---
pipeline_tag: sentence-similarity
tags:
- finetuner
- mteb
- sentence-transformers
- feature-extraction
- sentence-similarity
datasets:
- jinaai/negation-dataset
language: en
license: apache-2.0
model-index:
- name: jina-embedding-s-en-v1
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 64.82089552238806
- type: ap
value: 27.100981946230778
- type: f1
value: 58.3354886367184
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 64.282775
- type: ap
value: 60.350688924943796
- type: f1
value: 62.06346948494396
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 30.623999999999995
- type: f1
value: 29.427789186742153
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.119
- type: map_at_10
value: 35.609
- type: map_at_100
value: 36.935
- type: map_at_1000
value: 36.957
- type: map_at_3
value: 31.046000000000003
- type: map_at_5
value: 33.574
- type: mrr_at_1
value: 22.404
- type: mrr_at_10
value: 35.695
- type: mrr_at_100
value: 37.021
- type: mrr_at_1000
value: 37.043
- type: mrr_at_3
value: 31.093
- type: mrr_at_5
value: 33.635999999999996
- type: ndcg_at_1
value: 22.119
- type: ndcg_at_10
value: 43.566
- type: ndcg_at_100
value: 49.370000000000005
- type: ndcg_at_1000
value: 49.901
- type: ndcg_at_3
value: 34.06
- type: ndcg_at_5
value: 38.653999999999996
- type: precision_at_1
value: 22.119
- type: precision_at_10
value: 6.92
- type: precision_at_100
value: 0.95
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 14.272000000000002
- type: precision_at_5
value: 10.811
- type: recall_at_1
value: 22.119
- type: recall_at_10
value: 69.203
- type: recall_at_100
value: 95.021
- type: recall_at_1000
value: 99.075
- type: recall_at_3
value: 42.817
- type: recall_at_5
value: 54.054
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 34.1740289109719
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 23.985251383455463
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.24873612289029
- type: mrr
value: 74.65692740623489
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.22415390332444
- type: cos_sim_spearman
value: 82.9591191954711
- type: euclidean_pearson
value: 44.096317524324945
- type: euclidean_spearman
value: 42.95218351391625
- type: manhattan_pearson
value: 44.07766490545065
- type: manhattan_spearman
value: 42.78350497166606
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 74.64285714285714
- type: f1
value: 73.53680835577447
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 28.512813238490164
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 20.942214972649488
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.255999999999997
- type: map_at_10
value: 37.091
- type: map_at_100
value: 38.428000000000004
- type: map_at_1000
value: 38.559
- type: map_at_3
value: 34.073
- type: map_at_5
value: 35.739
- type: mrr_at_1
value: 34.907
- type: mrr_at_10
value: 42.769
- type: mrr_at_100
value: 43.607
- type: mrr_at_1000
value: 43.656
- type: mrr_at_3
value: 39.986
- type: mrr_at_5
value: 41.581
- type: ndcg_at_1
value: 34.907
- type: ndcg_at_10
value: 42.681000000000004
- type: ndcg_at_100
value: 48.213
- type: ndcg_at_1000
value: 50.464
- type: ndcg_at_3
value: 37.813
- type: ndcg_at_5
value: 39.936
- type: precision_at_1
value: 34.907
- type: precision_at_10
value: 7.911
- type: precision_at_100
value: 1.349
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 17.93
- type: precision_at_5
value: 12.732
- type: recall_at_1
value: 28.255999999999997
- type: recall_at_10
value: 53.49699999999999
- type: recall_at_100
value: 77.288
- type: recall_at_1000
value: 91.776
- type: recall_at_3
value: 39.18
- type: recall_at_5
value: 45.365
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.563999999999997
- type: map_at_10
value: 33.913
- type: map_at_100
value: 34.966
- type: map_at_1000
value: 35.104
- type: map_at_3
value: 31.413000000000004
- type: map_at_5
value: 32.854
- type: mrr_at_1
value: 31.72
- type: mrr_at_10
value: 39.391
- type: mrr_at_100
value: 40.02
- type: mrr_at_1000
value: 40.076
- type: mrr_at_3
value: 37.314
- type: mrr_at_5
value: 38.507999999999996
- type: ndcg_at_1
value: 31.72
- type: ndcg_at_10
value: 38.933
- type: ndcg_at_100
value: 43.024
- type: ndcg_at_1000
value: 45.556999999999995
- type: ndcg_at_3
value: 35.225
- type: ndcg_at_5
value: 36.984
- type: precision_at_1
value: 31.72
- type: precision_at_10
value: 7.248
- type: precision_at_100
value: 1.192
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_3
value: 16.943
- type: precision_at_5
value: 11.975
- type: recall_at_1
value: 25.563999999999997
- type: recall_at_10
value: 47.808
- type: recall_at_100
value: 65.182
- type: recall_at_1000
value: 81.831
- type: recall_at_3
value: 36.889
- type: recall_at_5
value: 41.829
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.662
- type: map_at_10
value: 44.096999999999994
- type: map_at_100
value: 45.153999999999996
- type: map_at_1000
value: 45.223
- type: map_at_3
value: 41.377
- type: map_at_5
value: 42.935
- type: mrr_at_1
value: 38.997
- type: mrr_at_10
value: 47.675
- type: mrr_at_100
value: 48.476
- type: mrr_at_1000
value: 48.519
- type: mrr_at_3
value: 45.549
- type: mrr_at_5
value: 46.884
- type: ndcg_at_1
value: 38.997
- type: ndcg_at_10
value: 49.196
- type: ndcg_at_100
value: 53.788000000000004
- type: ndcg_at_1000
value: 55.393
- type: ndcg_at_3
value: 44.67
- type: ndcg_at_5
value: 46.991
- type: precision_at_1
value: 38.997
- type: precision_at_10
value: 7.875
- type: precision_at_100
value: 1.102
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 19.854
- type: precision_at_5
value: 13.605
- type: recall_at_1
value: 33.662
- type: recall_at_10
value: 60.75899999999999
- type: recall_at_100
value: 81.11699999999999
