bge-micro-v2 / README.md
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metadata
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  - mteb
model-index:
  - name: bge_micro
    results:
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_counterfactual
          name: MTEB AmazonCounterfactualClassification (en)
          config: en
          split: test
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
        metrics:
          - type: accuracy
            value: 67.76119402985074
          - type: ap
            value: 29.637849284211114
          - type: f1
            value: 61.31181187111905
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_polarity
          name: MTEB AmazonPolarityClassification
          config: default
          split: test
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
        metrics:
          - type: accuracy
            value: 79.7547
          - type: ap
            value: 74.21401629809145
          - type: f1
            value: 79.65319615433783
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (en)
          config: en
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 37.452000000000005
          - type: f1
            value: 37.0245198854966
      - task:
          type: Retrieval
        dataset:
          type: arguana
          name: MTEB ArguAna
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 31.152
          - type: map_at_10
            value: 46.702
          - type: map_at_100
            value: 47.563
          - type: map_at_1000
            value: 47.567
          - type: map_at_3
            value: 42.058
          - type: map_at_5
            value: 44.608
          - type: mrr_at_1
            value: 32.006
          - type: mrr_at_10
            value: 47.064
          - type: mrr_at_100
            value: 47.910000000000004
          - type: mrr_at_1000
            value: 47.915
          - type: mrr_at_3
            value: 42.283
          - type: mrr_at_5
            value: 44.968
          - type: ndcg_at_1
            value: 31.152
          - type: ndcg_at_10
            value: 55.308
          - type: ndcg_at_100
            value: 58.965
          - type: ndcg_at_1000
            value: 59.067
          - type: ndcg_at_3
            value: 45.698
          - type: ndcg_at_5
            value: 50.296
          - type: precision_at_1
            value: 31.152
          - type: precision_at_10
            value: 8.279
          - type: precision_at_100
            value: 0.987
          - type: precision_at_1000
            value: 0.1
          - type: precision_at_3
            value: 18.753
          - type: precision_at_5
            value: 13.485
          - type: recall_at_1
            value: 31.152
          - type: recall_at_10
            value: 82.788
          - type: recall_at_100
            value: 98.72
          - type: recall_at_1000
            value: 99.502
          - type: recall_at_3
            value: 56.259
          - type: recall_at_5
            value: 67.425
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-p2p
          name: MTEB ArxivClusteringP2P
          config: default
          split: test
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
        metrics:
          - type: v_measure
            value: 44.52692241938116
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-s2s
          name: MTEB ArxivClusteringS2S
          config: default
          split: test
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
        metrics:
          - type: v_measure
            value: 33.245710292773595
      - task:
          type: Reranking
        dataset:
          type: mteb/askubuntudupquestions-reranking
          name: MTEB AskUbuntuDupQuestions
          config: default
          split: test
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
        metrics:
          - type: map
            value: 58.08493637155168
          - type: mrr
            value: 71.94378490084861
      - task:
          type: STS
        dataset:
          type: mteb/biosses-sts
          name: MTEB BIOSSES
          config: default
          split: test
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
        metrics:
          - type: cos_sim_pearson
            value: 84.1602804378326
          - type: cos_sim_spearman
            value: 82.92478106365587
          - type: euclidean_pearson
            value: 82.27930167277077
          - type: euclidean_spearman
            value: 82.18560759458093
          - type: manhattan_pearson
            value: 82.34277425888187
          - type: manhattan_spearman
            value: 81.72776583704467
      - task:
          type: Classification
        dataset:
          type: mteb/banking77
          name: MTEB Banking77Classification
          config: default
          split: test
