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---
tags:
- finetuner
- feature-extraction
- sentence-similarity
- mteb
datasets:
- jinaai/negation-dataset
language: en
license: apache-2.0
model-index:
- name: jina-embedding-b-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: 66.58208955223881
- type: ap
value: 28.455148149555754
- type: f1
value: 59.973775371110385
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 65.09505
- type: ap
value: 61.387245649832614
- type: f1
value: 62.96831291412068
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 30.633999999999993
- type: f1
value: 29.638828990078647
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.889
- type: map_at_10
value: 40.604
- type: map_at_100
value: 41.697
- type: map_at_1000
value: 41.705999999999996
- type: map_at_3
value: 35.217999999999996
- type: map_at_5
value: 38.326
- type: mrr_at_1
value: 26.245
- type: mrr_at_10
value: 40.736
- type: mrr_at_100
value: 41.829
- type: mrr_at_1000
value: 41.837999999999994
- type: mrr_at_3
value: 35.349000000000004
- type: mrr_at_5
value: 38.425
- type: ndcg_at_1
value: 25.889
- type: ndcg_at_10
value: 49.347
- type: ndcg_at_100
value: 53.956
- type: ndcg_at_1000
value: 54.2
- type: ndcg_at_3
value: 38.282
- type: ndcg_at_5
value: 43.895
- type: precision_at_1
value: 25.889
- type: precision_at_10
value: 7.752000000000001
- type: precision_at_100
value: 0.976
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 15.717999999999998
- type: precision_at_5
value: 12.162
- type: recall_at_1
value: 25.889
- type: recall_at_10
value: 77.525
- type: recall_at_100
value: 97.58200000000001
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 47.155
- type: recall_at_5
value: 60.81100000000001
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 39.2179862062943
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 29.87826673088078
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.72401299412015
- type: mrr
value: 75.45167743921206
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 85.96510928112639
- type: cos_sim_spearman
value: 82.64224450538681
- type: euclidean_pearson
value: 52.03458755006108
- type: euclidean_spearman
value: 52.83192670285616
- type: manhattan_pearson
value: 52.14561955040935
- type: manhattan_spearman
value: 52.9584356095438
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.11363636363636
- type: f1
value: 84.01098114920124
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 32.991971466919026
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 26.48807922559519
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.014000000000001
- type: map_at_10
value: 14.149999999999999
- type: map_at_100
value: 15.539
- type: map_at_1000
value: 15.711
- type: map_at_3
value: 11.913
- type: map_at_5
value: 12.982
- type: mrr_at_1
value: 18.046
- type: mrr_at_10
value: 28.224
- type: mrr_at_100
value: 29.293000000000003
- type: mrr_at_1000
value: 29.348999999999997
- type: mrr_at_3
value: 25.179000000000002
- type: mrr_at_5
value: 26.827
- type: ndcg_at_1
value: 18.046
- type: ndcg_at_10
value: 20.784
- type: ndcg_at_100
value: 26.939999999999998
- type: ndcg_at_1000
value: 30.453999999999997
- type: ndcg_at_3
value: 16.694
- type: ndcg_at_5
value: 18.049
- type: precision_at_1
value: 18.046
- type: precision_at_10
value: 6.5280000000000005
- type: precision_at_100
value: 1.2959999999999998
- type: precision_at_1000
value: 0.19499999999999998
- type: precision_at_3
value: 12.465
- type: precision_at_5
value: 9.511
- type: recall_at_1
value: 8.014000000000001
- type: recall_at_10
value: 26.021
- type: recall_at_100
value: 47.692
- type: recall_at_1000
value: 67.63
- type: recall_at_3
value: 16.122
- type: recall_at_5
value: 19.817
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.396
