---
pipeline_tag: sentence-similarity
tags:
- finetuner
- mteb
- feature-extraction
- sentence-similarity
datasets:
- jinaai/negation-dataset
language: en
license: apache-2.0
model-index:
- name: jina-embedding-l-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: 61.64179104477612
- type: ap
value: 24.63675721041911
- type: f1
value: 55.10036810049116
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 60.708125
- type: ap
value: 57.491681452557344
- type: f1
value: 58.046023443205655
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 28.12
- type: f1
value: 26.904734434317966
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.031
- type: map_at_10
value: 40.742
- type: map_at_100
value: 41.832
- type: map_at_1000
value: 41.844
- type: map_at_3
value: 35.526
- type: map_at_5
value: 38.567
- type: mrr_at_1
value: 26.316
- type: mrr_at_10
value: 40.855999999999995
- type: mrr_at_100
value: 41.946
- type: mrr_at_1000
value: 41.957
- type: mrr_at_3
value: 35.621
- type: mrr_at_5
value: 38.644
- type: ndcg_at_1
value: 26.031
- type: ndcg_at_10
value: 49.483
- type: ndcg_at_100
value: 54.074999999999996
- type: ndcg_at_1000
value: 54.344
- type: ndcg_at_3
value: 38.792
- type: ndcg_at_5
value: 44.24
- type: precision_at_1
value: 26.031
- type: precision_at_10
value: 7.76
- type: precision_at_100
value: 0.975
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 16.098000000000003
- type: precision_at_5
value: 12.29
- type: recall_at_1
value: 26.031
- type: recall_at_10
value: 77.596
- type: recall_at_100
value: 97.51100000000001
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 48.293
- type: recall_at_5
value: 61.451
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 41.76036539849672
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 34.27585676831497
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 63.47328704612227
- type: mrr
value: 76.63182078002022
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 87.42072640664271
- type: cos_sim_spearman
value: 84.31336692039407
- type: euclidean_pearson
value: 54.93250871487246
- type: euclidean_spearman
value: 55.91091252228738
- type: manhattan_pearson
value: 54.78812442894107
- type: manhattan_spearman
value: 55.35005636930548
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 86.28896103896103
- type: f1
value: 86.23389676482913
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 33.73729294301578
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 30.641078215958288
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.258000000000001
- type: map_at_10
value: 14.57
- type: map_at_100
value: 15.98
- type: map_at_1000
value: 16.149
- type: map_at_3
value: 11.993
- type: map_at_5
value: 13.383000000000001
- type: mrr_at_1
value: 18.176000000000002
- type: mrr_at_10
value: 28.560000000000002
- type: mrr_at_100
value: 29.656
- type: mrr_at_1000
value: 29.709999999999997
- type: mrr_at_3
value: 25.255
- type: mrr_at_5
value: 27.128000000000004
- type: ndcg_at_1
value: 18.176000000000002
- type: ndcg_at_10
value: 21.36
- type: ndcg_at_100
value: 27.619
- type: ndcg_at_1000
value: 31.086000000000002
- type: ndcg_at_3
value: 16.701
- type: ndcg_at_5
value: 18.559
- type: precision_at_1
value: 18.176000000000002
- type: precision_at_10
value: 6.683999999999999
- type: precision_at_100
value: 1.3339999999999999
- type: precision_at_1000
value: 0.197
- type: precision_at_3
value: 12.269
- type: precision_at_5
value: 9.798
- type: recall_at_1
value: 8.258000000000001
- type: recall_at_10
value: 27.060000000000002
- type: recall_at_100
value: 48.833
- type: recall_at_1000
value: 68.636
- type: recall_at_3
value: 15.895999999999999
- type: recall_at_5
value: 20.625
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.241
- type: map_at_10
value: 17.141000000000002
- type: map_at_100
value: 22.805
- type: map_at_1000
value: 24.189
- type: map_at_3
