---
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-triplets-large
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 68.92537313432835
- type: ap
value: 29.723758877632513
- type: f1
value: 61.909704211663794
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 69.13669999999999
- type: ap
value: 65.30216072238086
- type: f1
value: 67.1890891071034
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 31.384
- type: f1
value: 30.016752348953723
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.613
- type: map_at_10
value: 37.897
- type: map_at_100
value: 39.093
- type: map_at_1000
value: 39.109
- type: map_at_3
value: 32.824
- type: map_at_5
value: 35.679
- type: mrr_at_1
value: 23.826
- type: mrr_at_10
value: 37.997
- type: mrr_at_100
value: 39.186
- type: mrr_at_1000
value: 39.202
- type: mrr_at_3
value: 32.918
- type: mrr_at_5
value: 35.748999999999995
- type: ndcg_at_1
value: 23.613
- type: ndcg_at_10
value: 46.482
- type: ndcg_at_100
value: 51.55499999999999
- type: ndcg_at_1000
value: 51.974
- type: ndcg_at_3
value: 35.964
- type: ndcg_at_5
value: 41.144999999999996
- type: precision_at_1
value: 23.613
- type: precision_at_10
value: 7.417999999999999
- type: precision_at_100
value: 0.963
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 15.031
- type: precision_at_5
value: 11.55
- type: recall_at_1
value: 23.613
- type: recall_at_10
value: 74.182
- type: recall_at_100
value: 96.30199999999999
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 45.092
- type: recall_at_5
value: 57.752
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 40.51285742156528
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 31.5825964077496
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.830281630546835
- type: mrr
value: 75.93072593765115
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 87.26764516732737
- type: cos_sim_spearman
value: 84.42541766631741
- type: euclidean_pearson
value: 48.71357447655235
- type: euclidean_spearman
value: 49.2023259276511
- type: manhattan_pearson
value: 48.36366272727299
- type: manhattan_spearman
value: 48.457128224924354
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.3409090909091
- type: f1
value: 85.25262617676835
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 33.560193912974974
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 28.4426572644577
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.822999999999997
- type: map_at_10
value: 39.088
- type: map_at_100
value: 40.561
- type: map_at_1000
value: 40.69
- type: map_at_3
value: 35.701
- type: map_at_5
value: 37.556
- type: mrr_at_1
value: 33.906
- type: mrr_at_10
value: 44.527
- type: mrr_at_100
value: 45.403999999999996
- type: mrr_at_1000
value: 45.452
- type: mrr_at_3
value: 41.726
- type: mrr_at_5
value: 43.314
- type: ndcg_at_1
value: 33.906
- type: ndcg_at_10
value: 45.591
- type: ndcg_at_100
value: 51.041000000000004
- type: ndcg_at_1000
value: 53.1
- type: ndcg_at_3
value: 40.324
- type: ndcg_at_5
value: 42.723
- type: precision_at_1
value: 33.906
- type: precision_at_10
value: 8.655
- type: precision_at_100
value: 1.418
- type: precision_at_1000
value: 0.19499999999999998
- type: precision_at_3
value: 19.123
- type: precision_at_5
value: 13.963000000000001
- type: recall_at_1
value: 27.822999999999997
- type: recall_at_10
value: 58.63699999999999
- type: recall_at_100
value: 80.874
- type: recall_at_1000
value: 93.82000000000001
- type: recall_at_3
value: 44.116
- type: recall_at_5
value: 50.178999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.823999999999998
- type: map_at_10
value: 37.006
- type: map_at_100
value: 38.256
- type: map_at_1000
value: 38.397999999999996
- type: map_at_3
value: 34.011
- type: map_at_5
value: 35.643
- type: mrr_at_1
value: 34.268
- type: mrr_at_10
value: 43.374
- type: mrr_at_100
value: 44.096000000000004
- type: mrr_at_1000
value: 44.144
- type: mrr_at_3
value: 41.008
- type: mrr_at_5
value: 42.359
- type: ndcg_at_1
value: 34.268
- type: ndcg_at_10
value: 43.02
- type: ndcg_at_100
value: 47.747
- type: ndcg_at_1000
value: 50.019999999999996
- type: ndcg_at_3
value: 38.687
- type: ndcg_at_5
value: 40.647
- type: precision_at_1
value: 34.268
- type: precision_at_10
value: 8.261000000000001
- type: precision_at_100
value: 1.376
- type: precision_at_1000
value: 0.189
- type: precision_at_3
value: 19.108
- type: precision_at_5
value: 13.489999999999998
- type: recall_at_1
value: 26.823999999999998
- type: recall_at_10
value: 53.84100000000001
- type: recall_at_100
value: 73.992
- type: recall_at_1000
value: 88.524
- type: recall_at_3
value: 40.711000000000006
- type: recall_at_5
value: 46.477000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 34.307
- type: map_at_10
value: 45.144
- type: map_at_100
value: 46.351
- type: map_at_1000
value: 46.414
- type: map_at_3
value: 42.315000000000005
- type: map_at_5
value: 43.991
- type: mrr_at_1
value: 39.06