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
        metrics:
          - type: accuracy
            value: 81.17207792207792
          - type: f1
            value: 81.09893836310513
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-p2p
          name: MTEB BiorxivClusteringP2P
          config: default
          split: test
          revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
        metrics:
          - type: v_measure
            value: 36.109308463095516
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-s2s
          name: MTEB BiorxivClusteringS2S
          config: default
          split: test
          revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
        metrics:
          - type: v_measure
            value: 28.06048212317168
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackAndroidRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 28.233999999999998
          - type: map_at_10
            value: 38.092999999999996
          - type: map_at_100
            value: 39.473
          - type: map_at_1000
            value: 39.614
          - type: map_at_3
            value: 34.839
          - type: map_at_5
            value: 36.523
          - type: mrr_at_1
            value: 35.193000000000005
          - type: mrr_at_10
            value: 44.089
          - type: mrr_at_100
            value: 44.927
          - type: mrr_at_1000
            value: 44.988
          - type: mrr_at_3
            value: 41.559000000000005
          - type: mrr_at_5
            value: 43.162
          - type: ndcg_at_1
            value: 35.193000000000005
          - type: ndcg_at_10
            value: 44.04
          - type: ndcg_at_100
            value: 49.262
          - type: ndcg_at_1000
            value: 51.847
          - type: ndcg_at_3
            value: 39.248
          - type: ndcg_at_5
            value: 41.298
          - type: precision_at_1
            value: 35.193000000000005
          - type: precision_at_10
            value: 8.555
          - type: precision_at_100
            value: 1.3820000000000001
          - type: precision_at_1000
            value: 0.189
          - type: precision_at_3
            value: 19.123
          - type: precision_at_5
            value: 13.648
          - type: recall_at_1
            value: 28.233999999999998
          - type: recall_at_10
            value: 55.094
          - type: recall_at_100
            value: 76.85300000000001
          - type: recall_at_1000
            value: 94.163
          - type: recall_at_3
            value: 40.782000000000004
          - type: recall_at_5
            value: 46.796
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackEnglishRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 21.538
          - type: map_at_10
            value: 28.449
          - type: map_at_100
            value: 29.471000000000004
          - type: map_at_1000
            value: 29.599999999999998
          - type: map_at_3
            value: 26.371
          - type: map_at_5
            value: 27.58
          - type: mrr_at_1
            value: 26.815
          - type: mrr_at_10
            value: 33.331
          - type: mrr_at_100
            value: 34.114
          - type: mrr_at_1000
            value: 34.182
          - type: mrr_at_3
            value: 31.561
          - type: mrr_at_5
            value: 32.608
          - type: ndcg_at_1
            value: 26.815
          - type: ndcg_at_10
            value: 32.67
          - type: ndcg_at_100
            value: 37.039
          - type: ndcg_at_1000
            value: 39.769
          - type: ndcg_at_3
            value: 29.523
          - type: ndcg_at_5
            value: 31.048
          - type: precision_at_1
            value: 26.815
          - type: precision_at_10
            value: 5.955
          - type: precision_at_100
            value: 1.02
          - type: precision_at_1000
            value: 0.152
          - type: precision_at_3
            value: 14.033999999999999
          - type: precision_at_5
            value: 9.911
          - type: recall_at_1
            value: 21.538
          - type: recall_at_10
            value: 40.186
          - type: recall_at_100
            value: 58.948
          - type: recall_at_1000
            value: 77.158
          - type: recall_at_3
            value: 30.951
          - type: recall_at_5
            value: 35.276
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackGamingRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 35.211999999999996
          - type: map_at_10
            value: 46.562
          - type: map_at_100
            value: 47.579
          - type: map_at_1000
            value: 47.646
          - type: map_at_3
            value: 43.485
          - type: map_at_5
            value: 45.206