- type: map_at_10
value: 14.543000000000001
- type: map_at_100
value: 19.235
- type: map_at_1000
value: 20.384
- type: map_at_3
value: 10.886
- type: map_at_5
value: 12.61
- type: mrr_at_1
value: 55.50000000000001
- type: mrr_at_10
value: 63.731
- type: mrr_at_100
value: 64.256
- type: mrr_at_1000
value: 64.27000000000001
- type: mrr_at_3
value: 61.583
- type: mrr_at_5
value: 62.92100000000001
- type: ndcg_at_1
value: 43.375
- type: ndcg_at_10
value: 31.352000000000004
- type: ndcg_at_100
value: 34.717999999999996
- type: ndcg_at_1000
value: 41.959
- type: ndcg_at_3
value: 35.319
- type: ndcg_at_5
value: 33.222
- type: precision_at_1
value: 55.50000000000001
- type: precision_at_10
value: 24.15
- type: precision_at_100
value: 7.42
- type: precision_at_1000
value: 1.66
- type: precision_at_3
value: 37.917
- type: precision_at_5
value: 31.900000000000002
- type: recall_at_1
value: 7.396
- type: recall_at_10
value: 19.686999999999998
- type: recall_at_100
value: 40.465
- type: recall_at_1000
value: 63.79899999999999
- type: recall_at_3
value: 12.124
- type: recall_at_5
value: 15.28
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 41.33
- type: f1
value: 37.682972473685496
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 49.019
- type: map_at_10
value: 61.219
- type: map_at_100
value: 61.753
- type: map_at_1000
value: 61.771
- type: map_at_3
value: 58.952000000000005
- type: map_at_5
value: 60.239
- type: mrr_at_1
value: 53
- type: mrr_at_10
value: 65.678
- type: mrr_at_100
value: 66.147
- type: mrr_at_1000
value: 66.155
- type: mrr_at_3
value: 63.495999999999995
- type: mrr_at_5
value: 64.75800000000001
- type: ndcg_at_1
value: 53
- type: ndcg_at_10
value: 67.587
- type: ndcg_at_100
value: 69.877
- type: ndcg_at_1000
value: 70.25200000000001
- type: ndcg_at_3
value: 63.174
- type: ndcg_at_5
value: 65.351
- type: precision_at_1
value: 53
- type: precision_at_10
value: 9.067
- type: precision_at_100
value: 1.026
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 25.728
- type: precision_at_5
value: 16.637
- type: recall_at_1
value: 49.019
- type: recall_at_10
value: 82.962
- type: recall_at_100
value: 92.917
- type: recall_at_1000
value: 95.511
- type: recall_at_3
value: 70.838
- type: recall_at_5
value: 76.201
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.714000000000002
- type: map_at_10
value: 28.041
- type: map_at_100
value: 29.75
- type: map_at_1000
value: 29.944
- type: map_at_3
value: 23.884
- type: map_at_5
value: 26.468000000000004
- type: mrr_at_1
value: 33.796
- type: mrr_at_10
value: 42.757
- type: mrr_at_100
value: 43.705
- type: mrr_at_1000
value: 43.751
- type: mrr_at_3
value: 40.406
- type: mrr_at_5
value: 41.88
- type: ndcg_at_1
value: 33.796
- type: ndcg_at_10
value: 35.482
- type: ndcg_at_100
value: 42.44
- type: ndcg_at_1000
value: 45.903
- type: ndcg_at_3
value: 31.922
- type: ndcg_at_5
value: 33.516
- type: precision_at_1
value: 33.796
- type: precision_at_10
value: 10.108
- type: precision_at_100
value: 1.735
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 21.759
- type: precision_at_5
value: 16.605
- type: recall_at_1
value: 16.714000000000002
- type: recall_at_10
value: 42.38
- type: recall_at_100
value: 68.84700000000001
- type: recall_at_1000
value: 90.036
- type: recall_at_3
value: 28.776000000000003
- type: recall_at_5
value: 35.606
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.534
- type: map_at_10
value: 40.857
- type: map_at_100
value: 41.715999999999994
- type: map_at_1000
value: 41.795
- type: map_at_3
value: 38.415
- type: map_at_5
value: 39.833
- type: mrr_at_1
value: 59.068
- type: mrr_at_10
value: 66.034
- type: mrr_at_100
value: 66.479
- type: mrr_at_1000
value: 66.50399999999999
- type: mrr_at_3
value: 64.38000000000001
- type: mrr_at_5
value: 65.40599999999999
