: ndcg_at_1000
value: 60.846999999999994
- type: ndcg_at_3
value: 49.001
- type: ndcg_at_5
value: 51.245
- type: precision_at_1
value: 43.348
- type: precision_at_10
value: 10.658
- type: precision_at_100
value: 1.701
- type: precision_at_1000
value: 0.214
- type: precision_at_3
value: 23.701
- type: precision_at_5
value: 17.082
- type: recall_at_1
value: 34.975
- type: recall_at_10
value: 66.291
- type: recall_at_100
value: 88.727
- type: recall_at_1000
value: 97.26700000000001
- type: recall_at_3
value: 51.505
- type: recall_at_5
value: 57.833
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
metrics:
- type: map_at_1
value: 31.509999999999998
- type: map_at_10
value: 43.401
- type: map_at_100
value: 44.762
- type: map_at_1000
value: 44.906
- type: map_at_3
value: 39.83
- type: map_at_5
value: 41.784
- type: mrr_at_1
value: 39.936
- type: mrr_at_10
value: 49.534
- type: mrr_at_100
value: 50.126000000000005
- type: mrr_at_1000
value: 50.163999999999994
- type: mrr_at_3
value: 46.996
- type: mrr_at_5
value: 48.508
- type: ndcg_at_1
value: 39.936
- type: ndcg_at_10
value: 49.845
- type: ndcg_at_100
value: 54.25600000000001
- type: ndcg_at_1000
value: 56.227000000000004
- type: ndcg_at_3
value: 44.982
- type: ndcg_at_5
value: 47.187
- type: precision_at_1
value: 39.936
- type: precision_at_10
value: 9.771
- type: precision_at_100
value: 1.575
- type: precision_at_1000
value: 0.20600000000000002
- type: precision_at_3
value: 22.314
- type: precision_at_5
value: 15.975
- type: recall_at_1
value: 31.509999999999998
- type: recall_at_10
value: 61.468
- type: recall_at_100
value: 80.023
- type: recall_at_1000
value: 92.267
- type: recall_at_3
value: 46.698
- type: recall_at_5
value: 53.03600000000001
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
metrics:
- type: map_at_1
value: 38.577
- type: map_at_10
value: 51.041000000000004
- type: map_at_100
value: 52.141000000000005
- type: map_at_1000
value: 52.190000000000005
- type: map_at_3
value: 47.904
- type: map_at_5
value: 49.645
- type: mrr_at_1
value: 44.138
- type: mrr_at_10
value: 54.36
- type: mrr_at_100
value: 55.05799999999999
- type: mrr_at_1000
value: 55.084
- type: mrr_at_3
value: 52.017
- type: mrr_at_5
value: 53.321
- type: ndcg_at_1
value: 44.138
- type: ndcg_at_10
value: 56.855999999999995
- type: ndcg_at_100
value: 61.133
- type: ndcg_at_1000
value: 62.17399999999999
- type: ndcg_at_3
value: 51.624
- type: ndcg_at_5
value: 54.108999999999995
- type: precision_at_1
value: 44.138
- type: precision_at_10
value: 9.16
- type: precision_at_100
value: 1.2309999999999999
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 23.156
- type: precision_at_5
value: 15.762
- type: recall_at_1
value: 38.577
- type: recall_at_10
value: 70.638
- type: recall_at_100
value: 89.01
- type: recall_at_1000
value: 96.53699999999999
- type: recall_at_3
value: 56.635000000000005
- type: recall_at_5
value: 62.731
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
metrics:
- type: map_at_1
value: 27.038
- type: map_at_10
value: 36.108000000000004
- type: map_at_100
value: 37.316
- type: map_at_1000
value: 37.396
- type: map_at_3
value: 33.206
- type: map_at_5
value: 34.674
- type: mrr_at_1
value: 29.04
- type: mrr_at_10
value: 37.979
- type: mrr_at_100
value: 39.056000000000004
- type: mrr_at_1000
value: 39.11
- type: mrr_at_3
value: 35.348
- type: mrr_at_5
value: 36.675999999999995
- type: ndcg_at_1
value: 29.04
- type: ndcg_at_10
value: 41.408
- type: ndcg_at_100
value: 46.918
- type: ndcg_at_1000
value: 48.827
- type: ndcg_at_3
value: 35.699999999999996
- type: ndcg_at_5
value: 38.112
- type: precision_at_1
value: 29.04
- type: precision_at_10
value: 6.463000000000001
- type: precision_at_100
value: 0.9570000000000001
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 15.104000000000001
- type: precision_at_5
value: 10.508000000000001
- type: recall_at_1
value: 27.038
- type: recall_at_10
value: 55.989
- type: recall_at_100
value: 80.418
- type: recall_at_1000
value: 94.506
- type: recall_at_3
value: 40.388000000000005
- type: recall_at_5
value: 46.085
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
metrics:
- type: map_at_1
value: 17.264
- type: map_at_10
value: 26.157000000000004
- type: map_at_100
value: 27.503
- type: map_at_1000
value: 27.617000000000004
- type: map_at_3
value: 23.247999999999998
- type: map_at_5
value: 24.81
- type: mrr_at_1
value: 21.144
- type: mrr_at_10
value: 30.516
- type: mrr_at_100
value: 31.607000000000003
- type: mrr_at_1000
value: 31.673000000000002
- type: mrr_at_3
value: 27.716
- type: mrr_at_5
value: 29.357
- type: ndcg_at_1
value: 21.144
- type: ndcg_at_10
value: 31.86
- type: ndcg_at_100
value: 38.12
- type: ndcg_at_1000
value: 40.699000000000005
- type: ndcg_at_3
value: 26.411
- type: ndcg_at_5
value: 28.896
- type: precision_at_1
value: 21.144
- type: precision_at_10
value: 5.995
- type: precision_at_100
value: 1.058
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_3
value: 12.894
- type: precision_at_5
value: 9.428
- type: recall_at_1
value: 17.264
- type: recall_at_10
value: 45.074
- type: recall_at_100
value: 71.817
- type: recall_at_1000
value: 89.846
- type: recall_at_3
value: 30.031000000000002
- type: recall_at_5
value: 36.233
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
metrics:
- type: map_at_1
value: 28.668
- type: map_at_10
value: 40.382
- type: map_at_100
value: 41.836
- type: map_at_1000
value: 41.954
- type: map_at_3
value: 37.136
- type: map_at_5
value: 38.755
- type: mrr_at_1
value: 35.13
- type: mrr_at_10
value: 45.928999999999995
- type: mrr_at_100
value: 46.814
- type: mrr_at_1000
value: 46.854
- type: mrr_at_3
value: 43.423
- type: mrr_at_5
value: 44.79
- type: ndcg_at_1
value: 35.13
- type: ndcg_at_10
value: 46.81
- type: ndcg_at_100
value: 52.552
- type: ndcg_at_1000
value: 54.493
- type: ndcg_at_3
value: 41.732
- type: ndcg_at_5
value: 43.847
- type: precision_at_1
value: 35.13
- type: precision_at_10
value: 8.738999999999999
- type: precision_at_100
value: 1.373
- type: precision_at_1000
value: 0.174
- type: precision_at_3
value: 20.372
- type: precision_at_5
value: 14.302000000000001
- type: recall_at_1
value: 28.668
- type: recall_at_10
value: 60.038000000000004
- type: recall_at_100
value: 83.736
- type: recall_at_1000
value: 96.184
- type: recall_at_3
value: 45.647999999999996
- type: recall_at_5
value: 51.212
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
metrics:
- type: map_at_1
value: 25.287
- type: map_at_10
value: 35.351
- type: map_at_100
value: 36.867
- type: map_at_1000
value: 36.973
- type: map_at_3
value: 32.176
- type: map_at_5
value: 33.894999999999996
- type: mrr_at_1
value: 31.735000000000003
- type: mrr_at_10
value: 40.832
- type: mrr_at_100
value: 41.812
- type: mrr_at_1000
value: 41.864000000000004
- type: mrr_at_3
value: 38.489000000000004
- type: mrr_at_5
value: 39.654
- type: ndcg_at_1
value: 31.735000000000003
- type: ndcg_at_10
value: 41.327999999999996
- type: ndcg_at_100
value: 47.565000000000005
- type: ndcg_at_1000
value: 49.708000000000006
- type: ndcg_at_3
value: 36.391
- type: ndcg_at_5
value: 38.489000000000004
- type: precision_at_1
value: 31.735000000000003
- type: precision_at_10