          - type: mrr_at_1
            value: 40.627
          - type: mrr_at_10
            value: 49.928
          - type: mrr_at_100
            value: 50.647
          - type: mrr_at_1000
            value: 50.685
          - type: mrr_at_3
            value: 47.513
          - type: mrr_at_5
            value: 48.958
          - type: ndcg_at_1
            value: 40.627
          - type: ndcg_at_10
            value: 52.217
          - type: ndcg_at_100
            value: 56.423
          - type: ndcg_at_1000
            value: 57.821999999999996
          - type: ndcg_at_3
            value: 46.949000000000005
          - type: ndcg_at_5
            value: 49.534
          - type: precision_at_1
            value: 40.627
          - type: precision_at_10
            value: 8.476
          - type: precision_at_100
            value: 1.15
          - type: precision_at_1000
            value: 0.132
          - type: precision_at_3
            value: 21.003
          - type: precision_at_5
            value: 14.469999999999999
          - type: recall_at_1
            value: 35.211999999999996
          - type: recall_at_10
            value: 65.692
          - type: recall_at_100
            value: 84.011
          - type: recall_at_1000
            value: 94.03099999999999
          - type: recall_at_3
            value: 51.404
          - type: recall_at_5
            value: 57.882
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackGisRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 22.09
          - type: map_at_10
            value: 29.516
          - type: map_at_100
            value: 30.462
          - type: map_at_1000
            value: 30.56
          - type: map_at_3
            value: 26.945000000000004
          - type: map_at_5
            value: 28.421999999999997
          - type: mrr_at_1
            value: 23.616
          - type: mrr_at_10
            value: 31.221
          - type: mrr_at_100
            value: 32.057
          - type: mrr_at_1000
            value: 32.137
          - type: mrr_at_3
            value: 28.738000000000003
          - type: mrr_at_5
            value: 30.156
          - type: ndcg_at_1
            value: 23.616
          - type: ndcg_at_10
            value: 33.97
          - type: ndcg_at_100
            value: 38.806000000000004
          - type: ndcg_at_1000
            value: 41.393
          - type: ndcg_at_3
            value: 28.908
          - type: ndcg_at_5
            value: 31.433
          - type: precision_at_1
            value: 23.616
          - type: precision_at_10
            value: 5.299
          - type: precision_at_100
            value: 0.812
          - type: precision_at_1000
            value: 0.107
          - type: precision_at_3
            value: 12.015
          - type: precision_at_5
            value: 8.701
          - type: recall_at_1
            value: 22.09
          - type: recall_at_10
            value: 46.089999999999996
          - type: recall_at_100
            value: 68.729
          - type: recall_at_1000
            value: 88.435
          - type: recall_at_3
            value: 32.584999999999994
          - type: recall_at_5
            value: 38.550000000000004
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackMathematicaRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 15.469
          - type: map_at_10
            value: 22.436
          - type: map_at_100
            value: 23.465
          - type: map_at_1000
            value: 23.608999999999998
          - type: map_at_3
            value: 19.716
          - type: map_at_5
            value: 21.182000000000002
          - type: mrr_at_1
            value: 18.905
          - type: mrr_at_10
            value: 26.55
          - type: mrr_at_100
            value: 27.46
          - type: mrr_at_1000
            value: 27.553
          - type: mrr_at_3
            value: 23.921999999999997
          - type: mrr_at_5
            value: 25.302999999999997
          - type: ndcg_at_1
            value: 18.905
          - type: ndcg_at_10
            value: 27.437
          - type: ndcg_at_100
            value: 32.555
          - type: ndcg_at_1000
            value: 35.885
          - type: ndcg_at_3
            value: 22.439
          - type: ndcg_at_5
            value: 24.666
          - type: precision_at_1
            value: 18.905
          - type: precision_at_10
            value: 5.2490000000000006
          - type: precision_at_100
            value: 0.889
          - type: precision_at_1000
            value: 0.131
          - type: precision_at_3
            value: 10.862
          - type: precision_at_5
            value: 8.085
          - type: recall_at_1
            value: 15.469
          - type: recall_at_10
            value: 38.706
          - type: recall_at_100
