bwang0911's picture
Update README.md
610f5c9
|
raw
history blame
46.4 kB
metadata
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



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.

The text embedding suite trained by Jina AI, Finetuner team.

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:

!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:

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-b-en-v1')
model = AutoModel.from_pretrained('jinaai/jina-embedding-b-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.

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 and chat with other community members about ideas.