---
pipeline_tag: sentence-similarity
tags:
- finetuner
- feature-extraction
- sentence-similarity
- mteb
datasets:
- jinaai/negation-dataset
language: en
license: apache-2.0
model-index:
- name: jina-embedding-s-en-v1
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 64.58208955223881
- type: ap
value: 27.24359671025387
- type: f1
value: 58.201387941715495
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 61.926550000000006
- type: ap
value: 58.40954250092862
- type: f1
value: 59.921771639047904
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 28.499999999999996
- type: f1
value: 27.160929516206465
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.262
- type: map_at_10
value: 36.677
- type: map_at_100
value: 37.839
- type: map_at_1000
value: 37.857
- type: map_at_3
value: 31.685999999999996
- type: map_at_5
value: 34.544999999999995
- type: mrr_at_1
value: 22.404
- type: mrr_at_10
value: 36.713
- type: mrr_at_100
value: 37.881
- type: mrr_at_1000
value: 37.899
- type: mrr_at_3
value: 31.709
- type: mrr_at_5
value: 34.629
- type: ndcg_at_1
value: 22.262
- type: ndcg_at_10
value: 45.18
- type: ndcg_at_100
value: 50.4
- type: ndcg_at_1000
value: 50.841
- type: ndcg_at_3
value: 34.882000000000005
- type: ndcg_at_5
value: 40.036
- type: precision_at_1
value: 22.262
- type: precision_at_10
value: 7.255000000000001
- type: precision_at_100
value: 0.959
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 14.723
- type: precision_at_5
value: 11.337
- type: recall_at_1
value: 22.262
- type: recall_at_10
value: 72.54599999999999
- type: recall_at_100
value: 95.946
- type: recall_at_1000
value: 99.36
- type: recall_at_3
value: 44.168
- type: recall_at_5
value: 56.686
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 34.97570470844357
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 24.372872291698265
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.58753030525579
- type: mrr
value: 75.03484588664644
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 85.21378425036666
- type: cos_sim_spearman
value: 80.45665253651644
- type: euclidean_pearson
value: 46.71436482437946
- type: euclidean_spearman
value: 45.13476336596072
- type: manhattan_pearson
value: 47.06449770246884
- type: manhattan_spearman
value: 45.498627078529
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 74.48701298701299
- type: f1
value: 73.30813366682357
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 29.66289767477026
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 22.324367934720776
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.524000000000001
- type: map_at_10
value: 11.187
- type: map_at_100
value: 12.389999999999999
- type: map_at_1000
value: 12.559000000000001
- type: map_at_3
value: 9.386
- type: map_at_5
value: 10.295
- type: mrr_at_1
value: 13.941
- type: mrr_at_10
value: 22.742
- type: mrr_at_100
value: 23.896
- type: mrr_at_1000
value: 23.965
- type: mrr_at_3
value: 19.881
- type: mrr_at_5
value: 21.555
- type: ndcg_at_1
value: 13.941
- type: ndcg_at_10
value: 16.619999999999997
- type: ndcg_at_100
value: 22.415
- type: ndcg_at_1000
value: 26.05
- type: ndcg_at_3
value: 13.148000000000001
- type: ndcg_at_5
value: 14.433000000000002
- type: precision_at_1
value: 13.941
- type: precision_at_10
value: 5.153
- type: precision_at_100
value: 1.124
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 9.685
- type: precision_at_5
value: 7.582999999999999
- type: recall_at_1
value: 6.524000000000001
- type: recall_at_10
value: 21.041999999999998
- type: recall_at_100
value: 41.515
- type: recall_at_1000
value: 62.507999999999996
- type: recall_at_3
value: 12.549
- type: recall_at_5
value: 15.939999999999998
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.483
- type: map_at_10
value: 11.955
- type: map_at_100
value: 15.470999999999998