value: 7.7170000000000005
- type: precision_at_100
value: 1.2670000000000001
- type: precision_at_1000
value: 0.16199999999999998
- type: precision_at_3
value: 17.808
- type: precision_at_5
value: 12.534
- type: recall_at_1
value: 25.287
- type: recall_at_10
value: 53.735
- type: recall_at_100
value: 80.149
- type: recall_at_1000
value: 94.756
- type: recall_at_3
value: 39.475
- type: recall_at_5
value: 45.532000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
metrics:
- type: map_at_1
value: 26.613
- type: map_at_10
value: 36.747416666666666
- type: map_at_100
value: 38.091416666666674
- type: map_at_1000
value: 38.2075
- type: map_at_3
value: 33.630833333333335
- type: map_at_5
value: 35.28225
- type: mrr_at_1
value: 31.654
- type: mrr_at_10
value: 40.94166666666666
- type: mrr_at_100
value: 41.85883333333334
- type: mrr_at_1000
value: 41.910666666666664
- type: mrr_at_3
value: 38.44458333333334
- type: mrr_at_5
value: 39.84525000000001
- type: ndcg_at_1
value: 31.654
- type: ndcg_at_10
value: 42.533
- type: ndcg_at_100
value: 48.09741666666667
- type: ndcg_at_1000
value: 50.170166666666674
- type: ndcg_at_3
value: 37.37858333333333
- type: ndcg_at_5
value: 39.666666666666664
- type: precision_at_1
value: 31.654
- type: precision_at_10
value: 7.649500000000001
- type: precision_at_100
value: 1.2425
- type: precision_at_1000
value: 0.16175
- type: precision_at_3
value: 17.49625
- type: precision_at_5
value: 12.410333333333332
- type: recall_at_1
value: 26.613
- type: recall_at_10
value: 55.33375
- type: recall_at_100
value: 79.52791666666667
- type: recall_at_1000
value: 93.73391666666667
- type: recall_at_3
value: 40.861333333333334
- type: recall_at_5
value: 46.84675
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
metrics:
- type: map_at_1
value: 26.079
- type: map_at_10
value: 33.481
- type: map_at_100
value: 34.494
- type: map_at_1000
value: 34.589999999999996
- type: map_at_3
value: 31.165
- type: map_at_5
value: 32.482
- type: mrr_at_1
value: 29.293999999999997
- type: mrr_at_10
value: 36.303000000000004
- type: mrr_at_100
value: 37.183
- type: mrr_at_1000
value: 37.254
- type: mrr_at_3
value: 34.33
- type: mrr_at_5
value: 35.519
- type: ndcg_at_1
value: 29.293999999999997
- type: ndcg_at_10
value: 37.817
- type: ndcg_at_100
value: 42.91
- type: ndcg_at_1000
value: 45.342
- type: ndcg_at_3
value: 33.695
- type: ndcg_at_5
value: 35.747
- type: precision_at_1
value: 29.293999999999997
- type: precision_at_10
value: 5.951
- type: precision_at_100
value: 0.9400000000000001
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 14.519000000000002
- type: precision_at_5
value: 10.123
- type: recall_at_1
value: 26.079
- type: recall_at_10
value: 48.27
- type: recall_at_100
value: 71.64
- type: recall_at_1000
value: 89.775
- type: recall_at_3
value: 36.858000000000004
- type: recall_at_5
value: 42.013
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
metrics:
- type: map_at_1
value: 18.17
- type: map_at_10
value: 26.483
- type: map_at_100
value: 27.732
- type: map_at_1000
value: 27.864
- type: map_at_3
value: 23.76
- type: map_at_5
value: 25.290000000000003
- type: mrr_at_1
value: 22.436
- type: mrr_at_10
value: 30.448999999999998
- type: mrr_at_100
value: 31.476
- type: mrr_at_1000
value: 31.548
- type: mrr_at_3
value: 28.051
- type: mrr_at_5
value: 29.421999999999997
- type: ndcg_at_1
value: 22.436
- type: ndcg_at_10
value: 31.662000000000003
- type: ndcg_at_100
value: 37.611
- type: ndcg_at_1000
value: 40.439
- type: ndcg_at_3
value: 26.939999999999998
- type: ndcg_at_5
value: 29.177999999999997
- type: precision_at_1
value: 22.436
- type: precision_at_10
value: 5.908
- type: precision_at_100
value: 1.056
- type: precision_at_1000
value: 0.149
- type: precision_at_3
value: 12.962000000000002
- type: precision_at_5
value: 9.476999999999999
- type: recall_at_1
value: 18.17
- type: recall_at_10
value: 43.219
- type: recall_at_100
value: 70.106
- type: recall_at_1000
value: 90.04100000000001
- type: recall_at_3
value: 30.023
- type: recall_at_5
value: 35.845
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
metrics:
- type: map_at_1
value: 28.016999999999996
- type: map_at_10
value: 38.123000000000005
- type: map_at_100
value: 39.367000000000004
- type: map_at_1000
value: 39.467999999999996
- type: map_at_3
value: 34.836
- type: map_at_5
value: 36.661
- type: mrr_at_1
value: 33.116
- type: mrr_at_10
value: 42.211
- type: mrr_at_100
value: 43.118
- type: mrr_at_1000
value: 43.169000000000004
- type: mrr_at_3
value: 39.521
- type: mrr_at_5
value: 41.154
- type: ndcg_at_1
value: 33.116
- type: ndcg_at_10
value: 43.86
- type: ndcg_at_100
value: 49.486000000000004
- type: ndcg_at_1000
value: 51.487
- type: ndcg_at_3
value: 38.303
- type: ndcg_at_5
value: 40.927
- type: precision_at_1
value: 33.116
- type: precision_at_10
value: 7.649
- type: precision_at_100
value: 1.165
- type: precision_at_1000
value: 0.145
- type: precision_at_3
value: 17.724
- type: precision_at_5
value: 12.668
- type: recall_at_1
value: 28.016999999999996
- type: recall_at_10
value: 57.032000000000004
- type: recall_at_100
value: 81.828
- type: recall_at_1000
value: 95.273
- type: recall_at_3
value: 41.733
- type: recall_at_5
value: 48.496
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
metrics:
- type: map_at_1
value: 24.295
- type: map_at_10
value: 34.94
- type: map_at_100
value: 36.659000000000006
- type: map_at_1000
value: 36.902
- type: map_at_3
value: 31.562
- type: map_at_5
value: 33.28
- type: mrr_at_1
value: 29.644
- type: mrr_at_10
value: 39.467999999999996
- type: mrr_at_100
value: 40.561
- type: mrr_at_1000
value: 40.61
- type: mrr_at_3
value: 36.759
- type: mrr_at_5
value: 38.251000000000005
- type: ndcg_at_1
value: 29.644
- type: ndcg_at_10
value: 41.376000000000005
- type: ndcg_at_100
value: 47.701
- type: ndcg_at_1000
value: 49.925999999999995
- type: ndcg_at_3
value: 36.009
- type: ndcg_at_5
value: 38.23
- type: precision_at_1
value: 29.644
- type: precision_at_10
value: 8.182
- type: precision_at_100
value: 1.672
- type: precision_at_1000
value: 0.253
- type: precision_at_3
value: 17.325
- type: precision_at_5
value: 12.450999999999999
- type: recall_at_1
value: 24.295
- type: recall_at_10
value: 54.478
- type: recall_at_100
value: 81.85
- type: recall_at_1000
value: 95.395
- type: recall_at_3
value: 39.121
- type: recall_at_5
value: 45.465
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
metrics:
- type: map_at_1
value: 19.476
- type: map_at_10
value: 28.274
- type: map_at_100
value: 29.509999999999998
- type: map_at_1000
value: 29.614
- type: map_at_3
value: 25.413000000000004
- type: map_at_5
value: 26.758
- type: mrr_at_1
value: 20.887
- type: mrr_at_10
value: 29.975
- type: mrr_at_100
value: 31.063000000000002
- type: mrr_at_1000
value: 31.14
- type: mrr_at_3
value: 27.326
- type: mrr_at_5
value: 28.666000000000004
- type: ndcg_at_1
value: 20.887
- type: ndcg_at_10
value: 33.456
- type: ndcg_at_100
value: 39.421
- type: ndcg_at_1000
value: 41.873
- type: ndcg_at_3
value: 27.755000000000003
- type: ndcg_at_5
value: 30.032999999999998
- type: precision_at_1
value: 20.887
- type: precision_at_10
value: 5.601
- type: precision_at_100