value: 12.940999999999999
- type: map_at_5
value: 14.607000000000001
- type: mrr_at_1
value: 62.25000000000001
- type: mrr_at_10
value: 70.537
- type: mrr_at_100
value: 70.851
- type: mrr_at_1000
value: 70.875
- type: mrr_at_3
value: 68.75
- type: mrr_at_5
value: 69.77499999999999
- type: ndcg_at_1
value: 50.125
- type: ndcg_at_10
value: 36.032
- type: ndcg_at_100
value: 39.428999999999995
- type: ndcg_at_1000
value: 47.138999999999996
- type: ndcg_at_3
value: 40.99
- type: ndcg_at_5
value: 37.772
- type: precision_at_1
value: 62.25000000000001
- type: precision_at_10
value: 28.050000000000004
- type: precision_at_100
value: 8.527999999999999
- type: precision_at_1000
value: 1.82
- type: precision_at_3
value: 45.0
- type: precision_at_5
value: 36.0
- type: recall_at_1
value: 8.241
- type: recall_at_10
value: 22.583000000000002
- type: recall_at_100
value: 44.267
- type: recall_at_1000
value: 69.497
- type: recall_at_3
value: 14.326
- type: recall_at_5
value: 17.29
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 42.295
- type: f1
value: 38.32403088027173
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 58.553
- type: map_at_10
value: 69.632
- type: map_at_100
value: 69.95400000000001
- type: map_at_1000
value: 69.968
- type: map_at_3
value: 67.656
- type: map_at_5
value: 68.86
- type: mrr_at_1
value: 63.156
- type: mrr_at_10
value: 74.37700000000001
- type: mrr_at_100
value: 74.629
- type: mrr_at_1000
value: 74.63300000000001
- type: mrr_at_3
value: 72.577
- type: mrr_at_5
value: 73.71
- type: ndcg_at_1
value: 63.156
- type: ndcg_at_10
value: 75.345
- type: ndcg_at_100
value: 76.728
- type: ndcg_at_1000
value: 77.006
- type: ndcg_at_3
value: 71.67099999999999
- type: ndcg_at_5
value: 73.656
- type: precision_at_1
value: 63.156
- type: precision_at_10
value: 9.673
- type: precision_at_100
value: 1.045
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 28.393
- type: precision_at_5
value: 18.160999999999998
- type: recall_at_1
value: 58.553
- type: recall_at_10
value: 88.362
- type: recall_at_100
value: 94.401
- type: recall_at_1000
value: 96.256
- type: recall_at_3
value: 78.371
- type: recall_at_5
value: 83.32300000000001
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.302
- type: map_at_10
value: 31.887
- type: map_at_100
value: 33.727000000000004
- type: map_at_1000
value: 33.914
- type: map_at_3
value: 27.254
- type: map_at_5
value: 29.904999999999998
- type: mrr_at_1
value: 39.043
- type: mrr_at_10
value: 47.858000000000004
- type: mrr_at_100
value: 48.636
- type: mrr_at_1000
value: 48.677
- type: mrr_at_3
value: 45.062000000000005
- type: mrr_at_5
value: 46.775
- type: ndcg_at_1
value: 39.043
- type: ndcg_at_10
value: 39.899
- type: ndcg_at_100
value: 46.719
- type: ndcg_at_1000
value: 49.739
- type: ndcg_at_3
value: 35.666
- type: ndcg_at_5
value: 37.232
- type: precision_at_1
value: 39.043
- type: precision_at_10
value: 11.265
- type: precision_at_100
value: 1.864
- type: precision_at_1000
value: 0.23800000000000002
- type: precision_at_3
value: 24.227999999999998
- type: precision_at_5
value: 18.148
- type: recall_at_1
value: 19.302
- type: recall_at_10
value: 47.278
- type: recall_at_100
value: 72.648
- type: recall_at_1000
value: 90.793
- type: recall_at_3
value: 31.235000000000003
- type: recall_at_5
value: 38.603
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.398
- type: map_at_10
value: 44.635000000000005
- type: map_at_100
value: 45.513
- type: map_at_1000
value: 45.595
- type: map_at_3
value: 41.894
- type: map_at_5
value: 43.514
- type: mrr_at_1
value: 62.795
- type: mrr_at_10
value: 70.001
- type: mrr_at_100
value: 70.378
- type: mrr_at_1000
value: 70.399
- type: mrr_at_3
value: 68.542
- type: mrr_at_5
value: 69.394
- type: ndcg_at_1
value: 62.795
- type: ndcg_at_10
value: 53.635
- type: ndcg_at_100
value: 57.05
- type: ndcg_at_1000
value: 58.755
- type: ndcg_at_3