- type: map_at_1000
value: 16.308
- type: map_at_3
value: 9.292
- type: map_at_5
value: 10.459
- type: mrr_at_1
value: 50.74999999999999
- type: mrr_at_10
value: 58.743
- type: mrr_at_100
value: 59.41499999999999
- type: mrr_at_1000
value: 59.431999999999995
- type: mrr_at_3
value: 56.708000000000006
- type: mrr_at_5
value: 57.80800000000001
- type: ndcg_at_1
value: 39.0
- type: ndcg_at_10
value: 26.721
- type: ndcg_at_100
value: 29.366999999999997
- type: ndcg_at_1000
value: 35.618
- type: ndcg_at_3
value: 31.244
- type: ndcg_at_5
value: 28.614
- type: precision_at_1
value: 50.74999999999999
- type: precision_at_10
value: 20.45
- type: precision_at_100
value: 6.0600000000000005
- type: precision_at_1000
value: 1.346
- type: precision_at_3
value: 33.917
- type: precision_at_5
value: 26.950000000000003
- type: recall_at_1
value: 6.483
- type: recall_at_10
value: 16.215
- type: recall_at_100
value: 33.382
- type: recall_at_1000
value: 54.445
- type: recall_at_3
value: 10.6
- type: recall_at_5
value: 12.889999999999999
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 34.39
- type: f1
value: 31.334865751249474
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 44.698
- type: map_at_10
value: 55.30500000000001
- type: map_at_100
value: 55.838
- type: map_at_1000
value: 55.87
- type: map_at_3
value: 52.884
- type: map_at_5
value: 54.352000000000004
- type: mrr_at_1
value: 48.32
- type: mrr_at_10
value: 59.39
- type: mrr_at_100
value: 59.89
- type: mrr_at_1000
value: 59.913000000000004
- type: mrr_at_3
value: 56.977999999999994
- type: mrr_at_5
value: 58.44200000000001
- type: ndcg_at_1
value: 48.32
- type: ndcg_at_10
value: 61.23800000000001
- type: ndcg_at_100
value: 63.79
- type: ndcg_at_1000
value: 64.575
- type: ndcg_at_3
value: 56.489999999999995
- type: ndcg_at_5
value: 59.016999999999996
- type: precision_at_1
value: 48.32
- type: precision_at_10
value: 8.288
- type: precision_at_100
value: 0.964
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 22.867
- type: precision_at_5
value: 15.098
- type: recall_at_1
value: 44.698
- type: recall_at_10
value: 75.752
- type: recall_at_100
value: 87.402
- type: recall_at_1000
value: 93.316
- type: recall_at_3
value: 62.82600000000001
- type: recall_at_5
value: 69.01899999999999
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 12.119
- type: map_at_10
value: 20.299
- type: map_at_100
value: 21.863
- type: map_at_1000
value: 22.064
- type: map_at_3
value: 17.485999999999997
- type: map_at_5
value: 19.148
- type: mrr_at_1
value: 24.383
- type: mrr_at_10
value: 33.074
- type: mrr_at_100
value: 34.03
- type: mrr_at_1000
value: 34.102
- type: mrr_at_3
value: 30.736
- type: mrr_at_5
value: 32.202
- type: ndcg_at_1
value: 24.383
- type: ndcg_at_10
value: 26.645999999999997
- type: ndcg_at_100
value: 33.348
- type: ndcg_at_1000
value: 37.294
- type: ndcg_at_3
value: 23.677
- type: ndcg_at_5
value: 24.935
- type: precision_at_1
value: 24.383
- type: precision_at_10
value: 7.654
- type: precision_at_100
value: 1.461
- type: precision_at_1000
value: 0.214
- type: precision_at_3
value: 16.101
- type: precision_at_5
value: 12.222
- type: recall_at_1
value: 12.119
- type: recall_at_10
value: 32.531
- type: recall_at_100
value: 58.028999999999996
- type: recall_at_1000
value: 82.513
- type: recall_at_3
value: 21.787
- type: recall_at_5
value: 27.229999999999997
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.057000000000002
- type: map_at_10
value: 34.892
- type: map_at_100
value: 35.687000000000005
- type: map_at_1000
value: 35.763
- type: map_at_3
value: 32.879000000000005
- type: map_at_5
value: 34.105000000000004
- type: mrr_at_1
value: 52.113
- type: mrr_at_10
value: 58.940000000000005
- type: mrr_at_100
value: 59.438
- type: mrr_at_1000
value: 59.473
- type: mrr_at_3
value: 57.299
