michael-guenther's picture
Update README.md
eea245a
|
raw
history blame
67.6 kB
metadata
pipeline_tag: sentence-similarity
tags:
  - finetuner
  - sentence-transformers
  - 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: 66.73134328358208
          - type: ap
            value: 28.30575908745204
          - type: f1
            value: 60.02420130946191
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_polarity
          name: MTEB AmazonPolarityClassification
          config: default
          split: test
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
        metrics:
          - type: accuracy
            value: 67.6068
          - type: ap
            value: 63.5899352938589
          - type: f1
            value: 65.64285334357656
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (en)
          config: en
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 31.178
          - type: f1
            value: 29.68460843733487
      - task:
          type: Retrieval
        dataset:
          type: arguana
          name: MTEB ArguAna
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 24.964
          - type: map_at_10
            value: 40.217999999999996
          - type: map_at_100
            value: 41.263
          - type: map_at_1000
            value: 41.277
          - type: map_at_3
            value: 35.183
          - type: map_at_5
            value: 38.045
          - type: mrr_at_1
            value: 25.107000000000003
          - type: mrr_at_10
            value: 40.272999999999996
          - type: mrr_at_100
            value: 41.318
          - type: mrr_at_1000
            value: 41.333
          - type: mrr_at_3
            value: 35.242000000000004
          - type: mrr_at_5
            value: 38.101
          - type: ndcg_at_1
            value: 24.964
          - type: ndcg_at_10
            value: 49.006
          - type: ndcg_at_100
            value: 53.446000000000005
          - type: ndcg_at_1000
            value: 53.813
          - type: ndcg_at_3
            value: 38.598
          - type: ndcg_at_5
            value: 43.74
          - type: precision_at_1
            value: 24.964
          - type: precision_at_10
            value: 7.724
          - type: precision_at_100
            value: 0.966
          - type: precision_at_1000
            value: 0.099
          - type: precision_at_3
            value: 16.169
          - type: precision_at_5
            value: 12.191
          - type: recall_at_1
            value: 24.964
          - type: recall_at_10
            value: 77.24
          - type: recall_at_100
            value: 96.586
          - type: recall_at_1000
            value: 99.431
          - type: recall_at_3
            value: 48.506
          - type: recall_at_5
            value: 60.953
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-p2p
          name: MTEB ArxivClusteringP2P
          config: default
          split: test
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
        metrics:
          - type: v_measure
            value: 39.25203906042786
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-s2s
          name: MTEB ArxivClusteringS2S
          config: default
          split: test
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
        metrics:
          - type: v_measure
            value: 29.07648348376354
      - task:
          type: Reranking
        dataset:
          type: mteb/askubuntudupquestions-reranking
          name: MTEB AskUbuntuDupQuestions
          config: default
          split: test
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
        metrics:
          - type: map
            value: 62.4029266143623
          - type: mrr
            value: 75.45750340764191
      - task:
          type: STS
        dataset:
          type: mteb/biosses-sts
          name: MTEB BIOSSES
          config: default
          split: test
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
        metrics:
          - type: cos_sim_pearson
            value: 85.92280995704714
          - type: cos_sim_spearman
            value: 83.58082010833608
          - type: euclidean_pearson
            value: 48.64744162695948
          - type: euclidean_spearman
            value: 48.817377397301556
          - type: manhattan_pearson
            value: 48.87684776623195
          - type: manhattan_spearman
            value: 48.94268145725884
      - task:
          type: Classification
        dataset:
          type: mteb/banking77
          name: MTEB Banking77Classification
          config: default
          split: test
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
        metrics:
          - type: accuracy
            value: 84.05519480519482
          - type: f1
            value: 83.94978356890618
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-p2p
          name: MTEB BiorxivClusteringP2P
          config: default
          split: test
          revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
        metrics:
          - type: v_measure
            value: 32.2033276486685
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-s2s
          name: MTEB BiorxivClusteringS2S
          config: default
          split: test
          revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
        metrics:
          - type: v_measure
            value: 26.631954164406014
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackAndroidRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 29.625
          - type: map_at_10
            value: 40.037
          - type: map_at_100
            value: 41.52
          - type: map_at_1000
            value: 41.654
          - type: map_at_3
            value: 36.818
          - type: map_at_5
            value: 38.426
          - type: mrr_at_1
            value: 35.336
          - type: mrr_at_10
            value: 45.395
          - type: mrr_at_100
            value: 46.221000000000004
          - type: mrr_at_1000
            value: 46.264
          - type: mrr_at_3
            value: 42.823
          - type: mrr_at_5
            value: 44.204
          - type: ndcg_at_1
            value: 35.336
          - type: ndcg_at_10
            value: 46.326
          - type: ndcg_at_100
            value: 51.795
          - type: ndcg_at_1000
            value: 53.834
          - type: ndcg_at_3
            value: 41.299
          - type: ndcg_at_5
            value: 43.247
          - type: precision_at_1
            value: 35.336
          - type: precision_at_10
            value: 8.627
          - type: precision_at_100
            value: 1.428
          - type: precision_at_1000
            value: 0.197
          - type: precision_at_3
            value: 19.647000000000002
          - type: precision_at_5
            value: 13.733999999999998
          - type: recall_at_1
            value: 29.625
          - type: recall_at_10
            value: 59.165
          - type: recall_at_100
            value: 81.675
          - type: recall_at_1000
            value: 94.17
          - type: recall_at_3
            value: 44.485
          - type: recall_at_5
            value: 50.198
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackEnglishRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 26.687
          - type: map_at_10
            value: 36.062
          - type: map_at_100
            value: 37.263000000000005
          - type: map_at_1000
            value: 37.397999999999996
          - type: map_at_3
            value: 32.967
          - type: map_at_5
            value: 34.75
          - type: mrr_at_1
            value: 33.885
          - type: mrr_at_10
            value: 42.632999999999996
          - type: mrr_at_100
            value: 43.305
          - type: mrr_at_1000
            value: 43.354
          - type: mrr_at_3
            value: 39.958
          - type: mrr_at_5
            value: 41.63
          - type: ndcg_at_1
            value: 33.885
          - type: ndcg_at_10
            value: 42.001
          - type: ndcg_at_100
            value: 46.436
          - type: ndcg_at_1000
            value: 48.774
          - type: ndcg_at_3
            value: 37.183
          - type: ndcg_at_5
            value: 39.605000000000004
          - type: precision_at_1
            value: 33.885
          - type: precision_at_10
            value: 7.962
          - type: precision_at_100
            value: 1.283
          - type: precision_at_1000
            value: 0.18
          - type: precision_at_3
            value: 17.855999999999998
          - type: precision_at_5
            value: 13.083
          - type: recall_at_1
            value: 26.687
          - type: recall_at_10
            value: 52.75
          - type: recall_at_100
            value: 71.324
          - type: recall_at_1000
            value: 86.356
          - type: recall_at_3
            value: 38.83
          - type: recall_at_5
            value: 45.23
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackGamingRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 34.02
          - type: map_at_10
            value: 45.751999999999995
          - type: map_at_100
            value: 46.867
          - type: map_at_1000
            value: 46.93
          - type: map_at_3
            value: 42.409
          - type: map_at_5
            value: 44.464999999999996
          - type: mrr_at_1
            value: 38.307
          - type: mrr_at_10
            value: 48.718
          - type: mrr_at_100
            value: 49.509
          - type: mrr_at_1000
            value: 49.542
          - type: mrr_at_3
            value: 46.007999999999996
          - type: mrr_at_5
            value: 47.766999999999996
          - type: ndcg_at_1
            value: 38.307
          - type: ndcg_at_10
            value: 51.666999999999994
          - type: ndcg_at_100
            value: 56.242000000000004
          - type: ndcg_at_1000
            value: 57.477999999999994
          - type: ndcg_at_3
            value: 45.912
          - type: ndcg_at_5
            value: 49.106
          - type: precision_at_1
            value: 38.307
          - type: precision_at_10
            value: 8.476
          - type: precision_at_100