- type: ndcg_at_1
value: 59.068
- type: ndcg_at_10
value: 49.638
- type: ndcg_at_100
value: 53.093999999999994
- type: ndcg_at_1000
value: 54.813
- type: ndcg_at_3
value: 45.537
- type: ndcg_at_5
value: 47.671
- type: precision_at_1
value: 59.068
- type: precision_at_10
value: 10.313
- type: precision_at_100
value: 1.304
- type: precision_at_1000
value: 0.153
- type: precision_at_3
value: 28.278
- type: precision_at_5
value: 18.658
- type: recall_at_1
value: 29.534
- type: recall_at_10
value: 51.56699999999999
- type: recall_at_100
value: 65.199
- type: recall_at_1000
value: 76.678
- type: recall_at_3
value: 42.417
- type: recall_at_5
value: 46.644000000000005
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 65.74719999999999
- type: ap
value: 60.57322504947344
- type: f1
value: 65.37875006542282
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 15.695999999999998
- type: map_at_10
value: 26.661
- type: map_at_100
value: 27.982000000000003
- type: map_at_1000
value: 28.049000000000003
- type: map_at_3
value: 23.057
- type: map_at_5
value: 25.079
- type: mrr_at_1
value: 16.16
- type: mrr_at_10
value: 27.150999999999996
- type: mrr_at_100
value: 28.423
- type: mrr_at_1000
value: 28.483999999999998
- type: mrr_at_3
value: 23.577
- type: mrr_at_5
value: 25.585
- type: ndcg_at_1
value: 16.16
- type: ndcg_at_10
value: 33.017
- type: ndcg_at_100
value: 39.582
- type: ndcg_at_1000
value: 41.28
- type: ndcg_at_3
value: 25.607000000000003
- type: ndcg_at_5
value: 29.214000000000002
- type: precision_at_1
value: 16.16
- type: precision_at_10
value: 5.506
- type: precision_at_100
value: 0.882
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 11.199
- type: precision_at_5
value: 8.55
- type: recall_at_1
value: 15.695999999999998
- type: recall_at_10
value: 52.736000000000004
- type: recall_at_100
value: 83.523
- type: recall_at_1000
value: 96.588
- type: recall_at_3
value: 32.484
- type: recall_at_5
value: 41.117
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 91.71682626538988
- type: f1
value: 91.60647677401211
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 74.94756041951665
- type: f1
value: 57.26936028487369
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.43241425689307
- type: f1
value: 68.80370629448252
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.04774714189642
- type: f1
value: 76.93545888412446
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 30.009784989313765
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 25.568442512328872
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.013959341949697
- type: mrr
value: 31.998487836684575
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.316
- type: map_at_10
value: 10.287
- type: map_at_100
value: 12.817
- type: map_at_1000
value: 14.141
- type: map_at_3
value: 7.728
- type: map_at_5
value: 8.876000000000001
- type: mrr_at_1
value: 39.628
- type: mrr_at_10
value: 48.423
- type: mrr_at_100
value: 49.153999999999996
- type: mrr_at_1000
value: 49.198
- type: mrr_at_3
value: 45.666000000000004
- type: mrr_at_5
value: 47.477000000000004
- type: ndcg_at_1
value: 36.533
- type: ndcg_at_10
value: 29.304000000000002
- type: ndcg_at_100
value: 27.078000000000003
- type: ndcg_at_1000
value: 36.221
- type: ndcg_at_3
value: 33.256
- type: ndcg_at_5
value: 31.465
- type: precision_at_1
value: 39.009
- type: precision_at_10
value: 22.043
- type: precision_at_100
value: 7.115
- type: precision_at_1000
value: 1.991
- type: precision_at_3
value: 31.476
- type: precision_at_5
value: 27.616000000000003
- type: recall_at_1
value: 4.316
- type: recall_at_10
value: 14.507
- type: recall_at_100
value: 28.847
- type: recall_at_1000
value: 61.758
- type: recall_at_3
value: 8.753
- type: recall_at_5
value: 11.153
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.374
