sentence-transformers pytorch onnx safetensors bert feature-extraction sentence-similarity transformers mteb model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index:
- name: bge_micro
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 66.26865671641792
    - type: ap
      value: 28.174006539079688
    - type: f1
      value: 59.724963358211035
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 75.3691
    - type: ap
      value: 69.64182876373573
    - type: f1
      value: 75.2906345000088
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 35.806
    - type: f1
      value: 35.506516495961904
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: arguana
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 27.24
    - type: map_at_10
      value: 42.832
    - type: map_at_100
      value: 43.797000000000004
    - type: map_at_1000
      value: 43.804
    - type: map_at_3
      value: 38.134
    - type: map_at_5
      value: 40.744
    - type: mrr_at_1
      value: 27.951999999999998
    - type: mrr_at_10
      value: 43.111
    - type: mrr_at_100
      value: 44.083
    - type: mrr_at_1000
      value: 44.09
    - type: mrr_at_3
      value: 38.431
    - type: mrr_at_5
      value: 41.019
    - type: ndcg_at_1
      value: 27.24
    - type: ndcg_at_10
      value: 51.513
    - type: ndcg_at_100
      value: 55.762
    - type: ndcg_at_1000
      value: 55.938
    - type: ndcg_at_3
      value: 41.743
    - type: ndcg_at_5
      value: 46.454
    - type: precision_at_1
      value: 27.24
    - type: precision_at_10
      value: 7.93
    - type: precision_at_100
      value: 0.9820000000000001
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 17.402
    - type: precision_at_5
      value: 12.731
    - type: recall_at_1
      value: 27.24
    - type: recall_at_10
      value: 79.303
    - type: recall_at_100
      value: 98.151
    - type: recall_at_1000
      value: 99.502
    - type: recall_at_3
      value: 52.205
    - type: recall_at_5
      value: 63.656
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 44.59766397469585
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 34.480143023109626
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 58.09326229984527
    - type: mrr
      value: 72.18429846546191
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 85.47582391622187
    - type: cos_sim_spearman
      value: 83.41635852964214
    - type: euclidean_pearson
      value: 84.21969728559216
    - type: euclidean_spearman
      value: 83.46575724558684
    - type: manhattan_pearson
      value: 83.83107014910223
    - type: manhattan_spearman
      value: 83.13321954800792
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 80.58116883116882
    - type: f1
      value: 80.53335622619781
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 37.13458676004344
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 29.720429607514898
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 26.051000000000002
    - type: map_at_10
      value: 36.291000000000004
    - type: map_at_100
      value: 37.632
    - type: map_at_1000
      value: 37.772
    - type: map_at_3
      value: 33.288000000000004
    - type: map_at_5
      value: 35.035
    - type: mrr_at_1
      value: 33.333
    - type: mrr_at_10
      value: 42.642
    - type: mrr_at_100
      value: 43.401
    - type: mrr_at_1000
      value: 43.463
    - type: mrr_at_3
      value: 40.272000000000006
    - type: mrr_at_5
      value: 41.753
    - type: ndcg_at_1
      value: 33.333
    - type: ndcg_at_10
      value: 42.291000000000004
    - type: ndcg_at_100
      value: 47.602
    - type: ndcg_at_1000
      value: 50.109
    - type: ndcg_at_3
      value: 38.033
    - type: ndcg_at_5
      value: 40.052
    - type: precision_at_1
      value: 33.333
    - type: precision_at_10
      value: 8.254999999999999
    - type: precision_at_100
      value: 1.353
    - type: precision_at_1000
      value: 0.185
    - type: precision_at_3
      value: 18.884
    - type: precision_at_5
      value: 13.447999999999999
    - type: recall_at_1
      value: 26.051000000000002
    - type: recall_at_10
      value: 53.107000000000006
    - type: recall_at_100
      value: 76.22
    - type: recall_at_1000
      value: 92.92399999999999
    - type: recall_at_3
      value: 40.073
    - type: recall_at_5
      value: 46.327
    - type: map_at_1
      value: 19.698999999999998
    - type: map_at_10
      value: 26.186
    - type: map_at_100
      value: 27.133000000000003
    - type: map_at_1000
      value: 27.256999999999998
    - type: map_at_3
      value: 24.264
    - type: map_at_5
      value: 25.307000000000002
    - type: mrr_at_1
      value: 24.712999999999997
    - type: mrr_at_10
      value: 30.703999999999997
    - type: mrr_at_100
      value: 31.445
    - type: mrr_at_1000
      value: 31.517
    - type: mrr_at_3
      value: 28.992
    - type: mrr_at_5
      value: 29.963
    - type: ndcg_at_1
      value: 24.712999999999997
    - type: ndcg_at_10
      value: 30.198000000000004
    - type: ndcg_at_100
      value: 34.412
    - type: ndcg_at_1000
      value: 37.174
    - type: ndcg_at_3
      value: 27.148
    - type: ndcg_at_5
      value: 28.464
    - type: precision_at_1
      value: 24.712999999999997
    - type: precision_at_10
      value: 5.489999999999999
    - type: precision_at_100
      value: 0.955
    - type: precision_at_1000
      value: 0.14400000000000002
    - type: precision_at_3
      value: 12.803
    - type: precision_at_5
      value: 8.981
    - type: recall_at_1
      value: 19.698999999999998
    - type: recall_at_10
      value: 37.595
    - type: recall_at_100
      value: 55.962
    - type: recall_at_1000
      value: 74.836
    - type: recall_at_3
      value: 28.538999999999998
    - type: recall_at_5
      value: 32.279
    - type: map_at_1
      value: 34.224
    - type: map_at_10
      value: 44.867000000000004
    - type: map_at_100
      value: 45.944
    - type: map_at_1000
      value: 46.013999999999996
    - type: map_at_3
      value: 42.009
    - type: map_at_5
      value: 43.684
    - type: mrr_at_1
      value: 39.436
    - type: mrr_at_10
      value: 48.301
    - type: mrr_at_100
      value: 49.055
    - type: mrr_at_1000
      value: 49.099
    - type: mrr_at_3
      value: 45.956
    - type: mrr_at_5
      value: 47.445
    - type: ndcg_at_1
      value: 39.436
    - type: ndcg_at_10
      value: 50.214000000000006