            value: 61.242
          - type: recall_at_1000
            value: 84.84
          - type: recall_at_3
            value: 24.973
          - type: recall_at_5
            value: 30.603
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackPhysicsRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 24.918000000000003
          - type: map_at_10
            value: 34.296
          - type: map_at_100
            value: 35.632000000000005
          - type: map_at_1000
            value: 35.748999999999995
          - type: map_at_3
            value: 31.304
          - type: map_at_5
            value: 33.166000000000004
          - type: mrr_at_1
            value: 30.703000000000003
          - type: mrr_at_10
            value: 39.655
          - type: mrr_at_100
            value: 40.569
          - type: mrr_at_1000
            value: 40.621
          - type: mrr_at_3
            value: 37.023
          - type: mrr_at_5
            value: 38.664
          - type: ndcg_at_1
            value: 30.703000000000003
          - type: ndcg_at_10
            value: 39.897
          - type: ndcg_at_100
            value: 45.777
          - type: ndcg_at_1000
            value: 48.082
          - type: ndcg_at_3
            value: 35.122
          - type: ndcg_at_5
            value: 37.691
          - type: precision_at_1
            value: 30.703000000000003
          - type: precision_at_10
            value: 7.305000000000001
          - type: precision_at_100
            value: 1.208
          - type: precision_at_1000
            value: 0.159
          - type: precision_at_3
            value: 16.811
          - type: precision_at_5
            value: 12.203999999999999
          - type: recall_at_1
            value: 24.918000000000003
          - type: recall_at_10
            value: 51.31
          - type: recall_at_100
            value: 76.534
          - type: recall_at_1000
            value: 91.911
          - type: recall_at_3
            value: 37.855
          - type: recall_at_5
            value: 44.493
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackProgrammersRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 22.416
          - type: map_at_10
            value: 30.474
          - type: map_at_100
            value: 31.759999999999998
          - type: map_at_1000
            value: 31.891000000000002
          - type: map_at_3
            value: 27.728
          - type: map_at_5
            value: 29.247
          - type: mrr_at_1
            value: 28.881
          - type: mrr_at_10
            value: 36.418
          - type: mrr_at_100
            value: 37.347
          - type: mrr_at_1000
            value: 37.415
          - type: mrr_at_3
            value: 33.942
          - type: mrr_at_5
            value: 35.386
          - type: ndcg_at_1
            value: 28.881
          - type: ndcg_at_10
            value: 35.812
          - type: ndcg_at_100
            value: 41.574
          - type: ndcg_at_1000
            value: 44.289
          - type: ndcg_at_3
            value: 31.239
          - type: ndcg_at_5
            value: 33.302
          - type: precision_at_1
            value: 28.881
          - type: precision_at_10
            value: 6.598
          - type: precision_at_100
            value: 1.1079999999999999
          - type: precision_at_1000
            value: 0.151
          - type: precision_at_3
            value: 14.954
          - type: precision_at_5
            value: 10.776
          - type: recall_at_1
            value: 22.416
          - type: recall_at_10
            value: 46.243
          - type: recall_at_100
            value: 71.352
          - type: recall_at_1000
            value: 90.034
          - type: recall_at_3
            value: 32.873000000000005
          - type: recall_at_5
            value: 38.632
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 22.528166666666667
          - type: map_at_10
            value: 30.317833333333333
          - type: map_at_100
            value: 31.44108333333333
          - type: map_at_1000
            value: 31.566666666666666
          - type: map_at_3
            value: 27.84425
          - type: map_at_5
            value: 29.233333333333334
          - type: mrr_at_1
            value: 26.75733333333333
          - type: mrr_at_10
            value: 34.24425
          - type: mrr_at_100
            value: 35.11375
          - type: mrr_at_1000
            value: 35.184333333333335
          - type: mrr_at_3
            value: 32.01225
          - type: mrr_at_5
            value: 33.31225
          - type: ndcg_at_1
            value: 26.75733333333333
          - type: ndcg_at_10
            value: 35.072583333333334
          - type: ndcg_at_100
            value: 40.13358333333334
          - type: ndcg_at_1000