            value: 1.176
          - type: precision_at_1000
            value: 0.133
          - type: precision_at_3
            value: 20.522000000000002
          - type: precision_at_5
            value: 14.557999999999998
          - type: recall_at_1
            value: 34.02
          - type: recall_at_10
            value: 66.046
          - type: recall_at_100
            value: 85.817
          - type: recall_at_1000
            value: 94.453
          - type: recall_at_3
            value: 51.059
          - type: recall_at_5
            value: 58.667
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackGisRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 23.939
          - type: map_at_10
            value: 32.627
          - type: map_at_100
            value: 33.617999999999995
          - type: map_at_1000
            value: 33.701
          - type: map_at_3
            value: 30.11
          - type: map_at_5
            value: 31.380000000000003
          - type: mrr_at_1
            value: 25.989
          - type: mrr_at_10
            value: 34.655
          - type: mrr_at_100
            value: 35.502
          - type: mrr_at_1000
            value: 35.563
          - type: mrr_at_3
            value: 32.109
          - type: mrr_at_5
            value: 33.426
          - type: ndcg_at_1
            value: 25.989
          - type: ndcg_at_10
            value: 37.657000000000004
          - type: ndcg_at_100
            value: 42.467
          - type: ndcg_at_1000
            value: 44.677
          - type: ndcg_at_3
            value: 32.543
          - type: ndcg_at_5
            value: 34.74
          - type: precision_at_1
            value: 25.989
          - type: precision_at_10
            value: 5.876
          - type: precision_at_100
            value: 0.8710000000000001
          - type: precision_at_1000
            value: 0.11
          - type: precision_at_3
            value: 13.861
          - type: precision_at_5
            value: 9.626999999999999
          - type: recall_at_1
            value: 23.939
          - type: recall_at_10
            value: 51.28
          - type: recall_at_100
            value: 73.428
          - type: recall_at_1000
            value: 90.309
          - type: recall_at_3
            value: 37.245
          - type: recall_at_5
            value: 42.541000000000004
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackMathematicaRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 15.082
          - type: map_at_10
            value: 22.486
          - type: map_at_100
            value: 23.687
          - type: map_at_1000
            value: 23.807000000000002
          - type: map_at_3
            value: 20.076
          - type: map_at_5
            value: 21.362000000000002
          - type: mrr_at_1
            value: 18.532
          - type: mrr_at_10
            value: 26.605
          - type: mrr_at_100
            value: 27.628999999999998
          - type: mrr_at_1000
            value: 27.698
          - type: mrr_at_3
            value: 23.964
          - type: mrr_at_5
            value: 25.319000000000003
          - type: ndcg_at_1
            value: 18.532
          - type: ndcg_at_10
            value: 27.474999999999998
          - type: ndcg_at_100
            value: 33.357
          - type: ndcg_at_1000
            value: 36.361
          - type: ndcg_at_3
            value: 22.851
          - type: ndcg_at_5
            value: 24.87
          - type: precision_at_1
            value: 18.532
          - type: precision_at_10
            value: 5.210999999999999
          - type: precision_at_100
            value: 0.9329999999999999
          - type: precision_at_1000
            value: 0.134
          - type: precision_at_3
            value: 11.235000000000001
          - type: precision_at_5
            value: 8.134
          - type: recall_at_1
            value: 15.082
          - type: recall_at_10
            value: 38.759
          - type: recall_at_100
            value: 64.621
          - type: recall_at_1000
            value: 86.162
          - type: recall_at_3
            value: 26.055
          - type: recall_at_5
            value: 31.208999999999996
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackPhysicsRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 24.759999999999998
          - type: map_at_10
            value: 33.706
          - type: map_at_100
            value: 35
          - type: map_at_1000
            value: 35.134
          - type: map_at_3
            value: 30.789
          - type: map_at_5
            value: 32.427
          - type: mrr_at_1
            value: 29.548000000000002
          - type: mrr_at_10
            value: 38.521
          - type: mrr_at_100
            value: 39.432
          - type: mrr_at_1000
            value: 39.494
          - type: mrr_at_3
            value: 35.691
          - type: mrr_at_5
            value: 37.424
          - type: ndcg_at_1
            value: 29.548000000000002
          - type: ndcg_at_10
            value: 39.301
          - type: ndcg_at_100
            value: 44.907000000000004
          - type: ndcg_at_1000
            value: 47.494
          - type: ndcg_at_3
            value: 34.08
          - type: ndcg_at_5
            value: 36.649
          - type: precision_at_1
            value: 29.548000000000002
          - type: precision_at_10
            value: 7.084
          - type: precision_at_100
            value: 1.169
          - type: precision_at_1000
            value: 0.158
          - type: precision_at_3
            value: 15.881
          - type: precision_at_5
            value: 11.53
          - type: recall_at_1
            value: 24.759999999999998
          - type: recall_at_10
            value: 51.202000000000005
          - type: recall_at_100
            value: 74.542
          - type: recall_at_1000
            value: 91.669
          - type: recall_at_3
            value: 36.892
          - type: recall_at_5
            value: 43.333
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackProgrammersRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 23.247999999999998
          - type: map_at_10
            value: 31.878
          - type: map_at_100
            value: 33.135
          - type: map_at_1000
            value: 33.263999999999996
          - type: map_at_3
            value: 29.406
          - type: map_at_5
            value: 30.602
          - type: mrr_at_1
            value: 28.767
          - type: mrr_at_10
            value: 36.929
          - type: mrr_at_100
            value: 37.844
          - type: mrr_at_1000
            value: 37.913000000000004
          - type: mrr_at_3
            value: 34.589
          - type: mrr_at_5
            value: 35.908
          - type: ndcg_at_1
            value: 28.767
          - type: ndcg_at_10
            value: 37.172
          - type: ndcg_at_100
            value: 42.842
          - type: ndcg_at_1000
            value: 45.534
          - type: ndcg_at_3
            value: 32.981
          - type: ndcg_at_5
            value: 34.628
          - type: precision_at_1
            value: 28.767
          - type: precision_at_10
            value: 6.678000000000001
          - type: precision_at_100
            value: 1.1199999999999999
          - type: precision_at_1000
            value: 0.155
          - type: precision_at_3
            value: 15.715000000000002
          - type: precision_at_5
            value: 10.913
          - type: recall_at_1
            value: 23.247999999999998
          - type: recall_at_10
            value: 48.16
          - type: recall_at_100
            value: 72.753
          - type: recall_at_1000
            value: 90.8
          - type: recall_at_3
            value: 35.961999999999996
          - type: recall_at_5
            value: 40.504
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 23.825583333333334
          - type: map_at_10
            value: 32.2845
          - type: map_at_100
            value: 33.48566666666667
          - type: map_at_1000
            value: 33.60833333333333
          - type: map_at_3
            value: 29.604916666666664
          - type: map_at_5
            value: 31.015333333333334
          - type: mrr_at_1
            value: 27.850916666666663
          - type: mrr_at_10
            value: 36.122416666666666
          - type: mrr_at_100
            value: 37.01275
          - type: mrr_at_1000
            value: 37.07566666666667
          - type: mrr_at_3
            value: 33.665749999999996
          - type: mrr_at_5
            value: 35.00916666666667
          - type: ndcg_at_1
            value: 27.850916666666663
          - type: ndcg_at_10
            value: 37.47625
          - type: ndcg_at_100
            value: 42.74433333333334
          - type: ndcg_at_1000
            value: 45.21991666666667
          - type: ndcg_at_3
            value: 32.70916666666667
          - type: ndcg_at_5
            value: 34.80658333333333
          - type: precision_at_1
            value: 27.850916666666663
          - type: precision_at_10
            value: 6.5761666666666665
          - type: precision_at_100
            value: 1.0879999999999999
          - type: precision_at_1000
            value: 0.15058333333333332
          - type: precision_at_3
            value: 14.933833333333336
          - type: precision_at_5
            value: 10.607249999999999
          - type: recall_at_1
            value: 23.825583333333334
          - type: recall_at_10
            value: 49.100500000000004
          - type: recall_at_100
            value: 72.21133333333334
          - type: recall_at_1000
            value: 89.34791666666666
          - type: recall_at_3
            value: 35.90525
          - type: recall_at_5
            value: 41.24583333333334
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackStatsRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 21.343
          - type: map_at_10
            value: 27.313
          - type: map_at_100
            value: 28.316999999999997
          - type: map_at_1000
            value: 28.406
          - type: map_at_3
            value: 25.06