- type: mrr_at_5
value: 58.353
- type: ndcg_at_1
value: 52.113
- type: ndcg_at_10
value: 43.105
- type: ndcg_at_100
value: 46.44
- type: ndcg_at_1000
value: 48.241
- type: ndcg_at_3
value: 39.566
- type: ndcg_at_5
value: 41.508
- type: precision_at_1
value: 52.113
- type: precision_at_10
value: 8.892999999999999
- type: precision_at_100
value: 1.1520000000000001
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 24.398
- type: precision_at_5
value: 16.181
- type: recall_at_1
value: 26.057000000000002
- type: recall_at_10
value: 44.463
- type: recall_at_100
value: 57.616
- type: recall_at_1000
value: 69.65599999999999
- type: recall_at_3
value: 36.597
- type: recall_at_5
value: 40.452
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 58.268399999999986
- type: ap
value: 55.03852332714837
- type: f1
value: 57.23656436062262
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 14.273
- type: map_at_10
value: 23.953
- type: map_at_100
value: 25.207
- type: map_at_1000
value: 25.285999999999998
- type: map_at_3
value: 20.727
- type: map_at_5
value: 22.492
- type: mrr_at_1
value: 14.685
- type: mrr_at_10
value: 24.423000000000002
- type: mrr_at_100
value: 25.64
- type: mrr_at_1000
value: 25.713
- type: mrr_at_3
value: 21.213
- type: mrr_at_5
value: 22.979
- type: ndcg_at_1
value: 14.685
- type: ndcg_at_10
value: 29.698
- type: ndcg_at_100
value: 36.010999999999996
- type: ndcg_at_1000
value: 38.102999999999994
- type: ndcg_at_3
value: 23.0
- type: ndcg_at_5
value: 26.186
- type: precision_at_1
value: 14.685
- type: precision_at_10
value: 4.954
- type: precision_at_100
value: 0.815
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 10.038
- type: precision_at_5
value: 7.636
- type: recall_at_1
value: 14.273
- type: recall_at_10
value: 47.559000000000005
- type: recall_at_100
value: 77.375
- type: recall_at_1000
value: 93.616
- type: recall_at_3
value: 29.110999999999997
- type: recall_at_5
value: 36.825
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 89.85636114911081
- type: f1
value: 89.65403786390279
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 59.03784769721842
- type: f1
value: 42.57604111096128
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 65.00336247478144
- type: f1
value: 63.12578076844032
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.14862138533962
- type: f1
value: 71.91174720216141
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 28.259326082067094
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 23.874256261395775
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 29.251614283788385
- type: mrr
value: 29.9695581475798
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.9309999999999996
- type: map_at_10
value: 8.472
- type: map_at_100
value: 10.461
- type: map_at_1000
value: 11.588
- type: map_at_3
value: 6.343999999999999
- type: map_at_5
value: 7.379
- type: mrr_at_1
value: 35.913000000000004
- type: mrr_at_10
value: 43.91
- type: mrr_at_100
value: 44.519999999999996
- type: mrr_at_1000
value: 44.59
- type: mrr_at_3
value: 41.589
- type: mrr_at_5
value: 42.626
- type: ndcg_at_1
value: 34.52
- type: ndcg_at_10
value: 25.128
- type: ndcg_at_100
value: 22.917
- type: ndcg_at_1000
value: 31.64
- type: ndcg_at_3
value: 29.866999999999997
- type: ndcg_at_5
value: 27.494000000000003
- type: precision_at_1
value: 35.913000000000004
- type: precision_at_10
value: 18.607000000000003
- type: precision_at_100
value: 6.006
- type: precision_at_1000
value: 1.814
- type: precision_at_3
value: 28.277
- type: precision_at_5
value: 23.777
- type: recall_at_1
value: 3.9309999999999996
- type: recall_at_10
value: 11.684
- type: recall_at_100
value: 24.212
- type: recall_at_1000
value: 55.36
- type: recall_at_3
value: 7.329
- type: recall_at_5
value: 9.059000000000001
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.03