value: 0.915
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 12.076
- type: precision_at_5
value: 8.613999999999999
- type: recall_at_1
value: 19.476
- type: recall_at_10
value: 47.772999999999996
- type: recall_at_100
value: 75.031
- type: recall_at_1000
value: 92.96
- type: recall_at_3
value: 32.221
- type: recall_at_5
value: 37.68
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
metrics:
- type: map_at_1
value: 8.341999999999999
- type: map_at_10
value: 14.524000000000001
- type: map_at_100
value: 16.114
- type: map_at_1000
value: 16.301
- type: map_at_3
value: 11.904
- type: map_at_5
value: 13.175
- type: mrr_at_1
value: 18.892999999999997
- type: mrr_at_10
value: 29.185
- type: mrr_at_100
value: 30.368000000000002
- type: mrr_at_1000
value: 30.418
- type: mrr_at_3
value: 25.548
- type: mrr_at_5
value: 27.708
- type: ndcg_at_1
value: 18.892999999999997
- type: ndcg_at_10
value: 21.572
- type: ndcg_at_100
value: 28.51
- type: ndcg_at_1000
value: 32.204
- type: ndcg_at_3
value: 16.753
- type: ndcg_at_5
value: 18.5
- type: precision_at_1
value: 18.892999999999997
- type: precision_at_10
value: 6.997000000000001
- type: precision_at_100
value: 1.433
- type: precision_at_1000
value: 0.211
- type: precision_at_3
value: 12.53
- type: precision_at_5
value: 10.098
- type: recall_at_1
value: 8.341999999999999
- type: recall_at_10
value: 27.215
- type: recall_at_100
value: 51.534
- type: recall_at_1000
value: 72.655
- type: recall_at_3
value: 15.634
- type: recall_at_5
value: 20.227
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
metrics:
- type: map_at_1
value: 7.5920000000000005
- type: map_at_10
value: 15.42
- type: map_at_100
value: 21.269
- type: map_at_1000
value: 22.55
- type: map_at_3
value: 11.221
- type: map_at_5
value: 13.225999999999999
- type: mrr_at_1
value: 58.25
- type: mrr_at_10
value: 66.237
- type: mrr_at_100
value: 66.74799999999999
- type: mrr_at_1000
value: 66.762
- type: mrr_at_3
value: 64.167
- type: mrr_at_5
value: 65.229
- type: ndcg_at_1
value: 45.625
- type: ndcg_at_10
value: 33.355000000000004
- type: ndcg_at_100
value: 37.484
- type: ndcg_at_1000
value: 44.523
- type: ndcg_at_3
value: 37.879000000000005
- type: ndcg_at_5
value: 35.841
- type: precision_at_1
value: 58.25
- type: precision_at_10
value: 26.450000000000003
- type: precision_at_100
value: 8.290000000000001
- type: precision_at_1000
value: 1.744
- type: precision_at_3
value: 40.75
- type: precision_at_5
value: 35
- type: recall_at_1
value: 7.5920000000000005
- type: recall_at_10
value: 20.064
- type: recall_at_100
value: 43.187
- type: recall_at_1000
value: 66.154
- type: recall_at_3
value: 12.366000000000001
- type: recall_at_5
value: 15.631
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
metrics:
- type: accuracy
value: 41.17
- type: f1
value: 36.961926373935974
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
metrics:
- type: map_at_1
value: 37.361
- type: map_at_10
value: 49.407000000000004
- type: map_at_100
value: 50.11600000000001
- type: map_at_1000
value: 50.151999999999994
- type: map_at_3
value: 46.608
- type: map_at_5
value: 48.286
- type: mrr_at_1
value: 40.204
- type: mrr_at_10
value: 52.714000000000006
- type: mrr_at_100
value: 53.347
- type: mrr_at_1000
value: 53.373000000000005
- type: mrr_at_3
value: 49.935
- type: mrr_at_5
value: 51.626000000000005
- type: ndcg_at_1
value: 40.204
- type: ndcg_at_10
value: 55.905
- type: ndcg_at_100
value: 59.229
- type: ndcg_at_1000
value: 60.077000000000005
- type: ndcg_at_3
value: 50.367
- type: ndcg_at_5
value: 53.291999999999994
- type: precision_at_1
value: 40.204
- type: precision_at_10
value: 8
- type: precision_at_100
value: 0.979
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 20.997
- type: precision_at_5
value: 14.215
- type: recall_at_1
value: 37.361
- type: recall_at_10
value: 72.775
- type: recall_at_100
value: 87.883
- type: recall_at_1000
value: 94.204
- type: recall_at_3
value: 57.830000000000005
- type: recall_at_5
value: 64.888
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
metrics:
- type: map_at_1
value: 18.257
- type: map_at_10
value: 29.694
- type: map_at_100
value: 31.593
- type: map_at_1000
value: 31.795
- type: map_at_3
value: 25.778000000000002
- type: map_at_5
value: 27.901999999999997
- type: mrr_at_1
value: 36.574
- type: mrr_at_10
value: 45.562000000000005
- type: mrr_at_100
value: 46.479
- type: mrr_at_1000
value: 46.52
- type: mrr_at_3
value: 43.184
- type: mrr_at_5
value: 44.558
- type: ndcg_at_1
value: 36.574
- type: ndcg_at_10
value: 37.274
- type: ndcg_at_100
value: 44.379000000000005
- type: ndcg_at_1000
value: 47.803000000000004
- type: ndcg_at_3
value: 33.999
- type: ndcg_at_5
value: 34.927
- type: precision_at_1
value: 36.574
- type: precision_at_10
value: 10.571
- type: precision_at_100
value: 1.779
- type: precision_at_1000
value: 0.23700000000000002
- type: precision_at_3
value: 22.942
- type: precision_at_5
value: 16.944
- type: recall_at_1
value: 18.257
- type: recall_at_10
value: 43.46
- type: recall_at_100
value: 70.017
- type: recall_at_1000
value: 90.838
- type: recall_at_3
value: 30.520999999999997
- type: recall_at_5
value: 35.977
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
metrics:
- type: map_at_1
value: 25.935000000000002
- type: map_at_10
value: 35.96
- type: map_at_100
value: 36.811
- type: map_at_1000
value: 36.894
- type: map_at_3
value: 33.479
- type: map_at_5
value: 34.93
- type: mrr_at_1
value: 51.870000000000005
- type: mrr_at_10
value: 59.671
- type: mrr_at_100
value: 60.153
- type: mrr_at_1000
value: 60.183
- type: mrr_at_3
value: 57.815000000000005
- type: mrr_at_5
value: 58.965999999999994
- type: ndcg_at_1
value: 51.870000000000005
- type: ndcg_at_10
value: 44.589
- type: ndcg_at_100
value: 48.113
- type: ndcg_at_1000
value: 49.962
- type: ndcg_at_3
value: 40.304
- type: ndcg_at_5
value: 42.543
- type: precision_at_1
value: 51.870000000000005
- type: precision_at_10
value: 9.454
- type: precision_at_100
value: 1.225
- type: precision_at_1000
value: 0.147
- type: precision_at_3
value: 25.131999999999998
- type: precision_at_5
value: 16.851
- type: recall_at_1
value: 25.935000000000002
- type: recall_at_10
value: 47.272
- type: recall_at_100
value: 61.229
- type: recall_at_1000
value: 73.55199999999999
- type: recall_at_3
value: 37.698
- type: recall_at_5
value: 42.126999999999995
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
metrics:
- type: accuracy
value: 59.76079999999999
- type: ap
value: 55.90381572041755
- type: f1
value: 58.99832553463791
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
metrics:
- type: map_at_1
value: 20.666999999999998
- type: map_at_10
value: 32.425
- type: map_at_100
value: 33.586
- type: map_at_1000
value: 33.643
- type: map_at_3
value: 28.836000000000002
- type: map_at_5
value: 30.847
- type: mrr_at_1
value: 21.275
- type: mrr_at_10
value: 33.062999999999995
- type: mrr_at_100
value: 34.168
- type: mrr_at_1000
value: 34.217999999999996
- type: mrr_at_3
value: 29.491
- type: mrr_at_5
value: 31.502999999999997
- type: ndcg_at_1
value: 21.246000000000002
- type: ndcg_at_10
value: 39.034
- type: ndcg_at_100
value: 44.768
- type: ndcg_at_1000
value: 46.2