          - type: map_at_5
            value: 26.409
          - type: mrr_at_1
            value: 23.313
          - type: mrr_at_10
            value: 29.467
          - type: mrr_at_100
            value: 30.348999999999997
          - type: mrr_at_1000
            value: 30.42
          - type: mrr_at_3
            value: 27.173000000000002
          - type: mrr_at_5
            value: 28.461
          - type: ndcg_at_1
            value: 23.313
          - type: ndcg_at_10
            value: 31.183
          - type: ndcg_at_100
            value: 36.252
          - type: ndcg_at_1000
            value: 38.582
          - type: ndcg_at_3
            value: 26.838
          - type: ndcg_at_5
            value: 29.042
          - type: precision_at_1
            value: 23.313
          - type: precision_at_10
            value: 4.9079999999999995
          - type: precision_at_100
            value: 0.808
          - type: precision_at_1000
            value: 0.109
          - type: precision_at_3
            value: 11.299
          - type: precision_at_5
            value: 8.097999999999999
          - type: recall_at_1
            value: 21.343
          - type: recall_at_10
            value: 41.047
          - type: recall_at_100
            value: 64.372
          - type: recall_at_1000
            value: 81.499
          - type: recall_at_3
            value: 29.337000000000003
          - type: recall_at_5
            value: 34.756
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackTexRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 16.595
          - type: map_at_10
            value: 23.433
          - type: map_at_100
            value: 24.578
          - type: map_at_1000
            value: 24.709999999999997
          - type: map_at_3
            value: 21.268
          - type: map_at_5
            value: 22.393
          - type: mrr_at_1
            value: 20.131
          - type: mrr_at_10
            value: 27.026
          - type: mrr_at_100
            value: 28.003
          - type: mrr_at_1000
            value: 28.083999999999996
          - type: mrr_at_3
            value: 24.966
          - type: mrr_at_5
            value: 26.064999999999998
          - type: ndcg_at_1
            value: 20.131
          - type: ndcg_at_10
            value: 27.846
          - type: ndcg_at_100
            value: 33.318999999999996
          - type: ndcg_at_1000
            value: 36.403
          - type: ndcg_at_3
            value: 23.883
          - type: ndcg_at_5
            value: 25.595000000000002
          - type: precision_at_1
            value: 20.131
          - type: precision_at_10
            value: 5.034000000000001
          - type: precision_at_100
            value: 0.9079999999999999
          - type: precision_at_1000
            value: 0.13699999999999998
          - type: precision_at_3
            value: 11.23
          - type: precision_at_5
            value: 8.032
          - type: recall_at_1
            value: 16.595
          - type: recall_at_10
            value: 37.576
          - type: recall_at_100
            value: 62.044
          - type: recall_at_1000
            value: 83.97
          - type: recall_at_3
            value: 26.631
          - type: recall_at_5
            value: 31.002000000000002
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackUnixRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 24.85
          - type: map_at_10
            value: 32.762
          - type: map_at_100
            value: 33.896
          - type: map_at_1000
            value: 34.006
          - type: map_at_3
            value: 29.965000000000003
          - type: map_at_5
            value: 31.485999999999997
          - type: mrr_at_1
            value: 28.731
          - type: mrr_at_10
            value: 36.504999999999995
          - type: mrr_at_100
            value: 37.364999999999995
          - type: mrr_at_1000
            value: 37.431
          - type: mrr_at_3
            value: 34.033
          - type: mrr_at_5
            value: 35.4
          - type: ndcg_at_1
            value: 28.731
          - type: ndcg_at_10
            value: 37.788
          - type: ndcg_at_100
            value: 43.1
          - type: ndcg_at_1000
            value: 45.623999999999995
          - type: ndcg_at_3
            value: 32.717
          - type: ndcg_at_5
            value: 35.024
          - type: precision_at_1
            value: 28.731
          - type: precision_at_10
            value: 6.371
          - type: precision_at_100
            value: 1.02
          - type: precision_at_1000
            value: 0.135
          - type: precision_at_3
            value: 14.521
          - type: precision_at_5
            value: 10.41
          - type: recall_at_1
            value: 24.85
          - type: recall_at_10
            value: 49.335
          - type: recall_at_100
            value: 72.792
          - type: recall_at_1000
            value: 90.525
          - type: recall_at_3
            value: 35.698
          - type: recall_at_5
            value: 41.385
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackWebmastersRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 23.016000000000002
          - type: map_at_10
            value: 32.126
          - type: map_at_100
            value: 33.786
          - type: map_at_1000
            value: 34.012
          - type: map_at_3
            value: 29.256
          - type: map_at_5
            value: 30.552
          - type: mrr_at_1
            value: 27.272999999999996
          - type: mrr_at_10
            value: 35.967
          - type: mrr_at_100
            value: 37.082
          - type: mrr_at_1000
            value: 37.146
          - type: mrr_at_3
            value: 33.531
          - type: mrr_at_5
            value: 34.697
          - type: ndcg_at_1
            value: 27.272999999999996
          - type: ndcg_at_10
            value: 37.945
          - type: ndcg_at_100
            value: 43.928
          - type: ndcg_at_1000
            value: 46.772999999999996
          - type: ndcg_at_3
            value: 33.111000000000004
          - type: ndcg_at_5
            value: 34.794000000000004
          - type: precision_at_1
            value: 27.272999999999996
          - type: precision_at_10
            value: 7.53
          - type: precision_at_100
            value: 1.512
          - type: precision_at_1000
            value: 0.241
          - type: precision_at_3
            value: 15.547
          - type: precision_at_5
            value: 11.146
          - type: recall_at_1
            value: 23.016000000000002
          - type: recall_at_10
            value: 49.576
          - type: recall_at_100
            value: 75.74600000000001
          - type: recall_at_1000
            value: 94.069
          - type: recall_at_3
            value: 35.964
          - type: recall_at_5
            value: 40.455999999999996
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackWordpressRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 22.742
          - type: map_at_10
            value: 29.232000000000003
          - type: map_at_100
            value: 30.160999999999998
          - type: map_at_1000
            value: 30.278
          - type: map_at_3
            value: 27.134999999999998
          - type: map_at_5
            value: 27.932000000000002
          - type: mrr_at_1
            value: 24.399
          - type: mrr_at_10
            value: 31.048
          - type: mrr_at_100
            value: 31.912000000000003
          - type: mrr_at_1000
            value: 31.999
          - type: mrr_at_3
            value: 29.144
          - type: mrr_at_5
            value: 29.809
          - type: ndcg_at_1
            value: 24.399
          - type: ndcg_at_10
            value: 33.354
          - type: ndcg_at_100
            value: 38.287
          - type: ndcg_at_1000
            value: 41.105000000000004
          - type: ndcg_at_3
            value: 29.112
          - type: ndcg_at_5
            value: 30.379
          - type: precision_at_1
            value: 24.399
          - type: precision_at_10
            value: 5.157
          - type: precision_at_100
            value: 0.828
          - type: precision_at_1000
            value: 0.11800000000000001
          - type: precision_at_3
            value: 11.892
          - type: precision_at_5
            value: 8.022
          - type: recall_at_1
            value: 22.742
          - type: recall_at_10
            value: 44.31
          - type: recall_at_100
            value: 67.422
          - type: recall_at_1000
            value: 88.193
          - type: recall_at_3
            value: 32.705
          - type: recall_at_5
            value: 35.669000000000004
      - task:
          type: Retrieval
        dataset:
          type: climate-fever
          name: MTEB ClimateFEVER
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 9.067
          - type: map_at_10
            value: 14.821000000000002
          - type: map_at_100
            value: 16.195
          - type: map_at_1000
            value: 16.359
          - type: map_at_3
            value: 12.666
          - type: map_at_5
            value: 13.675999999999998
          - type: mrr_at_1
            value: 20.326
          - type: mrr_at_10
            value: 29.798000000000002
          - type: mrr_at_100
            value: 30.875000000000004
          - type: mrr_at_1000
            value: 30.928
          - type: mrr_at_3
            value: 26.678
          - type: mrr_at_5
            value: 28.433000000000003
          - type: ndcg_at_1
            value: 20.326
          - type: ndcg_at_10
            value: 21.477
          - type: ndcg_at_100
            value: 27.637
          - type: ndcg_at_1000
            value: 30.953000000000003
          - type: ndcg_at_3
            value: 17.456
          - type: ndcg_at_5
            value: 18.789
          - type: precision_at_1
            value: 20.326
          - type: precision_at_10
            value: 6.482
          - type: precision_at_100
            value: 1.302
          - type: precision_at_1000
            value: 0.191