- type: recall_at_1000
value: 92.805
- type: recall_at_3
value: 48.577999999999996
- type: recall_at_5
value: 54.384
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.313
- type: map_at_10
value: 29.036
- type: map_at_100
value: 29.975
- type: map_at_1000
value: 30.063000000000002
- type: map_at_3
value: 26.878999999999998
- type: map_at_5
value: 28.005999999999997
- type: mrr_at_1
value: 23.39
- type: mrr_at_10
value: 31.072
- type: mrr_at_100
value: 31.922
- type: mrr_at_1000
value: 31.995
- type: mrr_at_3
value: 28.908
- type: mrr_at_5
value: 30.104999999999997
- type: ndcg_at_1
value: 23.39
- type: ndcg_at_10
value: 33.448
- type: ndcg_at_100
value: 38.255
- type: ndcg_at_1000
value: 40.542
- type: ndcg_at_3
value: 29.060000000000002
- type: ndcg_at_5
value: 31.023
- type: precision_at_1
value: 23.39
- type: precision_at_10
value: 5.175
- type: precision_at_100
value: 0.8049999999999999
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 12.504999999999999
- type: precision_at_5
value: 8.61
- type: recall_at_1
value: 21.313
- type: recall_at_10
value: 45.345
- type: recall_at_100
value: 67.752
- type: recall_at_1000
value: 84.937
- type: recall_at_3
value: 33.033
- type: recall_at_5
value: 37.929
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.255999999999998
- type: map_at_10
value: 20.339
- type: map_at_100
value: 21.491
- type: map_at_1000
value: 21.616
- type: map_at_3
value: 18.481
- type: map_at_5
value: 19.594
- type: mrr_at_1
value: 17.413
- type: mrr_at_10
value: 24.146
- type: mrr_at_100
value: 25.188
- type: mrr_at_1000
value: 25.273
- type: mrr_at_3
value: 22.264
- type: mrr_at_5
value: 23.302
- type: ndcg_at_1
value: 17.413
- type: ndcg_at_10
value: 24.272
- type: ndcg_at_100
value: 29.82
- type: ndcg_at_1000
value: 33.072
- type: ndcg_at_3
value: 20.826
- type: ndcg_at_5
value: 22.535
- type: precision_at_1
value: 17.413
- type: precision_at_10
value: 4.366
- type: precision_at_100
value: 0.818
- type: precision_at_1000
value: 0.124
- type: precision_at_3
value: 9.866999999999999
- type: precision_at_5
value: 7.164
- type: recall_at_1
value: 14.255999999999998
- type: recall_at_10
value: 32.497
- type: recall_at_100
value: 56.592
- type: recall_at_1000
value: 80.17699999999999
- type: recall_at_3
value: 23.195
- type: recall_at_5
value: 27.392
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.709
- type: map_at_10
value: 31.377
- type: map_at_100
value: 32.536
- type: map_at_1000
value: 32.669
- type: map_at_3
value: 28.572999999999997
- type: map_at_5
value: 30.205
- type: mrr_at_1
value: 27.815
- type: mrr_at_10
value: 36.452
- type: mrr_at_100
value: 37.302
- type: mrr_at_1000
value: 37.364000000000004
- type: mrr_at_3
value: 33.75
- type: mrr_at_5
value: 35.43
- type: ndcg_at_1
value: 27.815
- type: ndcg_at_10
value: 36.84
- type: ndcg_at_100
value: 42.092
- type: ndcg_at_1000
value: 44.727
- type: ndcg_at_3
value: 31.964
- type: ndcg_at_5
value: 34.428
- type: precision_at_1
value: 27.815
- type: precision_at_10
value: 6.67
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.151
- type: precision_at_3
value: 14.982000000000001
- type: precision_at_5
value: 10.857
- type: recall_at_1
value: 22.709
- type: recall_at_10
value: 48.308
- type: recall_at_100
value: 70.866
- type: recall_at_1000
value: 88.236
- type: recall_at_3
value: 34.709
- type: recall_at_5
value: 40.996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.348000000000003
- type: map_at_10
value: 29.427999999999997
- type: map_at_100
value: 30.499
- type: map_at_1000
value: 30.631999999999998
- type: map_at_3
value: 27.035999999999998
- type: map_at_5
value: 28.351
- type: mrr_at_1
value: 27.74
- type: mrr_at_10
value: 34.424
- type: mrr_at_100
value: 35.341
- type: mrr_at_1000
value: 35.419
- type: mrr_at_3
value: 32.401
- type: mrr_at_5
value: 33.497
- type: ndcg_at_1
value: 27.74
- type: ndcg_at_10
value: 34.136
- type: ndcg_at_100
value: 39.269
- type: ndcg_at_1000
value: 42.263
- type: ndcg_at_3
value: 30.171999999999997
- type: ndcg_at_5
value: 31.956
- type: precision_at_1
value: 27.74
- type: precision_at_10
value: 6.062
- type: precision_at_100
value: 1.014
- type: precision_at_1000
value: 0.146
- type: precision_at_3
value: 14.079
- type: precision_at_5
value: 9.977
- type: recall_at_1
value: 22.348000000000003
- type: recall_at_10
value: 43.477
- type: recall_at_100
value: 65.945
- type: recall_at_1000
value: 86.587
- type: recall_at_3
value: 32.107
- type: recall_at_5
value: 36.974000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.688499999999998
- type: map_at_10
value: 29.164666666666665
- type: map_at_100
value: 30.22575
- type: map_at_1000
value: 30.350833333333334
- type: map_at_3
value: 26.82025
- type: map_at_5
value: 28.14966666666667
- type: mrr_at_1
value: 25.779249999999998
- type: mrr_at_10
value: 32.969
- type: mrr_at_100
value: 33.81725
- type: mrr_at_1000
value: 33.88825
- type: mrr_at_3
value: 30.831250000000004
- type: mrr_at_5
value: 32.065000000000005
- type: ndcg_at_1
value: 25.779249999999998
- type: ndcg_at_10
value: 33.73675
- type: ndcg_at_100
value: 38.635666666666665
- type: ndcg_at_1000
value: 41.353500000000004
- type: ndcg_at_3
value: 29.66283333333333
- type: ndcg_at_5
value: 31.607249999999997
- type: precision_at_1
value: 25.779249999999998
- type: precision_at_10
value: 5.861416666666667
- type: precision_at_100
value: 0.9852500000000002
- type: precision_at_1000
value: 0.14108333333333334
- type: precision_at_3
value: 13.563583333333332
- type: precision_at_5
value: 9.630333333333335
- type: recall_at_1
value: 21.688499999999998
- type: recall_at_10
value: 43.605
- type: recall_at_100
value: 65.52366666666667
- type: recall_at_1000