- type: mrr_at_10
value: 48.612
- type: mrr_at_100
value: 49.425000000000004
- type: mrr_at_1000
value: 49.458999999999996
- type: mrr_at_3
value: 46.144
- type: mrr_at_5
value: 47.654999999999994
- type: ndcg_at_1
value: 39.06
- type: ndcg_at_10
value: 50.647
- type: ndcg_at_100
value: 55.620000000000005
- type: ndcg_at_1000
value: 56.976000000000006
- type: ndcg_at_3
value: 45.705
- type: ndcg_at_5
value: 48.269
- type: precision_at_1
value: 39.06
- type: precision_at_10
value: 8.082
- type: precision_at_100
value: 1.161
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 20.376
- type: precision_at_5
value: 14.069
- type: recall_at_1
value: 34.307
- type: recall_at_10
value: 63.497
- type: recall_at_100
value: 85.038
- type: recall_at_1000
value: 94.782
- type: recall_at_3
value: 50.209
- type: recall_at_5
value: 56.525000000000006
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.448
- type: map_at_10
value: 34.86
- type: map_at_100
value: 36.004999999999995
- type: map_at_1000
value: 36.081
- type: map_at_3
value: 32.527
- type: map_at_5
value: 33.955
- type: mrr_at_1
value: 28.701
- type: mrr_at_10
value: 36.909
- type: mrr_at_100
value: 37.89
- type: mrr_at_1000
value: 37.945
- type: mrr_at_3
value: 34.576
- type: mrr_at_5
value: 35.966
- type: ndcg_at_1
value: 28.701
- type: ndcg_at_10
value: 39.507999999999996
- type: ndcg_at_100
value: 45.056000000000004
- type: ndcg_at_1000
value: 47.034
- type: ndcg_at_3
value: 34.985
- type: ndcg_at_5
value: 37.384
- type: precision_at_1
value: 28.701
- type: precision_at_10
value: 5.921
- type: precision_at_100
value: 0.914
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 14.689
- type: precision_at_5
value: 10.237
- type: recall_at_1
value: 26.448
- type: recall_at_10
value: 51.781
- type: recall_at_100
value: 77.142
- type: recall_at_1000
value: 92.10000000000001
- type: recall_at_3
value: 39.698
- type: recall_at_5
value: 45.469
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.174000000000001
- type: map_at_10
value: 22.019
- type: map_at_100
value: 23.18
- type: map_at_1000
value: 23.304
- type: map_at_3
value: 19.332
- type: map_at_5
value: 20.816000000000003
- type: mrr_at_1
value: 17.785999999999998
- type: mrr_at_10
value: 26.233
- type: mrr_at_100
value: 27.254
- type: mrr_at_1000
value: 27.328000000000003
- type: mrr_at_3
value: 23.653
- type: mrr_at_5
value: 25.095
- type: ndcg_at_1
value: 17.785999999999998
- type: ndcg_at_10
value: 27.236
- type: ndcg_at_100
value: 32.932
- type: ndcg_at_1000
value: 36.134
- type: ndcg_at_3
value: 22.33
- type: ndcg_at_5
value: 24.573999999999998
- type: precision_at_1
value: 17.785999999999998
- type: precision_at_10
value: 5.286
- type: precision_at_100
value: 0.9369999999999999
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 11.07
- type: precision_at_5
value: 8.308
- type: recall_at_1
value: 14.174000000000001
- type: recall_at_10
value: 39.135
- type: recall_at_100
value: 64.095
- type: recall_at_1000
value: 87.485
- type: recall_at_3
value: 25.496999999999996
- type: recall_at_5
value: 31.148999999999997
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.371000000000002
- type: map_at_10
value: 33.074999999999996
- type: map_at_100
value: 34.486
- type: map_at_1000
value: 34.608
- type: map_at_3
value: 30.483
- type: map_at_5
value: 31.972
- type: mrr_at_1
value: 29.548000000000002
- type: mrr_at_10
value: 38.431
- type: mrr_at_100
value: 39.347
- type: mrr_at_1000
value: 39.4
- type: mrr_at_3
value: 35.980000000000004
- type: mrr_at_5
value: 37.413999999999994
- type: ndcg_at_1
value: 29.548000000000002
- type: ndcg_at_10
value: 38.552
- type: ndcg_at_100
value: 44.598
- type: ndcg_at_1000
value: 47.0
- type: ndcg_at_3
value: 34.109
- type: ndcg_at_5
value: 36.263
- type: precision_at_1
value: 29.548000000000002
- type: precision_at_10
value: 6.92
- type: precision_at_100
value: 1.179
- type: precision_at_1000
value: 0.159
- type: precision_at_3
value: 16.137
- type: precision_at_5
value: 11.511000000000001
- type: recall_at_1
value: 24.371000000000002
- type: recall_at_10
value: 49.586999999999996
- type: recall_at_100
value: 75.15899999999999
- type: recall_at_1000
value: 91.06
- type: recall_at_3
value: 37.09
- type: recall_at_5
value: 42.588
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.517
- type: map_at_10
value: 32.969
- type: map_at_100
value: 34.199
- type: map_at_1000
value: 34.322
- type: map_at_3
value: 30.270999999999997
- type: map_at_5
value: 31.863000000000003
- type: mrr_at_1
value: 30.479
- type: mrr_at_10
value: 38.633
- type: mrr_at_100
value: 39.522
- type: mrr_at_1000
value: 39.583
- type: mrr_at_3
value: 36.454
- type: mrr_at_5
value: 37.744
- type: ndcg_at_1
value: 30.479
- type: ndcg_at_10
value: 38.269
- type: ndcg_at_100
value: 43.91
- type: ndcg_at_1000
value: 46.564
- type: ndcg_at_3
value: 34.03
- type: ndcg_at_5
value: 36.155
- type: precision_at_1
value: 30.479