          - type: precision_at_3
            value: 12.53
          - type: precision_at_5
            value: 9.603
          - type: recall_at_1
            value: 9.067
          - type: recall_at_10
            value: 26.246000000000002
          - type: recall_at_100
            value: 47.837
          - type: recall_at_1000
            value: 66.637
          - type: recall_at_3
            value: 16.468
          - type: recall_at_5
            value: 20.088
      - task:
          type: Retrieval
        dataset:
          type: dbpedia-entity
          name: MTEB DBPedia
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 7.563000000000001
          - type: map_at_10
            value: 15.22
          - type: map_at_100
            value: 20.048
          - type: map_at_1000
            value: 21.17
          - type: map_at_3
            value: 11.627
          - type: map_at_5
            value: 13.239
          - type: mrr_at_1
            value: 56.25
          - type: mrr_at_10
            value: 64.846
          - type: mrr_at_100
            value: 65.405
          - type: mrr_at_1000
            value: 65.41799999999999
          - type: mrr_at_3
            value: 63.125
          - type: mrr_at_5
            value: 64.1
          - type: ndcg_at_1
            value: 45
          - type: ndcg_at_10
            value: 32.437
          - type: ndcg_at_100
            value: 35.483
          - type: ndcg_at_1000
            value: 42.186
          - type: ndcg_at_3
            value: 37.297000000000004
          - type: ndcg_at_5
            value: 34.697
          - type: precision_at_1
            value: 56.25
          - type: precision_at_10
            value: 25.15
          - type: precision_at_100
            value: 7.539999999999999
          - type: precision_at_1000
            value: 1.678
          - type: precision_at_3
            value: 40.666999999999994
          - type: precision_at_5
            value: 33.45
          - type: recall_at_1
            value: 7.563000000000001
          - type: recall_at_10
            value: 19.969
          - type: recall_at_100
            value: 40.113
          - type: recall_at_1000
            value: 61.72299999999999
          - type: recall_at_3
            value: 12.950999999999999
          - type: recall_at_5
            value: 15.690999999999999
      - task:
          type: Classification
        dataset:
          type: mteb/emotion
          name: MTEB EmotionClassification
          config: default
          split: test
          revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
        metrics:
          - type: accuracy
            value: 44.675000000000004
          - type: f1
            value: 40.779372586075105
      - task:
          type: Retrieval
        dataset:
          type: fever
          name: MTEB FEVER
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 57.406
          - type: map_at_10
            value: 67.69500000000001
          - type: map_at_100
            value: 68.08
          - type: map_at_1000
            value: 68.095
          - type: map_at_3
            value: 65.688
          - type: map_at_5
            value: 66.93
          - type: mrr_at_1
            value: 61.941
          - type: mrr_at_10
            value: 72.513
          - type: mrr_at_100
            value: 72.83699999999999
          - type: mrr_at_1000
            value: 72.844
          - type: mrr_at_3
            value: 70.60499999999999
          - type: mrr_at_5
            value: 71.807
          - type: ndcg_at_1
            value: 61.941
          - type: ndcg_at_10
            value: 73.29
          - type: ndcg_at_100
            value: 74.96300000000001
          - type: ndcg_at_1000
            value: 75.28200000000001
          - type: ndcg_at_3
            value: 69.491
          - type: ndcg_at_5
            value: 71.573
          - type: precision_at_1
            value: 61.941
          - type: precision_at_10
            value: 9.388
          - type: precision_at_100
            value: 1.0290000000000001
          - type: precision_at_1000
            value: 0.107
          - type: precision_at_3
            value: 27.423
          - type: precision_at_5
            value: 17.627000000000002
          - type: recall_at_1
            value: 57.406
          - type: recall_at_10
            value: 85.975
          - type: recall_at_100
            value: 93.29899999999999
          - type: recall_at_1000
            value: 95.531
          - type: recall_at_3
            value: 75.624
          - type: recall_at_5
            value: 80.78999999999999
      - task:
          type: Retrieval
        dataset:
          type: fiqa
          name: MTEB FiQA2018
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 16.314999999999998
          - type: map_at_10
            value: 26.678
          - type: map_at_100
            value: 28.322000000000003
          - type: map_at_1000
            value: 28.519
          - type: map_at_3
            value: 23.105
          - type: map_at_5
            value: 24.808
          - type: mrr_at_1
            value: 33.333
          - type: mrr_at_10
            value: 41.453
          - type: mrr_at_100
            value: 42.339
          - type: mrr_at_1000
            value: 42.39
          - type: mrr_at_3
            value: 38.863
          - type: mrr_at_5
            value: 40.159
          - type: ndcg_at_1
            value: 33.333
          - type: ndcg_at_10
            value: 34.062
          - type: ndcg_at_100
            value: 40.595
          - type: ndcg_at_1000
            value: 44.124
          - type: ndcg_at_3
            value: 30.689
          - type: ndcg_at_5
            value: 31.255
          - type: precision_at_1
            value: 33.333
          - type: precision_at_10
            value: 9.722
          - type: precision_at_100
            value: 1.6480000000000001
          - type: precision_at_1000
            value: 0.22699999999999998
          - type: precision_at_3
            value: 20.936
          - type: precision_at_5
            value: 15.154
          - type: recall_at_1
            value: 16.314999999999998
          - type: recall_at_10
            value: 41.221000000000004
          - type: recall_at_100
            value: 65.857
          - type: recall_at_1000
            value: 87.327
          - type: recall_at_3
            value: 27.435
          - type: recall_at_5
            value: 32.242
      - task:
          type: Retrieval
        dataset:
          type: hotpotqa
          name: MTEB HotpotQA
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 31.978
          - type: map_at_10
            value: 43.784
          - type: map_at_100
            value: 44.547
          - type: map_at_1000
            value: 44.614
          - type: map_at_3
            value: 41.317
          - type: map_at_5
            value: 42.812
          - type: mrr_at_1
            value: 63.956999999999994
          - type: mrr_at_10
            value: 70.502
          - type: mrr_at_100
            value: 70.845
          - type: mrr_at_1000
            value: 70.865
          - type: mrr_at_3
            value: 69.192
          - type: mrr_at_5
            value: 69.994
          - type: ndcg_at_1
            value: 63.956999999999994
          - type: ndcg_at_10
            value: 52.782
          - type: ndcg_at_100
            value: 55.78999999999999
          - type: ndcg_at_1000
            value: 57.289
          - type: ndcg_at_3
            value: 48.864000000000004
          - type: ndcg_at_5
            value: 50.964
          - type: precision_at_1
            value: 63.956999999999994
          - type: precision_at_10
            value: 10.809000000000001
          - type: precision_at_100
            value: 1.319
          - type: precision_at_1000
            value: 0.152
          - type: precision_at_3
            value: 30.2
          - type: precision_at_5
            value: 19.787
          - type: recall_at_1
            value: 31.978
          - type: recall_at_10
            value: 54.045
          - type: recall_at_100
            value: 65.928
          - type: recall_at_1000
            value: 75.976
          - type: recall_at_3
            value: 45.300000000000004
          - type: recall_at_5
            value: 49.467
      - task:
          type: Classification
        dataset:
          type: mteb/imdb
          name: MTEB ImdbClassification
          config: default
          split: test
          revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
        metrics:
          - type: accuracy
            value: 63.8708
          - type: ap
            value: 59.02002684158838
          - type: f1
            value: 63.650055896985315
      - task:
          type: Retrieval
        dataset:
          type: msmarco
          name: MTEB MSMARCO
          config: default
          split: dev
          revision: None
        metrics:
          - type: map_at_1
            value: 19.834
          - type: map_at_10
            value: 31.317
          - type: map_at_100
            value: 32.576
          - type: map_at_1000
            value: 32.631
          - type: map_at_3
            value: 27.728
          - type: map_at_5
            value: 29.720000000000002
          - type: mrr_at_1
            value: 20.43
          - type: mrr_at_10
            value: 31.868999999999996
          - type: mrr_at_100
            value: 33.074999999999996
          - type: mrr_at_1000
            value: 33.123999999999995
          - type: mrr_at_3
            value: 28.333000000000002
          - type: mrr_at_5
            value: 30.305
          - type: ndcg_at_1
            value: 20.43
          - type: ndcg_at_10
            value: 37.769000000000005
          - type: ndcg_at_100
            value: 43.924
          - type: ndcg_at_1000
            value: 45.323
          - type: ndcg_at_3
            value: 30.422
          - type: ndcg_at_5
            value: 33.98
          - type: precision_at_1
            value: 20.43
          - type: precision_at_10
            value: 6.027
          - type: precision_at_100
            value: 0.9119999999999999
          - type: precision_at_1000