value: 84.69683333333332
- type: recall_at_3
value: 32.195499999999996
- type: recall_at_5
value: 37.25325
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.279
- type: map_at_10
value: 23.238
- type: map_at_100
value: 24.026
- type: map_at_1000
value: 24.13
- type: map_at_3
value: 20.730999999999998
- type: map_at_5
value: 22.278000000000002
- type: mrr_at_1
value: 19.017999999999997
- type: mrr_at_10
value: 25.188
- type: mrr_at_100
value: 25.918999999999997
- type: mrr_at_1000
value: 25.996999999999996
- type: mrr_at_3
value: 22.776
- type: mrr_at_5
value: 24.256
- type: ndcg_at_1
value: 19.017999999999997
- type: ndcg_at_10
value: 27.171
- type: ndcg_at_100
value: 31.274
- type: ndcg_at_1000
value: 34.016000000000005
- type: ndcg_at_3
value: 22.442
- type: ndcg_at_5
value: 24.955
- type: precision_at_1
value: 19.017999999999997
- type: precision_at_10
value: 4.494
- type: precision_at_100
value: 0.712
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 9.611
- type: precision_at_5
value: 7.331
- type: recall_at_1
value: 17.279
- type: recall_at_10
value: 37.464999999999996
- type: recall_at_100
value: 56.458
- type: recall_at_1000
value: 76.759
- type: recall_at_3
value: 24.659
- type: recall_at_5
value: 30.672
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.901
- type: map_at_10
value: 20.268
- type: map_at_100
value: 21.143
- type: map_at_1000
value: 21.264
- type: map_at_3
value: 18.557000000000002
- type: map_at_5
value: 19.483
- type: mrr_at_1
value: 17.997
- type: mrr_at_10
value: 23.591
- type: mrr_at_100
value: 24.387
- type: mrr_at_1000
value: 24.471
- type: mrr_at_3
value: 21.874
- type: mrr_at_5
value: 22.797
- type: ndcg_at_1
value: 17.997
- type: ndcg_at_10
value: 23.87
- type: ndcg_at_100
value: 28.459
- type: ndcg_at_1000
value: 31.66
- type: ndcg_at_3
value: 20.779
- type: ndcg_at_5
value: 22.137
- type: precision_at_1
value: 17.997
- type: precision_at_10
value: 4.25
- type: precision_at_100
value: 0.761
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 9.716
- type: precision_at_5
value: 6.909999999999999
- type: recall_at_1
value: 14.901
- type: recall_at_10
value: 31.44
- type: recall_at_100
value: 52.717000000000006
- type: recall_at_1000
value: 76.102
- type: recall_at_3
value: 22.675
- type: recall_at_5
value: 26.336
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.52
- type: map_at_10
value: 28.397
- type: map_at_100
value: 29.443
- type: map_at_1000
value: 29.56
- type: map_at_3
value: 26.501
- type: map_at_5
value: 27.375
- type: mrr_at_1
value: 25.28
- type: mrr_at_10
value: 32.102000000000004
- type: mrr_at_100
value: 33.005
- type: mrr_at_1000
value: 33.084
- type: mrr_at_3
value: 30.208000000000002
- type: mrr_at_5
value: 31.146
- type: ndcg_at_1
value: 25.28
- type: ndcg_at_10
value: 32.635
- type: ndcg_at_100
value: 37.672
- type: ndcg_at_1000
value: 40.602
- type: ndcg_at_3
value: 28.951999999999998
- type: ndcg_at_5
value: 30.336999999999996
- type: precision_at_1
value: 25.28
- type: precision_at_10
value: 5.3260000000000005
- type: precision_at_100
value: 0.8840000000000001
- type: precision_at_1000
value: 0.126
- type: precision_at_3
value: 12.687000000000001
- type: precision_at_5
value: 8.638
- type: recall_at_1
value: 21.52
- type: recall_at_10
value: 41.955
- type: recall_at_100
value: 64.21
- type: recall_at_1000
value: 85.28099999999999
- type: recall_at_3
value: 31.979999999999997
- type: recall_at_5
value: 35.406
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.296
- type: map_at_10
value: 28.449999999999996
- type: map_at_100
value: 29.847
- type: map_at_1000
value: 30.073
- type: map_at_3
value: 25.995
- type: map_at_5
value: 27.603
- type: mrr_at_1
value: 25.296000000000003
- type: mrr_at_10
value: 32.751999999999995
- type: mrr_at_100
value: 33.705
- type: mrr_at_1000
value: 33.783
- type: mrr_at_3
value: 30.731
- type: mrr_at_5
value: 32.006
- type: ndcg_at_1
value: 25.296000000000003
- type: ndcg_at_10
value: 33.555
- type: ndcg_at_100
value: 38.891999999999996
- type: ndcg_at_1000
value: 42.088
- type: ndcg_at_3
value: 29.944
- type: ndcg_at_5
value: 31.997999999999998
- type: precision_at_1
value: 25.296000000000003
- type: precision_at_10
value: 6.542000000000001
- type: precision_at_100
value: 1.354
- type: precision_at_1000
value: 0.22599999999999998
- type: precision_at_3
value: 14.360999999999999
- type: precision_at_5
value: 10.593
- type: recall_at_1
value: 20.296
- type: recall_at_10
value: 42.742000000000004
- type: recall_at_100
value: 67.351
- type: recall_at_1000
value: 88.774
- type: recall_at_3
value: 32.117000000000004
- type: recall_at_5
value: 37.788
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.157999999999998
- type: map_at_10
value: 24.342
- type: map_at_100
value: 25.201
- type: map_at_1000
value: 25.317
- type: map_at_3
value: 22.227
- type: map_at_5
value: 23.372999999999998
- type: mrr_at_1
value: 19.778000000000002
- type: mrr_at_10
value: 26.066
- type: mrr_at_100
value: 26.935
- type: mrr_at_1000
value: 27.022000000000002
- type: mrr_at_3
value: 24.214
- type: mrr_at_5
value: 25.268
- type: ndcg_at_1
value: 19.778000000000002
- type: ndcg_at_10
value: 28.104000000000003
- type: ndcg_at_100
value: 32.87
- type: ndcg_at_1000
value: 35.858000000000004
- type: ndcg_at_3
value: 24.107
- type: ndcg_at_5
value: 26.007
- type: precision_at_1
value: 19.778000000000002
- type: precision_at_10
value: 4.417999999999999
- type: precision_at_100
value: 0.739
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 10.228
- type: precision_at_5
value: 7.172000000000001
- type: recall_at_1
value: 18.157999999999998
- type: recall_at_10
value: 37.967
- type: recall_at_100
value: 60.806000000000004