value: 49.267
- type: ndcg_at_5
value: 51.522
- type: precision_at_1
value: 62.795
- type: precision_at_10
value: 11.196
- type: precision_at_100
value: 1.389
- type: precision_at_1000
value: 0.16199999999999998
- type: precision_at_3
value: 30.804
- type: precision_at_5
value: 20.265
- type: recall_at_1
value: 31.398
- type: recall_at_10
value: 55.982
- type: recall_at_100
value: 69.453
- type: recall_at_1000
value: 80.756
- type: recall_at_3
value: 46.205
- type: recall_at_5
value: 50.662
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 63.803200000000004
- type: ap
value: 59.04397034963468
- type: f1
value: 63.4675375611795
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 17.671
- type: map_at_10
value: 29.152
- type: map_at_100
value: 30.422
- type: map_at_1000
value: 30.481
- type: map_at_3
value: 25.417
- type: map_at_5
value: 27.448
- type: mrr_at_1
value: 18.195
- type: mrr_at_10
value: 29.67
- type: mrr_at_100
value: 30.891999999999996
- type: mrr_at_1000
value: 30.944
- type: mrr_at_3
value: 25.974000000000004
- type: mrr_at_5
value: 27.996
- type: ndcg_at_1
value: 18.195
- type: ndcg_at_10
value: 35.795
- type: ndcg_at_100
value: 42.117
- type: ndcg_at_1000
value: 43.585
- type: ndcg_at_3
value: 28.122000000000003
- type: ndcg_at_5
value: 31.757
- type: precision_at_1
value: 18.195
- type: precision_at_10
value: 5.89
- type: precision_at_100
value: 0.9079999999999999
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 12.24
- type: precision_at_5
value: 9.178
- type: recall_at_1
value: 17.671
- type: recall_at_10
value: 56.373
- type: recall_at_100
value: 86.029
- type: recall_at_1000
value: 97.246
- type: recall_at_3
value: 35.414
- type: recall_at_5
value: 44.149
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.80255357957135
- type: f1
value: 90.79256308087807
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 71.20611035111719
- type: f1
value: 54.075483897190836
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.79354404841965
- type: f1
value: 68.53816551555609
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.6072629455279
- type: f1
value: 77.04997715738867
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 30.432745003633016
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.95493811839366
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.63516074152514
- type: mrr
value: 32.73091425241894
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.379
- type: map_at_10
value: 12.051
- type: map_at_100
value: 15.176
- type: map_at_1000
value: 16.662
- type: map_at_3
value: 8.588
- type: map_at_5
value: 10.274
- type: mrr_at_1
value: 44.891999999999996
- type: mrr_at_10
value: 53.06999999999999
- type: mrr_at_100
value: 53.675
- type: mrr_at_1000
value: 53.717999999999996
- type: mrr_at_3
value: 50.671
- type: mrr_at_5
value: 52.25
- type: ndcg_at_1
value: 42.879
- type: ndcg_at_10
value: 33.291
- type: ndcg_at_100
value: 30.567
- type: ndcg_at_1000
value: 39.598
- type: ndcg_at_3
value: 37.713
- type: ndcg_at_5
value: 36.185
- type: precision_at_1
value: 44.891999999999996
- type: precision_at_10
value: 24.923000000000002
- type: precision_at_100
value: 8.015
- type: precision_at_1000
value: 2.083
- type: precision_at_3
value: 35.088
- type: precision_at_5
value: 31.765
- type: recall_at_1
value: 5.379
- type: recall_at_10
value: 16.346
- type: recall_at_100
value: 31.887999999999998
- type: recall_at_1000
value: 64.90599999999999
- type: recall_at_3
value: 9.543
- type: recall_at_5
value: 12.369
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.654
- type: map_at_10
value: 40.163
- type: map_at_100
value: 41.376000000000005
- type: map_at_1000
value: 41.411
- type: map_at_3
value: 35.677
- type: map_at_5
value: 38.238
- type: mrr_at_1
value: 29.055999999999997
- type: mrr_at_10
value: 42.571999999999996
- type: mrr_at_100
value: 43.501