            value: 42.81825
          - type: ndcg_at_3
            value: 30.79275000000001
          - type: ndcg_at_5
            value: 32.822
          - type: precision_at_1
            value: 26.75733333333333
          - type: precision_at_10
            value: 6.128083333333334
          - type: precision_at_100
            value: 1.019
          - type: precision_at_1000
            value: 0.14391666666666664
          - type: precision_at_3
            value: 14.129916666666665
          - type: precision_at_5
            value: 10.087416666666668
          - type: recall_at_1
            value: 22.528166666666667
          - type: recall_at_10
            value: 45.38341666666667
          - type: recall_at_100
            value: 67.81791666666668
          - type: recall_at_1000
            value: 86.71716666666666
          - type: recall_at_3
            value: 33.38741666666667
          - type: recall_at_5
            value: 38.62041666666667
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackStatsRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 21.975
          - type: map_at_10
            value: 28.144999999999996
          - type: map_at_100
            value: 28.994999999999997
          - type: map_at_1000
            value: 29.086000000000002
          - type: map_at_3
            value: 25.968999999999998
          - type: map_at_5
            value: 27.321
          - type: mrr_at_1
            value: 25
          - type: mrr_at_10
            value: 30.822
          - type: mrr_at_100
            value: 31.647
          - type: mrr_at_1000
            value: 31.712
          - type: mrr_at_3
            value: 28.860000000000003
          - type: mrr_at_5
            value: 30.041
          - type: ndcg_at_1
            value: 25
          - type: ndcg_at_10
            value: 31.929999999999996
          - type: ndcg_at_100
            value: 36.258
          - type: ndcg_at_1000
            value: 38.682
          - type: ndcg_at_3
            value: 27.972
          - type: ndcg_at_5
            value: 30.089
          - type: precision_at_1
            value: 25
          - type: precision_at_10
            value: 4.923
          - type: precision_at_100
            value: 0.767
          - type: precision_at_1000
            value: 0.106
          - type: precision_at_3
            value: 11.860999999999999
          - type: precision_at_5
            value: 8.466
          - type: recall_at_1
            value: 21.975
          - type: recall_at_10
            value: 41.102
          - type: recall_at_100
            value: 60.866
          - type: recall_at_1000
            value: 78.781
          - type: recall_at_3
            value: 30.268
          - type: recall_at_5
            value: 35.552
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackTexRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 15.845999999999998
          - type: map_at_10
            value: 21.861
          - type: map_at_100
            value: 22.798
          - type: map_at_1000
            value: 22.925
          - type: map_at_3
            value: 19.922
          - type: map_at_5
            value: 21.054000000000002
          - type: mrr_at_1
            value: 19.098000000000003
          - type: mrr_at_10
            value: 25.397
          - type: mrr_at_100
            value: 26.246000000000002
          - type: mrr_at_1000
            value: 26.33
          - type: mrr_at_3
            value: 23.469
          - type: mrr_at_5
            value: 24.646
          - type: ndcg_at_1
            value: 19.098000000000003
          - type: ndcg_at_10
            value: 25.807999999999996
          - type: ndcg_at_100
            value: 30.445
          - type: ndcg_at_1000
            value: 33.666000000000004
          - type: ndcg_at_3
            value: 22.292
          - type: ndcg_at_5
            value: 24.075
          - type: precision_at_1
            value: 19.098000000000003
          - type: precision_at_10
            value: 4.58
          - type: precision_at_100
            value: 0.8099999999999999
          - type: precision_at_1000
            value: 0.126
          - type: precision_at_3
            value: 10.346
          - type: precision_at_5
            value: 7.542999999999999
          - type: recall_at_1
            value: 15.845999999999998
          - type: recall_at_10
            value: 34.172999999999995
          - type: recall_at_100
            value: 55.24099999999999
          - type: recall_at_1000
            value: 78.644
          - type: recall_at_3
            value: 24.401
          - type: recall_at_5
            value: 28.938000000000002
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackUnixRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 22.974
          - type: map_at_10