- type: precision_at_10
value: 6.815
- type: precision_at_100
value: 1.138
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 16.058
- type: precision_at_5
value: 11.416
- type: recall_at_1
value: 24.517
- type: recall_at_10
value: 48.559000000000005
- type: recall_at_100
value: 73.307
- type: recall_at_1000
value: 91.508
- type: recall_at_3
value: 36.563
- type: recall_at_5
value: 42.375
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.388
- type: map_at_10
value: 29.408
- type: map_at_100
value: 30.452
- type: map_at_1000
value: 30.546
- type: map_at_3
value: 27.139000000000003
- type: map_at_5
value: 28.402
- type: mrr_at_1
value: 25.46
- type: mrr_at_10
value: 31.966
- type: mrr_at_100
value: 32.879999999999995
- type: mrr_at_1000
value: 32.944
- type: mrr_at_3
value: 29.755
- type: mrr_at_5
value: 30.974
- type: ndcg_at_1
value: 25.46
- type: ndcg_at_10
value: 33.449
- type: ndcg_at_100
value: 38.67
- type: ndcg_at_1000
value: 41.035
- type: ndcg_at_3
value: 29.048000000000002
- type: ndcg_at_5
value: 31.127
- type: precision_at_1
value: 25.46
- type: precision_at_10
value: 5.199
- type: precision_at_100
value: 0.8670000000000001
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 12.168
- type: precision_at_5
value: 8.62
- type: recall_at_1
value: 23.388
- type: recall_at_10
value: 43.428
- type: recall_at_100
value: 67.245
- type: recall_at_1000
value: 84.75399999999999
- type: recall_at_3
value: 31.416
- type: recall_at_5
value: 36.451
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.136000000000003
- type: map_at_10
value: 24.102999999999998
- type: map_at_100
value: 25.219
- type: map_at_1000
value: 25.344
- type: map_at_3
value: 22.004
- type: map_at_5
value: 23.145
- type: mrr_at_1
value: 20.613
- type: mrr_at_10
value: 27.753
- type: mrr_at_100
value: 28.698
- type: mrr_at_1000
value: 28.776000000000003
- type: mrr_at_3
value: 25.711000000000002
- type: mrr_at_5
value: 26.795
- type: ndcg_at_1
value: 20.613
- type: ndcg_at_10
value: 28.510999999999996
- type: ndcg_at_100
value: 33.924
- type: ndcg_at_1000
value: 36.849
- type: ndcg_at_3
value: 24.664
- type: ndcg_at_5
value: 26.365
- type: precision_at_1
value: 20.613
- type: precision_at_10
value: 5.069
- type: precision_at_100
value: 0.918
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 11.574
- type: precision_at_5
value: 8.211
- type: recall_at_1
value: 17.136000000000003
- type: recall_at_10
value: 38.232
- type: recall_at_100
value: 62.571
- type: recall_at_1000
value: 83.23
- type: recall_at_3
value: 27.468999999999998
- type: recall_at_5
value: 31.852999999999998
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.580000000000002
- type: map_at_10
value: 33.449
- type: map_at_100
value: 34.58
- type: map_at_1000
value: 34.692
- type: map_at_3
value: 30.660999999999998
- type: map_at_5
value: 32.425
- type: mrr_at_1
value: 30.037000000000003
- type: mrr_at_10
value: 37.443
- type: mrr_at_100
value: 38.32
- type: mrr_at_1000
value: 38.384
- type: mrr_at_3
value: 34.778999999999996
- type: mrr_at_5
value: 36.458
- type: ndcg_at_1
value: 30.037000000000003
- type: ndcg_at_10
value: 38.46
- type: ndcg_at_100
value: 43.746
- type: ndcg_at_1000
value: 46.28
- type: ndcg_at_3
value: 33.52
- type: ndcg_at_5
value: 36.175000000000004
- type: precision_at_1
value: 30.037000000000003
- type: precision_at_10
value: 6.418
- type: precision_at_100
value: 1.0210000000000001
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 15.018999999999998
- type: precision_at_5
value: 10.877
- type: recall_at_1
value: 25.580000000000002
- type: recall_at_10
value: 49.830000000000005
- type: recall_at_100
value: 73.04899999999999
- type: recall_at_1000
value: 90.751
- type: recall_at_3
value: 36.370999999999995
- type: recall_at_5
value: 43.104
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.071
- type: map_at_10
value: 33.384
- type: map_at_100
value: 35.004999999999995
- type: map_at_1000
value: 35.215999999999994
- type: map_at_3
value: 30.459000000000003
- type: map_at_5
value: 31.769
- type: mrr_at_1
value: 28.854000000000003
- type: mrr_at_10
value: 37.512
- type: mrr_at_100
value: 38.567
- type: mrr_at_1000
value: 38.618
- type: mrr_at_3
value: 35.211
- type: mrr_at_5
value: 36.13
- type: ndcg_at_1
value: 28.854000000000003
- type: ndcg_at_10
value: 39.216
- type: ndcg_at_100
value: 45.214
- type: ndcg_at_1000
value: 47.573
- type: ndcg_at_3
value: 34.597
- type: ndcg_at_5
value: 36.063
- type: precision_at_1
value: 28.854000000000003
- type: precision_at_10
value: 7.648000000000001
- type: precision_at_100
value: 1.545
- type: precision_at_1000
value: 0.241
- type: precision_at_3
value: 16.667
- type: precision_at_5
value: 11.818
- type: recall_at_1
value: 24.071
- type: recall_at_10
value: 50.802
- type: recall_at_100
value: 77.453
- type: recall_at_1000
value: 92.304
- type: recall_at_3
value: 36.846000000000004
- type: recall_at_5