- type: ndcg_at_3
value: 31.652
- type: ndcg_at_5
value: 35.257
- type: precision_at_1
value: 21.246000000000002
- type: precision_at_10
value: 6.196
- type: precision_at_100
value: 0.909
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 13.547999999999998
- type: precision_at_5
value: 9.946000000000002
- type: recall_at_1
value: 20.666999999999998
- type: recall_at_10
value: 59.321999999999996
- type: recall_at_100
value: 86.158
- type: recall_at_1000
value: 97.154
- type: recall_at_3
value: 39.160000000000004
- type: recall_at_5
value: 47.82
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
metrics:
- type: accuracy
value: 91.89922480620154
- type: f1
value: 91.66762682851963
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (de)
config: de
split: test
metrics:
- type: accuracy
value: 72.03719357565511
- type: f1
value: 68.75742308679864
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (es)
config: es
split: test
metrics:
- type: accuracy
value: 72.98532354903269
- type: f1
value: 71.33173021994274
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (fr)
config: fr
split: test
metrics:
- type: accuracy
value: 75.59348575007829
- type: f1
value: 73.1511918522243
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (hi)
config: hi
split: test
metrics:
- type: accuracy
value: 40.36213696665471
- type: f1
value: 37.865703085609475
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (th)
config: th
split: test
metrics:
- type: accuracy
value: 17.099457504520796
- type: f1
value: 12.86835498185132
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
metrics:
- type: accuracy
value: 62.83629730962153
- type: f1
value: 44.241027031016735
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (de)
config: de
split: test
metrics:
- type: accuracy
value: 43.412228796844175
- type: f1
value: 25.96122949091921
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (es)
config: es
split: test
metrics:
- type: accuracy
value: 41.8812541694463
- type: f1
value: 27.93481154758236
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (fr)
config: fr
split: test
metrics:
- type: accuracy
value: 38.93830253679925
- type: f1
value: 25.820783392796052
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (hi)
config: hi
split: test
metrics:
- type: accuracy
value: 17.7518823951237
- type: f1
value: 11.681226129204576
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (th)
config: th
split: test
metrics:
- type: accuracy
value: 5.631103074141048
- type: f1
value: 2.046543337618445
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (af)
config: af
split: test
metrics:
- type: accuracy
value: 38.94082044384667
- type: f1
value: 36.222023448848596
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (am)
config: am
split: test
metrics:
- type: accuracy
value: 2.451244115669133
- type: f1
value: 1.1859369824825732
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (ar)
config: ar
split: test
metrics:
- type: accuracy
value: 20.938130464021523
- type: f1
value: 17.984223607695032
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (az)
config: az
split: test
metrics:
- type: accuracy
value: 34.25016812373907
- type: f1
value: 33.954933856088616
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (bn)
config: bn
split: test
metrics:
- type: accuracy
value: 13.665097511768659
- type: f1
value: 12.091606412618153
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (cy)
config: cy
split: test
metrics:
- type: accuracy
value: 35.7128446536651
- type: f1
value: 33.62071051640523
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (da)
config: da
split: test
metrics:
- type: accuracy
value: 44.425016812373904
- type: f1
value: 41.20770166767181
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (de)
config: de
split: test
metrics:
- type: accuracy
value: 44.1661062542031
- type: f1
value: 40.374580049860995
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (el)
config: el
split: test
metrics:
- type: accuracy
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type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (te)
config: te
split: test
metrics:
- type: accuracy
value: 7.737054472091459
- type: f1
value: 3.8594459698077364
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (th)
config: th
split: test
metrics:
- type: accuracy
value: 18.31540013449899
- type: f1
value: 13.491482848005418
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (tl)
config: tl
split: test
metrics:
- type: accuracy
value: 48.305312710154666
- type: f1
value: 45.48790821413181
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (tr)
config: tr
split: test
metrics:
- type: accuracy
value: 41.792199058507066
- type: f1
value: 41.24552662271258
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ur)
config: ur
split: test
metrics:
- type: accuracy
value: 24.462004034969738
- type: f1
value: 22.270575649981797
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (vi)
config: vi
split: test
metrics:
- type: accuracy
value: 40.94149293880296
- type: f1
value: 39.08540872012287
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-CN)
config: zh-CN
split: test
metrics:
- type: accuracy
value: 33.17753866845998
- type: f1
value: 31.64001182395128
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-TW)
config: zh-TW
split: test
metrics:
- type: accuracy
value: 31.15669132481506
- type: f1
value: 30.89137619124565
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
metrics:
- type: v_measure
value: 34.24621118290122
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
metrics:
- type: v_measure
value: 32.24202424478886
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
metrics:
- type: map
value: 31.024522945679166
- type: mrr
value: 32.018722362966635
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
metrics:
- type: map_at_1
value: 5.156000000000001
- type: map_at_10
value: 11.551
- type: map_at_100
value: 14.938
- type: map_at_1000
value: 16.366
- type: map_at_3
value: 8.118
- type: map_at_5
value: 9.918000000000001
- type: mrr_at_1
value: 42.415000000000006
- type: mrr_at_10
value: 51.571999999999996
- type: mrr_at_100
value: 52.126
- type: mrr_at_1000
value: 52.171
- type: mrr_at_3
value: 49.02
- type: mrr_at_5
value: 50.50599999999999
- type: ndcg_at_1
value: 39.783
- type: ndcg_at_10
value: 32.25
- type: ndcg_at_100
value: 30.089
- type: ndcg_at_1000
value: 38.86
- type: ndcg_at_3
value: 36.962
- type: ndcg_at_5
value: 35.292
- type: precision_at_1
value: 41.796
- type: precision_at_10
value: 24.272
- type: precision_at_100
value: 7.963000000000001
- type: precision_at_1000
value: 2.07
- type: precision_at_3
value: 35.397
- type: precision_at_5
value: 31.022
- type: recall_at_1
value: 5.156000000000001
- type: recall_at_10
value: 15.468000000000002
- type: recall_at_100
value: 31.049
- type: recall_at_1000
value: 63.148
- type: recall_at_3
value: 9.078999999999999
- type: recall_at_5
value: 12.275
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
metrics:
- type: map_at_1
value: 23.672
- type: map_at_10
value: 38.452
- type: map_at_100
value: 39.705
- type: map_at_1000
value: 39.742
- type: map_at_3
value: 33.806999999999995
- type: map_at_5
value: 36.576