            value: 30.108
          - type: map_at_100
            value: 31.208000000000002
          - type: map_at_1000
            value: 31.330999999999996
          - type: map_at_3
            value: 27.889999999999997
          - type: map_at_5
            value: 29.023
          - type: mrr_at_1
            value: 26.493
          - type: mrr_at_10
            value: 33.726
          - type: mrr_at_100
            value: 34.622
          - type: mrr_at_1000
            value: 34.703
          - type: mrr_at_3
            value: 31.575999999999997
          - type: mrr_at_5
            value: 32.690999999999995
          - type: ndcg_at_1
            value: 26.493
          - type: ndcg_at_10
            value: 34.664
          - type: ndcg_at_100
            value: 39.725
          - type: ndcg_at_1000
            value: 42.648
          - type: ndcg_at_3
            value: 30.447999999999997
          - type: ndcg_at_5
            value: 32.145
          - type: precision_at_1
            value: 26.493
          - type: precision_at_10
            value: 5.7090000000000005
          - type: precision_at_100
            value: 0.9199999999999999
          - type: precision_at_1000
            value: 0.129
          - type: precision_at_3
            value: 13.464
          - type: precision_at_5
            value: 9.384
          - type: recall_at_1
            value: 22.974
          - type: recall_at_10
            value: 45.097
          - type: recall_at_100
            value: 66.908
          - type: recall_at_1000
            value: 87.495
          - type: recall_at_3
            value: 33.338
          - type: recall_at_5
            value: 37.499
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackWebmastersRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 22.408
          - type: map_at_10
            value: 29.580000000000002
          - type: map_at_100
            value: 31.145
          - type: map_at_1000
            value: 31.369000000000003
          - type: map_at_3
            value: 27.634999999999998
          - type: map_at_5
            value: 28.766000000000002
          - type: mrr_at_1
            value: 27.272999999999996
          - type: mrr_at_10
            value: 33.93
          - type: mrr_at_100
            value: 34.963
          - type: mrr_at_1000
            value: 35.031
          - type: mrr_at_3
            value: 32.016
          - type: mrr_at_5
            value: 33.221000000000004
          - type: ndcg_at_1
            value: 27.272999999999996
          - type: ndcg_at_10
            value: 33.993
          - type: ndcg_at_100
            value: 40.333999999999996
          - type: ndcg_at_1000
            value: 43.361
          - type: ndcg_at_3
            value: 30.918
          - type: ndcg_at_5
            value: 32.552
          - type: precision_at_1
            value: 27.272999999999996
          - type: precision_at_10
            value: 6.285
          - type: precision_at_100
            value: 1.389
          - type: precision_at_1000
            value: 0.232
          - type: precision_at_3
            value: 14.427000000000001
          - type: precision_at_5
            value: 10.356
          - type: recall_at_1
            value: 22.408
          - type: recall_at_10
            value: 41.318
          - type: recall_at_100
            value: 70.539
          - type: recall_at_1000
            value: 90.197
          - type: recall_at_3
            value: 32.513
          - type: recall_at_5
            value: 37
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackWordpressRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 17.258000000000003
          - type: map_at_10
            value: 24.294
          - type: map_at_100
            value: 25.305
          - type: map_at_1000
            value: 25.419999999999998
          - type: map_at_3
            value: 22.326999999999998
          - type: map_at_5
            value: 23.31
          - type: mrr_at_1
            value: 18.484
          - type: mrr_at_10
            value: 25.863999999999997
          - type: mrr_at_100
            value: 26.766000000000002
          - type: mrr_at_1000
            value: 26.855
          - type: mrr_at_3
            value: 23.968
          - type: mrr_at_5
            value: 24.911
          - type: ndcg_at_1
            value: 18.484
          - type: ndcg_at_10
            value: 28.433000000000003
          - type: ndcg_at_100
            value: 33.405
          - type: ndcg_at_1000
            value: 36.375
          - type: ndcg_at_3
            value: 24.455
          - type: ndcg_at_5
            value: 26.031
          - type: precision_at_1
            value: 18.484
          - type: precision_at_10
            value: 4.603
          - type: precision_at_100
            value: 0.773