value: 41.14
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.395
- type: map_at_10
value: 29.189999999999998
- type: map_at_100
value: 30.226999999999997
- type: map_at_1000
value: 30.337999999999997
- type: map_at_3
value: 27.342
- type: map_at_5
value: 28.116999999999997
- type: mrr_at_1
value: 25.323
- type: mrr_at_10
value: 31.241000000000003
- type: mrr_at_100
value: 32.225
- type: mrr_at_1000
value: 32.304
- type: mrr_at_3
value: 29.452
- type: mrr_at_5
value: 30.209000000000003
- type: ndcg_at_1
value: 25.323
- type: ndcg_at_10
value: 33.024
- type: ndcg_at_100
value: 38.279
- type: ndcg_at_1000
value: 41.026
- type: ndcg_at_3
value: 29.243000000000002
- type: ndcg_at_5
value: 30.564000000000004
- type: precision_at_1
value: 25.323
- type: precision_at_10
value: 4.972
- type: precision_at_100
value: 0.8210000000000001
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 12.076
- type: precision_at_5
value: 8.133
- type: recall_at_1
value: 23.395
- type: recall_at_10
value: 42.994
- type: recall_at_100
value: 66.985
- type: recall_at_1000
value: 87.483
- type: recall_at_3
value: 32.505
- type: recall_at_5
value: 35.721000000000004
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.322000000000001
- type: map_at_10
value: 14.491000000000001
- type: map_at_100
value: 16.066
- type: map_at_1000
value: 16.238
- type: map_at_3
value: 12.235
- type: map_at_5
value: 13.422999999999998
- type: mrr_at_1
value: 19.479
- type: mrr_at_10
value: 29.38
- type: mrr_at_100
value: 30.520999999999997
- type: mrr_at_1000
value: 30.570999999999998
- type: mrr_at_3
value: 26.395000000000003
- type: mrr_at_5
value: 27.982000000000003
- type: ndcg_at_1
value: 19.479
- type: ndcg_at_10
value: 21.215
- type: ndcg_at_100
value: 27.966
- type: ndcg_at_1000
value: 31.324
- type: ndcg_at_3
value: 17.194000000000003
- type: ndcg_at_5
value: 18.593
- type: precision_at_1
value: 19.479
- type: precision_at_10
value: 6.5280000000000005
- type: precision_at_100
value: 1.359
- type: precision_at_1000
value: 0.198
- type: precision_at_3
value: 12.703999999999999
- type: precision_at_5
value: 9.655
- type: recall_at_1
value: 8.322000000000001
- type: recall_at_10
value: 26.165
- type: recall_at_100
value: 49.573
- type: recall_at_1000
value: 68.501
- type: recall_at_3
value: 16.179
- type: recall_at_5
value: 20.175
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.003
- type: map_at_10
value: 16.087
- type: map_at_100
value: 21.363
- type: map_at_1000
value: 22.64
- type: map_at_3
value: 12.171999999999999
- type: map_at_5
value: 13.866
- type: mrr_at_1
value: 61.25000000000001
- type: mrr_at_10
value: 68.626
- type: mrr_at_100
value: 69.134
- type: mrr_at_1000
value: 69.144
- type: mrr_at_3
value: 67.042
- type: mrr_at_5
value: 67.929
- type: ndcg_at_1
value: 49.0
- type: ndcg_at_10
value: 34.132
- type: ndcg_at_100
value: 37.545
- type: ndcg_at_1000
value: 44.544
- type: ndcg_at_3
value: 38.946999999999996
- type: ndcg_at_5
value: 36.317
- type: precision_at_1
value: 61.25000000000001
- type: precision_at_10
value: 26.325
- type: precision_at_100
value: 8.173
- type: precision_at_1000
value: 1.778
- type: precision_at_3
value: 41.667
- type: precision_at_5
value: 34.300000000000004
- type: recall_at_1
value: 8.003
- type: recall_at_10
value: 20.577
- type: recall_at_100
value: 41.884
- type: recall_at_1000
value: 64.36500000000001
- type: recall_at_3
value: 13.602
- type: recall_at_5
value: 16.41
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 45.835
- type: f1
value: 41.66455981281837
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 55.717000000000006
- type: map_at_10
value: 66.34100000000001
- type: map_at_100
value: 66.776
- type: map_at_1000
value: 66.794
- type: map_at_3
value: 64.386
- type: map_at_5
value: 65.566
- type: mrr_at_1
value: 60.141
- type: mrr_at_10
value: 70.928
- type: mrr_at_100
value: 71.29299999999999
- type: mrr_at_1000
value: 71.30199999999999
- type: mrr_at_3
value: 69.07900000000001
- type: mrr_at_5
value: 70.244
- type: ndcg_at_1
value: 60.141
- type: ndcg_at_10
value: 71.90100000000001
- type: ndcg_at_100
value: 73.836
- type: ndcg_at_1000
value: 74.214
- type: ndcg_at_3
value: 68.203
- type: ndcg_at_5
value: 70.167
- type: precision_at_1
value: 60.141
- type: precision_at_10
value: 9.268
- type: precision_at_100
value: 1.03
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 27.028000000000002
- type: precision_at_5
value: 17.342
- type: recall_at_1
value: 55.717000000000006
- type: recall_at_10
value: 84.66799999999999
- type: recall_at_100
value: 93.28
- type: recall_at_1000
value: 95.887
- type: recall_at_3
value: 74.541
- type: recall_at_5
value: 79.389
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.744
- type: map_at_10
value: 29.554000000000002
- type: map_at_100
value: 31.180000000000003
- type: map_at_1000
value: 31.372
- type: map_at_3
value: 25.6
- type: map_at_5
value: 27.642