- type: recall_at_1000
value: 83.097
- type: recall_at_3
value: 27.223999999999997
- type: recall_at_5
value: 31.968000000000004
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.055
- type: map_at_10
value: 11.609
- type: map_at_100
value: 12.83
- type: map_at_1000
value: 12.995000000000001
- type: map_at_3
value: 9.673
- type: map_at_5
value: 10.761999999999999
- type: mrr_at_1
value: 15.309000000000001
- type: mrr_at_10
value: 23.655
- type: mrr_at_100
value: 24.785
- type: mrr_at_1000
value: 24.856
- type: mrr_at_3
value: 20.499000000000002
- type: mrr_at_5
value: 22.425
- type: ndcg_at_1
value: 15.309000000000001
- type: ndcg_at_10
value: 17.252000000000002
- type: ndcg_at_100
value: 22.976
- type: ndcg_at_1000
value: 26.480999999999998
- type: ndcg_at_3
value: 13.418
- type: ndcg_at_5
value: 15.084
- type: precision_at_1
value: 15.309000000000001
- type: precision_at_10
value: 5.309
- type: precision_at_100
value: 1.1320000000000001
- type: precision_at_1000
value: 0.17600000000000002
- type: precision_at_3
value: 9.62
- type: precision_at_5
value: 7.883
- type: recall_at_1
value: 7.055
- type: recall_at_10
value: 21.891
- type: recall_at_100
value: 41.979
- type: recall_at_1000
value: 62.239999999999995
- type: recall_at_3
value: 12.722
- type: recall_at_5
value: 16.81
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.909
- type: map_at_10
value: 12.844
- type: map_at_100
value: 16.435
- type: map_at_1000
value: 17.262
- type: map_at_3
value: 10.131
- type: map_at_5
value: 11.269
- type: mrr_at_1
value: 54.50000000000001
- type: mrr_at_10
value: 62.202
- type: mrr_at_100
value: 62.81
- type: mrr_at_1000
value: 62.824000000000005
- type: mrr_at_3
value: 60.5
- type: mrr_at_5
value: 61.324999999999996
- type: ndcg_at_1
value: 42.125
- type: ndcg_at_10
value: 28.284
- type: ndcg_at_100
value: 30.444
- type: ndcg_at_1000
value: 36.397
- type: ndcg_at_3
value: 33.439
- type: ndcg_at_5
value: 30.473
- type: precision_at_1
value: 54.50000000000001
- type: precision_at_10
value: 21.4
- type: precision_at_100
value: 6.192
- type: precision_at_1000
value: 1.398
- type: precision_at_3
value: 36.583
- type: precision_at_5
value: 28.799999999999997
- type: recall_at_1
value: 6.909
- type: recall_at_10
value: 17.296
- type: recall_at_100
value: 33.925
- type: recall_at_1000
value: 53.786
- type: recall_at_3
value: 11.333
- type: recall_at_5
value: 13.529
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 36.08
- type: f1
value: 33.016420191943766
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 52.605000000000004
- type: map_at_10
value: 63.31400000000001
- type: map_at_100
value: 63.678000000000004
- type: map_at_1000
value: 63.699
- type: map_at_3
value: 61.141
- type: map_at_5
value: 62.517999999999994
- type: mrr_at_1
value: 56.871
- type: mrr_at_10
value: 67.915
- type: mrr_at_100
value: 68.24900000000001
- type: mrr_at_1000
value: 68.262
- type: mrr_at_3
value: 65.809
- type: mrr_at_5
value: 67.171
- type: ndcg_at_1
value: 56.871
- type: ndcg_at_10
value: 69.122
- type: ndcg_at_100
value: 70.855
- type: ndcg_at_1000
value: 71.368
- type: ndcg_at_3
value: 64.974
- type: ndcg_at_5
value: 67.318
- type: precision_at_1
value: 56.871
- type: precision_at_10
value: 9.029
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 25.893
- type: precision_at_5
value: 16.838
- type: recall_at_1
value: 52.605000000000004
- type: recall_at_10
value: 82.679
- type: recall_at_100
value: 90.586
- type: recall_at_1000
value: 94.38
- type: recall_at_3
value: 71.447
- type: recall_at_5
value: 77.218
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.759
- type: map_at_10
value: 18.877
- type: map_at_100
value: 20.498
- type: map_at_1000
value: 20.682000000000002
- type: map_at_3
value: 16.159000000000002
- type: map_at_5
value: 17.575
- type: mrr_at_1
value: 22.531000000000002
- type: mrr_at_10
value: 31.155
- type: mrr_at_100
value: 32.188
- type: mrr_at_1000
value: 32.245000000000005
- type: mrr_at_3
value: 28.781000000000002
- type: mrr_at_5
value: 30.054
- type: ndcg_at_1
value: 22.531000000000002
- type: ndcg_at_10
value: 25.189
- type: ndcg_at_100
value: 31.958
- type: ndcg_at_1000
value: 35.693999999999996
- type: ndcg_at_3
value: 22.235
- type: ndcg_at_5
value: 23.044999999999998
- type: precision_at_1
value: 22.531000000000002
- type: precision_at_10
value: 7.438000000000001
- type: precision_at_100
value: 1.418
- type: precision_at_1000
value: 0.208
- type: precision_at_3
value: 15.329
- type: precision_at_5
value: 11.451
- type: recall_at_1
value: 10.759
- type: recall_at_10
value: 31.416
- type: recall_at_100
value: 56.989000000000004
- type: recall_at_1000
value: 80.33200000000001
- type: recall_at_3
value: 20.61
- type: recall_at_5
value: 24.903
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.21
- type: map_at_10
value: 38.765
- type: map_at_100
value: 39.498
- type: map_at_1000
value: 39.568
- type: map_at_3
value: 36.699
- type: map_at_5
value: 37.925
- type: mrr_at_1
value: 58.42
- type: mrr_at_10
value: 65.137
- type: mrr_at_100
value: 65.542
- type: mrr_at_1000
value: 65.568
- type: mrr_at_3
value: 63.698
- type: mrr_at_5
value: 64.575
- type: ndcg_at_1
value: 58.42
- type: ndcg_at_10
value: 47.476
- type: ndcg_at_100
value: 50.466
- type: ndcg_at_1000
value: 52.064
- type: ndcg_at_3
value: 43.986
- type: ndcg_at_5
value: 45.824
- type: precision_at_1
value: 58.42
- type: precision_at_10
value: 9.649000000000001
- type: precision_at_100
value: 1.201
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_3
value: 26.977
- type: precision_at_5
value: 17.642
- type: recall_at_1
value: 29.21
- type: recall_at_10
value: 48.244
- type: recall_at_100
value: 60.041