          - type: precision_at_1000
            value: 0.11299999999999999
          - type: precision_at_3
            value: 10.659
          - type: precision_at_5
            value: 7.505000000000001
          - type: recall_at_1
            value: 17.258000000000003
          - type: recall_at_10
            value: 39.589999999999996
          - type: recall_at_100
            value: 62.592000000000006
          - type: recall_at_1000
            value: 84.917
          - type: recall_at_3
            value: 28.706
          - type: recall_at_5
            value: 32.224000000000004
      - task:
          type: Retrieval
        dataset:
          type: climate-fever
          name: MTEB ClimateFEVER
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 10.578999999999999
          - type: map_at_10
            value: 17.642
          - type: map_at_100
            value: 19.451
          - type: map_at_1000
            value: 19.647000000000002
          - type: map_at_3
            value: 14.618
          - type: map_at_5
            value: 16.145
          - type: mrr_at_1
            value: 23.322000000000003
          - type: mrr_at_10
            value: 34.204
          - type: mrr_at_100
            value: 35.185
          - type: mrr_at_1000
            value: 35.235
          - type: mrr_at_3
            value: 30.847
          - type: mrr_at_5
            value: 32.824
          - type: ndcg_at_1
            value: 23.322000000000003
          - type: ndcg_at_10
            value: 25.352999999999998
          - type: ndcg_at_100
            value: 32.574
          - type: ndcg_at_1000
            value: 36.073
          - type: ndcg_at_3
            value: 20.318
          - type: ndcg_at_5
            value: 22.111
          - type: precision_at_1
            value: 23.322000000000003
          - type: precision_at_10
            value: 8.02
          - type: precision_at_100
            value: 1.5730000000000002
          - type: precision_at_1000
            value: 0.22200000000000003
          - type: precision_at_3
            value: 15.049000000000001
          - type: precision_at_5
            value: 11.87
          - type: recall_at_1
            value: 10.578999999999999
          - type: recall_at_10
            value: 30.964999999999996
          - type: recall_at_100
            value: 55.986000000000004
          - type: recall_at_1000
            value: 75.565
          - type: recall_at_3
            value: 18.686
          - type: recall_at_5
            value: 23.629
      - task:
          type: Retrieval
        dataset:
          type: dbpedia-entity
          name: MTEB DBPedia
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 7.327
          - type: map_at_10
            value: 14.904
          - type: map_at_100
            value: 20.29
          - type: map_at_1000
            value: 21.42
          - type: map_at_3
            value: 10.911
          - type: map_at_5
            value: 12.791
          - type: mrr_at_1
            value: 57.25
          - type: mrr_at_10
            value: 66.62700000000001
          - type: mrr_at_100
            value: 67.035
          - type: mrr_at_1000
            value: 67.052
          - type: mrr_at_3
            value: 64.833
          - type: mrr_at_5
            value: 65.908
          - type: ndcg_at_1
            value: 43.75
          - type: ndcg_at_10
            value: 32.246
          - type: ndcg_at_100
            value: 35.774
          - type: ndcg_at_1000
            value: 42.872
          - type: ndcg_at_3
            value: 36.64
          - type: ndcg_at_5
            value: 34.487
          - type: precision_at_1
            value: 57.25
          - type: precision_at_10
            value: 25.924999999999997
          - type: precision_at_100
            value: 7.670000000000001
          - type: precision_at_1000
            value: 1.599
          - type: precision_at_3
            value: 41.167
          - type: precision_at_5
            value: 34.65
          - type: recall_at_1
            value: 7.327
          - type: recall_at_10
            value: 19.625
          - type: recall_at_100
            value: 41.601
          - type: recall_at_1000
            value: 65.117
          - type: recall_at_3
            value: 12.308
          - type: recall_at_5
            value: 15.437999999999999
      - task:
          type: Classification
        dataset:
          type: mteb/emotion
          name: MTEB EmotionClassification
          config: default
          split: test
          revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
        metrics:
          - type: accuracy
            value: 44.53
          - type: f1
            value: 39.39884255816736
      - task:
          type: Retrieval
        dataset:
          type: fever