- type: mrr_at_1
value: 35.802
- type: mrr_at_10
value: 44.812999999999995
- type: mrr_at_100
value: 45.56
- type: mrr_at_1000
value: 45.606
- type: mrr_at_3
value: 42.181000000000004
- type: mrr_at_5
value: 43.516
- type: ndcg_at_1
value: 35.802
- type: ndcg_at_10
value: 37.269999999999996
- type: ndcg_at_100
value: 43.575
- type: ndcg_at_1000
value: 46.916000000000004
- type: ndcg_at_3
value: 33.511
- type: ndcg_at_5
value: 34.504000000000005
- type: precision_at_1
value: 35.802
- type: precision_at_10
value: 10.448
- type: precision_at_100
value: 1.7129999999999999
- type: precision_at_1000
value: 0.231
- type: precision_at_3
value: 22.531000000000002
- type: precision_at_5
value: 16.512
- type: recall_at_1
value: 17.744
- type: recall_at_10
value: 44.616
- type: recall_at_100
value: 68.51899999999999
- type: recall_at_1000
value: 88.495
- type: recall_at_3
value: 30.235
- type: recall_at_5
value: 35.821999999999996
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.315
- type: map_at_10
value: 45.932
- type: map_at_100
value: 46.708
- type: map_at_1000
value: 46.778999999999996
- type: map_at_3
value: 43.472
- type: map_at_5
value: 45.022
- type: mrr_at_1
value: 66.631
- type: mrr_at_10
value: 73.083
- type: mrr_at_100
value: 73.405
- type: mrr_at_1000
value: 73.421
- type: mrr_at_3
value: 71.756
- type: mrr_at_5
value: 72.616
- type: ndcg_at_1
value: 66.631
- type: ndcg_at_10
value: 54.949000000000005
- type: ndcg_at_100
value: 57.965
- type: ndcg_at_1000
value: 59.467000000000006
- type: ndcg_at_3
value: 51.086
- type: ndcg_at_5
value: 53.272
- type: precision_at_1
value: 66.631
- type: precision_at_10
value: 11.178
- type: precision_at_100
value: 1.3559999999999999
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 31.582
- type: precision_at_5
value: 20.678
- type: recall_at_1
value: 33.315
- type: recall_at_10
value: 55.888000000000005
- type: recall_at_100
value: 67.812
- type: recall_at_1000
value: 77.839
- type: recall_at_3
value: 47.373
- type: recall_at_5
value: 51.695
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 66.424
- type: ap
value: 61.132235499939256
- type: f1
value: 66.07094958225315
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.575
- type: map_at_10
value: 33.509
- type: map_at_100
value: 34.725
- type: map_at_1000
value: 34.775
- type: map_at_3
value: 29.673
- type: map_at_5
value: 31.805
- type: mrr_at_1
value: 22.235
- type: mrr_at_10
value: 34.1
- type: mrr_at_100
value: 35.254999999999995
- type: mrr_at_1000
value: 35.299
- type: mrr_at_3
value: 30.334
- type: mrr_at_5
value: 32.419
- type: ndcg_at_1
value: 22.235
- type: ndcg_at_10
value: 40.341
- type: ndcg_at_100
value: 46.161
- type: ndcg_at_1000
value: 47.400999999999996
- type: ndcg_at_3
value: 32.482
- type: ndcg_at_5
value: 36.269
- type: precision_at_1
value: 22.235
- type: precision_at_10
value: 6.422999999999999
- type: precision_at_100
value: 0.9329999999999999
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 13.835
- type: precision_at_5
value: 10.226
- type: recall_at_1
value: 21.575
- type: recall_at_10
value: 61.448
- type: recall_at_100
value: 88.289
- type: recall_at_1000
value: 97.76899999999999
- type: recall_at_3
value: 39.971000000000004
- type: recall_at_5
value: 49.053000000000004
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.83401732786137
- type: f1
value: 92.47678691291068
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 76.08983128134975
- type: f1
value: 59.782936393820904
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.73032952252858
- type: f1
value: 70.72684765888265
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.08473436449226
- type: f1
value: 77.31457411257054
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 30.11980959210532
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 25.2587629106119
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.48268319779204
- type: mrr
value: 32.501885728964304
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.284
- type: map_at_10
value: 11.509
- type: map_at_100
value: 14.624
- type: map_at_1000
value: 16.035
- type: map_at_3
value: 8.347999999999999
- type: map_at_5
value: 9.919
- type: mrr_at_1
value: 43.344
- type: mrr_at_10
value: 52.303999999999995
- type: mrr_at_100
value: 52.994
- type: mrr_at_1000
value: 53.032999999999994
- type: mrr_at_3
value: 50.361
- type: mrr_at_5
value: 51.754
- type: ndcg_at_1
value: 41.176
- type: ndcg_at_10
value: 32.244
- type: ndcg_at_100
value: 29.916999999999998
- type: ndcg_at_1000
value: 38.753
- type: ndcg_at_3
value: 36.856
- type: ndcg_at_5
value: 35.394999999999996
- type: precision_at_1
value: 43.034
- type: precision_at_10
value: 24.118000000000002
- type: precision_at_100
value: 7.926
- type: precision_at_1000
value: 2.045
- type: precision_at_3
value: 34.675
- type: precision_at_5
value: 31.146
- type: recall_at_1
value: 5.284
- type: recall_at_10
value: 15.457