- type: map_at_10
value: 30.990000000000002
- type: map_at_100
value: 32.211
- type: map_at_1000
value: 32.267
- type: map_at_3
value: 26.833000000000002
- type: map_at_5
value: 29.128
- type: mrr_at_1
value: 21.523999999999997
- type: mrr_at_10
value: 33.085
- type: mrr_at_100
value: 34.096
- type: mrr_at_1000
value: 34.139
- type: mrr_at_3
value: 29.354999999999997
- type: mrr_at_5
value: 31.441999999999997
- type: ndcg_at_1
value: 21.495
- type: ndcg_at_10
value: 37.971
- type: ndcg_at_100
value: 43.492999999999995
- type: ndcg_at_1000
value: 44.925
- type: ndcg_at_3
value: 29.808
- type: ndcg_at_5
value: 33.748
- type: precision_at_1
value: 21.495
- type: precision_at_10
value: 6.819
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 13.886000000000001
- type: precision_at_5
value: 10.574
- type: recall_at_1
value: 19.03
- type: recall_at_10
value: 57.493
- type: recall_at_100
value: 82.03200000000001
- type: recall_at_1000
value: 92.879
- type: recall_at_3
value: 35.899
- type: recall_at_5
value: 45.092
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 67.97
- type: map_at_10
value: 81.478
- type: map_at_100
value: 82.147
- type: map_at_1000
value: 82.172
- type: map_at_3
value: 78.456
- type: map_at_5
value: 80.337
- type: mrr_at_1
value: 78.24
- type: mrr_at_10
value: 84.941
- type: mrr_at_100
value: 85.08099999999999
- type: mrr_at_1000
value: 85.083
- type: mrr_at_3
value: 83.743
- type: mrr_at_5
value: 84.553
- type: ndcg_at_1
value: 78.24
- type: ndcg_at_10
value: 85.61999999999999
- type: ndcg_at_100
value: 87.113
- type: ndcg_at_1000
value: 87.318
- type: ndcg_at_3
value: 82.403
- type: ndcg_at_5
value: 84.15700000000001
- type: precision_at_1
value: 78.24
- type: precision_at_10
value: 12.979
- type: precision_at_100
value: 1.503
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 35.9
- type: precision_at_5
value: 23.704
- type: recall_at_1
value: 67.97
- type: recall_at_10
value: 93.563
- type: recall_at_100
value: 98.834
- type: recall_at_1000
value: 99.901
- type: recall_at_3
value: 84.319
- type: recall_at_5
value: 89.227
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 35.853649010160694
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 47.270443152349415
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.803
- type: map_at_10
value: 8.790000000000001
- type: map_at_100
value: 10.313
- type: map_at_1000
value: 10.562000000000001
- type: map_at_3
value: 6.483
- type: map_at_5
value: 7.591
- type: mrr_at_1
value: 18.7
- type: mrr_at_10
value: 27.349
- type: mrr_at_100
value: 28.474
- type: mrr_at_1000
value: 28.544999999999998
- type: mrr_at_3
value: 24.567
- type: mrr_at_5
value: 26.172
- type: ndcg_at_1
value: 18.7
- type: ndcg_at_10
value: 15.155
- type: ndcg_at_100
value: 21.63
- type: ndcg_at_1000
value: 26.595999999999997
- type: ndcg_at_3
value: 14.706
- type: ndcg_at_5
value: 12.681999999999999
- type: precision_at_1
value: 18.7
- type: precision_at_10
value: 7.6899999999999995
- type: precision_at_100
value: 1.7080000000000002
- type: precision_at_1000
value: 0.291
- type: precision_at_3
value: 13.567000000000002
- type: precision_at_5
value: 10.9
- type: recall_at_1
value: 3.803
- type: recall_at_10
value: 15.607
- type: recall_at_100
value: 34.717999999999996
- type: recall_at_1000
value: 59.150000000000006
- type: recall_at_3
value: 8.258000000000001
- type: recall_at_5
value: 11.063
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 81.05755556071047
- type: cos_sim_spearman
value: 72.44408263672771
- type: euclidean_pearson
value: 71.65314814604668
- type: euclidean_spearman
value: 65.1833695751109
- type: manhattan_pearson
value: 71.81874115177355
- type: manhattan_spearman
value: 65.45940792270201
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 81.75836272926722