- type: recall_at_1000
value: 70.743
- type: recall_at_3
value: 40.466
- type: recall_at_5
value: 44.105
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 58.7064
- type: ap
value: 55.36326227125519
- type: f1
value: 57.46763115215848
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 15.889000000000001
- type: map_at_10
value: 25.979000000000003
- type: map_at_100
value: 27.21
- type: map_at_1000
value: 27.284000000000002
- type: map_at_3
value: 22.665
- type: map_at_5
value: 24.578
- type: mrr_at_1
value: 16.39
- type: mrr_at_10
value: 26.504
- type: mrr_at_100
value: 27.689999999999998
- type: mrr_at_1000
value: 27.758
- type: mrr_at_3
value: 23.24
- type: mrr_at_5
value: 25.108000000000004
- type: ndcg_at_1
value: 16.39
- type: ndcg_at_10
value: 31.799
- type: ndcg_at_100
value: 38.034
- type: ndcg_at_1000
value: 39.979
- type: ndcg_at_3
value: 25.054
- type: ndcg_at_5
value: 28.463
- type: precision_at_1
value: 16.39
- type: precision_at_10
value: 5.189
- type: precision_at_100
value: 0.835
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 10.84
- type: precision_at_5
value: 8.238
- type: recall_at_1
value: 15.889000000000001
- type: recall_at_10
value: 49.739
- type: recall_at_100
value: 79.251
- type: recall_at_1000
value: 94.298
- type: recall_at_3
value: 31.427
- type: recall_at_5
value: 39.623000000000005
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 88.81668946648426
- type: f1
value: 88.55200075528438
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 58.611491108071135
- type: f1
value: 42.12391403999353
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.67047747141896
- type: f1
value: 62.88410885922258
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.78547410894419
- type: f1
value: 71.69467869218154
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 27.23799937752035
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 23.26502601343789
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.680711484149832
- type: mrr
value: 31.705059795117307
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.077
- type: map_at_10
value: 8.657
- type: map_at_100
value: 10.753
- type: map_at_1000
value: 11.885
- type: map_at_3
value: 6.5089999999999995
- type: map_at_5
value: 7.405
- type: mrr_at_1
value: 38.7
- type: mrr_at_10
value: 46.065
- type: mrr_at_100
value: 46.772000000000006
- type: mrr_at_1000
value: 46.83
- type: mrr_at_3
value: 44.118
- type: mrr_at_5
value: 45.015
- type: ndcg_at_1
value: 36.997
- type: ndcg_at_10
value: 25.96
- type: ndcg_at_100
value: 23.607
- type: ndcg_at_1000
value: 32.317
- type: ndcg_at_3
value: 31.06
- type: ndcg_at_5
value: 28.921000000000003
- type: precision_at_1
value: 38.7
- type: precision_at_10
value: 19.195
- type: precision_at_100
value: 6.164
- type: precision_at_1000
value: 1.839
- type: precision_at_3
value: 28.999000000000002
- type: precision_at_5
value: 25.014999999999997
- type: recall_at_1
value: 4.077
- type: recall_at_10
value: 11.802
- type: recall_at_100
value: 24.365000000000002
- type: recall_at_1000
value: 55.277
- type: recall_at_3
value: 7.435
- type: recall_at_5
value: 8.713999999999999
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.588
- type: map_at_10
value: 32.08
- type: map_at_100
value: 33.32
- type: map_at_1000
value: 33.377
- type: map_at_3
value: 28.166000000000004
- type: map_at_5
value: 30.383
- type: mrr_at_1
value: 22.161
- type: mrr_at_10
value: 34.121
- type: mrr_at_100
value: 35.171
- type: mrr_at_1000
value: 35.214
- type: mrr_at_3
value: 30.692000000000004
- type: mrr_at_5
value: 32.706
- type: ndcg_at_1
value: 22.131999999999998
- type: ndcg_at_10
value: 38.887
- type: ndcg_at_100
value: 44.433
- type: ndcg_at_1000
value: 45.823
- type: ndcg_at_3
value: 31.35
- type: ndcg_at_5
value: 35.144
- type: precision_at_1
value: 22.131999999999998
- type: precision_at_10
value: 6.8629999999999995
- type: precision_at_100
value: 0.993
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 14.706
- type: precision_at_5
value: 10.972999999999999
- type: recall_at_1
value: 19.588
- type: recall_at_10
value: 57.703
- type: recall_at_100
value: 82.194
- type: recall_at_1000
value: 92.623
- type: recall_at_3
value: 38.012
- type: recall_at_5
value: 46.847
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 68.038
- type: map_at_10
value: 81.572
- type: map_at_100
value: 82.25200000000001
- type: map_at_1000
value: 82.27600000000001
- type: map_at_3
value: 78.618
- type: map_at_5
value: 80.449
- type: mrr_at_1
value: 78.31
- type: mrr_at_10
value: 84.98
- type: mrr_at_100
value: 85.122
- type: mrr_at_1000
value: 85.124
- type: mrr_at_3
value: 83.852
- type: mrr_at_5
value: 84.6
- type: ndcg_at_1
value: 78.31
- type: ndcg_at_10
value: 85.693
- type: ndcg_at_100
value: 87.191
- type: ndcg_at_1000
value: 87.386
- type: ndcg_at_3
value: 82.585
- type: ndcg_at_5
value: 84.255
- type: precision_at_1
value: 78.31
- type: precision_at_10
value: 12.986
- type: precision_at_100
value: 1.505
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.007
- type: precision_at_5
value: 23.735999999999997
- type: recall_at_1
value: 68.038
- type: recall_at_10
value: 93.598
- type: recall_at_100
value: 98.869
- type: recall_at_1000
value: 99.86500000000001
- type: recall_at_3
value: 84.628
- type: recall_at_5
value: 89.316
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 37.948231664922865
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 49.90597913763894
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.753
- type: map_at_10
value: 8.915
- type: map_at_100
value: 10.374
- type: map_at_1000
value: 10.612
- type: map_at_3
value: 6.577
- type: map_at_5
value: 7.8
- type: mrr_at_1
value: 18.4