- type: mrr_at_1
value: 26.854
- type: mrr_at_10
value: 40.822
- type: mrr_at_100
value: 41.801
- type: mrr_at_1000
value: 41.827999999999996
- type: mrr_at_3
value: 36.824
- type: mrr_at_5
value: 39.312000000000005
- type: ndcg_at_1
value: 26.854
- type: ndcg_at_10
value: 46.469
- type: ndcg_at_100
value: 51.756
- type: ndcg_at_1000
value: 52.601
- type: ndcg_at_3
value: 37.623
- type: ndcg_at_5
value: 42.324
- type: precision_at_1
value: 26.854
- type: precision_at_10
value: 8.189
- type: precision_at_100
value: 1.11
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 17.718999999999998
- type: precision_at_5
value: 13.291
- type: recall_at_1
value: 23.672
- type: recall_at_10
value: 68.639
- type: recall_at_100
value: 91.546
- type: recall_at_1000
value: 97.794
- type: recall_at_3
value: 45.643
- type: recall_at_5
value: 56.523
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
metrics:
- type: map_at_1
value: 69.667
- type: map_at_10
value: 83.83500000000001
- type: map_at_100
value: 84.479
- type: map_at_1000
value: 84.494
- type: map_at_3
value: 80.759
- type: map_at_5
value: 82.657
- type: mrr_at_1
value: 80.46
- type: mrr_at_10
value: 86.83800000000001
- type: mrr_at_100
value: 86.944
- type: mrr_at_1000
value: 86.945
- type: mrr_at_3
value: 85.815
- type: mrr_at_5
value: 86.508
- type: ndcg_at_1
value: 80.46
- type: ndcg_at_10
value: 87.752
- type: ndcg_at_100
value: 88.973
- type: ndcg_at_1000
value: 89.072
- type: ndcg_at_3
value: 84.735
- type: ndcg_at_5
value: 86.371
- type: precision_at_1
value: 80.46
- type: precision_at_10
value: 13.452
- type: precision_at_100
value: 1.532
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.187
- type: precision_at_5
value: 24.5
- type: recall_at_1
value: 69.667
- type: recall_at_10
value: 95.329
- type: recall_at_100
value: 99.52
- type: recall_at_1000
value: 99.991
- type: recall_at_3
value: 86.696
- type: recall_at_5
value: 91.346
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
metrics:
- type: v_measure
value: 51.177545122684634
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
metrics:
- type: v_measure
value: 54.804652123126985
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
metrics:
- type: map_at_1
value: 5.162
- type: map_at_10
value: 13.168
- type: map_at_100
value: 15.766
- type: map_at_1000
value: 16.136
- type: map_at_3
value: 9.180000000000001
- type: map_at_5
value: 11.205
- type: mrr_at_1
value: 25.5
- type: mrr_at_10
value: 36.617
- type: mrr_at_100
value: 37.814
- type: mrr_at_1000
value: 37.86
- type: mrr_at_3
value: 33.15
- type: mrr_at_5
value: 35.29
- type: ndcg_at_1
value: 25.5
- type: ndcg_at_10
value: 21.818
- type: ndcg_at_100
value: 31.302999999999997
- type: ndcg_at_1000
value: 37.175000000000004
- type: ndcg_at_3
value: 20.358999999999998
- type: ndcg_at_5
value: 18.169
- type: precision_at_1
value: 25.5
- type: precision_at_10
value: 11.32
- type: precision_at_100
value: 2.495
- type: precision_at_1000
value: 0.38899999999999996
- type: precision_at_3
value: 18.833
- type: precision_at_5
value: 16.06
- type: recall_at_1
value: 5.162
- type: recall_at_10
value: 22.932
- type: recall_at_100
value: 50.598
- type: recall_at_1000
value: 79.053
- type: recall_at_3
value: 11.442
- type: recall_at_5
value: 16.272000000000002
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
metrics:
- type: cos_sim_pearson
value: 84.73414727754201
- type: cos_sim_spearman
value: 79.3180820145488
- type: euclidean_pearson
value: 81.33251162244008
- type: euclidean_spearman
value: 79.31808410123591
- type: manhattan_pearson
value: 81.24535628962194
- type: manhattan_spearman
value: 79.18643136990889
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
metrics:
- type: cos_sim_pearson
value: 82.89241604274538
- type: cos_sim_spearman
value: 73.08329002776462
- type: euclidean_pearson
value: 78.75856902522398
- type: euclidean_spearman
value: 73.0808569122323
- type: manhattan_pearson
value: 78.81165127939924
- type: manhattan_spearman
value: 73.13358160467396
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
metrics:
- type: cos_sim_pearson
value: 81.65439991719452
- type: cos_sim_spearman
value: 82.13398891011764
- type: euclidean_pearson
value: 81.63807492339613
- type: euclidean_spearman
value: 82.13398891011764
- type: manhattan_pearson
value: 81.5983078333819
- type: manhattan_spearman
value: 82.11893098949203
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
metrics:
- type: cos_sim_pearson
value: 81.66945263546415
- type: cos_sim_spearman
value: 76.7342099954029
- type: euclidean_pearson
value: 79.98454905286438
- type: euclidean_spearman
value: 76.73420731947648
- type: manhattan_pearson
value: 79.98121513026915
- type: manhattan_spearman
value: 76.74818574618494
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
metrics:
- type: cos_sim_pearson
value: 84.80085528616004
- type: cos_sim_spearman
value: 85.57752600637704
- type: euclidean_pearson
value: 84.88803602633503
- type: euclidean_spearman
value: 85.57753174543699
- type: manhattan_pearson
value: 84.77107707460819
- type: manhattan_spearman
value: 85.4531691739887
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
metrics:
- type: cos_sim_pearson
value: 79.32666585707851
- type: cos_sim_spearman
value: 80.22692417222228
- type: euclidean_pearson
value: 79.847799005588
- type: euclidean_spearman
value: 80.22692417222228
- type: manhattan_pearson
value: 79.86640649752613
- type: manhattan_spearman
value: 80.25939898948658
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ko-ko)
config: ko-ko
split: test
metrics:
- type: cos_sim_pearson
value: 36.97351108396674
- type: cos_sim_spearman
value: 43.373159642451846
- type: euclidean_pearson
value: 42.343251342924724
- type: euclidean_spearman
value: 43.37383732365708
- type: manhattan_pearson
value: 42.21420013714062
- type: manhattan_spearman
value: 43.27093471564943
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ar-ar)
config: ar-ar
split: test
metrics:
- type: cos_sim_pearson
value: 54.25766812232355
- type: cos_sim_spearman
value: 58.70907752953121
- type: euclidean_pearson
value: 57.74925638384565
- type: euclidean_spearman
value: 58.70907752953121
- type: manhattan_pearson
value: 57.53107164585081
- type: manhattan_spearman
value: 58.18399071690873
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-ar)
config: en-ar
split: test
metrics:
- type: cos_sim_pearson
value: 2.000902150291317
- type: cos_sim_spearman
value: 0.5442319876381565
- type: euclidean_pearson
value: 2.0061692624223886
- type: euclidean_spearman
value: 0.5442319876381565
- type: manhattan_pearson
value: 1.6005243901065973
- type: manhattan_spearman
value: 0.8261501538578374
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-de)
config: en-de
split: test
metrics:
- type: cos_sim_pearson
value: 31.103076250241756
- type: cos_sim_spearman
value: 27.538399556865983
- type: euclidean_pearson
value: 31.299966953719917
- type: euclidean_spearman
value: 27.538399556865983
- type: manhattan_pearson
value: 29.252983940152795
- type: manhattan_spearman
value: 24.545142053308506
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
metrics:
- type: cos_sim_pearson
value: 88.92662843843466