- type: map_at_10
value: 36.095
- type: map_at_100
value: 37.413999999999994
- type: map_at_1000
value: 37.46
- type: map_at_3
value: 31.711
- type: map_at_5
value: 34.294999999999995
- type: mrr_at_1
value: 25.406000000000002
- type: mrr_at_10
value: 38.424
- type: mrr_at_100
value: 39.456
- type: mrr_at_1000
value: 39.488
- type: mrr_at_3
value: 34.613
- type: mrr_at_5
value: 36.864999999999995
- type: ndcg_at_1
value: 25.406000000000002
- type: ndcg_at_10
value: 43.614000000000004
- type: ndcg_at_100
value: 49.166
- type: ndcg_at_1000
value: 50.212
- type: ndcg_at_3
value: 35.221999999999994
- type: ndcg_at_5
value: 39.571
- type: precision_at_1
value: 25.406000000000002
- type: precision_at_10
value: 7.654
- type: precision_at_100
value: 1.0699999999999998
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 16.425
- type: precision_at_5
value: 12.352
- type: recall_at_1
value: 22.374
- type: recall_at_10
value: 64.337
- type: recall_at_100
value: 88.374
- type: recall_at_1000
value: 96.101
- type: recall_at_3
value: 42.5
- type: recall_at_5
value: 52.556000000000004
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 69.301
- type: map_at_10
value: 83.128
- type: map_at_100
value: 83.779
- type: map_at_1000
value: 83.798
- type: map_at_3
value: 80.11399999999999
- type: map_at_5
value: 82.00699999999999
- type: mrr_at_1
value: 79.81
- type: mrr_at_10
value: 86.28
- type: mrr_at_100
value: 86.399
- type: mrr_at_1000
value: 86.401
- type: mrr_at_3
value: 85.26
- type: mrr_at_5
value: 85.93499999999999
- type: ndcg_at_1
value: 79.80000000000001
- type: ndcg_at_10
value: 87.06700000000001
- type: ndcg_at_100
value: 88.41799999999999
- type: ndcg_at_1000
value: 88.554
- type: ndcg_at_3
value: 84.052
- type: ndcg_at_5
value: 85.711
- type: precision_at_1
value: 79.80000000000001
- type: precision_at_10
value: 13.224
- type: precision_at_100
value: 1.5230000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 36.723
- type: precision_at_5
value: 24.192
- type: recall_at_1
value: 69.301
- type: recall_at_10
value: 94.589
- type: recall_at_100
value: 99.29299999999999
- type: recall_at_1000
value: 99.965
- type: recall_at_3
value: 86.045
- type: recall_at_5
value: 90.656
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 43.09903181165838
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 51.710378422887594
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.138
- type: map_at_10
value: 10.419
- type: map_at_100
value: 12.321
- type: map_at_1000
value: 12.605
- type: map_at_3
value: 7.445
- type: map_at_5
value: 8.859
- type: mrr_at_1
value: 20.4
- type: mrr_at_10
value: 30.148999999999997
- type: mrr_at_100
value: 31.357000000000003
- type: mrr_at_1000
value: 31.424999999999997
- type: mrr_at_3
value: 26.983
- type: mrr_at_5
value: 28.883
- type: ndcg_at_1
value: 20.4
- type: ndcg_at_10
value: 17.713
- type: ndcg_at_100
value: 25.221
- type: ndcg_at_1000
value: 30.381999999999998
- type: ndcg_at_3
value: 16.607
- type: ndcg_at_5
value: 14.559
- type: precision_at_1
value: 20.4
- type: precision_at_10
value: 9.3
- type: precision_at_100
value: 2.0060000000000002
- type: precision_at_1000
value: 0.32399999999999995
- type: precision_at_3
value: 15.5
- type: precision_at_5
value: 12.839999999999998
- type: recall_at_1
value: 4.138
- type: recall_at_10
value: 18.813
- type: recall_at_100
value: 40.692
- type: recall_at_1000
value: 65.835
- type: recall_at_3
value: 9.418
- type: recall_at_5
value: 12.983
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.25944192442188
- type: cos_sim_spearman
value: 75.04296759426568
- type: euclidean_pearson
value: 74.8130340249869
- type: euclidean_spearman
value: 68.40180320816793
- type: manhattan_pearson
value: 74.9149619199144
- type: manhattan_spearman