- type: mrr_at_10
value: 27.325
- type: mrr_at_100
value: 28.419
- type: mrr_at_1000
value: 28.494000000000003
- type: mrr_at_3
value: 24.349999999999998
- type: mrr_at_5
value: 26.205000000000002
- type: ndcg_at_1
value: 18.4
- type: ndcg_at_10
value: 15.293000000000001
- type: ndcg_at_100
value: 21.592
- type: ndcg_at_1000
value: 26.473000000000003
- type: ndcg_at_3
value: 14.748
- type: ndcg_at_5
value: 12.98
- type: precision_at_1
value: 18.4
- type: precision_at_10
value: 7.779999999999999
- type: precision_at_100
value: 1.693
- type: precision_at_1000
value: 0.28800000000000003
- type: precision_at_3
value: 13.700000000000001
- type: precision_at_5
value: 11.379999999999999
- type: recall_at_1
value: 3.753
- type: recall_at_10
value: 15.806999999999999
- type: recall_at_100
value: 34.37
- type: recall_at_1000
value: 58.463
- type: recall_at_3
value: 8.338
- type: recall_at_5
value: 11.538
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.58843987639705
- type: cos_sim_spearman
value: 76.33071660715956
- type: euclidean_pearson
value: 72.8029921002978
- type: euclidean_spearman
value: 69.34534284782808
- type: manhattan_pearson
value: 72.49781034973653
- type: manhattan_spearman
value: 69.24754112621694
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.31673079903189
- type: cos_sim_spearman
value: 74.27699263517789
- type: euclidean_pearson
value: 69.4008910999579
- type: euclidean_spearman
value: 59.0716984643048
- type: manhattan_pearson
value: 68.87342686919199
- type: manhattan_spearman
value: 58.904612865335025
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 77.59122302327788
- type: cos_sim_spearman
value: 78.55383586979005
- type: euclidean_pearson
value: 68.18338642204289
- type: euclidean_spearman
value: 68.95092864180276
- type: manhattan_pearson
value: 68.08807059822706
- type: manhattan_spearman
value: 68.86135938270193
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 78.51766841424501
- type: cos_sim_spearman
value: 73.84318001499558
- type: euclidean_pearson
value: 67.2007138855177
- type: euclidean_spearman
value: 63.98672842723766
- type: manhattan_pearson
value: 67.17773810895949
- type: manhattan_spearman
value: 64.07359154832962
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 82.73438541570299
- type: cos_sim_spearman
value: 83.71357922283677
- type: euclidean_pearson
value: 57.50131347498546
- type: euclidean_spearman
value: 57.73623619252132
- type: manhattan_pearson
value: 58.082992079000725
- type: manhattan_spearman
value: 58.42728201167522
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 78.14794654172421
- type: cos_sim_spearman
value: 80.025736165043
- type: euclidean_pearson
value: 65.87773913985473
- type: euclidean_spearman
value: 66.69337751784794
- type: manhattan_pearson
value: 66.01039761004415
- type: manhattan_spearman
value: 66.89215027952318
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.10554507136152
- type: cos_sim_spearman
value: 87.4898082140765
- type: euclidean_pearson
value: 72.19391114541367
- type: euclidean_spearman
value: 70.36647944993783
- type: manhattan_pearson
value: 72.18680758133698
- type: manhattan_spearman
value: 70.3871215447305
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 64.54868111501618
- type: cos_sim_spearman
value: 64.25173617448473
- type: euclidean_pearson
value: 39.116088900637116
- type: euclidean_spearman
value: 53.300772929884
- type: manhattan_pearson
value: 38.3844195287959
- type: manhattan_spearman
value: 52.846675312001246
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 80.04396610550214
- type: cos_sim_spearman
value: 79.19504854997832
- type: euclidean_pearson
value: 66.3284657637072
- type: euclidean_spearman
value: 63.69531796729492
- type: manhattan_pearson
value: 66.82324081038026
- type: manhattan_spearman
value: 64.18254512904923
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 74.16264051781705
- type: mrr
value: 91.80864796060874
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.983000000000004
- type: map_at_10
value: 47.858000000000004
- type: map_at_100
value: 48.695
- type: map_at_1000
value: 48.752
- type: map_at_3
value: 45.444
- type: map_at_5
value: 46.906
- type: mrr_at_1
value: 41.333
- type: mrr_at_10
value: 49.935
- type: mrr_at_100
value: 50.51
- type: mrr_at_1000
value: 50.55500000000001
- type: mrr_at_3
value: 47.833
- type: mrr_at_5
value: 49.117
- type: ndcg_at_1
value: 41.333
- type: ndcg_at_10
value: 52.398999999999994
- type: ndcg_at_100
value: 56.196
- type: ndcg_at_1000
value: 57.838
- type: ndcg_at_3
value: 47.987
- type: ndcg_at_5
value: 50.356
- type: precision_at_1
value: 41.333
- type: precision_at_10
value: 7.167
- type: precision_at_100
value: 0.9299999999999999
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 19.0
- type: precision_at_5
value: 12.8
- type: recall_at_1
value: 38.983000000000004
- type: recall_at_10
value: 64.183
- type: recall_at_100
value: 82.02199999999999
- type: recall_at_1000
value: 95.167
- type: recall_at_3
value: 52.383
- type: recall_at_5
value: 58.411
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.8019801980198
- type: cos_sim_ap
value: 94.9287554635848
- type: cos_sim_f1
value: 89.83739837398375
- type: cos_sim_precision
value: 91.32231404958677
- type: cos_sim_recall
value: 88.4
- type: dot_accuracy
value: 99.23762376237623
- type: dot_ap
value: 55.22534191245801
- type: dot_f1
value: 54.054054054054056
- type: dot_precision
value: 55.15088449531738
- type: dot_recall
value: 53.0
- type: euclidean_accuracy
value: 99.6108910891089
- type: euclidean_ap
value: 82.5195111329438
- type: euclidean_f1
value: 78.2847718526663
- type: euclidean_precision
value: 86.93528693528694