    - type: ndcg_at_100
      value: 54.63
    - type: ndcg_at_1000
      value: 56.165
    - type: ndcg_at_3
      value: 45.272
    - type: ndcg_at_5
      value: 47.826
    - type: precision_at_1
      value: 39.436
    - type: precision_at_10
      value: 8.037999999999998
    - type: precision_at_100
      value: 1.118
    - type: precision_at_1000
      value: 0.13
    - type: precision_at_3
      value: 20.125
    - type: precision_at_5
      value: 13.918
    - type: recall_at_1
      value: 34.224
    - type: recall_at_10
      value: 62.690999999999995
    - type: recall_at_100
      value: 81.951
    - type: recall_at_1000
      value: 92.93299999999999
    - type: recall_at_3
      value: 49.299
    - type: recall_at_5
      value: 55.533
    - type: map_at_1
      value: 21.375
    - type: map_at_10
      value: 28.366000000000003
    - type: map_at_100
      value: 29.363
    - type: map_at_1000
      value: 29.458000000000002
    - type: map_at_3
      value: 26.247
    - type: map_at_5
      value: 27.439000000000004
    - type: mrr_at_1
      value: 22.938
    - type: mrr_at_10
      value: 30.072
    - type: mrr_at_100
      value: 30.993
    - type: mrr_at_1000
      value: 31.070999999999998
    - type: mrr_at_3
      value: 28.004
    - type: mrr_at_5
      value: 29.179
    - type: ndcg_at_1
      value: 22.938
    - type: ndcg_at_10
      value: 32.516
    - type: ndcg_at_100
      value: 37.641999999999996
    - type: ndcg_at_1000
      value: 40.150999999999996
    - type: ndcg_at_3
      value: 28.341
    - type: ndcg_at_5
      value: 30.394
    - type: precision_at_1
      value: 22.938
    - type: precision_at_10
      value: 5.028
    - type: precision_at_100
      value: 0.8
    - type: precision_at_1000
      value: 0.105
    - type: precision_at_3
      value: 12.052999999999999
    - type: precision_at_5
      value: 8.497
    - type: recall_at_1
      value: 21.375
    - type: recall_at_10
      value: 43.682
    - type: recall_at_100
      value: 67.619
    - type: recall_at_1000
      value: 86.64699999999999
    - type: recall_at_3
      value: 32.478
    - type: recall_at_5
      value: 37.347
    - type: map_at_1
      value: 14.95
    - type: map_at_10
      value: 21.417
    - type: map_at_100
      value: 22.525000000000002
    - type: map_at_1000
      value: 22.665
    - type: map_at_3
      value: 18.684
    - type: map_at_5
      value: 20.275000000000002
    - type: mrr_at_1
      value: 18.159
    - type: mrr_at_10
      value: 25.373
    - type: mrr_at_100
      value: 26.348
    - type: mrr_at_1000
      value: 26.432
    - type: mrr_at_3
      value: 22.698999999999998
    - type: mrr_at_5
      value: 24.254
    - type: ndcg_at_1
      value: 18.159
    - type: ndcg_at_10
      value: 26.043
    - type: ndcg_at_100
      value: 31.491999999999997
    - type: ndcg_at_1000
      value: 34.818
    - type: ndcg_at_3
      value: 21.05
    - type: ndcg_at_5
      value: 23.580000000000002
    - type: precision_at_1
      value: 18.159
    - type: precision_at_10
      value: 4.938
    - type: precision_at_100
      value: 0.872
    - type: precision_at_1000
      value: 0.129
    - type: precision_at_3
      value: 9.908999999999999
    - type: precision_at_5
      value: 7.611999999999999
    - type: recall_at_1
      value: 14.95
    - type: recall_at_10
      value: 36.285000000000004
    - type: recall_at_100
      value: 60.431999999999995
    - type: recall_at_1000
      value: 84.208
    - type: recall_at_3
      value: 23.006
    - type: recall_at_5
      value: 29.304999999999996
    - type: map_at_1
      value: 23.580000000000002
    - type: map_at_10
      value: 32.906
    - type: map_at_100
      value: 34.222
    - type: map_at_1000
      value: 34.346
    - type: map_at_3
      value: 29.891000000000002
    - type: map_at_5
      value: 31.679000000000002
    - type: mrr_at_1
      value: 28.778
    - type: mrr_at_10
      value: 37.783
    - type: mrr_at_100
      value: 38.746
    - type: mrr_at_1000
      value: 38.804
    - type: mrr_at_3
      value: 35.098
    - type: mrr_at_5
      value: 36.739
    - type: ndcg_at_1
      value: 28.778
    - type: ndcg_at_10
      value: 38.484
    - type: ndcg_at_100
      value: 44.322
    - type: ndcg_at_1000
      value: 46.772000000000006
    - type: ndcg_at_3
      value: 33.586
    - type: ndcg_at_5
      value: 36.098
    - type: precision_at_1
      value: 28.778
    - type: precision_at_10
      value: 7.151000000000001
    - type: precision_at_100
      value: 1.185
    - type: precision_at_1000
      value: 0.158
    - type: precision_at_3
      value: 16.105
    - type: precision_at_5
      value: 11.704
    - type: recall_at_1
      value: 23.580000000000002
    - type: recall_at_10
      value: 50.151999999999994
    - type: recall_at_100
      value: 75.114
    - type: recall_at_1000
      value: 91.467
    - type: recall_at_3
      value: 36.552
    - type: recall_at_5
      value: 43.014
    - type: map_at_1
      value: 20.669999999999998
    - type: map_at_10
      value: 28.687
    - type: map_at_100
      value: 30.061
    - type: map_at_1000
      value: 30.197000000000003
    - type: map_at_3
      value: 26.134
    - type: map_at_5
      value: 27.508
    - type: mrr_at_1
      value: 26.256
    - type: mrr_at_10
      value: 34.105999999999995
    - type: mrr_at_100
      value: 35.137
    - type: mrr_at_1000
      value: 35.214
    - type: mrr_at_3
      value: 31.791999999999998
    - type: mrr_at_5
      value: 33.145
    - type: ndcg_at_1
      value: 26.256
    - type: ndcg_at_10
      value: 33.68
    - type: ndcg_at_100
      value: 39.7
    - type: ndcg_at_1000
      value: 42.625
    - type: ndcg_at_3
      value: 29.457
    - type: ndcg_at_5
      value: 31.355
    - type: precision_at_1
      value: 26.256
    - type: precision_at_10
      value: 6.2330000000000005
    - type: precision_at_100
      value: 1.08
    - type: precision_at_1000
      value: 0.149
    - type: precision_at_3
      value: 14.193
    - type: precision_at_5
      value: 10.113999999999999
    - type: recall_at_1
      value: 20.669999999999998
    - type: recall_at_10
      value: 43.254999999999995
    - type: recall_at_100
      value: 69.118
    - type: recall_at_1000
      value: 89.408
    - type: recall_at_3
      value: 31.135
    - type: recall_at_5
      value: 36.574
    - type: map_at_1
      value: 21.488833333333336
    - type: map_at_10