            value: 0.10300000000000001
          - type: precision_at_3
            value: 12.985
          - type: precision_at_5
            value: 9.593
          - type: recall_at_1
            value: 19.834
          - type: recall_at_10
            value: 57.647000000000006
          - type: recall_at_100
            value: 86.276
          - type: recall_at_1000
            value: 97.065
          - type: recall_at_3
            value: 37.616
          - type: recall_at_5
            value: 46.171
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_domain
          name: MTEB MTOPDomainClassification (en)
          config: en
          split: test
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
        metrics:
          - type: accuracy
            value: 91.52530779753762
          - type: f1
            value: 91.4004687820246
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_intent
          name: MTEB MTOPIntentClassification (en)
          config: en
          split: test
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
        metrics:
          - type: accuracy
            value: 72.82717738258093
          - type: f1
            value: 56.791387113030346
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (en)
          config: en
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 71.09280430396772
          - type: f1
            value: 68.92843467363518
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_scenario
          name: MTEB MassiveScenarioClassification (en)
          config: en
          split: test
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
        metrics:
          - type: accuracy
            value: 76.2542030934768
          - type: f1
            value: 76.22211319699834
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-p2p
          name: MTEB MedrxivClusteringP2P
          config: default
          split: test
          revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
        metrics:
          - type: v_measure
            value: 29.604407852989457
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-s2s
          name: MTEB MedrxivClusteringS2S
          config: default
          split: test
          revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
        metrics:
          - type: v_measure
            value: 25.011863718751183
      - task:
          type: Reranking
        dataset:
          type: mteb/mind_small
          name: MTEB MindSmallReranking
          config: default
          split: test
          revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
        metrics:
          - type: map
            value: 31.55552172383111
          - type: mrr
            value: 32.65475731770242
      - task:
          type: Retrieval
        dataset:
          type: nfcorpus
          name: MTEB NFCorpus
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 4.968
          - type: map_at_10
            value: 10.703999999999999
          - type: map_at_100
            value: 13.316
          - type: map_at_1000
            value: 14.674000000000001
          - type: map_at_3
            value: 7.809000000000001
          - type: map_at_5
            value: 9.268
          - type: mrr_at_1
            value: 41.796
          - type: mrr_at_10
            value: 50.558
          - type: mrr_at_100
            value: 51.125
          - type: mrr_at_1000
            value: 51.184
          - type: mrr_at_3
            value: 48.349
          - type: mrr_at_5
            value: 49.572
          - type: ndcg_at_1
            value: 39.783
          - type: ndcg_at_10
            value: 30.375999999999998
          - type: ndcg_at_100
            value: 27.648
          - type: ndcg_at_1000
            value: 36.711
          - type: ndcg_at_3
            value: 35.053
          - type: ndcg_at_5
            value: 33.278999999999996
          - type: precision_at_1
            value: 41.796
          - type: precision_at_10
            value: 22.663
          - type: precision_at_100
            value: 7.210999999999999
          - type: precision_at_1000
            value: 1.984
          - type: precision_at_3
            value: 33.127
          - type: precision_at_5
            value: 29.102
          - type: recall_at_1
            value: 4.968
          - type: recall_at_10
            value: 14.469999999999999
          - type: recall_at_100
            value: 28.188000000000002
          - type: recall_at_1000
            value: 60.769
          - type: recall_at_3
            value: 8.737
          - type: recall_at_5
            value: 11.539000000000001
      - task:
          type: Retrieval
        dataset:
          type: nq
          name: MTEB NQ
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 26.958
          - type: map_at_10
            value: 40.6
          - type: map_at_100
            value: 41.754000000000005
          - type: map_at_1000
            value: 41.792
          - type: map_at_3
            value: 36.521
          - type: map_at_5
            value: 38.866
          - type: mrr_at_1
            value: 30.330000000000002
          - type: mrr_at_10
            value: 43.013
          - type: mrr_at_100
            value: 43.89
          - type: mrr_at_1000
            value: 43.917
          - type: mrr_at_3
            value: 39.489000000000004
          - type: mrr_at_5
            value: 41.504999999999995
          - type: ndcg_at_1
            value: 30.330000000000002
          - type: ndcg_at_10
            value: 47.878
          - type: ndcg_at_100
            value: 52.761
          - type: ndcg_at_1000
            value: 53.69500000000001
          - type: ndcg_at_3
            value: 40.061
          - type: ndcg_at_5
            value: 43.980000000000004
          - type: precision_at_1
            value: 30.330000000000002
          - type: precision_at_10
            value: 8.048
          - type: precision_at_100
            value: 1.076
          - type: precision_at_1000
            value: 0.117
          - type: precision_at_3
            value: 18.299000000000003
          - type: precision_at_5
            value: 13.25
          - type: recall_at_1
            value: 26.958
          - type: recall_at_10
            value: 67.72399999999999
          - type: recall_at_100
            value: 89.02600000000001
          - type: recall_at_1000
            value: 96.029
          - type: recall_at_3
            value: 47.332
          - type: recall_at_5
            value: 56.36600000000001
      - task:
          type: Retrieval
        dataset:
          type: quora
          name: MTEB QuoraRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 69.926
          - type: map_at_10
            value: 83.797
          - type: map_at_100
            value: 84.42699999999999
          - type: map_at_1000
            value: 84.446
          - type: map_at_3
            value: 80.78
          - type: map_at_5
            value: 82.669
          - type: mrr_at_1
            value: 80.44
          - type: mrr_at_10
            value: 86.79
          - type: mrr_at_100
            value: 86.90299999999999
          - type: mrr_at_1000
            value: 86.904
          - type: mrr_at_3
            value: 85.753
          - type: mrr_at_5
            value: 86.478
          - type: ndcg_at_1
            value: 80.44
          - type: ndcg_at_10
            value: 87.634
          - type: ndcg_at_100
            value: 88.9
          - type: ndcg_at_1000
            value: 89.03
          - type: ndcg_at_3
            value: 84.622
          - type: ndcg_at_5
            value: 86.29
          - type: precision_at_1
            value: 80.44
          - type: precision_at_10
            value: 13.305
          - type: precision_at_100
            value: 1.524
          - type: precision_at_1000
            value: 0.157
          - type: precision_at_3
            value: 36.957
          - type: precision_at_5
            value: 24.328
          - type: recall_at_1
            value: 69.926
          - type: recall_at_10
            value: 94.99300000000001
          - type: recall_at_100
            value: 99.345
          - type: recall_at_1000
            value: 99.97
          - type: recall_at_3
            value: 86.465
          - type: recall_at_5
            value: 91.121
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering
          name: MTEB RedditClustering
          config: default
          split: test
          revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
        metrics:
          - type: v_measure
            value: 42.850644235471144
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering-p2p
          name: MTEB RedditClusteringP2P
          config: default
          split: test
          revision: 282350215ef01743dc01b456c7f5241fa8937f16
        metrics:
          - type: v_measure
            value: 52.547875398320734
      - task:
          type: Retrieval
        dataset:
          type: scidocs
          name: MTEB SCIDOCS
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 4.328
          - type: map_at_10
            value: 10.479
          - type: map_at_100
            value: 12.25
          - type: map_at_1000
            value: 12.522
          - type: map_at_3
            value: 7.548000000000001
          - type: map_at_5
            value: 9.039
          - type: mrr_at_1
            value: 21.3
          - type: mrr_at_10
            value: 30.678
          - type: mrr_at_100
            value: 31.77
          - type: mrr_at_1000
            value: 31.831
          - type: mrr_at_3
            value: 27.500000000000004
          - type: mrr_at_5
            value: 29.375
          - type: ndcg_at_1
            value: 21.3
          - type: ndcg_at_10
            value: 17.626
          - type: ndcg_at_100
            value: 25.03
          - type: ndcg_at_1000
            value: 30.055
          - type: ndcg_at_3
            value: 16.744999999999997
          - type: ndcg_at_5
            value: 14.729999999999999
          - type: precision_at_1
            value: 21.3
          - type: precision_at_10
            value: 9.09
          - type: precision_at_100