value: 68.52380798258379
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 81.91983072545858
- type: cos_sim_spearman
value: 73.5129498787296
- type: euclidean_pearson
value: 66.76535523270856
- type: euclidean_spearman
value: 56.64797879544097
- type: manhattan_pearson
value: 66.12191731384162
- type: manhattan_spearman
value: 56.37753861965956
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 77.71164758747632
- type: cos_sim_spearman
value: 79.1530762030973
- type: euclidean_pearson
value: 69.50621786400177
- type: euclidean_spearman
value: 70.44898083428744
- type: manhattan_pearson
value: 69.04018458995307
- type: manhattan_spearman
value: 70.00888532086853
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 78.90774995778577
- type: cos_sim_spearman
value: 75.24229403562713
- type: euclidean_pearson
value: 68.5838924571539
- type: euclidean_spearman
value: 65.06652398167358
- type: manhattan_pearson
value: 68.23143277902628
- type: manhattan_spearman
value: 64.79624516012709
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 83.78074322110155
- type: cos_sim_spearman
value: 85.12071478276958
- type: euclidean_pearson
value: 65.00147804089737
- type: euclidean_spearman
value: 66.02559342831921
- type: manhattan_pearson
value: 65.01270190203297
- type: manhattan_spearman
value: 66.13038450207748
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 77.29395327338185
- type: cos_sim_spearman
value: 80.07128686563352
- type: euclidean_pearson
value: 65.97939065455975
- type: euclidean_spearman
value: 66.80283051081129
- type: manhattan_pearson
value: 65.6750450606584
- type: manhattan_spearman
value: 66.55805829330733
- 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: 87.64956503192369
- type: cos_sim_spearman
value: 87.95719598052727
- type: euclidean_pearson
value: 73.35178669405819
- type: euclidean_spearman
value: 71.58959083579994
- type: manhattan_pearson
value: 73.24156949179472
- type: manhattan_spearman
value: 71.35933730170666
- 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: 66.61640922485357
- type: cos_sim_spearman
value: 66.08406266387749
- type: euclidean_pearson
value: 43.684972836995776
- type: euclidean_spearman
value: 60.26686390609082
- type: manhattan_pearson
value: 43.694268683941154
- type: manhattan_spearman
value: 59.61419719435629
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 81.73624666044613
- type: cos_sim_spearman
value: 81.68869881979401
- type: euclidean_pearson
value: 72.47205990508046
- type: euclidean_spearman
value: 71.02381428101695
- type: manhattan_pearson
value: 72.4947870027535
- type: manhattan_spearman
value: 71.0789806652577
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 79.53671929012175
- type: mrr
value: 93.96566033820936
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 43.761
- type: map_at_10
value: 53.846000000000004
- type: map_at_100
value: 54.55799999999999
- type: map_at_1000
value: 54.620999999999995
- type: map_at_3
value: 51.513
- type: map_at_5
value: 52.591
- type: mrr_at_1
value: 46.666999999999994
- type: mrr_at_10
value: 55.461000000000006
- type: mrr_at_100
value: 56.008
- type: mrr_at_1000
value: 56.069
- type: mrr_at_3
value: 53.5
- type: mrr_at_5
value: 54.417
- type: ndcg_at_1
value: 46.666999999999994
- type: ndcg_at_10
value: 58.599000000000004
- type: ndcg_at_100
value: 61.538000000000004
- type: ndcg_at_1000
value: 63.22
- type: ndcg_at_3
value: 54.254999999999995
- type: ndcg_at_5
value: 55.861000000000004
- type: precision_at_1
value: 46.666999999999994
- type: precision_at_10
value: 8.033
- type: precision_at_100
value: 0.963
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 21.667
- type: precision_at_5
value: 14.066999999999998
- type: recall_at_1
value: 43.761
- type: recall_at_10
value: 71.65599999999999