- type: cos_sim_spearman
value: 73.63905703662927
- type: euclidean_pearson
value: 67.58539517215293
- type: euclidean_spearman
value: 58.88440181413321
- type: manhattan_pearson
value: 66.56872028174024
- type: manhattan_spearman
value: 58.48195528793699
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 76.58680032464127
- type: cos_sim_spearman
value: 78.03760988363273
- type: euclidean_pearson
value: 68.23192805876019
- type: euclidean_spearman
value: 69.21753515532978
- type: manhattan_pearson
value: 68.07876685109447
- type: manhattan_spearman
value: 69.08026107263751
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 78.72357139489792
- type: cos_sim_spearman
value: 74.53681843472086
- type: euclidean_pearson
value: 66.73161230236408
- type: euclidean_spearman
value: 63.81392957525887
- type: manhattan_pearson
value: 66.33322201893088
- type: manhattan_spearman
value: 63.55218357111819
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 82.62456549757793
- type: cos_sim_spearman
value: 83.89301877076606
- type: euclidean_pearson
value: 58.128415035981554
- type: euclidean_spearman
value: 58.47993973876889
- type: manhattan_pearson
value: 58.37634990795807
- type: manhattan_spearman
value: 58.89541748905865
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 76.79731685895317
- type: cos_sim_spearman
value: 79.04240201103201
- type: euclidean_pearson
value: 64.26869512572189
- type: euclidean_spearman
value: 65.09728500847595
- type: manhattan_pearson
value: 64.2772185991136
- type: manhattan_spearman
value: 65.18852760227209
- 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: 86.30962737077412
- type: cos_sim_spearman
value: 86.77386963770132
- type: euclidean_pearson
value: 70.0534100015362
- type: euclidean_spearman
value: 68.17903243639661
- type: manhattan_pearson
value: 70.03048392176451
- type: manhattan_spearman
value: 68.19594588464386
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 64.77791754851359
- type: cos_sim_spearman
value: 64.28210927783513
- type: euclidean_pearson
value: 36.337603238543956
- type: euclidean_spearman
value: 52.70617012481411
- type: manhattan_pearson
value: 35.49141141164909
- type: manhattan_spearman
value: 52.084744319382835
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 79.741579322503
- type: cos_sim_spearman
value: 78.83687709048151
- type: euclidean_pearson
value: 66.59151974274772
- type: euclidean_spearman
value: 63.76907648545863
- type: manhattan_pearson
value: 66.91555116739791
- type: manhattan_spearman
value: 64.2024945118848
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 74.31125049985503
- type: mrr
value: 91.5911222038673
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.983000000000004
- type: map_at_10
value: 48.79
- type: map_at_100
value: 49.419999999999995
- type: map_at_1000
value: 49.495
- type: map_at_3
value: 46.394000000000005
- type: map_at_5
value: 47.772999999999996
- type: mrr_at_1
value: 42.667
- type: mrr_at_10
value: 51.088
- type: mrr_at_100
value: 51.498999999999995
- type: mrr_at_1000
value: 51.564
- type: mrr_at_3
value: 49.111
- type: mrr_at_5
value: 50.278
- type: ndcg_at_1
value: 42.667
- type: ndcg_at_10
value: 53.586999999999996
- type: ndcg_at_100
value: 56.519
- type: ndcg_at_1000
value: 58.479000000000006
- type: ndcg_at_3
value: 49.053000000000004
- type: ndcg_at_5
value: 51.209
- type: precision_at_1
value: 42.667
- type: precision_at_10
value: 7.3999999999999995
- type: precision_at_100
value: 0.9129999999999999
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 19.444
- type: precision_at_5
value: 13.067
- type: recall_at_1
value: 39.983000000000004
- type: recall_at_10
value: 66.333
- type: recall_at_100
value: 80.256
- type: recall_at_1000
value: 95.667
- type: recall_at_3
value: 53.449999999999996
- type: recall_at_5