            value: 1.989
          - type: precision_at_1000
            value: 0.32
          - type: precision_at_3
            value: 15.467
          - type: precision_at_5
            value: 12.879999999999999
          - type: recall_at_1
            value: 4.328
          - type: recall_at_10
            value: 18.412
          - type: recall_at_100
            value: 40.363
          - type: recall_at_1000
            value: 64.997
          - type: recall_at_3
            value: 9.408
          - type: recall_at_5
            value: 13.048000000000002
      - task:
          type: STS
        dataset:
          type: mteb/sickr-sts
          name: MTEB SICK-R
          config: default
          split: test
          revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
        metrics:
          - type: cos_sim_pearson
            value: 84.1338589503896
          - type: cos_sim_spearman
            value: 79.1378154534123
          - type: euclidean_pearson
            value: 73.17857462509251
          - type: euclidean_spearman
            value: 70.79268955610539
          - type: manhattan_pearson
            value: 72.8280251705823
          - type: manhattan_spearman
            value: 70.60323787229834
      - task:
          type: STS
        dataset:
          type: mteb/sts12-sts
          name: MTEB STS12
          config: default
          split: test
          revision: a0d554a64d88156834ff5ae9920b964011b16384
        metrics:
          - type: cos_sim_pearson
            value: 84.21604641858598
          - type: cos_sim_spearman
            value: 75.06080146054282
          - type: euclidean_pearson
            value: 69.44429285856924
          - type: euclidean_spearman
            value: 58.240130690046456
          - type: manhattan_pearson
            value: 69.07597314234852
          - type: manhattan_spearman
            value: 58.08224335836159
      - task:
          type: STS
        dataset:
          type: mteb/sts13-sts
          name: MTEB STS13
          config: default
          split: test
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
        metrics:
          - type: cos_sim_pearson
            value: 80.2252849321165
          - type: cos_sim_spearman
            value: 80.85907200101076
          - type: euclidean_pearson
            value: 70.85619832878055
          - type: euclidean_spearman
            value: 71.59417341887324
          - type: manhattan_pearson
            value: 70.55842192345895
          - type: manhattan_spearman
            value: 71.30332994715893
      - task:
          type: STS
        dataset:
          type: mteb/sts14-sts
          name: MTEB STS14
          config: default
          split: test
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
        metrics:
          - type: cos_sim_pearson
            value: 80.50469360654135
          - type: cos_sim_spearman
            value: 76.12917164308409
          - type: euclidean_pearson
            value: 70.4070213910491
          - type: euclidean_spearman
            value: 66.97320451942113
          - type: manhattan_pearson
            value: 70.24834290119863
          - type: manhattan_spearman
            value: 66.9047074173091
      - task:
          type: STS
        dataset:
          type: mteb/sts15-sts
          name: MTEB STS15
          config: default
          split: test
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
        metrics:
          - type: cos_sim_pearson
            value: 84.70140350059746
          - type: cos_sim_spearman
            value: 85.55427877110485
          - type: euclidean_pearson
            value: 63.4780453371435
          - type: euclidean_spearman
            value: 64.65485395077273
          - type: manhattan_pearson
            value: 63.64869846572011
          - type: manhattan_spearman
            value: 64.87219311596813
      - task:
          type: STS
        dataset:
          type: mteb/sts16-sts
          name: MTEB STS16
          config: default
          split: test
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
        metrics:
          - type: cos_sim_pearson
            value: 79.4416477676503
          - type: cos_sim_spearman
            value: 81.2094925260351
          - type: euclidean_pearson
            value: 68.372257553367
          - type: euclidean_spearman
            value: 69.47792807911692
          - type: manhattan_pearson
            value: 68.17773583183664
          - type: manhattan_spearman
            value: 69.31505452732998
      - 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: 88.94688403351994
          - type: cos_sim_spearman
            value: 88.97626967707933
          - type: euclidean_pearson
            value: 74.09942728422159
          - type: euclidean_spearman
            value: 72.91022362666948
          - type: manhattan_pearson
            value: 74.11262432880199
          - type: manhattan_spearman
            value: 72.82115894578564
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (en)
          config: en
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 67.42605802805606
          - type: cos_sim_spearman
            value: 66.22330559222408
          - type: euclidean_pearson
            value: 50.15272876367891
          - type: euclidean_spearman
            value: 60.695400782452715
          - type: manhattan_pearson
            value: 50.17076569264417
          - type: manhattan_spearman
            value: 60.3761281869747
      - task:
          type: STS
        dataset:
          type: mteb/stsbenchmark-sts
          name: MTEB STSBenchmark
          config: default
          split: test
          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
        metrics:
          - type: cos_sim_pearson
            value: 82.85939227596093
          - type: cos_sim_spearman
            value: 82.57071649593358
          - type: euclidean_pearson
            value: 72.18291316100125
          - type: euclidean_spearman
            value: 70.70702024402348
          - type: manhattan_pearson
            value: 72.36789718833687
          - type: manhattan_spearman
            value: 70.92789721402387
      - task:
          type: Reranking
        dataset:
          type: mteb/scidocs-reranking
          name: MTEB SciDocsRR
          config: default
          split: test
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
        metrics:
          - type: map
            value: 79.31107201598611
          - type: mrr
            value: 93.66321314850727
      - task:
          type: Retrieval
        dataset:
          type: scifact
          name: MTEB SciFact
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 45.428000000000004
          - type: map_at_10
            value: 54.730000000000004
          - type: map_at_100
            value: 55.421
          - type: map_at_1000
            value: 55.47299999999999
          - type: map_at_3
            value: 52.333
          - type: map_at_5
            value: 53.72
          - type: mrr_at_1
            value: 48.333
          - type: mrr_at_10
            value: 56.601
          - type: mrr_at_100
            value: 57.106
          - type: mrr_at_1000
            value: 57.154
          - type: mrr_at_3
            value: 54.611
          - type: mrr_at_5
            value: 55.87800000000001
          - type: ndcg_at_1
            value: 48.333
          - type: ndcg_at_10
            value: 59.394999999999996
          - type: ndcg_at_100
            value: 62.549
          - type: ndcg_at_1000
            value: 63.941
          - type: ndcg_at_3
            value: 55.096000000000004
          - type: ndcg_at_5
            value: 57.325
          - type: precision_at_1
            value: 48.333
          - type: precision_at_10
            value: 8.1
          - type: precision_at_100
            value: 0.983
          - type: precision_at_1000
            value: 0.11
          - type: precision_at_3
            value: 21.889
          - type: precision_at_5
            value: 14.533
          - type: recall_at_1
            value: 45.428000000000004
          - type: recall_at_10
            value: 71.806
          - type: recall_at_100
            value: 86.533
          - type: recall_at_1000
            value: 97.5
          - type: recall_at_3
            value: 60.228
          - type: recall_at_5
            value: 65.90599999999999
      - task:
          type: PairClassification
        dataset:
          type: mteb/sprintduplicatequestions-pairclassification
          name: MTEB SprintDuplicateQuestions
          config: default
          split: test
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
        metrics:
          - type: cos_sim_accuracy
            value: 99.8029702970297
          - type: cos_sim_ap
            value: 95.48085242816634
          - type: cos_sim_f1
            value: 89.86653484923382
          - type: cos_sim_precision
            value: 88.85630498533725
          - type: cos_sim_recall
            value: 90.9
          - type: dot_accuracy
            value: 99.21881188118812
          - type: dot_ap
            value: 55.14126603018576
          - type: dot_f1
            value: 55.22458628841608
          - type: dot_precision
            value: 52.37668161434977
          - type: dot_recall
            value: 58.4
          - type: euclidean_accuracy
            value: 99.64356435643565
          - type: euclidean_ap
            value: 84.52487064474103
          - type: euclidean_f1
            value: 80.53908355795149
          - type: euclidean_precision
            value: 87.36842105263159
          - type: euclidean_recall
            value: 74.7
          - type: manhattan_accuracy
            value: 99.63861386138613
          - type: manhattan_ap
            value: 84.1994288662172
          - type: manhattan_f1
            value: 80.38482095136291
          - type: manhattan_precision
            value: 86.33754305396096
          - type: manhattan_recall
            value: 75.2
          - type: max_accuracy
            value: 99.8029702970297
          - type: max_ap
            value: 95.48085242816634
          - type: max_f1