- type: mrr_at_1000
value: 43.527
- type: mrr_at_3
value: 38.775
- type: mrr_at_5
value: 40.953
- type: ndcg_at_1
value: 29.026999999999997
- type: ndcg_at_10
value: 47.900999999999996
- type: ndcg_at_100
value: 52.941
- type: ndcg_at_1000
value: 53.786
- type: ndcg_at_3
value: 39.387
- type: ndcg_at_5
value: 43.65
- type: precision_at_1
value: 29.026999999999997
- type: precision_at_10
value: 8.247
- type: precision_at_100
value: 1.102
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 18.231
- type: precision_at_5
value: 13.378
- type: recall_at_1
value: 25.654
- type: recall_at_10
value: 69.175
- type: recall_at_100
value: 90.85600000000001
- type: recall_at_1000
value: 97.18
- type: recall_at_3
value: 47.043
- type: recall_at_5
value: 56.86600000000001
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.785
- type: map_at_10
value: 84.509
- type: map_at_100
value: 85.17
- type: map_at_1000
value: 85.187
- type: map_at_3
value: 81.628
- type: map_at_5
value: 83.422
- type: mrr_at_1
value: 81.43
- type: mrr_at_10
value: 87.506
- type: mrr_at_100
value: 87.616
- type: mrr_at_1000
value: 87.617
- type: mrr_at_3
value: 86.598
- type: mrr_at_5
value: 87.215
- type: ndcg_at_1
value: 81.44
- type: ndcg_at_10
value: 88.208
- type: ndcg_at_100
value: 89.49000000000001
- type: ndcg_at_1000
value: 89.59700000000001
- type: ndcg_at_3
value: 85.471
- type: ndcg_at_5
value: 86.955
- type: precision_at_1
value: 81.44
- type: precision_at_10
value: 13.347000000000001
- type: precision_at_100
value: 1.53
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.330000000000005
- type: precision_at_5
value: 24.506
- type: recall_at_1
value: 70.785
- type: recall_at_10
value: 95.15
- type: recall_at_100
value: 99.502
- type: recall_at_1000
value: 99.993
- type: recall_at_3
value: 87.234
- type: recall_at_5
value: 91.467
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 52.40682777853522
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 56.61834429208595
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.918
- type: map_at_10
value: 11.562
- type: map_at_100
value: 13.636999999999999
- type: map_at_1000
value: 13.918
- type: map_at_3
value: 8.353
- type: map_at_5
value: 9.878
- type: mrr_at_1
value: 24.3
- type: mrr_at_10
value: 33.914
- type: mrr_at_100
value: 35.079
- type: mrr_at_1000
value: 35.134
- type: mrr_at_3
value: 30.833
- type: mrr_at_5
value: 32.528
- type: ndcg_at_1
value: 24.3
- type: ndcg_at_10
value: 19.393
- type: ndcg_at_100
value: 27.471
- type: ndcg_at_1000
value: 32.543
- type: ndcg_at_3
value: 18.648
- type: ndcg_at_5
value: 16.064999999999998
- type: precision_at_1
value: 24.3
- type: precision_at_10
value: 9.92
- type: precision_at_100
value: 2.152
- type: precision_at_1000
value: 0.338
- type: precision_at_3
value: 17.1
- type: precision_at_5
value: 13.819999999999999
- type: recall_at_1
value: 4.918
- type: recall_at_10
value: 20.102
- type: recall_at_100
value: 43.69
- type: recall_at_1000
value: 68.568
- type: recall_at_3
value: 10.383000000000001
- type: recall_at_5
value: 13.977999999999998
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 86.02374279770862
- type: cos_sim_spearman
value: 80.3123278821752
- type: euclidean_pearson
value: 78.150387301923
- type: euclidean_spearman
value: 74.27020095240543
- type: manhattan_pearson
value: 78.00212720962597
- type: manhattan_spearman
value: 74.27996355049189
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.56832604166104
- type: cos_sim_spearman
value: 73.85172437109456
- type: euclidean_pearson
value: 70.77037821156355
- type: euclidean_spearman
value: 58.32603602271459
- type: manhattan_pearson
value: 70.6019035905572
- type: manhattan_spearman
value: 58.18758998109944
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 83.97624603590171
- type: cos_sim_spearman
value: 84.3654403570941
- type: euclidean_pearson
value: 77.37734191552401