      value: 29.025416666666665
    - type: map_at_100
      value: 30.141249999999992
    - type: map_at_1000
      value: 30.264083333333335
    - type: map_at_3
      value: 26.599333333333337
    - type: map_at_5
      value: 28.004666666666665
    - type: mrr_at_1
      value: 25.515
    - type: mrr_at_10
      value: 32.8235
    - type: mrr_at_100
      value: 33.69958333333333
    - type: mrr_at_1000
      value: 33.77191666666668
    - type: mrr_at_3
      value: 30.581000000000003
    - type: mrr_at_5
      value: 31.919666666666668
    - type: ndcg_at_1
      value: 25.515
    - type: ndcg_at_10
      value: 33.64241666666666
    - type: ndcg_at_100
      value: 38.75816666666667
    - type: ndcg_at_1000
      value: 41.472166666666666
    - type: ndcg_at_3
      value: 29.435083333333335
    - type: ndcg_at_5
      value: 31.519083333333338
    - type: precision_at_1
      value: 25.515
    - type: precision_at_10
      value: 5.89725
    - type: precision_at_100
      value: 0.9918333333333335
    - type: precision_at_1000
      value: 0.14075
    - type: precision_at_3
      value: 13.504000000000001
    - type: precision_at_5
      value: 9.6885
    - type: recall_at_1
      value: 21.488833333333336
    - type: recall_at_10
      value: 43.60808333333333
    - type: recall_at_100
      value: 66.5045
    - type: recall_at_1000
      value: 85.70024999999998
    - type: recall_at_3
      value: 31.922166666666662
    - type: recall_at_5
      value: 37.29758333333334
    - type: map_at_1
      value: 20.781
    - type: map_at_10
      value: 27.173000000000002
    - type: map_at_100
      value: 27.967
    - type: map_at_1000
      value: 28.061999999999998
    - type: map_at_3
      value: 24.973
    - type: map_at_5
      value: 26.279999999999998
    - type: mrr_at_1
      value: 23.773
    - type: mrr_at_10
      value: 29.849999999999998
    - type: mrr_at_100
      value: 30.595
    - type: mrr_at_1000
      value: 30.669
    - type: mrr_at_3
      value: 27.761000000000003
    - type: mrr_at_5
      value: 29.003
    - type: ndcg_at_1
      value: 23.773
    - type: ndcg_at_10
      value: 31.033
    - type: ndcg_at_100
      value: 35.174
    - type: ndcg_at_1000
      value: 37.72
    - type: ndcg_at_3
      value: 26.927
    - type: ndcg_at_5
      value: 29.047
    - type: precision_at_1
      value: 23.773
    - type: precision_at_10
      value: 4.8469999999999995
    - type: precision_at_100
      value: 0.75
    - type: precision_at_1000
      value: 0.104
    - type: precision_at_3
      value: 11.452
    - type: precision_at_5
      value: 8.129
    - type: recall_at_1
      value: 20.781
    - type: recall_at_10
      value: 40.463
    - type: recall_at_100
      value: 59.483
    - type: recall_at_1000
      value: 78.396
    - type: recall_at_3
      value: 29.241
    - type: recall_at_5
      value: 34.544000000000004
    - type: map_at_1
      value: 15.074000000000002
    - type: map_at_10
      value: 20.757
    - type: map_at_100
      value: 21.72
    - type: map_at_1000
      value: 21.844
    - type: map_at_3
      value: 18.929000000000002
    - type: map_at_5
      value: 19.894000000000002
    - type: mrr_at_1
      value: 18.307000000000002
    - type: mrr_at_10
      value: 24.215
    - type: mrr_at_100
      value: 25.083
    - type: mrr_at_1000
      value: 25.168000000000003
    - type: mrr_at_3
      value: 22.316
    - type: mrr_at_5
      value: 23.36
    - type: ndcg_at_1
      value: 18.307000000000002
    - type: ndcg_at_10
      value: 24.651999999999997
    - type: ndcg_at_100
      value: 29.296
    - type: ndcg_at_1000
      value: 32.538
    - type: ndcg_at_3
      value: 21.243000000000002
    - type: ndcg_at_5
      value: 22.727
    - type: precision_at_1
      value: 18.307000000000002
    - type: precision_at_10
      value: 4.446
    - type: precision_at_100
      value: 0.792
    - type: precision_at_1000
      value: 0.124
    - type: precision_at_3
      value: 9.945
    - type: precision_at_5
      value: 7.123
    - type: recall_at_1
      value: 15.074000000000002
    - type: recall_at_10
      value: 33.031
    - type: recall_at_100
      value: 53.954
    - type: recall_at_1000
      value: 77.631
    - type: recall_at_3
      value: 23.253
    - type: recall_at_5
      value: 27.218999999999998
    - type: map_at_1
      value: 21.04
    - type: map_at_10
      value: 28.226000000000003
    - type: map_at_100
      value: 29.337999999999997
    - type: map_at_1000
      value: 29.448999999999998
    - type: map_at_3
      value: 25.759
    - type: map_at_5
      value: 27.226
    - type: mrr_at_1
      value: 24.067
    - type: mrr_at_10
      value: 31.646
    - type: mrr_at_100
      value: 32.592999999999996
    - type: mrr_at_1000
      value: 32.668
    - type: mrr_at_3
      value: 29.26
    - type: mrr_at_5
      value: 30.725
    - type: ndcg_at_1
      value: 24.067
    - type: ndcg_at_10
      value: 32.789
    - type: ndcg_at_100
      value: 38.253
    - type: ndcg_at_1000
      value: 40.961
    - type: ndcg_at_3
      value: 28.189999999999998
    - type: ndcg_at_5
      value: 30.557000000000002
    - type: precision_at_1
      value: 24.067
    - type: precision_at_10
      value: 5.532
    - type: precision_at_100
      value: 0.928
    - type: precision_at_1000
      value: 0.128
    - type: precision_at_3
      value: 12.5
    - type: precision_at_5
      value: 9.16
    - type: recall_at_1
      value: 21.04
    - type: recall_at_10
      value: 43.167
    - type: recall_at_100
      value: 67.569
    - type: recall_at_1000
      value: 86.817
    - type: recall_at_3
      value: 31.178
    - type: recall_at_5
      value: 36.730000000000004
    - type: map_at_1
      value: 21.439
    - type: map_at_10
      value: 28.531000000000002
    - type: map_at_100
      value: 29.953999999999997
    - type: map_at_1000
      value: 30.171
    - type: map_at_3
      value: 26.546999999999997
    - type: map_at_5
      value: 27.71
    - type: mrr_at_1
      value: 26.087
    - type: mrr_at_10
      value: 32.635
    - type: mrr_at_100
      value: 33.629999999999995
    - type: mrr_at_1000
      value: 33.71
    - type: mrr_at_3
      value: 30.731
    - type: mrr_at_5
      value: 31.807999999999996
    - type: ndcg_at_1
      value: 26.087
    - type: ndcg_at_10
      value: 32.975
    - type: ndcg_at_100
      value: 38.853
    - type: ndcg_at_1000
      value: 42.158