            value: 89.86653484923382
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering
          name: MTEB StackExchangeClustering
          config: default
          split: test
          revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
        metrics:
          - type: v_measure
            value: 48.06508273111389
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering-p2p
          name: MTEB StackExchangeClusteringP2P
          config: default
          split: test
          revision: 815ca46b2622cec33ccafc3735d572c266efdb44
        metrics:
          - type: v_measure
            value: 31.36169910951664
      - task:
          type: Reranking
        dataset:
          type: mteb/stackoverflowdupquestions-reranking
          name: MTEB StackOverflowDupQuestions
          config: default
          split: test
          revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
        metrics:
          - type: map
            value: 50.110601218420356
          - type: mrr
            value: 50.90277777777777
      - task:
          type: Summarization
        dataset:
          type: mteb/summeval
          name: MTEB SummEval
          config: default
          split: test
          revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
        metrics:
          - type: cos_sim_pearson
            value: 29.63669555287747
          - type: cos_sim_spearman
            value: 30.708042454053853
          - type: dot_pearson
            value: 20.309025749838924
          - type: dot_spearman
            value: 21.511758746817165
      - task:
          type: Retrieval
        dataset:
          type: trec-covid
          name: MTEB TRECCOVID
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 0.201
          - type: map_at_10
            value: 1.405
          - type: map_at_100
            value: 7.359999999999999
          - type: map_at_1000
            value: 17.858
          - type: map_at_3
            value: 0.494
          - type: map_at_5
            value: 0.757
          - type: mrr_at_1
            value: 74
          - type: mrr_at_10
            value: 84.89999999999999
          - type: mrr_at_100
            value: 84.89999999999999
          - type: mrr_at_1000
            value: 84.89999999999999
          - type: mrr_at_3
            value: 84
          - type: mrr_at_5
            value: 84.89999999999999
          - type: ndcg_at_1
            value: 68
          - type: ndcg_at_10
            value: 60.571
          - type: ndcg_at_100
            value: 46.016
          - type: ndcg_at_1000
            value: 41.277
          - type: ndcg_at_3
            value: 63.989
          - type: ndcg_at_5
            value: 61.41
          - type: precision_at_1
            value: 74
          - type: precision_at_10
            value: 65.2
          - type: precision_at_100
            value: 47.04
          - type: precision_at_1000
            value: 18.416
          - type: precision_at_3
            value: 68
          - type: precision_at_5
            value: 66.4
          - type: recall_at_1
            value: 0.201
          - type: recall_at_10
            value: 1.763
          - type: recall_at_100
            value: 11.008999999999999
          - type: recall_at_1000
            value: 38.509
          - type: recall_at_3
            value: 0.551
          - type: recall_at_5
            value: 0.881
      - task:
          type: Retrieval
        dataset:
          type: webis-touche2020
          name: MTEB Touche2020
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 1.4040000000000001
          - type: map_at_10
            value: 7.847999999999999
          - type: map_at_100
            value: 12.908
          - type: map_at_1000
            value: 14.37
          - type: map_at_3
            value: 3.6450000000000005
          - type: map_at_5
            value: 4.93
          - type: mrr_at_1
            value: 18.367
          - type: mrr_at_10
            value: 32.576
          - type: mrr_at_100
            value: 34.163
          - type: mrr_at_1000
            value: 34.18
          - type: mrr_at_3
            value: 28.571
          - type: mrr_at_5
            value: 30.918
          - type: ndcg_at_1
            value: 15.306000000000001
          - type: ndcg_at_10
            value: 18.59
          - type: ndcg_at_100
            value: 30.394
          - type: ndcg_at_1000
            value: 42.198
          - type: ndcg_at_3
            value: 18.099
          - type: ndcg_at_5
            value: 16.955000000000002
          - type: precision_at_1
            value: 16.326999999999998
          - type: precision_at_10
            value: 17.959
          - type: precision_at_100
            value: 6.755
          - type: precision_at_1000
            value: 1.4529999999999998
          - type: precision_at_3
            value: 20.408
          - type: precision_at_5
            value: 18.367
          - type: recall_at_1
            value: 1.4040000000000001
          - type: recall_at_10
            value: 14.048
          - type: recall_at_100
            value: 42.150999999999996
          - type: recall_at_1000
            value: 77.85600000000001
          - type: recall_at_3
            value: 4.819
          - type: recall_at_5
            value: 7.13
      - task:
          type: Classification
        dataset:
          type: mteb/toxic_conversations_50k
          name: MTEB ToxicConversationsClassification
          config: default
          split: test
          revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
        metrics:
          - type: accuracy
            value: 66.1456
          - type: ap
            value: 11.631023858569064
          - type: f1
            value: 50.128196455722254
      - task:
          type: Classification
        dataset:
          type: mteb/tweet_sentiment_extraction
          name: MTEB TweetSentimentExtractionClassification
          config: default
          split: test
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
        metrics:
          - type: accuracy
            value: 56.850594227504246
          - type: f1
            value: 56.82313689360827
      - task:
          type: Clustering
        dataset:
          type: mteb/twentynewsgroups-clustering
          name: MTEB TwentyNewsgroupsClustering
          config: default
          split: test
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
        metrics:
          - type: v_measure
            value: 38.060423744064764
      - task:
          type: PairClassification
        dataset:
          type: mteb/twittersemeval2015-pairclassification
          name: MTEB TwitterSemEval2015
          config: default
          split: test
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
        metrics:
          - type: cos_sim_accuracy
            value: 84.43702688204088
          - type: cos_sim_ap
            value: 68.30176948820142
          - type: cos_sim_f1
            value: 64.25430330443524
          - type: cos_sim_precision
            value: 61.33365315423362
          - type: cos_sim_recall
            value: 67.46701846965699
          - type: dot_accuracy
            value: 77.76718126005842
          - type: dot_ap
            value: 37.510516716176305
          - type: dot_f1
            value: 43.53859496964441
          - type: dot_precision
            value: 32.428940568475454
          - type: dot_recall
            value: 66.2269129287599
          - type: euclidean_accuracy
            value: 82.10049472492102
          - type: euclidean_ap
            value: 61.64354520687271
          - type: euclidean_f1
            value: 59.804144841721694
          - type: euclidean_precision
            value: 52.604166666666664
          - type: euclidean_recall
            value: 69.28759894459104
          - type: manhattan_accuracy
            value: 82.22566609048101
          - type: manhattan_ap
            value: 61.753431124879974
          - type: manhattan_f1
            value: 59.77735297424941
          - type: manhattan_precision
            value: 52.0870076425632
          - type: manhattan_recall
            value: 70.13192612137203
          - type: max_accuracy
            value: 84.43702688204088
          - type: max_ap
            value: 68.30176948820142
          - type: max_f1
            value: 64.25430330443524
      - task:
          type: PairClassification
        dataset:
          type: mteb/twitterurlcorpus-pairclassification
          name: MTEB TwitterURLCorpus
          config: default
          split: test
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
        metrics:
          - type: cos_sim_accuracy
            value: 88.81515116233942
          - type: cos_sim_ap
            value: 85.33305785100573
          - type: cos_sim_f1
            value: 78.11202938475667
          - type: cos_sim_precision
            value: 74.68567816253424
          - type: cos_sim_recall
            value: 81.86787804126887
          - type: dot_accuracy
            value: 82.50475414289595
          - type: dot_ap
            value: 69.87015340174045
          - type: dot_f1
            value: 65.94174480373633
          - type: dot_precision
            value: 61.40362525728703
          - type: dot_recall
            value: 71.20418848167539
          - type: euclidean_accuracy
            value: 83.05778709201692
          - type: euclidean_ap
            value: 70.54206653977498
          - type: euclidean_f1
            value: 62.98969847356943
          - type: euclidean_precision
            value: 61.55033063923585
          - type: euclidean_recall
            value: 64.49799815214044
          - type: manhattan_accuracy
            value: 83.0034540303489
          - type: manhattan_ap
            value: 70.53997987198404
          - type: manhattan_f1
            value: 62.95875898600075
          - type: manhattan_precision
            value: 61.89555125725339
          - type: manhattan_recall
            value: 64.05913150600554
          - type: max_accuracy
            value: 88.81515116233942
          - type: max_ap
            value: 85.33305785100573
          - type: max_f1
            value: 78.11202938475667