- type: recall_at_100
value: 84.433
- type: recall_at_1000
value: 97.5
- type: recall_at_3
value: 59.522
- type: recall_at_5
value: 63.632999999999996
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.68811881188118
- type: cos_sim_ap
value: 91.08077352794682
- type: cos_sim_f1
value: 84.38570729319628
- type: cos_sim_precision
value: 82.64621284755513
- type: cos_sim_recall
value: 86.2
- type: dot_accuracy
value: 99.14653465346535
- type: dot_ap
value: 45.24942149367904
- type: dot_f1
value: 46.470062555853445
- type: dot_precision
value: 42.003231017770595
- type: dot_recall
value: 52
- type: euclidean_accuracy
value: 99.56930693069307
- type: euclidean_ap
value: 80.28575652582506
- type: euclidean_f1
value: 75.52054023635341
- type: euclidean_precision
value: 86.35778635778635
- type: euclidean_recall
value: 67.10000000000001
- type: manhattan_accuracy
value: 99.56039603960396
- type: manhattan_ap
value: 79.74630510301085
- type: manhattan_f1
value: 74.67569091934575
- type: manhattan_precision
value: 85.64036222509702
- type: manhattan_recall
value: 66.2
- type: max_accuracy
value: 99.68811881188118
- type: max_ap
value: 91.08077352794682
- type: max_f1
value: 84.38570729319628
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 52.0788049295693
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 31.606006030205545
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.87384988372756
- type: mrr
value: 51.62476922587217
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.355859978837156
- type: cos_sim_spearman
value: 30.0847548337847
- type: dot_pearson
value: 19.391736817587557
- type: dot_spearman
value: 20.732256259543014
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.19
- type: map_at_10
value: 1.2850000000000001
- type: map_at_100
value: 6.376999999999999
- type: map_at_1000
value: 15.21
- type: map_at_3
value: 0.492
- type: map_at_5
value: 0.776
- type: mrr_at_1
value: 68
- type: mrr_at_10
value: 79.783
- type: mrr_at_100
value: 79.783
- type: mrr_at_1000
value: 79.783
- type: mrr_at_3
value: 77.333
- type: mrr_at_5
value: 79.533
- type: ndcg_at_1
value: 62
- type: ndcg_at_10
value: 54.635
- type: ndcg_at_100
value: 40.939
- type: ndcg_at_1000
value: 37.716
- type: ndcg_at_3
value: 58.531
- type: ndcg_at_5
value: 58.762
- type: precision_at_1
value: 68
- type: precision_at_10
value: 58.8
- type: precision_at_100
value: 41.74
- type: precision_at_1000
value: 16.938
- type: precision_at_3
value: 64
- type: precision_at_5
value: 64.8
- type: recall_at_1
value: 0.19
- type: recall_at_10
value: 1.547
- type: recall_at_100
value: 9.739
- type: recall_at_1000
value: 35.815000000000005
- type: recall_at_3
value: 0.528
- type: recall_at_5
value: 0.894
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.514
- type: map_at_10
value: 7.163
- type: map_at_100
value: 11.623999999999999
- type: map_at_1000
value: 13.062999999999999
- type: map_at_3
value: 3.51
- type: map_at_5
value: 4.661
- type: mrr_at_1
value: 20.408
- type: mrr_at_10
value: 33.993
- type: mrr_at_100
value: 35.257
- type: mrr_at_1000
value: 35.313
- type: mrr_at_3
value: 30.272
- type: mrr_at_5
value: 31.701
- type: ndcg_at_1
value: 18.367
- type: ndcg_at_10
value: 18.062
- type: ndcg_at_100
value: 28.441
- type: ndcg_at_1000
value: 40.748
- type: ndcg_at_3
value: 18.651999999999997
- type: ndcg_at_5
value: 17.055
- type: precision_at_1
value: 20.408
- type: precision_at_10
value: 17.551
- type: precision_at_100
value: 6.223999999999999
- type: precision_at_1000
value: 1.427
- type: precision_at_3
value: 20.408
- type: precision_at_5
value: 17.959
- type: recall_at_1
value: 1.514
- type: recall_at_10
value: 13.447000000000001
- type: recall_at_100
value: 39.77
- type: recall_at_1000
value: 76.95
- type: recall_at_3
value: 4.806