    - type: ndcg_at_3
      value: 29.894
    - type: ndcg_at_5
      value: 31.397000000000002
    - type: precision_at_1
      value: 26.087
    - type: precision_at_10
      value: 6.2059999999999995
    - type: precision_at_100
      value: 1.298
    - type: precision_at_1000
      value: 0.22200000000000003
    - type: precision_at_3
      value: 14.097000000000001
    - type: precision_at_5
      value: 9.959999999999999
    - type: recall_at_1
      value: 21.439
    - type: recall_at_10
      value: 40.519
    - type: recall_at_100
      value: 68.073
    - type: recall_at_1000
      value: 89.513
    - type: recall_at_3
      value: 31.513
    - type: recall_at_5
      value: 35.702
    - type: map_at_1
      value: 18.983
    - type: map_at_10
      value: 24.898
    - type: map_at_100
      value: 25.836
    - type: map_at_1000
      value: 25.934
    - type: map_at_3
      value: 22.467000000000002
    - type: map_at_5
      value: 24.019
    - type: mrr_at_1
      value: 20.333000000000002
    - type: mrr_at_10
      value: 26.555
    - type: mrr_at_100
      value: 27.369
    - type: mrr_at_1000
      value: 27.448
    - type: mrr_at_3
      value: 24.091
    - type: mrr_at_5
      value: 25.662000000000003
    - type: ndcg_at_1
      value: 20.333000000000002
    - type: ndcg_at_10
      value: 28.834
    - type: ndcg_at_100
      value: 33.722
    - type: ndcg_at_1000
      value: 36.475
    - type: ndcg_at_3
      value: 24.08
    - type: ndcg_at_5
      value: 26.732
    - type: precision_at_1
      value: 20.333000000000002
    - type: precision_at_10
      value: 4.603
    - type: precision_at_100
      value: 0.771
    - type: precision_at_1000
      value: 0.11100000000000002
    - type: precision_at_3
      value: 9.982000000000001
    - type: precision_at_5
      value: 7.6160000000000005
    - type: recall_at_1
      value: 18.983
    - type: recall_at_10
      value: 39.35
    - type: recall_at_100
      value: 62.559
    - type: recall_at_1000
      value: 83.623
    - type: recall_at_3
      value: 26.799
    - type: recall_at_5
      value: 32.997
  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: climate-fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 10.621
    - type: map_at_10
      value: 17.298
    - type: map_at_100
      value: 18.983
    - type: map_at_1000
      value: 19.182
    - type: map_at_3
      value: 14.552999999999999
    - type: map_at_5
      value: 15.912
    - type: mrr_at_1
      value: 23.453
    - type: mrr_at_10
      value: 33.932
    - type: mrr_at_100
      value: 34.891
    - type: mrr_at_1000
      value: 34.943000000000005
    - type: mrr_at_3
      value: 30.770999999999997
    - type: mrr_at_5
      value: 32.556000000000004
    - type: ndcg_at_1
      value: 23.453
    - type: ndcg_at_10
      value: 24.771
    - type: ndcg_at_100
      value: 31.738
    - type: ndcg_at_1000
      value: 35.419
    - type: ndcg_at_3
      value: 20.22
    - type: ndcg_at_5
      value: 21.698999999999998
    - type: precision_at_1
      value: 23.453
    - type: precision_at_10
      value: 7.785
    - type: precision_at_100
      value: 1.5270000000000001
    - type: precision_at_1000
      value: 0.22
    - type: precision_at_3
      value: 14.962
    - type: precision_at_5
      value: 11.401
    - type: recall_at_1
      value: 10.621
    - type: recall_at_10
      value: 29.726000000000003
    - type: recall_at_100
      value: 53.996
    - type: recall_at_1000
      value: 74.878
    - type: recall_at_3
      value: 18.572
    - type: recall_at_5
      value: 22.994999999999997
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia
      type: dbpedia-entity
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 6.819
    - type: map_at_10
      value: 14.188
    - type: map_at_100
      value: 19.627
    - type: map_at_1000
      value: 20.757
    - type: map_at_3
      value: 10.352
    - type: map_at_5
      value: 12.096
    - type: mrr_at_1
      value: 54.25
    - type: mrr_at_10
      value: 63.798
    - type: mrr_at_100
      value: 64.25
    - type: mrr_at_1000
      value: 64.268
    - type: mrr_at_3
      value: 61.667
    - type: mrr_at_5
      value: 63.153999999999996
    - type: ndcg_at_1
      value: 39.5
    - type: ndcg_at_10
      value: 31.064999999999998
    - type: ndcg_at_100
      value: 34.701
    - type: ndcg_at_1000
      value: 41.687000000000005
    - type: ndcg_at_3
      value: 34.455999999999996
    - type: ndcg_at_5
      value: 32.919
    - type: precision_at_1
      value: 54.25
    - type: precision_at_10
      value: 25.4
    - type: precision_at_100
      value: 7.79
    - type: precision_at_1000
      value: 1.577
    - type: precision_at_3
      value: 39.333
    - type: precision_at_5
      value: 33.6
    - type: recall_at_1
      value: 6.819
    - type: recall_at_10
      value: 19.134
    - type: recall_at_100
      value: 41.191
    - type: recall_at_1000
      value: 64.699
    - type: recall_at_3
      value: 11.637
    - type: recall_at_5
      value: 14.807
  - task:
      type: Classification
    dataset:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 42.474999999999994
    - type: f1
      value: 37.79154895614037
  - task:
      type: Retrieval
    dataset:
      name: MTEB FEVER
      type: fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 53.187
    - type: map_at_10
      value: 64.031
    - type: map_at_100
      value: 64.507
    - type: map_at_1000
      value: 64.526
    - type: map_at_3
      value: 61.926
    - type: map_at_5
      value: 63.278999999999996
    - type: mrr_at_1
      value: 57.396
    - type: mrr_at_10
      value: 68.296
    - type: mrr_at_100
      value: 68.679
    - type: mrr_at_1000
      value: 68.688
    - type: mrr_at_3
      value: 66.289
    - type: mrr_at_5
      value: 67.593
    - type: ndcg_at_1
      value: 57.396
    - type: ndcg_at_10
      value: 69.64
    - type: ndcg_at_100
      value: 71.75399999999999
    - type: ndcg_at_1000
      value: 72.179
    - type: ndcg_at_3
      value: 65.66199999999999
    - type: ndcg_at_5
      value: 67.932
    - type: precision_at_1
      value: 57.396
    - type: precision_at_10
      value: 9.073
    - type: precision_at_100
      value: 1.024
    - type: precision_at_1000
      value: 0.107
    - type: precision_at_3
      value: 26.133
    - type: precision_at_5
      value: 16.943
    - type: recall_at_1
      value: 53.187
    - type: recall_at_10