value: 58.989000000000004
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.6930693069307
- type: cos_sim_ap
value: 90.94265768188356
- type: cos_sim_f1
value: 84.15792103948026
- type: cos_sim_precision
value: 84.11588411588411
- type: cos_sim_recall
value: 84.2
- type: dot_accuracy
value: 99.12178217821783
- type: dot_ap
value: 42.77306613711772
- type: dot_f1
value: 44.23963133640553
- type: dot_precision
value: 38.0677721701514
- type: dot_recall
value: 52.800000000000004
- type: euclidean_accuracy
value: 99.55049504950495
- type: euclidean_ap
value: 78.83886818298362
- type: euclidean_f1
value: 74.54645409565696
- type: euclidean_precision
value: 82.78388278388277
- type: euclidean_recall
value: 67.80000000000001
- type: manhattan_accuracy
value: 99.54257425742574
- type: manhattan_ap
value: 77.98046807031727
- type: manhattan_f1
value: 74.18822234452395
- type: manhattan_precision
value: 82.4969400244798
- type: manhattan_recall
value: 67.4
- type: max_accuracy
value: 99.6930693069307
- type: max_ap
value: 90.94265768188356
- type: max_f1
value: 84.15792103948026
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 47.81120799399627
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 29.82642033698617
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 47.861728758923675
- type: mrr
value: 48.53185213479331
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.09237795780992
- type: cos_sim_spearman
value: 28.95547545518808
- type: dot_pearson
value: 19.99986205111785
- type: dot_spearman
value: 21.34033389331779
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.169
- type: map_at_10
value: 1.077
- type: map_at_100
value: 4.9750000000000005
- type: map_at_1000
value: 11.802
- type: map_at_3
value: 0.48700000000000004
- type: map_at_5
value: 0.679
- type: mrr_at_1
value: 62.0
- type: mrr_at_10
value: 76.25
- type: mrr_at_100
value: 76.337
- type: mrr_at_1000
value: 76.337
- type: mrr_at_3
value: 74.333
- type: mrr_at_5
value: 75.333
- type: ndcg_at_1
value: 56.00000000000001
- type: ndcg_at_10
value: 50.631
- type: ndcg_at_100
value: 36.39
- type: ndcg_at_1000
value: 32.879000000000005
- type: ndcg_at_3
value: 59.961
- type: ndcg_at_5
value: 55.913999999999994
- type: precision_at_1
value: 62.0
- type: precision_at_10
value: 53.0
- type: precision_at_100
value: 37.2
- type: precision_at_1000
value: 14.804
- type: precision_at_3
value: 67.333
- type: precision_at_5
value: 60.4
- type: recall_at_1
value: 0.169
- type: recall_at_10
value: 1.324
- type: recall_at_100
value: 8.352
- type: recall_at_1000
value: 31.041999999999998
- type: recall_at_3
value: 0.532
- type: recall_at_5
value: 0.777
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.018
- type: map_at_10
value: 8.036
- type: map_at_100
value: 12.814
- type: map_at_1000
value: 14.204
- type: map_at_3
value: 3.9759999999999995
- type: map_at_5
value: 5.585
- type: mrr_at_1
value: 24.490000000000002
- type: mrr_at_10
value: 38.903
- type: mrr_at_100
value: 39.893
- type: mrr_at_1000
value: 39.895
- type: mrr_at_3
value: 35.034
- type: mrr_at_5
value: 37.789
- type: ndcg_at_1
value: 21.429000000000002
- type: ndcg_at_10
value: 20.082
- type: ndcg_at_100
value: 30.299
- type: ndcg_at_1000
value: 42.323
- type: ndcg_at_3
value: 19.826
- type: ndcg_at_5
value: 19.861
- type: precision_at_1
value: 24.490000000000002
- type: precision_at_10
value: 18.776
- type: precision_at_100
value: 6.551
- type: precision_at_1000
value: 1.455
- type: precision_at_3
value: 21.088
- type: precision_at_5
value: 21.633
- type: recall_at_1
value: 2.018
- type: recall_at_10
value: 14.094999999999999
- type: recall_at_100
value: 40.482
- type: recall_at_1000
value: 78.214
- type: recall_at_3
value: 4.884
- type: recall_at_5
value: 8.203000000000001
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 59.69140000000001