- type: euclidean_spearman
value: 77.83492278107906
- type: manhattan_pearson
value: 77.38406845115612
- type: manhattan_spearman
value: 77.80429501178632
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.5175806484823
- type: cos_sim_spearman
value: 77.84074419393815
- type: euclidean_pearson
value: 75.31514179994578
- type: euclidean_spearman
value: 71.06564963155697
- type: manhattan_pearson
value: 75.25016497298036
- type: manhattan_spearman
value: 71.0503867625097
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 85.15312065200007
- type: cos_sim_spearman
value: 86.28786282283781
- type: euclidean_pearson
value: 69.93961446583728
- type: euclidean_spearman
value: 70.99565144007187
- type: manhattan_pearson
value: 70.06338127800244
- type: manhattan_spearman
value: 71.15328825585216
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 80.48261723093232
- type: cos_sim_spearman
value: 82.13997187275378
- type: euclidean_pearson
value: 72.01034058956992
- type: euclidean_spearman
value: 72.90423890320797
- type: manhattan_pearson
value: 71.91819389305805
- type: manhattan_spearman
value: 72.804333901611
- 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: 89.89094326696411
- type: cos_sim_spearman
value: 89.5679328484923
- type: euclidean_pearson
value: 77.27326226557433
- type: euclidean_spearman
value: 75.44670270858582
- type: manhattan_pearson
value: 77.49623029933024
- type: manhattan_spearman
value: 75.6317127686177
- 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: 67.03259798800852
- type: cos_sim_spearman
value: 66.17683868865686
- type: euclidean_pearson
value: 49.154524473561416
- type: euclidean_spearman
value: 58.82796771905756
- type: manhattan_pearson
value: 48.97445679282608
- type: manhattan_spearman
value: 58.69653501728678
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.01368632144246
- type: cos_sim_spearman
value: 83.64169080274549
- type: euclidean_pearson
value: 75.84021692605727
- type: euclidean_spearman
value: 74.69132304226987
- type: manhattan_pearson
value: 75.9627059404693
- type: manhattan_spearman
value: 74.83616979158057
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 81.63017243645893
- type: mrr
value: 94.79274900843528
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 47.094
- type: map_at_10
value: 56.047000000000004
- type: map_at_100
value: 56.701
- type: map_at_1000
value: 56.742000000000004
- type: map_at_3
value: 53.189
- type: map_at_5
value: 54.464
- type: mrr_at_1
value: 50.0
- type: mrr_at_10
value: 57.567
- type: mrr_at_100
value: 58.104
- type: mrr_at_1000
value: 58.142
- type: mrr_at_3
value: 55.222
- type: mrr_at_5
value: 56.355999999999995
- type: ndcg_at_1
value: 50.0
- type: ndcg_at_10
value: 60.84
- type: ndcg_at_100
value: 63.983999999999995
- type: ndcg_at_1000
value: 65.19500000000001
- type: ndcg_at_3
value: 55.491
- type: ndcg_at_5
value: 57.51500000000001
- type: precision_at_1
value: 50.0
- type: precision_at_10
value: 8.366999999999999
- type: precision_at_100
value: 1.013
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 21.556
- type: precision_at_5
value: 14.2
- type: recall_at_1
value: 47.094
- type: recall_at_10
value: 74.239
- type: recall_at_100
value: 89.0
- type: recall_at_1000
value: 98.667
- type: recall_at_3
value: 59.606
- type: recall_at_5
value: 64.756
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.7128712871287
- type: cos_sim_ap
value: 91.8391173412632
- type: cos_sim_f1
value: 85.23421588594704
- type: cos_sim_precision
value: 86.82572614107885
- type: cos_sim_recall
value: 83.7
- type: dot_accuracy
value: 99.23960396039604
- type: dot_ap
value: 58.07268940033783
- type: dot_f1
value: 58.00486618004865
- type: dot_precision
value: 56.49289099526066
- type: dot_recall
value: 59.599999999999994
- type: euclidean_accuracy
value: 99.62574257425743
- type: euclidean_ap