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 set 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:

Data & Parameters

Please checkout our technical blog.

Metrics

We compared the model against all-minilm-l6-v2/all-mpnet-base-v2 from sbert and text-embeddings-ada-002 from OpenAI:

Name param dimension
all-minilm-l6-v2 23m 384
all-mpnet-base-v2 110m 768
ada-embedding-002 Unknown/OpenAI API 1536
jina-embedding-t-en-v1 14m 312
jina-embedding-s-en-v1 35m 512
jina-embedding-b-en-v1 110m 768
jina-embedding-l-en-v1 330m 1024
Name STS12 STS13 STS14 STS15 STS16 STS17 TRECOVID Quora SciFact
all-minilm-l6-v2 0.724 0.806 0.756 0.854 0.79 0.876 0.473 0.876 0.645
all-mpnet-base-v2 0.726 0.835 0.78 0.857 0.8 0.906 0.513 0.875 0.656
ada-embedding-002 0.698 0.833 0.761 0.861 0.86 0.903 0.685 0.876 0.726
jina-embedding-t-en-v1 0.717 0.773 0.731 0.829 0.777 0.860 0.482 0.840 0.522
jina-embedding-s-en-v1 0.743 0.786 0.738 0.837 0.80 0.875 0.523 0.857 0.524
jina-embedding-b-en-v1 0.751 0.809 0.761 0.856 0.812 0.890 0.606 0.876 0.594
jina-embedding-l-en-v1 0.745 0.832 0.781 0.869 0.837 0.902 0.573 0.881 0.598

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 with sentence-transformers:

from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

sentences = ['how is the weather today', 'What is the current weather like today?']

model = SentenceTransformer('jinaai/jina-embedding-b-en-v1')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))

Fine-tuning

Please consider Finetuner.

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.

Citation

If you find Jina Embeddings useful in your research, please cite the following paper:

@misc{günther2023jina,
      title={Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models},
      author={Michael Günther and Louis Milliken and Jonathan Geuter and Georgios Mastrapas and Bo Wang and Han Xiao},
      year={2023},
      eprint={2307.11224},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}