      value: 82.839
    - type: recall_at_100
      value: 92.231
    - type: recall_at_1000
      value: 95.249
    - type: recall_at_3
      value: 72.077
    - type: recall_at_5
      value: 77.667
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA2018
      type: fiqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 10.957
    - type: map_at_10
      value: 18.427
    - type: map_at_100
      value: 19.885
    - type: map_at_1000
      value: 20.088
    - type: map_at_3
      value: 15.709000000000001
    - type: map_at_5
      value: 17.153
    - type: mrr_at_1
      value: 22.377
    - type: mrr_at_10
      value: 30.076999999999998
    - type: mrr_at_100
      value: 31.233
    - type: mrr_at_1000
      value: 31.311
    - type: mrr_at_3
      value: 27.521
    - type: mrr_at_5
      value: 29.025000000000002
    - type: ndcg_at_1
      value: 22.377
    - type: ndcg_at_10
      value: 24.367
    - type: ndcg_at_100
      value: 31.04
    - type: ndcg_at_1000
      value: 35.106
    - type: ndcg_at_3
      value: 21.051000000000002
    - type: ndcg_at_5
      value: 22.231
    - type: precision_at_1
      value: 22.377
    - type: precision_at_10
      value: 7.005999999999999
    - type: precision_at_100
      value: 1.3599999999999999
    - type: precision_at_1000
      value: 0.208
    - type: precision_at_3
      value: 13.991999999999999
    - type: precision_at_5
      value: 10.833
    - type: recall_at_1
      value: 10.957
    - type: recall_at_10
      value: 30.274
    - type: recall_at_100
      value: 55.982
    - type: recall_at_1000
      value: 80.757
    - type: recall_at_3
      value: 19.55
    - type: recall_at_5
      value: 24.105999999999998
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA
      type: hotpotqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 29.526999999999997
    - type: map_at_10
      value: 40.714
    - type: map_at_100
      value: 41.655
    - type: map_at_1000
      value: 41.744
    - type: map_at_3
      value: 38.171
    - type: map_at_5
      value: 39.646
    - type: mrr_at_1
      value: 59.055
    - type: mrr_at_10
      value: 66.411
    - type: mrr_at_100
      value: 66.85900000000001
    - type: mrr_at_1000
      value: 66.88300000000001
    - type: mrr_at_3
      value: 64.846
    - type: mrr_at_5
      value: 65.824
    - type: ndcg_at_1
      value: 59.055
    - type: ndcg_at_10
      value: 49.732
    - type: ndcg_at_100
      value: 53.441
    - type: ndcg_at_1000
      value: 55.354000000000006
    - type: ndcg_at_3
      value: 45.551
    - type: ndcg_at_5
      value: 47.719
    - type: precision_at_1
      value: 59.055
    - type: precision_at_10
      value: 10.366
    - type: precision_at_100
      value: 1.328
    - type: precision_at_1000
      value: 0.158
    - type: precision_at_3
      value: 28.322999999999997
    - type: precision_at_5
      value: 18.709
    - type: recall_at_1
      value: 29.526999999999997
    - type: recall_at_10
      value: 51.83
    - type: recall_at_100
      value: 66.42099999999999
    - type: recall_at_1000
      value: 79.176
    - type: recall_at_3
      value: 42.485
    - type: recall_at_5
      value: 46.772000000000006
  - task:
      type: Classification
    dataset:
      name: MTEB ImdbClassification
      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 70.69959999999999
    - type: ap
      value: 64.95539314492567
    - type: f1
      value: 70.5554935943308
  - task:
      type: Retrieval
    dataset:
      name: MTEB MSMARCO
      type: msmarco
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 13.153
    - type: map_at_10
      value: 22.277
    - type: map_at_100
      value: 23.462
    - type: map_at_1000
      value: 23.546
    - type: map_at_3
      value: 19.026
    - type: map_at_5
      value: 20.825
    - type: mrr_at_1
      value: 13.539000000000001
    - type: mrr_at_10
      value: 22.753
    - type: mrr_at_100
      value: 23.906
    - type: mrr_at_1000
      value: 23.982999999999997
    - type: mrr_at_3
      value: 19.484
    - type: mrr_at_5
      value: 21.306
    - type: ndcg_at_1
      value: 13.553
    - type: ndcg_at_10
      value: 27.848
    - type: ndcg_at_100
      value: 33.900999999999996
    - type: ndcg_at_1000
      value: 36.155
    - type: ndcg_at_3
      value: 21.116
    - type: ndcg_at_5
      value: 24.349999999999998
    - type: precision_at_1
      value: 13.553
    - type: precision_at_10
      value: 4.695
    - type: precision_at_100
      value: 0.7779999999999999
    - type: precision_at_1000
      value: 0.097
    - type: precision_at_3
      value: 9.207
    - type: precision_at_5
      value: 7.155
    - type: recall_at_1
      value: 13.153
    - type: recall_at_10
      value: 45.205
    - type: recall_at_100
      value: 73.978
    - type: recall_at_1000
      value: 91.541
    - type: recall_at_3
      value: 26.735
    - type: recall_at_5
      value: 34.493
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 90.2530779753762
    - type: f1
      value: 89.59402328284126
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 67.95029639762883
    - type: f1
      value: 48.99988836758662
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 67.77740416946874
    - type: f1
      value: 66.21341120969817
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 73.03631472763955
    - type: f1
      value: 72.5779336237941
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 31.98182669158824
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 29.259462874407582
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 31.29342377286548
    - type: mrr
      value: 32.32805799117226
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: nfcorpus
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 4.692
    - type: map_at_10
      value: 10.559000000000001
    - type: map_at_100
      value: 13.665
    - type: map_at_1000
      value: 15.082
    - type: map_at_3
      value: 7.68
    - type: map_at_5
      value: 8.844000000000001
    - type: mrr_at_1
      value: 38.7
    - type: mrr_at_10
      value: 47.864000000000004
    - type: mrr_at_100
      value: 48.583999999999996
    - type: mrr_at_1000
      value: 48.636
    - type: mrr_at_3
      value: 45.975
    - type: mrr_at_5
      value: 47.074
    - type: ndcg_at_1
      value: 36.378
    - type: ndcg_at_10
      value: 30.038999999999998
    - type: ndcg_at_100
      value: 28.226000000000003