- type: cos_sim_spearman
value: 88.6282754793921
- type: euclidean_pearson
value: 88.9663425476392
- type: euclidean_spearman
value: 88.6282754793921
- type: manhattan_pearson
value: 89.04213757202741
- type: manhattan_spearman
value: 88.8029452722001
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-tr)
config: en-tr
split: test
metrics:
- type: cos_sim_pearson
value: 6.699439791440673
- type: cos_sim_spearman
value: 0.42741621491041054
- type: euclidean_pearson
value: 7.0939749740816485
- type: euclidean_spearman
value: 0.42741621491041054
- type: manhattan_pearson
value: 3.7604205840813005
- type: manhattan_spearman
value: -1.7995925853478083
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-en)
config: es-en
split: test
metrics:
- type: cos_sim_pearson
value: 22.332768127048812
- type: cos_sim_spearman
value: 22.011862055263386
- type: euclidean_pearson
value: 22.275743114886957
- type: euclidean_spearman
value: 22.011862055263386
- type: manhattan_pearson
value: 21.382471306976754
- type: manhattan_spearman
value: 20.5220742340821
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-es)
config: es-es
split: test
metrics:
- type: cos_sim_pearson
value: 78.59529102081041
- type: cos_sim_spearman
value: 78.36515013988296
- type: euclidean_pearson
value: 79.6578967101581
- type: euclidean_spearman
value: 78.36388790924713
- type: manhattan_pearson
value: 79.54080618487365
- type: manhattan_spearman
value: 78.03366107978795
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (fr-en)
config: fr-en
split: test
metrics:
- type: cos_sim_pearson
value: 34.19498070710533
- type: cos_sim_spearman
value: 30.702559767030923
- type: euclidean_pearson
value: 34.28061977250095
- type: euclidean_spearman
value: 30.702559767030923
- type: manhattan_pearson
value: 34.8122111793038
- type: manhattan_spearman
value: 31.40796587790667
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (it-en)
config: it-en
split: test
metrics:
- type: cos_sim_pearson
value: 25.84186641167081
- type: cos_sim_spearman
value: 24.28452119168039
- type: euclidean_pearson
value: 25.866557000478302
- type: euclidean_spearman
value: 24.28452119168039
- type: manhattan_pearson
value: 24.273876016721925
- type: manhattan_spearman
value: 23.66844883927423
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (nl-en)
config: nl-en
split: test
metrics:
- type: cos_sim_pearson
value: 31.68262883322153
- type: cos_sim_spearman
value: 24.508086225784982
- type: euclidean_pearson
value: 32.07775246994894
- type: euclidean_spearman
value: 24.508086225784982
- type: manhattan_pearson
value: 33.20196765495327
- type: manhattan_spearman
value: 27.383641505403627
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
metrics:
- type: cos_sim_pearson
value: 66.82398288868168
- type: cos_sim_spearman
value: 65.6697261994716
- type: euclidean_pearson
value: 66.84746542331361
- type: euclidean_spearman
value: 65.6697261994716
- type: manhattan_pearson
value: 66.89947196080837
- type: manhattan_spearman
value: 65.61734245758937
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de)
config: de
split: test
metrics:
- type: cos_sim_pearson
value: 18.956935297479266
- type: cos_sim_spearman
value: 22.525438836468805
- type: euclidean_pearson
value: 13.676185827963197
- type: euclidean_spearman
value: 22.525438836468805
- type: manhattan_pearson
value: 13.749488574260106
- type: manhattan_spearman
value: 22.49725541226794
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es)
config: es
split: test
metrics:
- type: cos_sim_pearson
value: 43.159634114474954
- type: cos_sim_spearman
value: 43.97530387822291
- type: euclidean_pearson
value: 42.45018759035119
- type: euclidean_spearman
value: 43.97530387822291
- type: manhattan_pearson
value: 43.88212906018782
- type: manhattan_spearman
value: 44.2344991447187
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl)
config: pl
split: test
metrics:
- type: cos_sim_pearson
value: 2.9506287366804567
- type: cos_sim_spearman
value: 19.21860340477442
- type: euclidean_pearson
value: 6.306031200912426
- type: euclidean_spearman
value: 19.21860340477442
- type: manhattan_pearson
value: 5.968058806485322
- type: manhattan_spearman
value: 18.496966556101356
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (tr)
config: tr
split: test
metrics:
- type: cos_sim_pearson
value: 17.494702940326327
- type: cos_sim_spearman
value: 21.600665598855933
- type: euclidean_pearson
value: 19.949878763475876
- type: euclidean_spearman
value: 21.600665598855933
- type: manhattan_pearson
value: 20.562737979747386
- type: manhattan_spearman
value: 21.548415116687096
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ar)
config: ar
split: test
metrics:
- type: cos_sim_pearson
value: 21.455304899947475
- type: cos_sim_spearman
value: 17.54247841644246
- type: euclidean_pearson
value: 19.954769470444862
- type: euclidean_spearman
value: 17.54247841644246
- type: manhattan_pearson
value: 20.491628523649304
- type: manhattan_spearman
value: 17.984509706975498
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ru)
config: ru
split: test
metrics:
- type: cos_sim_pearson
value: 5.725870260172754
- type: cos_sim_spearman
value: 11.187514830423046
- type: euclidean_pearson
value: 5.917124931676964
- type: euclidean_spearman
value: 11.187514830423046
- type: manhattan_pearson
value: 6.374841892742465
- type: manhattan_spearman
value: 10.769670996439327
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
metrics:
- type: cos_sim_pearson
value: 23.644675903928903
- type: cos_sim_spearman
value: 33.1476054705555
- type: euclidean_pearson
value: 27.486723401317015
- type: euclidean_spearman
value: 33.14559867176513
- type: manhattan_pearson
value: 28.905530853992335
- type: manhattan_spearman
value: 32.97179552695711
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr)
config: fr
split: test
metrics:
- type: cos_sim_pearson
value: 68.19096417445061
- type: cos_sim_spearman
value: 69.51402658537921
- type: euclidean_pearson
value: 65.89836450895854
- type: euclidean_spearman
value: 69.51402658537921
- type: manhattan_pearson
value: 65.95918282706997
- type: manhattan_spearman
value: 69.66631782067878
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-en)
config: de-en
split: test
metrics:
- type: cos_sim_pearson
value: 47.02727261965111
- type: cos_sim_spearman
value: 42.85739641224728
- type: euclidean_pearson
value: 47.55857919944314
- type: euclidean_spearman
value: 42.85739641224728
- type: manhattan_pearson
value: 50.24947623020984
- type: manhattan_spearman
value: 44.34581665268886
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-en)
config: es-en
split: test
metrics:
- type: cos_sim_pearson
value: 52.54253509229287
- type: cos_sim_spearman
value: 53.98864875959218
- type: euclidean_pearson
value: 52.771474843725464
- type: euclidean_spearman
value: 53.98864875959218
- type: manhattan_pearson
value: 53.39728391060008
- type: manhattan_spearman
value: 54.65413858996554
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (it)
config: it
split: test
metrics:
- type: cos_sim_pearson
value: 48.017241684543656
- type: cos_sim_spearman
value: 47.47536430344332