- type: recall_at_100
value: 30.914
- type: recall_at_1000
value: 63.788999999999994
- type: recall_at_3
value: 9.596
- type: recall_at_5
value: 12.391
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.537999999999997
- type: map_at_10
value: 43.99
- type: map_at_100
value: 45.003
- type: map_at_1000
value: 45.04
- type: map_at_3
value: 39.814
- type: map_at_5
value: 42.166
- type: mrr_at_1
value: 33.256
- type: mrr_at_10
value: 46.487
- type: mrr_at_100
value: 47.264
- type: mrr_at_1000
value: 47.29
- type: mrr_at_3
value: 43.091
- type: mrr_at_5
value: 45.013999999999996
- type: ndcg_at_1
value: 33.256
- type: ndcg_at_10
value: 51.403
- type: ndcg_at_100
value: 55.706999999999994
- type: ndcg_at_1000
value: 56.586000000000006
- type: ndcg_at_3
value: 43.559
- type: ndcg_at_5
value: 47.426
- type: precision_at_1
value: 33.256
- type: precision_at_10
value: 8.540000000000001
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 19.834
- type: precision_at_5
value: 14.143
- type: recall_at_1
value: 29.537999999999997
- type: recall_at_10
value: 71.5
- type: recall_at_100
value: 90.25
- type: recall_at_1000
value: 96.82600000000001
- type: recall_at_3
value: 51.108
- type: recall_at_5
value: 60.006
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.526
- type: map_at_10
value: 84.342
- type: map_at_100
value: 84.985
- type: map_at_1000
value: 85.003
- type: map_at_3
value: 81.472
- type: map_at_5
value: 83.292
- type: mrr_at_1
value: 81.17
- type: mrr_at_10
value: 87.33999999999999
- type: mrr_at_100
value: 87.445
- type: mrr_at_1000
value: 87.446
- type: mrr_at_3
value: 86.387
- type: mrr_at_5
value: 87.042
- type: ndcg_at_1
value: 81.19
- type: ndcg_at_10
value: 88.088
- type: ndcg_at_100
value: 89.35
- type: ndcg_at_1000
value: 89.462
- type: ndcg_at_3
value: 85.319
- type: ndcg_at_5
value: 86.858
- type: precision_at_1
value: 81.19
- type: precision_at_10
value: 13.33
- type: precision_at_100
value: 1.528
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.31
- type: precision_at_5
value: 24.512
- type: recall_at_1
value: 70.526
- type: recall_at_10
value: 95.166
- type: recall_at_100
value: 99.479
- type: recall_at_1000
value: 99.984
- type: recall_at_3
value: 87.124
- type: recall_at_5
value: 91.53
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 45.049073872893494
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 55.13810914528368
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.593
- type: map_at_10
value: 10.907
- type: map_at_100
value: 12.888
- type: map_at_1000
value: 13.167000000000002
- type: map_at_3
value: 7.936
- type: map_at_5
value: 9.31
- type: mrr_at_1
value: 22.7
- type: mrr_at_10
value: 32.509
- type: mrr_at_100
value: 33.69
- type: mrr_at_1000
value: 33.747
- type: mrr_at_3
value: 29.599999999999998
- type: mrr_at_5
value: 31.155
- type: ndcg_at_1
value: 22.7
- type: ndcg_at_10
value: 18.445
- type: ndcg_at_100
value: 26.241999999999997
- type: ndcg_at_1000
value: 31.409
- type: ndcg_at_3
value: 17.864
- type: ndcg_at_5
value: 15.232999999999999
- type: precision_at_1
value: 22.7
- type: precision_at_10
value: 9.43
- type: precision_at_100
value: 2.061
- type: precision_at_1000
value: 0.331
- type: precision_at_3
value: 16.467000000000002
- type: precision_at_5
value: 13.08
- type: recall_at_1
value: 4.593
- type: recall_at_10
value: 19.115
- type: recall_at_100
value: 41.82
- type: recall_at_1000
value: 67.167
- type: recall_at_3
value: 9.983
- type: recall_at_5
value: 13.218
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.94432059816452
- type: cos_sim_spearman
value: 79.19993315048852
- type: euclidean_pearson
value: 72.43261099671753
- type: euclidean_spearman
value: 71.51531114998619
- type: manhattan_pearson
value: 71.83604124130447
- type: manhattan_spearman
value: 71.24460392842295
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.25401068481673
- type: cos_sim_spearman
value: 74.5249604699309
- type: euclidean_pearson
value: 71.1324859629043
- type: euclidean_spearman
value: 58.77041705276752
- type: manhattan_pearson
value: 71.01471521586141
- type: manhattan_spearman
value: 58.69949381017865
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 82.85731544223766
- type: cos_sim_spearman
value: 83.15607264736185
- type: euclidean_pearson
value: 75.8803249521361
- type: euclidean_spearman
value: 76.4862168799065
- type: manhattan_pearson
value: 75.80451454386811
- type: manhattan_spearman
value: 76.35986831074699
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.40669043798857
- type: cos_sim_spearman
value: 78.08686090667834
- type: euclidean_pearson
value: 74.48574712193803
- type: euclidean_spearman
value: 70.79423012045118
- type: manhattan_pearson
value: 74.39099211477354
- type: manhattan_spearman
value: 70.73135427277684
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.03027014209859
- type: cos_sim_spearman
value: 86.91082847840946