- type: euclidean_recall
value: 71.2
- type: manhattan_accuracy
value: 99.5970297029703
- type: manhattan_ap
value: 81.96876777875492
- type: manhattan_f1
value: 77.33773377337734
- type: manhattan_precision
value: 85.94132029339853
- type: manhattan_recall
value: 70.3
- type: max_accuracy
value: 99.8019801980198
- type: max_ap
value: 94.9287554635848
- type: max_f1
value: 89.83739837398375
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 46.34997003954114
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 31.462336020554893
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 47.1757817459526
- type: mrr
value: 47.941057104660054
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.56106249068471
- type: cos_sim_spearman
value: 31.24613190558528
- type: dot_pearson
value: 20.486610035794257
- type: dot_spearman
value: 23.115667545894546
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.182
- type: map_at_10
value: 1.155
- type: map_at_100
value: 5.118
- type: map_at_1000
value: 11.827
- type: map_at_3
value: 0.482
- type: map_at_5
value: 0.712
- type: mrr_at_1
value: 70.0
- type: mrr_at_10
value: 79.483
- type: mrr_at_100
value: 79.637
- type: mrr_at_1000
value: 79.637
- type: mrr_at_3
value: 77.667
- type: mrr_at_5
value: 78.567
- type: ndcg_at_1
value: 63.0
- type: ndcg_at_10
value: 52.303
- type: ndcg_at_100
value: 37.361
- type: ndcg_at_1000
value: 32.84
- type: ndcg_at_3
value: 58.274
- type: ndcg_at_5
value: 55.601
- type: precision_at_1
value: 70.0
- type: precision_at_10
value: 55.60000000000001
- type: precision_at_100
value: 37.96
- type: precision_at_1000
value: 14.738000000000001
- type: precision_at_3
value: 62.666999999999994
- type: precision_at_5
value: 60.0
- type: recall_at_1
value: 0.182
- type: recall_at_10
value: 1.4120000000000001
- type: recall_at_100
value: 8.533
- type: recall_at_1000
value: 30.572
- type: recall_at_3
value: 0.5309999999999999
- type: recall_at_5
value: 0.814
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.385
- type: map_at_10
value: 7.185999999999999
- type: map_at_100
value: 11.642
- type: map_at_1000
value: 12.953000000000001
- type: map_at_3
value: 3.496
- type: map_at_5
value: 4.82
- type: mrr_at_1
value: 16.326999999999998
- type: mrr_at_10
value: 29.461
- type: mrr_at_100
value: 31.436999999999998
- type: mrr_at_1000
value: 31.436999999999998
- type: mrr_at_3
value: 24.490000000000002
- type: mrr_at_5
value: 27.857
- type: ndcg_at_1
value: 14.285999999999998
- type: ndcg_at_10
value: 16.672
- type: ndcg_at_100
value: 28.691
- type: ndcg_at_1000
value: 39.817
- type: ndcg_at_3
value: 15.277
- type: ndcg_at_5
value: 15.823
- type: precision_at_1
value: 16.326999999999998
- type: precision_at_10
value: 15.509999999999998
- type: precision_at_100
value: 6.49
- type: precision_at_1000
value: 1.4080000000000001
- type: precision_at_3
value: 16.326999999999998
- type: precision_at_5
value: 16.735
- type: recall_at_1
value: 1.385
- type: recall_at_10
value: 12.586
- type: recall_at_100
value: 40.765
- type: recall_at_1000
value: 75.198
- type: recall_at_3
value: 4.326
- type: recall_at_5
value: 7.074999999999999
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 59.4402
- type: ap
value: 10.16922814263879
- type: f1
value: 45.374485104940476
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 54.25863044708545
- type: f1
value: 54.20154252609619
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 34.3883169293051
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 81.76670441676104
- type: cos_sim_ap
value: 59.29878710961347
- type: cos_sim_f1
value: 57.33284971587474
- type: cos_sim_precision
value: 52.9122963624191
- type: cos_sim_recall
value: 62.559366754617415
- type: dot_accuracy
value: 77.52279907015557
- type: dot_ap
value: 34.17588904643467
- type: dot_f1
value: 41.063567529494634
- type: dot_precision
value: 30.813953488372093
- type: dot_recall
value: 61.53034300791557
- type: euclidean_accuracy
value: 80.61631996185254
- type: euclidean_ap
value: 54.00362361479352
- type: euclidean_f1
value: 53.99111751290361
- type: euclidean_precision
value: 49.52653600528518
- type: euclidean_recall
value: 59.340369393139845
- type: manhattan_accuracy
value: 80.65208320915539
- type: manhattan_ap
value: 54.18329507159467
- type: manhattan_f1
value: 53.85550960836779
- type: manhattan_precision
value: 49.954873646209386
- type: manhattan_recall
value: 58.41688654353562
- type: max_accuracy
value: 81.76670441676104
- type: max_ap
value: 59.29878710961347
- type: max_f1
value: 57.33284971587474
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 87.99433383785463
- type: cos_sim_ap
value: 83.43513915159009
- type: cos_sim_f1
value: 76.3906784964842
- type: cos_sim_precision
value: 73.19223985890653
- type: cos_sim_recall
value: 79.88142901139513
- type: dot_accuracy
value: 81.96142352621571
- type: dot_ap
value: 67.78764755689359
- type: dot_f1
value: 64.42823356983445
- type: dot_precision
value: 56.77801913931779
- type: dot_recall
value: 74.46104096088698
- type: euclidean_accuracy
value: 81.9478402607987
- type: euclidean_ap
value: 67.13958457373279
- type: euclidean_f1
value: 60.45118343195266
- type: euclidean_precision
value: 58.1625391403359
- type: euclidean_recall
value: 62.92731752386819
- type: manhattan_accuracy
value: 82.01769705437188
- type: manhattan_ap
value: 67.24709477497046
- type: manhattan_f1
value: 60.4103846436714
- type: manhattan_precision
value: 57.82063916654935
- type: manhattan_recall
value: 63.24299353249153
- type: max_accuracy
value: 87.99433383785463
- type: max_ap
value: 83.43513915159009
- type: max_f1
value: 76.3906784964842
---
The text embedding set trained by Jina AI, Finetuner team.