- type: euclidean_pearson
value: 46.94098755337956
- type: euclidean_spearman
value: 47.47536430344332
- type: manhattan_pearson
value: 47.27489495136295
- type: manhattan_spearman
value: 47.75408075281176
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl-en)
config: pl-en
split: test
metrics:
- type: cos_sim_pearson
value: 43.16723254329198
- type: cos_sim_spearman
value: 42.6695846628273
- type: euclidean_pearson
value: 43.37634781317223
- type: euclidean_spearman
value: 42.6695846628273
- type: manhattan_pearson
value: 46.43632735525556
- type: manhattan_spearman
value: 44.399080708250175
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh-en)
config: zh-en
split: test
metrics:
- type: cos_sim_pearson
value: 42.614472380988
- type: cos_sim_spearman
value: 44.386615916921755
- type: euclidean_pearson
value: 42.602921485579536
- type: euclidean_spearman
value: 44.386615916921755
- type: manhattan_pearson
value: 39.57742966805997
- type: manhattan_spearman
value: 41.12937281700849
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-it)
config: es-it
split: test
metrics:
- type: cos_sim_pearson
value: 41.19025498086497
- type: cos_sim_spearman
value: 40.70511339346037
- type: euclidean_pearson
value: 41.757361379987536
- type: euclidean_spearman
value: 40.70511339346037
- type: manhattan_pearson
value: 42.12654868854391
- type: manhattan_spearman
value: 40.16977290096036
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-fr)
config: de-fr
split: test
metrics:
- type: cos_sim_pearson
value: 42.58930629526249
- type: cos_sim_spearman
value: 43.51970789091437
- type: euclidean_pearson
value: 42.79780567751299
- type: euclidean_spearman
value: 43.51970789091437
- type: manhattan_pearson
value: 43.11190678703615
- type: manhattan_spearman
value: 43.921331076552214
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-pl)
config: de-pl
split: test
metrics:
- type: cos_sim_pearson
value: 9.14354524166508
- type: cos_sim_spearman
value: 1.632087485480262
- type: euclidean_pearson
value: 9.808059926397586
- type: euclidean_spearman
value: 1.632087485480262
- type: manhattan_pearson
value: 15.655877492684972
- type: manhattan_spearman
value: 9.084260532390138
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr-pl)
config: fr-pl
split: test
metrics:
- type: cos_sim_pearson
value: 16.116974803470246
- type: cos_sim_spearman
value: 16.903085094570333
- type: euclidean_pearson
value: 16.277560475636694
- type: euclidean_spearman
value: 16.903085094570333
- type: manhattan_pearson
value: 20.321632312194925
- type: manhattan_spearman
value: 28.17180849095055
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
metrics:
- type: cos_sim_pearson
value: 83.75945741541354
- type: cos_sim_spearman
value: 83.08944658809418
- type: euclidean_pearson
value: 83.5587988852494
- type: euclidean_spearman
value: 83.08938533093635
- type: manhattan_pearson
value: 83.56896467262781
- type: manhattan_spearman
value: 83.11516183577004
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
metrics:
- type: map
value: 87.20127714147824
- type: mrr
value: 96.44415315983943
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
metrics:
- type: map_at_1
value: 47.483
- type: map_at_10
value: 57.18600000000001
- type: map_at_100
value: 57.863
- type: map_at_1000
value: 57.901
- type: map_at_3
value: 53.909
- type: map_at_5
value: 55.57299999999999
- type: mrr_at_1
value: 50
- type: mrr_at_10
value: 58.607
- type: mrr_at_100
value: 59.169000000000004
- type: mrr_at_1000
value: 59.207
- type: mrr_at_3
value: 56.056
- type: mrr_at_5
value: 57.422
- type: ndcg_at_1
value: 50
- type: ndcg_at_10
value: 62.639
- type: ndcg_at_100
value: 65.549
- type: ndcg_at_1000
value: 66.497
- type: ndcg_at_3
value: 56.602
- type: ndcg_at_5
value: 59.270999999999994
- type: precision_at_1
value: 50
- type: precision_at_10
value: 8.833
- type: precision_at_100
value: 1.0370000000000001
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 22.222
- type: precision_at_5
value: 15
- type: recall_at_1
value: 47.483
- type: recall_at_10
value: 78.233
- type: recall_at_100
value: 91.167
- type: recall_at_1000
value: 98.333
- type: recall_at_3
value: 61.956
- type: recall_at_5
value: 68.43900000000001
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
metrics:
- type: cos_sim_accuracy
value: 99.72871287128713
- type: cos_sim_ap
value: 92.44554820122362
- type: cos_sim_f1
value: 85.89083419155509
- type: cos_sim_precision
value: 88.53503184713377
- type: cos_sim_recall
value: 83.39999999999999
- type: dot_accuracy
value: 99.72871287128713
- type: dot_ap
value: 92.44554820122363
- type: dot_f1
value: 85.89083419155509
- type: dot_precision
value: 88.53503184713377
- type: dot_recall
value: 83.39999999999999
- type: euclidean_accuracy
value: 99.72871287128713
- type: euclidean_ap
value: 92.44554820122362
- type: euclidean_f1
value: 85.89083419155509
- type: euclidean_precision
value: 88.53503184713377
- type: euclidean_recall
value: 83.39999999999999
- type: manhattan_accuracy
value: 99.73267326732673
- type: manhattan_ap
value: 92.57860510428624
- type: manhattan_f1
value: 86.20170597089813
- type: manhattan_precision
value: 86.5055387713998
- type: manhattan_recall
value: 85.9
- type: max_accuracy
value: 99.73267326732673
- type: max_ap
value: 92.57860510428624
- type: max_f1
value: 86.20170597089813
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
metrics:
- type: v_measure
value: 53.04887987709521
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
metrics:
- type: v_measure
value: 33.133116286225686
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
metrics:
- type: map
value: 51.4732035634667
- type: mrr
value: 52.263880931160344
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
metrics:
- type: cos_sim_pearson
value: 29.365093191497525
- type: cos_sim_spearman
value: 27.90160600683062
- type: dot_pearson
value: 29.36509564650472
- type: dot_spearman
value: 27.90160600683062
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
metrics:
- type: map_at_1
value: 0.17600000000000002
- type: map_at_10
value: 1.164
- type: map_at_100
value: 6.048
- type: map_at_1000
value: 14.913000000000002
- type: map_at_3
value: 0.44799999999999995
- type: map_at_5
value: 0.658
- type: mrr_at_1
value: 64
- type: mrr_at_10
value: 73.538
- type: mrr_at_100
value: 73.752
- type: mrr_at_1000
value: 73.752
- type: mrr_at_3
value: 70.667
- type: mrr_at_5
value: 72.467
- type: ndcg_at_1
value: 59
- type: ndcg_at_10
value: 50.815999999999995
- type: ndcg_at_100
value: 37.662
- type: ndcg_at_1000
value: 35.907
- type: ndcg_at_3
value: 54.112
- type: ndcg_at_5
value: 51.19200000000001
- type: precision_at_1
value: 64
- type: precision_at_10
value: 55.400000000000006
- type: precision_at_100
value: 38.48
- type: precision_at_1000
value: 16.012
- type: precision_at_3
value: 57.99999999999999
- type: precision_at_5
value: 54.800000000000004
- type: recall_at_1
value: 0.17600000000000002
- type: recall_at_10
value: 1.435
- type: recall_at_100
value: 9.122
- type: recall_at_1000
value: 34.378
- type: recall_at_3
value: 0.47400000000000003
- type: recall_at_5
value: 0.736
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
metrics:
- type: map_at_1
value: 1.813
- type: map_at_10
value: 6.632000000000001
- type: map_at_100
value: 11.485
- type: map_at_1000
value: 13.031
- type: map_at_3
value: 3.5069999999999997
- type: map_at_5
value: 5.183
- type: mrr_at_1
value: 18.367
- type: mrr_at_10
value: 33.035
- type: mrr_at_100
value: 34.117
- type: mrr_at_1000
value: 34.168
- type: mrr_at_3
value: 27.551
- type: mrr_at_5
value: 31.326999999999998
- type: ndcg_at_1
value: 15.306000000000001
- type: ndcg_at_10
value: 17.224
- type: ndcg_at_100
value: 29.287999999999997
- type: ndcg_at_1000
value: 41.613
- type: ndcg_at_3
value: 15.786
- type: ndcg_at_5
value: 16.985
- type: precision_at_1
value: 18.367
- type: precision_at_10
value: 15.714
- type: precision_at_100
value: 6.4079999999999995
- type: precision_at_1000
value: 1.451
- type: precision_at_3
value: 17.687
- type: precision_at_5
value: 18.776
- type: recall_at_1
value: 1.813
- type: recall_at_10
value: 12.006
- type: recall_at_100
value: 41.016999999999996
- type: recall_at_1000
value: 78.632
- type: recall_at_3
value: 4.476999999999999
- type: recall_at_5
value: 7.904999999999999
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
metrics:
- type: accuracy
value: 67.4694
- type: ap
value: 12.602604676283388
- type: f1
value: 51.82471949507483
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
metrics:
- type: accuracy
value: 54.25297113752122
- type: f1
value: 54.50148311546008
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
metrics:
- type: v_measure
value: 47.467044776612376
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
metrics:
- type: cos_sim_accuracy
value: 84.78869881385229
- type: cos_sim_ap
value: 70.01722500181003
- type: cos_sim_f1
value: 65.943384461903
- type: cos_sim_precision
value: 62.52069047056041
- type: cos_sim_recall
value: 69.76253298153034
- type: dot_accuracy
value: 84.78869881385229
- type: dot_ap
value: 70.01721947474665
- type: dot_f1
value: 65.943384461903
- type: dot_precision
value: 62.52069047056041
- type: dot_recall
value: 69.76253298153034
- type: euclidean_accuracy
value: 84.78869881385229
- type: euclidean_ap
value: 70.01721811552584
- type: euclidean_f1
value: 65.943384461903
- type: euclidean_precision
value: 62.52069047056041
- type: euclidean_recall
value: 69.76253298153034
- type: manhattan_accuracy
value: 84.68140907194373
- type: manhattan_ap
value: 69.90669388421887
- type: manhattan_f1
value: 66.00842865743527
- type: manhattan_precision
value: 60.70874861572536
- type: manhattan_recall
value: 72.32189973614776
- type: max_accuracy
value: 84.78869881385229
- type: max_ap
value: 70.01722500181003
- type: max_f1
value: 66.00842865743527
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
metrics:
- type: cos_sim_accuracy
value: 88.4367601971514
- type: cos_sim_ap
value: 84.77318195783158
- type: cos_sim_f1
value: 77.13502703503444
- type: cos_sim_precision
value: 74.31140288283146
- type: cos_sim_recall
value: 80.18170619032954
- type: dot_accuracy
value: 88.4367601971514
- type: dot_ap
value: 84.77317449778201
- type: dot_f1
value: 77.13502703503444
- type: dot_precision
value: 74.31140288283146
- type: dot_recall
value: 80.18170619032954
- type: euclidean_accuracy
value: 88.4367601971514
- type: euclidean_ap
value: 84.77314948093711
- type: euclidean_f1
value: 77.13502703503444
- type: euclidean_precision
value: 74.31140288283146
- type: euclidean_recall
value: 80.18170619032954
- type: manhattan_accuracy
value: 88.43287926417511
- type: manhattan_ap
value: 84.71097141640011
- type: manhattan_f1
value: 77.08356453223837
- type: manhattan_precision
value: 74.18298326806692
- type: manhattan_recall
value: 80.2202032645519
- type: max_accuracy
value: 88.4367601971514
- type: max_ap
value: 84.77318195783158
- type: max_f1
value: 77.13502703503444
all-MiniLM-L12-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')
embeddings = model.encode(sentences)
print(embeddings)
Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
#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('sentence-transformers/all-MiniLM-L12-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L12-v2')
# 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
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# Normalize embeddings
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:")
print(sentence_embeddings)
Evaluation Results
For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net
Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained microsoft/MiniLM-L12-H384-uncased model and fine-tuned in on a
1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developped this model during the Community week using JAX/Flax for NLP & CV, organized by Hugging Face. We developped this model as part of the project: Train the Best Sentence Embedding Model Ever with 1B Training Pairs. We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
Intended uses
Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
By default, input text longer than 256 word pieces is truncated.
Training procedure
Pre-training
We use the pretrained microsoft/MiniLM-L12-H384-uncased model. Please refer to the model card for more detailed information about the pre-training procedure.
Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs.
Hyper parameters
We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository: train_script.py.
Training data
We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
We sampled each dataset given a weighted probability which configuration is detailed in the data_config.json file.
| Dataset | Paper | Number of training tuples |
|---|---|---|
| Reddit comments (2015-2018) | paper | 726,484,430 |
| S2ORC Citation pairs (Abstracts) | paper | 116,288,806 |
| WikiAnswers Duplicate question pairs | paper | 77,427,422 |
| PAQ (Question, Answer) pairs | paper | 64,371,441 |
| S2ORC Citation pairs (Titles) | paper | 52,603,982 |
| S2ORC (Title, Abstract) | paper | 41,769,185 |
| Stack Exchange (Title, Body) pairs | - | 25,316,456 |
| Stack Exchange (Title+Body, Answer) pairs | - | 21,396,559 |
| Stack Exchange (Title, Answer) pairs | - | 21,396,559 |
| MS MARCO triplets | paper | 9,144,553 |
| GOOAQ: Open Question Answering with Diverse Answer Types | paper | 3,012,496 |
| Yahoo Answers (Title, Answer) | paper | 1,198,260 |
| Code Search | - | 1,151,414 |
| COCO Image captions | paper | 828,395 |
| SPECTER citation triplets | paper | 684,100 |
| Yahoo Answers (Question, Answer) | paper | 681,164 |
| Yahoo Answers (Title, Question) | paper | 659,896 |
| SearchQA | paper | 582,261 |
| Eli5 | paper | 325,475 |
| Flickr 30k | paper | 317,695 |
| Stack Exchange Duplicate questions (titles) | 304,525 | |
| AllNLI (SNLI and MultiNLI | paper SNLI, paper MultiNLI | 277,230 |
| Stack Exchange Duplicate questions (bodies) | 250,519 | |
| Stack Exchange Duplicate questions (titles+bodies) | 250,460 | |
| Sentence Compression | paper | 180,000 |
| Wikihow | paper | 128,542 |
| Altlex | paper | 112,696 |
| Quora Question Triplets | - | 103,663 |
| Simple Wikipedia | paper | 102,225 |
| Natural Questions (NQ) | paper | 100,231 |
| SQuAD2.0 | paper | 87,599 |
| TriviaQA | - | 73,346 |
| Total | 1,170,060,424 |