    - type: ndcg_at_1000
      value: 36.958
    - type: ndcg_at_3
      value: 33.469
    - type: ndcg_at_5
      value: 32.096999999999994
    - type: precision_at_1
      value: 38.080000000000005
    - type: precision_at_10
      value: 22.941
    - type: precision_at_100
      value: 7.632
    - type: precision_at_1000
      value: 2.0420000000000003
    - type: precision_at_3
      value: 31.579
    - type: precision_at_5
      value: 28.235
    - type: recall_at_1
      value: 4.692
    - type: recall_at_10
      value: 14.496
    - type: recall_at_100
      value: 29.69
    - type: recall_at_1000
      value: 61.229
    - type: recall_at_3
      value: 8.871
    - type: recall_at_5
      value: 10.825999999999999
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ
      type: nq
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 13.120000000000001
    - type: map_at_10
      value: 24.092
    - type: map_at_100
      value: 25.485999999999997
    - type: map_at_1000
      value: 25.557999999999996
    - type: map_at_3
      value: 20.076
    - type: map_at_5
      value: 22.368
    - type: mrr_at_1
      value: 15.093
    - type: mrr_at_10
      value: 26.142
    - type: mrr_at_100
      value: 27.301
    - type: mrr_at_1000
      value: 27.357
    - type: mrr_at_3
      value: 22.364
    - type: mrr_at_5
      value: 24.564
    - type: ndcg_at_1
      value: 15.093
    - type: ndcg_at_10
      value: 30.734
    - type: ndcg_at_100
      value: 37.147999999999996
    - type: ndcg_at_1000
      value: 38.997
    - type: ndcg_at_3
      value: 22.82
    - type: ndcg_at_5
      value: 26.806
    - type: precision_at_1
      value: 15.093
    - type: precision_at_10
      value: 5.863
    - type: precision_at_100
      value: 0.942
    - type: precision_at_1000
      value: 0.11199999999999999
    - type: precision_at_3
      value: 11.047
    - type: precision_at_5
      value: 8.863999999999999
    - type: recall_at_1
      value: 13.120000000000001
    - type: recall_at_10
      value: 49.189
    - type: recall_at_100
      value: 78.032
    - type: recall_at_1000
      value: 92.034
    - type: recall_at_3
      value: 28.483000000000004
    - type: recall_at_5
      value: 37.756
  - task:
      type: Retrieval
    dataset:
      name: MTEB QuoraRetrieval
      type: quora
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 67.765
    - type: map_at_10
      value: 81.069
    - type: map_at_100
      value: 81.757
    - type: map_at_1000
      value: 81.782
    - type: map_at_3
      value: 78.148
    - type: map_at_5
      value: 79.95400000000001
    - type: mrr_at_1
      value: 77.8
    - type: mrr_at_10
      value: 84.639
    - type: mrr_at_100
      value: 84.789
    - type: mrr_at_1000
      value: 84.79100000000001
    - type: mrr_at_3
      value: 83.467
    - type: mrr_at_5
      value: 84.251
    - type: ndcg_at_1
      value: 77.82
    - type: ndcg_at_10
      value: 85.286
    - type: ndcg_at_100
      value: 86.86500000000001
    - type: ndcg_at_1000
      value: 87.062
    - type: ndcg_at_3
      value: 82.116
    - type: ndcg_at_5
      value: 83.811
    - type: precision_at_1
      value: 77.82
    - type: precision_at_10
      value: 12.867999999999999
    - type: precision_at_100
      value: 1.498
    - type: precision_at_1000
      value: 0.156
    - type: precision_at_3
      value: 35.723
    - type: precision_at_5
      value: 23.52
    - type: recall_at_1
      value: 67.765
    - type: recall_at_10
      value: 93.381
    - type: recall_at_100
      value: 98.901
    - type: recall_at_1000
      value: 99.864
    - type: recall_at_3
      value: 84.301
    - type: recall_at_5
      value: 89.049
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 45.27190981742137
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 54.47444004585028
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS
      type: scidocs
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 4.213
    - type: map_at_10
      value: 10.166
    - type: map_at_100
      value: 11.987
    - type: map_at_1000
      value: 12.285
    - type: map_at_3
      value: 7.538
    - type: map_at_5
      value: 8.606
    - type: mrr_at_1
      value: 20.8
    - type: mrr_at_10
      value: 30.066
    - type: mrr_at_100
      value: 31.290000000000003
    - type: mrr_at_1000
      value: 31.357000000000003
    - type: mrr_at_3
      value: 27.083000000000002
    - type: mrr_at_5
      value: 28.748
    - type: ndcg_at_1
      value: 20.8
    - type: ndcg_at_10
      value: 17.258000000000003
    - type: ndcg_at_100
      value: 24.801000000000002
    - type: ndcg_at_1000
      value: 30.348999999999997
    - type: ndcg_at_3
      value: 16.719
    - type: ndcg_at_5
      value: 14.145
    - type: precision_at_1
      value: 20.8
    - type: precision_at_10
      value: 8.88
    - type: precision_at_100
      value: 1.9789999999999999
    - type: precision_at_1000
      value: 0.332
    - type: precision_at_3
      value: 15.5
    - type: precision_at_5
      value: 12.1
    - type: recall_at_1
      value: 4.213
    - type: recall_at_10
      value: 17.983
    - type: recall_at_100
      value: 40.167
    - type: recall_at_1000
      value: 67.43
    - type: recall_at_3
      value: 9.433
    - type: recall_at_5
      value: 12.267999999999999
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 80.36742239848913
    - type: cos_sim_spearman
      value: 72.39470010828755
    - type: euclidean_pearson
      value: 77.26919895870947
    - type: euclidean_spearman
      value: 72.26534999077315
    - type: manhattan_pearson
      value: 77.04066349814258
    - type: manhattan_spearman
      value: 72.0072248699278
  - task:
      type: STS
    dataset:
      name: MTEB STS12
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 80.26991474037257
    - type: cos_sim_spearman
      value: 71.90287122017716
    - type: euclidean_pearson
      value: 76.68006075912453
    - type: euclidean_spearman
      value: 71.69301858764365
    - type: manhattan_pearson
      value: 76.72277285842371
    - type: manhattan_spearman
      value: 71.73265239703795
  - task:
      type: STS
    dataset:
      name: MTEB STS13
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 79.74371413317881
    - type: cos_sim_spearman
      value: 80.9279612820358
    - type: euclidean_pearson
      value: 80.6417435294782
    - type: euclidean_spearman
      value: 81.17460969254459