## Intented Usage & Model Info
`jina-embedding-s-en-v1` is a language model that has been trained using Jina AI's Linnaeus-Clean dataset.
This dataset consists of 380 million pairs of sentences, which include both query-document pairs.
These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
The Linnaeus-Full dataset, from which the Linnaeus-Clean dataset is derived, originally contained 1.6 billion sentence pairs.
The model has a range of use cases, including information retrieval, semantic textual similarity, text reranking, and more.
With a compact size of just 35 million parameters,
the model enables lightning-fast inference while still delivering impressive performance.
Additionally, we provide the following options:
- [`jina-embedding-t-en-v1`](https://huggingface.co/jinaai/jina-embedding-t-en-v1): 14 million parameters.
- [`jina-embedding-s-en-v1`](https://huggingface.co/jinaai/jina-embedding-s-en-v1): 35 million parameters **(you are here)**.
- [`jina-embedding-b-en-v1`](https://huggingface.co/jinaai/jina-embedding-b-en-v1): 110 million parameters.
- [`jina-embedding-l-en-v1`](https://huggingface.co/jinaai/jina-embedding-l-en-v1): 330 million parameters.
- `jina-embedding-1b-en-v1`: 1.2 billion parameters, 10 times bert-base (soon).
- `jina-embedding-6b-en-v1`: 6 billion parameters, 30 times bert-base (soon).
## Data & Parameters
Please checkout our [technical blog](https://arxiv.org/abs/2307.11224).
## Metrics
We compared the model against `all-minilm-l6-v2`/`all-mpnet-base-v2` from sbert and `text-embeddings-ada-002` from OpenAI:
|Name|param |dimension|
|------------------------------|-----|------|
|all-minilm-l6-v2|23m |384|
|all-mpnet-base-v2 |110m |768|
|ada-embedding-002|Unknown/OpenAI API |1536|
|jina-embedding-t-en-v1|14m |312|
|jina-embedding-s-en-v1|35m |512|
|jina-embedding-b-en-v1|110m |768|
|jina-embedding-l-en-v1|330m |1024|
|Name|STS12|STS13|STS14|STS15|STS16|STS17|TRECOVID|Quora|SciFact|
|------------------------------|-----|-----|-----|-----|-----|-----|--------|-----|-----|
|all-minilm-l6-v2|0.724|0.806|0.756|0.854|0.79 |0.876|0.473 |0.876|0.645 |
|all-mpnet-base-v2|0.726|**0.835**|0.78 |0.857|0.8 |**0.906**|0.513 |0.875|0.656 |
|ada-embedding-002|0.698|0.833|0.761|0.861|**0.86** |0.903|**0.685** |0.876|**0.726** |
|jina-embedding-t-en-v1|0.717|0.773|0.731|0.829|0.777|0.860|0.482 |0.840|0.522 |
|jina-embedding-s-en-v1|0.743|0.786|0.738|0.837|0.80|0.875|0.523 |0.857|0.524 |
|jina-embedding-b-en-v1|**0.751**|0.809|0.761|0.856|0.812|0.890|0.606 |0.876|0.594 |
|jina-embedding-l-en-v1|0.745|0.832|**0.781**|**0.869**|0.837|0.902|0.573 |**0.881**|0.598 |
## Usage
Use with Jina AI Finetuner
```python
!pip install finetuner
import finetuner
model = finetuner.build_model('jinaai/jina-embedding-s-en-v1')
embeddings = finetuner.encode(
model=model,
data=['how is the weather today', 'What is the current weather like today?']
)
print(finetuner.cos_sim(embeddings[0], embeddings[1]))
```
Use with sentence-transformers:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = ['how is the weather today', 'What is the current weather like today?']
model = SentenceTransformer('jinaai/jina-embedding-s-en-v1')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```
## Fine-tuning
Please consider [Finetuner](https://github.com/jina-ai/finetuner).
## Plans
1. The development of `jina-embedding-s-en-v2` is currently underway with two main objectives: improving performance and increasing the maximum sequence length.
2. We are currently working on a bilingual embedding model that combines English and X language. The upcoming model will be called `jina-embedding-s/b/l-de-v1`.
## Contact
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
## Citation
If you find Jina Embeddings useful in your research, please cite the following paper:
``` latex
@misc{günther2023jina,
title={Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models},
author={Michael Günther and Louis Milliken and Jonathan Geuter and Georgios Mastrapas and Bo Wang and Han Xiao},
year={2023},
eprint={2307.11224},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```