value: 86.31145319031712
- type: euclidean_f1
value: 80.12486992715921
- type: euclidean_precision
value: 83.51409978308027
- type: euclidean_recall
value: 77.0
- type: manhattan_accuracy
value: 99.62178217821783
- type: manhattan_ap
value: 85.96697606381338
- type: manhattan_f1
value: 80.24193548387099
- type: manhattan_precision
value: 80.89430894308943
- type: manhattan_recall
value: 79.60000000000001
- type: max_accuracy
value: 99.7128712871287
- type: max_ap
value: 91.8391173412632
- type: max_f1
value: 85.23421588594704
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 54.98955943181893
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.72837687387049
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 51.02207528482775
- type: mrr
value: 51.8842044393515
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.250596893094876
- type: cos_sim_spearman
value: 30.609457706010158
- type: dot_pearson
value: 19.739579843052162
- type: dot_spearman
value: 20.27834051930579
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.187
- type: map_at_10
value: 1.239
- type: map_at_100
value: 6.388000000000001
- type: map_at_1000
value: 15.507000000000001
- type: map_at_3
value: 0.5
- type: map_at_5
value: 0.712
- type: mrr_at_1
value: 70.0
- type: mrr_at_10
value: 83.0
- type: mrr_at_100
value: 83.0
- type: mrr_at_1000
value: 83.0
- type: mrr_at_3
value: 81.667
- type: mrr_at_5
value: 82.667
- type: ndcg_at_1
value: 65.0
- type: ndcg_at_10
value: 56.57600000000001
- type: ndcg_at_100
value: 42.054
- type: ndcg_at_1000
value: 38.269999999999996
- type: ndcg_at_3
value: 63.134
- type: ndcg_at_5
value: 58.792
- type: precision_at_1
value: 70.0
- type: precision_at_10
value: 59.8
- type: precision_at_100
value: 42.5
- type: precision_at_1000
value: 17.304
- type: precision_at_3
value: 67.333
- type: precision_at_5
value: 62.4
- type: recall_at_1
value: 0.187
- type: recall_at_10
value: 1.529
- type: recall_at_100
value: 9.673
- type: recall_at_1000
value: 35.807
- type: recall_at_3
value: 0.5459999999999999
- type: recall_at_5
value: 0.8130000000000001
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.646
- type: map_at_10
value: 6.569999999999999
- type: map_at_100
value: 11.530999999999999
- type: map_at_1000
value: 13.009
- type: map_at_3
value: 3.234
- type: map_at_5
value: 4.956
- type: mrr_at_1
value: 18.367
- type: mrr_at_10
value: 35.121
- type: mrr_at_100
value: 36.142
- type: mrr_at_1000
value: 36.153
- type: mrr_at_3
value: 29.252
- type: mrr_at_5
value: 33.434999999999995
- type: ndcg_at_1
value: 16.326999999999998
- type: ndcg_at_10
value: 17.336
- type: ndcg_at_100
value: 28.925
- type: ndcg_at_1000
value: 41.346
- type: ndcg_at_3
value: 16.131999999999998
- type: ndcg_at_5
value: 18.107
- type: precision_at_1
value: 18.367
- type: precision_at_10
value: 16.531000000000002
- type: precision_at_100
value: 6.449000000000001
- type: precision_at_1000
value: 1.451
- type: precision_at_3
value: 17.687
- type: precision_at_5
value: 20.0
- type: recall_at_1
value: 1.646
- type: recall_at_10
value: 12.113
- type: recall_at_100
value: 40.261
- type: recall_at_1000
value: 77.878
- type: recall_at_3
value: 4.181
- type: recall_at_5
value: 7.744
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 66.61500000000001
- type: ap
value: 11.70707762285034
- type: f1
value: 50.53259935502312
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 54.89247311827958
- type: f1
value: 55.044186334629586
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 46.95851882042766
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.01978899684092
- type: cos_sim_ap
value: 68.10404793439619
- type: cos_sim_f1
value: 63.93145891154821
- type: cos_sim_precision
value: 58.905937291527685
- type: cos_sim_recall
value: 69.89445910290237
- type: dot_accuracy
value: 77.78506288370984
- type: dot_ap
value: 38.55636213255057
- type: dot_f1