    - type: manhattan_pearson
      value: 80.51820155178402
    - type: manhattan_spearman
      value: 81.08028700017084
  - task:
      type: STS
    dataset:
      name: MTEB STS14
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 80.37085777051112
    - type: cos_sim_spearman
      value: 76.60308382518285
    - type: euclidean_pearson
      value: 79.59684787227351
    - type: euclidean_spearman
      value: 76.8769048249242
    - type: manhattan_pearson
      value: 79.55617632538295
    - type: manhattan_spearman
      value: 76.90186497973124
  - task:
      type: STS
    dataset:
      name: MTEB STS15
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 83.99513105301321
    - type: cos_sim_spearman
      value: 84.92034548133665
    - type: euclidean_pearson
      value: 84.70872540095195
    - type: euclidean_spearman
      value: 85.14591726040749
    - type: manhattan_pearson
      value: 84.65707417430595
    - type: manhattan_spearman
      value: 85.10407163865375
  - task:
      type: STS
    dataset:
      name: MTEB STS16
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 79.40758449150897
    - type: cos_sim_spearman
      value: 80.71692246880549
    - type: euclidean_pearson
      value: 80.51658552062683
    - type: euclidean_spearman
      value: 80.87118389043233
    - type: manhattan_pearson
      value: 80.41534690825016
    - type: manhattan_spearman
      value: 80.73925282537256
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (en-en)
      type: mteb/sts17-crosslingual-sts
      config: en-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 84.93617076910748
    - type: cos_sim_spearman
      value: 85.61118538966805
    - type: euclidean_pearson
      value: 85.56187558635287
    - type: euclidean_spearman
      value: 85.21910090757267
    - type: manhattan_pearson
      value: 85.29916699037645
    - type: manhattan_spearman
      value: 84.96820527868671
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      config: en
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 64.22294088543077
    - type: cos_sim_spearman
      value: 65.89748502901078
    - type: euclidean_pearson
      value: 66.15637850660805
    - type: euclidean_spearman
      value: 65.86095841381278
    - type: manhattan_pearson
      value: 66.80966197857856
    - type: manhattan_spearman
      value: 66.48325202219692
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 81.75298158703048
    - type: cos_sim_spearman
      value: 81.32168373072322
    - type: euclidean_pearson
      value: 82.3251793712207
    - type: euclidean_spearman
      value: 81.31655163330606
    - type: manhattan_pearson
      value: 82.14136865023298
    - type: manhattan_spearman
      value: 81.13410964028606
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 78.77937068780793
    - type: mrr
      value: 93.334709952357
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: scifact
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 50.705999999999996
    - type: map_at_10
      value: 60.699999999999996
    - type: map_at_100
      value: 61.256
    - type: map_at_1000
      value: 61.285000000000004
    - type: map_at_3
      value: 57.633
    - type: map_at_5
      value: 59.648
    - type: mrr_at_1
      value: 53.0
    - type: mrr_at_10
      value: 61.717999999999996
    - type: mrr_at_100
      value: 62.165000000000006
    - type: mrr_at_1000
      value: 62.190999999999995
    - type: mrr_at_3
      value: 59.389
    - type: mrr_at_5
      value: 60.922
    - type: ndcg_at_1
      value: 53.0
    - type: ndcg_at_10
      value: 65.413
    - type: ndcg_at_100
      value: 68.089
    - type: ndcg_at_1000
      value: 69.01899999999999
    - type: ndcg_at_3
      value: 60.327
    - type: ndcg_at_5
      value: 63.263999999999996
    - type: precision_at_1
      value: 53.0
    - type: precision_at_10
      value: 8.933
    - type: precision_at_100
      value: 1.04
    - type: precision_at_1000
      value: 0.11199999999999999
    - type: precision_at_3
      value: 23.778
    - type: precision_at_5
      value: 16.2
    - type: recall_at_1
      value: 50.705999999999996
    - type: recall_at_10
      value: 78.633
    - type: recall_at_100
      value: 91.333
    - type: recall_at_1000
      value: 99.0
    - type: recall_at_3
      value: 65.328
    - type: recall_at_5
      value: 72.583
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.82178217821782
    - type: cos_sim_ap
      value: 95.30078788098801
    - type: cos_sim_f1
      value: 91.11549851924975
    - type: cos_sim_precision
      value: 89.96101364522417
    - type: cos_sim_recall
      value: 92.30000000000001
    - type: dot_accuracy
      value: 99.74851485148515
    - type: dot_ap
      value: 93.12383012680787
    - type: dot_f1
      value: 87.17171717171716
    - type: dot_precision
      value: 88.06122448979592
    - type: dot_recall
      value: 86.3
    - type: euclidean_accuracy
      value: 99.82673267326733
    - type: euclidean_ap
      value: 95.29507269622621
    - type: euclidean_f1
      value: 91.3151364764268
    - type: euclidean_precision
      value: 90.64039408866995
    - type: euclidean_recall
      value: 92.0
    - type: manhattan_accuracy
      value: 99.82178217821782
    - type: manhattan_ap
      value: 95.34300712110257
    - type: manhattan_f1
      value: 91.05367793240556
    - type: manhattan_precision
      value: 90.51383399209486
    - type: manhattan_recall
      value: 91.60000000000001
    - type: max_accuracy
      value: 99.82673267326733
    - type: max_ap
      value: 95.34300712110257
    - type: max_f1
      value: 91.3151364764268
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 53.10993894014712
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 34.67216071080345
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 48.96344255085851
    - type: mrr
      value: 49.816123419064596
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 30.580410074992177
    - type: cos_sim_spearman
      value: 31.155995112739966
    - type: dot_pearson
      value: 31.112094423048998
    - type: dot_spearman
      value: 31.29974829801922
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID
      type: trec-covid
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.17700000000000002
    - type: map_at_10
      value: 1.22
    - type: map_at_100
      value: 6.2170000000000005
    - type: map_at_1000
      value: 15.406