- type: ap
value: 10.299275820958274
- type: f1
value: 45.697311005218154
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 53.542727787209955
- type: f1
value: 53.59495510018717
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 32.405659957745534
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 82.34487691482386
- type: cos_sim_ap
value: 61.4880638625752
- type: cos_sim_f1
value: 59.350775193798455
- type: cos_sim_precision
value: 54.858934169278996
- type: cos_sim_recall
value: 64.64379947229551
- type: dot_accuracy
value: 77.68373368301842
- type: dot_ap
value: 36.846940578266626
- type: dot_f1
value: 42.67407473787974
- type: dot_precision
value: 32.311032704573215
- type: dot_recall
value: 62.82321899736147
- type: euclidean_accuracy
value: 80.40770101925256
- type: euclidean_ap
value: 53.51906185864526
- type: euclidean_f1
value: 53.24030024315466
- type: euclidean_precision
value: 44.41700476274475
- type: euclidean_recall
value: 66.43799472295514
- type: manhattan_accuracy
value: 80.31829290099542
- type: manhattan_ap
value: 53.67183195163967
- type: manhattan_f1
value: 53.28358208955224
- type: manhattan_precision
value: 44.70483005366726
- type: manhattan_recall
value: 65.93667546174143
- type: max_accuracy
value: 82.34487691482386
- type: max_ap
value: 61.4880638625752
- type: max_f1
value: 59.350775193798455
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 87.71684713005007
- type: cos_sim_ap
value: 82.85441942604702
- type: cos_sim_f1
value: 75.69942543843179
- type: cos_sim_precision
value: 73.88754490140019
- type: cos_sim_recall
value: 77.60240221743148
- type: dot_accuracy
value: 82.23696976753212
- type: dot_ap
value: 68.47562727147806
- type: dot_f1
value: 64.99698249849123
- type: dot_precision
value: 57.566219265946074
- type: dot_recall
value: 74.63042808746535
- type: euclidean_accuracy
value: 81.52481856638336
- type: euclidean_ap
value: 65.96678666430529
- type: euclidean_f1
value: 59.14671467146715
- type: euclidean_precision
value: 55.54879285859201
- type: euclidean_recall
value: 63.24299353249153
- type: manhattan_accuracy
value: 81.56750882912253
- type: manhattan_ap
value: 66.07646774834106
- type: manhattan_f1
value: 59.161485036907756
- type: manhattan_precision
value: 56.05319368841728
- type: manhattan_recall
value: 62.634739759778256
- type: max_accuracy
value: 87.71684713005007
- type: max_ap
value: 82.85441942604702
- type: max_f1
value: 75.69942543843179
---
---
The text embedding suite trained by Jina AI, Finetuner team.
## Intented Usage & Model Info
`jina-embedding-s-en-v1` is a language model that has been trained using Jina AI's Linnaeus-Clean dataset.
This dataset consists of 380 million pairs of sentences, which include both query-document pairs.
These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
The Linnaeus-Full dataset, from which the Linnaeus-Clean dataset is derived, originally contained 1.6 billion sentence pairs.
The model has a range of use cases, including information retrieval, semantic textual similarity, text reranking, and more.
With a compact size of just 35 million parameters,
the model enables lightning-fast inference while still delivering impressive performance.
Additionally, we provide the following options:
- `jina-embedding-s-en-v1`: 35 million parameters **(you are here)**.
- `jina-embedding-b-en-v1`: 110 million parameters.
- `jina-embedding-l-en-v1`: 330 million parameters.
- `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-s-en-v1')
embeddings = finetuner.encode(
model=model,
data=['how is the weather today', 'What is the current weather like today?']
)
print(finetuner.cos_sim(embeddings[0], embeddings[1]))
```
Use 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-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.