- type: euclidean_pearson
value: 69.13187603971996
- type: euclidean_spearman
value: 70.0370035340552
- type: manhattan_pearson
value: 69.2586635812031
- type: manhattan_spearman
value: 70.18638387118486
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.41190748361883
- type: cos_sim_spearman
value: 83.64850851235231
- type: euclidean_pearson
value: 71.60523243575282
- type: euclidean_spearman
value: 72.26134033805099
- type: manhattan_pearson
value: 71.50771482066683
- type: manhattan_spearman
value: 72.13707967973161
- 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: 90.42838477648627
- type: cos_sim_spearman
value: 90.15798155439076
- type: euclidean_pearson
value: 77.09619972244516
- type: euclidean_spearman
value: 75.5953488548861
- type: manhattan_pearson
value: 77.36892406451771
- type: manhattan_spearman
value: 75.76625156149356
- 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: 65.76151154879307
- type: cos_sim_spearman
value: 64.8846800918359
- type: euclidean_pearson
value: 50.23302700257155
- type: euclidean_spearman
value: 58.89455187289583
- type: manhattan_pearson
value: 50.05498582284945
- type: manhattan_spearman
value: 58.75893793871576
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.72381109169437
- type: cos_sim_spearman
value: 84.59820928231167
- type: euclidean_pearson
value: 74.85450857429493
- type: euclidean_spearman
value: 73.83634052565915
- type: manhattan_pearson
value: 74.97349743979106
- type: manhattan_spearman
value: 73.9636470375881
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 80.96736259172798
- type: mrr
value: 94.48378781712114
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 46.344
- type: map_at_10
value: 54.962
- type: map_at_100
value: 55.772
- type: map_at_1000
value: 55.81700000000001
- type: map_at_3
value: 51.832
- type: map_at_5
value: 53.718999999999994
- type: mrr_at_1
value: 49.0
- type: mrr_at_10
value: 56.721
- type: mrr_at_100
value: 57.287
- type: mrr_at_1000
value: 57.330000000000005
- type: mrr_at_3
value: 54.056000000000004
- type: mrr_at_5
value: 55.822
- type: ndcg_at_1
value: 49.0
- type: ndcg_at_10
value: 59.757000000000005
- type: ndcg_at_100
value: 63.149
- type: ndcg_at_1000
value: 64.43100000000001
- type: ndcg_at_3
value: 54.105000000000004
- type: ndcg_at_5
value: 57.196999999999996
- type: precision_at_1
value: 49.0
- type: precision_at_10
value: 8.200000000000001
- type: precision_at_100
value: 1.0070000000000001
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 20.889
- type: precision_at_5
value: 14.399999999999999
- type: recall_at_1
value: 46.344
- type: recall_at_10
value: 72.722
- type: recall_at_100
value: 88.167
- type: recall_at_1000
value: 98.333
- type: recall_at_3
value: 57.994
- type: recall_at_5
value: 65.506
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.83366336633664
- type: cos_sim_ap
value: 96.09329747251944
- type: cos_sim_f1
value: 91.66255550074001
- type: cos_sim_precision
value: 90.45764362220059
- type: cos_sim_recall
value: 92.9
- type: dot_accuracy
value: 99.32871287128712
- type: dot_ap
value: 63.95436644147969
- type: dot_f1
value: 60.61814556331008
- type: dot_precision
value: 60.437375745526836
- type: dot_recall
value: 60.8
- type: euclidean_accuracy
value: 99.66534653465347
- type: euclidean_ap
value: 85.85143979761818
- type: euclidean_f1
value: 81.57033805888769
- type: euclidean_precision
value: 89.68824940047962
- type: euclidean_recall
value: 74.8
- type: manhattan_accuracy
value: 99.65742574257426
- type: manhattan_ap
value: 85.55693926348405
- type: manhattan_f1
value: 81.13804004214963
- type: manhattan_precision
value: 85.74610244988864
- type: manhattan_recall
value: 77.0
- type: max_accuracy
value: 99.83366336633664
- type: max_ap
value: 96.09329747251944
- type: max_f1
value: 91.66255550074001
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 45.23573510003245
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 33.37478638401161
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.375920467392476
- type: mrr
value: 51.17302223919871
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.768864092288343
- type: cos_sim_spearman
value: 29.854278347043266
- type: dot_pearson
value: 20.51281723837505
- type: dot_spearman
value: 21.799102540913665
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.2
- type: map_at_10
value: 1.202
- type: map_at_100
value: 6.729
- type: map_at_1000
value: 15.928
- type: map_at_3
value: 0.492
- type: map_at_5
value: 0.712
- type: mrr_at_1
value: 76.0
- type: mrr_at_10
value: 84.75
- type: mrr_at_100
value: 84.75
- type: mrr_at_1000
value: 84.75
- type: mrr_at_3
value: 83.0
- type: mrr_at_5
value: 84.5
- type: ndcg_at_1
value: 71.0
- type: ndcg_at_10
value: 57.253
- type: ndcg_at_100
value: 44.383
- type: ndcg_at_1000
value: 38.666
- type: ndcg_at_3
value: 64.324
- type: ndcg_at_5
value: 60.791
- type: precision_at_1
value: 76.0