value: 44.6866485013624
- type: dot_precision
value: 34.07202216066482
- type: dot_recall
value: 64.90765171503958
- type: euclidean_accuracy
value: 82.94093103653812
- type: euclidean_ap
value: 63.65596102723866
- type: euclidean_f1
value: 61.444903916322055
- type: euclidean_precision
value: 56.994584837545126
- type: euclidean_recall
value: 66.64907651715039
- type: manhattan_accuracy
value: 82.99457590749239
- type: manhattan_ap
value: 63.77653539498376
- type: manhattan_f1
value: 61.48299483235189
- type: manhattan_precision
value: 56.455528580887226
- type: manhattan_recall
value: 67.4934036939314
- type: max_accuracy
value: 84.01978899684092
- type: max_ap
value: 68.10404793439619
- type: max_f1
value: 63.93145891154821
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 87.75177552683665
- type: cos_sim_ap
value: 83.75899853399007
- type: cos_sim_f1
value: 76.25022931572188
- type: cos_sim_precision
value: 72.83241045769958
- type: cos_sim_recall
value: 80.00461964890668
- type: dot_accuracy
value: 81.8197694725812
- type: dot_ap
value: 67.6851675345571
- type: dot_f1
value: 64.04501820589209
- type: dot_precision
value: 56.17233770758332
- type: dot_recall
value: 74.48413920542039
- type: euclidean_accuracy
value: 83.3003454030349
- type: euclidean_ap
value: 72.80186670461116
- type: euclidean_f1
value: 65.38000218078727
- type: euclidean_precision
value: 61.92082616179002
- type: euclidean_recall
value: 69.24853711117956
- type: manhattan_accuracy
value: 83.32169053440447
- type: manhattan_ap
value: 72.8243559753097
- type: manhattan_f1
value: 65.45939901157966
- type: manhattan_precision
value: 61.58284124075205
- type: manhattan_recall
value: 69.85679088389283
- type: max_accuracy
value: 87.75177552683665
- type: max_ap
value: 83.75899853399007
- type: max_f1
value: 76.25022931572188
---
The text embedding suite 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-s-en-v1`: 35 million parameters.
- `jina-embedding-b-en-v1`: 110 million parameters.
- `jina-embedding-l-en-v1`: 330 million parameters **(you are here)**.
- `jina-embedding-1b-en-v1`: 1.2 billion parameters, 10* bert-base size (soon).
- `jina-embedding-6b-en-v1`: 6 billion parameters 30* bert-base size(soon).
## Data & Parameters
More info will be released together with the technique report.
## 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 |context|
|------------------------------|-----|------|
|all-minilm-l6-v2|33m |128|
|all-mpnet-base-v2 |110m |128|
|ada-embedding-002|Unknown/OpenAI API |8192|
|jina-embedding-s-en-v1|35m |512|
|jina-embedding-b-en-v1|110m |512|
|jina-embedding-l-en-v1|330m |512|
|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-s-en-v1|0.742|0.786|0.738|0.837|0.80|0.875|0.543 |0.857|0.608 |
|jina-embedding-b-en-v1|**0.751**|0.809|0.761|0.856|0.812|0.89|0.601 |0.876|0.645 |
|jina-embedding-l-en-v1|0.739|**0.844**|0.778|**0.863**|0.829|0.896|0.526 |**0.882**|0.652 |
*update: we have updated the checkpoints for small/base model, re-evaluation of large model and BEIR is running in progress.*
## 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 directly with Huggingface Transformers:
```python
import torch
from transformers import AutoModel, AutoTokenizer
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
)
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
input_mask_expanded.sum(1), min=1e-9
)
sentences = ['how is the weather today', 'What is the current weather like today?']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embedding-l-en-v1')
model = AutoModel.from_pretrained('jinaai/jina-embedding-l-en-v1')
with torch.inference_mode():
encoded_input = tokenizer(
sentences, padding=True, truncation=True, return_tensors='pt'
)
model_output = model.encoder(**encoded_input)
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
```
## 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.