    - type: map_at_3
      value: 0.483
    - type: map_at_5
      value: 0.729
    - type: mrr_at_1
      value: 64.0
    - type: mrr_at_10
      value: 76.333
    - type: mrr_at_100
      value: 76.47
    - type: mrr_at_1000
      value: 76.47
    - type: mrr_at_3
      value: 75.0
    - type: mrr_at_5
      value: 76.0
    - type: ndcg_at_1
      value: 59.0
    - type: ndcg_at_10
      value: 52.62
    - type: ndcg_at_100
      value: 39.932
    - type: ndcg_at_1000
      value: 37.317
    - type: ndcg_at_3
      value: 57.123000000000005
    - type: ndcg_at_5
      value: 56.376000000000005
    - type: precision_at_1
      value: 64.0
    - type: precision_at_10
      value: 55.800000000000004
    - type: precision_at_100
      value: 41.04
    - type: precision_at_1000
      value: 17.124
    - type: precision_at_3
      value: 63.333
    - type: precision_at_5
      value: 62.0
    - type: recall_at_1
      value: 0.17700000000000002
    - type: recall_at_10
      value: 1.46
    - type: recall_at_100
      value: 9.472999999999999
    - type: recall_at_1000
      value: 35.661
    - type: recall_at_3
      value: 0.527
    - type: recall_at_5
      value: 0.8250000000000001
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020
      type: webis-touche2020
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 1.539
    - type: map_at_10
      value: 7.178
    - type: map_at_100
      value: 12.543000000000001
    - type: map_at_1000
      value: 14.126
    - type: map_at_3
      value: 3.09
    - type: map_at_5
      value: 5.008
    - type: mrr_at_1
      value: 18.367
    - type: mrr_at_10
      value: 32.933
    - type: mrr_at_100
      value: 34.176
    - type: mrr_at_1000
      value: 34.176
    - type: mrr_at_3
      value: 27.551
    - type: mrr_at_5
      value: 30.714000000000002
    - type: ndcg_at_1
      value: 15.306000000000001
    - type: ndcg_at_10
      value: 18.343
    - type: ndcg_at_100
      value: 30.076000000000004
    - type: ndcg_at_1000
      value: 42.266999999999996
    - type: ndcg_at_3
      value: 17.233999999999998
    - type: ndcg_at_5
      value: 18.677
    - type: precision_at_1
      value: 18.367
    - type: precision_at_10
      value: 18.367
    - type: precision_at_100
      value: 6.837
    - type: precision_at_1000
      value: 1.467
    - type: precision_at_3
      value: 19.048000000000002
    - type: precision_at_5
      value: 21.224
    - type: recall_at_1
      value: 1.539
    - type: recall_at_10
      value: 13.289000000000001
    - type: recall_at_100
      value: 42.480000000000004
    - type: recall_at_1000
      value: 79.463
    - type: recall_at_3
      value: 4.202999999999999
    - type: recall_at_5
      value: 7.9030000000000005
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 69.2056
    - type: ap
      value: 13.564165903349778
    - type: f1
      value: 53.303385089202656
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 56.71477079796264
    - type: f1
      value: 57.01563439439609
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 39.373040570976514
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 83.44757703999524
    - type: cos_sim_ap
      value: 65.78689843625949
    - type: cos_sim_f1
      value: 62.25549384206713
    - type: cos_sim_precision
      value: 57.39091718610864
    - type: cos_sim_recall
      value: 68.02110817941951
    - type: dot_accuracy
      value: 81.3971508612982
    - type: dot_ap
      value: 58.42933051967154
    - type: dot_f1
      value: 57.85580214198962
    - type: dot_precision
      value: 49.74368710841086
    - type: dot_recall
      value: 69.12928759894459
    - type: euclidean_accuracy
      value: 83.54294569946951
    - type: euclidean_ap
      value: 66.10612585693795
    - type: euclidean_f1
      value: 62.66666666666667
    - type: euclidean_precision
      value: 58.88631090487239
    - type: euclidean_recall
      value: 66.96569920844327
    - type: manhattan_accuracy
      value: 83.43565595756095
    - type: manhattan_ap
      value: 65.88532290329134
    - type: manhattan_f1
      value: 62.58408721874276
    - type: manhattan_precision
      value: 55.836092715231786
    - type: manhattan_recall
      value: 71.18733509234828
    - type: max_accuracy
      value: 83.54294569946951
    - type: max_ap
      value: 66.10612585693795
    - type: max_f1
      value: 62.66666666666667
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 88.02344083517679
    - type: cos_sim_ap
      value: 84.21589190889944
    - type: cos_sim_f1
      value: 76.36723039754007
    - type: cos_sim_precision
      value: 72.79134682484299
    - type: cos_sim_recall
      value: 80.31259624268556
    - type: dot_accuracy
      value: 87.43353902278108
    - type: dot_ap
      value: 82.08962394120071
    - type: dot_f1
      value: 74.97709923664122
    - type: dot_precision
      value: 74.34150772025431
    - type: dot_recall
      value: 75.62365260240222
    - type: euclidean_accuracy
      value: 87.97686963946133
    - type: euclidean_ap
      value: 84.20578083922416
    - type: euclidean_f1
      value: 76.4299182903834
    - type: euclidean_precision
      value: 73.51874244256348
    - type: euclidean_recall
      value: 79.58115183246073
    - type: manhattan_accuracy
      value: 88.00209570380719
    - type: manhattan_ap
      value: 84.14700304263556
    - type: manhattan_f1
      value: 76.36429345861944
    - type: manhattan_precision
      value: 71.95886119057349
    - type: manhattan_recall
      value: 81.34431783184478
    - type: max_accuracy
      value: 88.02344083517679
    - type: max_ap
      value: 84.21589190889944
    - type: max_f1
      value: 76.4299182903834
---

# bge-micro

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
It is distilled from [bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5/blob/main/config.json), with 1/4 the non-embedding parameters.
It has 1/2 the parameters of the smallest commonly-used embedding model, all-MiniLM-L6-v2, with similar performance.

<!--- Describe your model here -->

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```



## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```



## Evaluation Results

<!--- Describe how your model was evaluated -->

For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})



## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```

## Citing & Authors

<!--- Describe where people can find more information -->