- type: precision_at_10
value: 59.599999999999994
- type: precision_at_100
value: 45.440000000000005
- type: precision_at_1000
value: 17.458000000000002
- type: precision_at_3
value: 69.333
- type: precision_at_5
value: 63.2
- type: recall_at_1
value: 0.2
- type: recall_at_10
value: 1.4949999999999999
- type: recall_at_100
value: 10.266
- type: recall_at_1000
value: 35.853
- type: recall_at_3
value: 0.5349999999999999
- type: recall_at_5
value: 0.8109999999999999
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.0140000000000002
- type: map_at_10
value: 8.474
- type: map_at_100
value: 14.058000000000002
- type: map_at_1000
value: 15.381
- type: map_at_3
value: 4.508
- type: map_at_5
value: 5.87
- type: mrr_at_1
value: 22.448999999999998
- type: mrr_at_10
value: 37.242
- type: mrr_at_100
value: 38.291
- type: mrr_at_1000
value: 38.311
- type: mrr_at_3
value: 32.312999999999995
- type: mrr_at_5
value: 34.762
- type: ndcg_at_1
value: 20.408
- type: ndcg_at_10
value: 20.729
- type: ndcg_at_100
value: 33.064
- type: ndcg_at_1000
value: 44.324999999999996
- type: ndcg_at_3
value: 21.251
- type: ndcg_at_5
value: 20.28
- type: precision_at_1
value: 22.448999999999998
- type: precision_at_10
value: 18.98
- type: precision_at_100
value: 7.224
- type: precision_at_1000
value: 1.471
- type: precision_at_3
value: 22.448999999999998
- type: precision_at_5
value: 20.816000000000003
- type: recall_at_1
value: 2.0140000000000002
- type: recall_at_10
value: 13.96
- type: recall_at_100
value: 44.187
- type: recall_at_1000
value: 79.328
- type: recall_at_3
value: 5.345
- type: recall_at_5
value: 7.979
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.1312
- type: ap
value: 12.606776505497608
- type: f1
value: 52.4112415600534
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 58.16072439162422
- type: f1
value: 58.29152785435414
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 40.421119289825924
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.48012159504083
- type: cos_sim_ap
value: 72.31974877212102
- type: cos_sim_f1
value: 67.96846573681019
- type: cos_sim_precision
value: 62.89562289562289
- type: cos_sim_recall
value: 73.93139841688654
- type: dot_accuracy
value: 78.52416999463551
- type: dot_ap
value: 43.65271285411479
- type: dot_f1
value: 46.94641449960599
- type: dot_precision
value: 37.456774599182644
- type: dot_recall
value: 62.875989445910285
- type: euclidean_accuracy
value: 83.90057817249806
- type: euclidean_ap
value: 65.96278727778665
- type: euclidean_f1
value: 63.35733232284957
- type: euclidean_precision
value: 60.770535497940394
- type: euclidean_recall
value: 66.17414248021109
- type: manhattan_accuracy
value: 83.96614412588663
- type: manhattan_ap
value: 66.03670273156699
- type: manhattan_f1
value: 63.49128406579917
- type: manhattan_precision
value: 59.366391184573
- type: manhattan_recall
value: 68.23218997361478
- type: max_accuracy
value: 85.48012159504083
- type: max_ap
value: 72.31974877212102
- type: max_f1
value: 67.96846573681019
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.97038848139093
- type: cos_sim_ap
value: 85.982764495556
- type: cos_sim_f1
value: 78.73283281450284
- type: cos_sim_precision
value: 75.07857791436754
- type: cos_sim_recall
value: 82.7610101632276
- type: dot_accuracy
value: 83.21108394458028
- type: dot_ap
value: 70.97956937273386
- type: dot_f1
value: 66.53083038279111
- type: dot_precision
value: 58.7551622418879
- type: dot_recall
value: 76.67847243609486
- type: euclidean_accuracy
value: 84.31520937633407
- type: euclidean_ap
value: 74.67323411319909
- type: euclidean_f1
value: 67.21935410935676
- type: euclidean_precision
value: 65.82773636430733
- type: euclidean_recall
value: 68.67108099784416
- type: manhattan_accuracy
value: 84.35013777312066
- type: manhattan_ap
value: 74.66508905354597
- type: manhattan_f1
value: 67.28264162375038
- type: manhattan_precision
value: 66.19970193740686
- type: manhattan_recall
value: 68.40160147828766
- type: max_accuracy
value: 88.97038848139093
- type: max_ap
value: 85.982764495556
- type: max_f1
value: 78.73283281450284
---
The text embedding set trained by Jina AI, Finetuner team.
## Intented Usage & Model Info
`jina-embedding-l-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 size of 330 million parameters,
the model enables single-gpu inference while delivering better performance than our small and base model.
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
- [`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 **(you are here)**.
- `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|0.739|**0.844**|0.778|**0.863**|0.821|0.896|0.566 |**0.882**|0.608 |
## Usage
Use with Jina AI Finetuner
```python
!pip install finetuner
import finetuner
model = finetuner.build_model('jinaai/jina-embedding-l-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-b-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}
}
```