- type: recall_at_5
value: 6.873
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 65.53179999999999
- type: ap
value: 11.504743595308318
- type: f1
value: 49.74264614001562
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 56.47425014148275
- type: f1
value: 56.555750746223346
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 39.27004599453324
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.47875067056088
- type: cos_sim_ap
value: 68.630858164926
- type: cos_sim_f1
value: 64.5112402121748
- type: cos_sim_precision
value: 61.87015503875969
- type: cos_sim_recall
value: 67.38786279683377
- type: dot_accuracy
value: 77.68969422423557
- type: dot_ap
value: 37.28838556128439
- type: dot_f1
value: 43.27918525376652
- type: dot_precision
value: 31.776047460140898
- type: dot_recall
value: 67.83641160949868
- type: euclidean_accuracy
value: 82.67866722298385
- type: euclidean_ap
value: 62.72011158877603
- type: euclidean_f1
value: 60.39579770339605
- type: euclidean_precision
value: 56.23293903548681
- type: euclidean_recall
value: 65.22427440633246
- type: manhattan_accuracy
value: 82.67866722298385
- type: manhattan_ap
value: 62.80364769571995
- type: manhattan_f1
value: 60.413827282864574
- type: manhattan_precision
value: 56.94931090866619
- type: manhattan_recall
value: 64.32717678100263
- type: max_accuracy
value: 84.47875067056088
- type: max_ap
value: 68.630858164926
- type: max_f1
value: 64.5112402121748
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.4192959987581
- type: cos_sim_ap
value: 84.81803796578367
- type: cos_sim_f1
value: 77.1643709825528
- type: cos_sim_precision
value: 73.77958839643183
- type: cos_sim_recall
value: 80.874653526332
- type: dot_accuracy
value: 81.99441145651414
- type: dot_ap
value: 67.908510950511
- type: dot_f1
value: 64.4734255193656
- type: dot_precision
value: 56.120935539075866
- type: dot_recall
value: 75.74684323991376
- type: euclidean_accuracy
value: 82.67163426087632
- type: euclidean_ap
value: 70.1466353903414
- type: euclidean_f1
value: 62.686024087617795
- type: euclidean_precision
value: 59.42738875474301
- type: euclidean_recall
value: 66.32275947028026
- type: manhattan_accuracy
value: 82.6483486630186
- type: manhattan_ap
value: 70.12958345267741
- type: manhattan_f1
value: 62.5966218150587
- type: manhattan_precision
value: 58.47820272800214
- type: manhattan_recall
value: 67.33908222975053
- type: max_accuracy
value: 88.4192959987581
- type: max_ap
value: 84.81803796578367
- type: max_f1
value: 77.1643709825528
---
---
<br><br>
<p align="center">
<img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
</p>
<p align="center">
<b>The text embedding suite trained by Jina AI, Finetuner team.</b>
</p>
## Intented Usage & Model Info
`jina-embedding-b-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 standard size of 110 million parameters,
the model enables fast inference while delivering better performance than our small model.
It is recommended to use a single GPU for inference.
Additionally, we provide the following options:
- `jina-embedding-s-en-v1`: 35 million parameters.
- `jina-embedding-b-en-v1`: 110 million parameters **(you are here)**.
- `jina-embedding-l-en-v1`: 330 million parameters.
- `jina-embedding-xl-en-v1`: 1.2 billion parameters (soon).
- `jina-embedding-xxl-en-v1`: 6 billion parameters (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
Usage with Jina AI Finetuner:
```python
!pip install finetuner
import finetuner
model = finetuner.build_model('jinaai/jina-embedding-b-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 we want sentence embeddings for
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-s-en-v1')
model = AutoModel.from_pretrained('jinaai/jina-embedding-s-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. |