          name: MTEB FEVER
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 58.913000000000004
          - type: map_at_10
            value: 69.592
          - type: map_at_100
            value: 69.95599999999999
          - type: map_at_1000
            value: 69.973
          - type: map_at_3
            value: 67.716
          - type: map_at_5
            value: 68.899
          - type: mrr_at_1
            value: 63.561
          - type: mrr_at_10
            value: 74.2
          - type: mrr_at_100
            value: 74.468
          - type: mrr_at_1000
            value: 74.47500000000001
          - type: mrr_at_3
            value: 72.442
          - type: mrr_at_5
            value: 73.58
          - type: ndcg_at_1
            value: 63.561
          - type: ndcg_at_10
            value: 74.988
          - type: ndcg_at_100
            value: 76.52799999999999
          - type: ndcg_at_1000
            value: 76.88000000000001
          - type: ndcg_at_3
            value: 71.455
          - type: ndcg_at_5
            value: 73.42699999999999
          - type: precision_at_1
            value: 63.561
          - type: precision_at_10
            value: 9.547
          - type: precision_at_100
            value: 1.044
          - type: precision_at_1000
            value: 0.109
          - type: precision_at_3
            value: 28.143
          - type: precision_at_5
            value: 18.008
          - type: recall_at_1
            value: 58.913000000000004
          - type: recall_at_10
            value: 87.18
          - type: recall_at_100
            value: 93.852
          - type: recall_at_1000
            value: 96.256
          - type: recall_at_3
            value: 77.55199999999999
          - type: recall_at_5
            value: 82.42399999999999
      - task:
          type: Retrieval
        dataset:
          type: fiqa
          name: MTEB FiQA2018
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 11.761000000000001
          - type: map_at_10
            value: 19.564999999999998
          - type: map_at_100
            value: 21.099
          - type: map_at_1000
            value: 21.288999999999998
          - type: map_at_3
            value: 16.683999999999997
          - type: map_at_5
            value: 18.307000000000002
          - type: mrr_at_1
            value: 23.302
          - type: mrr_at_10
            value: 30.979
          - type: mrr_at_100
            value: 32.121
          - type: mrr_at_1000
            value: 32.186
          - type: mrr_at_3
            value: 28.549000000000003
          - type: mrr_at_5
            value: 30.038999999999998
          - type: ndcg_at_1
            value: 23.302
          - type: ndcg_at_10
            value: 25.592
          - type: ndcg_at_100
            value: 32.416
          - type: ndcg_at_1000
            value: 36.277
          - type: ndcg_at_3
            value: 22.151
          - type: ndcg_at_5
            value: 23.483999999999998
          - type: precision_at_1
            value: 23.302
          - type: precision_at_10
            value: 7.377000000000001
          - type: precision_at_100
            value: 1.415
          - type: precision_at_1000
            value: 0.212
          - type: precision_at_3
            value: 14.712
          - type: precision_at_5
            value: 11.358
          - type: recall_at_1
            value: 11.761000000000001
          - type: recall_at_10
            value: 31.696
          - type: recall_at_100
            value: 58.01500000000001
          - type: recall_at_1000
            value: 81.572
          - type: recall_at_3
            value: 20.742
          - type: recall_at_5
            value: 25.707
      - task:
          type: Retrieval
        dataset:
          type: hotpotqa
          name: MTEB HotpotQA
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 32.275
          - type: map_at_10
            value: 44.712
          - type: map_at_100
            value: 45.621
          - type: map_at_1000
            value: 45.698
          - type: map_at_3
            value: 42.016999999999996
          - type: map_at_5
            value: 43.659
          - type: mrr_at_1
            value: 64.551
          - type: mrr_at_10
            value: 71.58099999999999
          - type: mrr_at_100
            value: 71.952
          - type: mrr_at_1000
            value: 71.96900000000001
          - type: mrr_at_3
            value: 70.236
          - type: mrr_at_5
            value: 71.051
          - type: ndcg_at_1
            value: 64.551
          - type: ndcg_at_10
            value: 53.913999999999994
          - type: ndcg_at_100
            value: 57.421
          - type: ndcg_at_1000
            value: 59.06
          - type: ndcg_at_3
            value: 49.716
          - type: ndcg_at_5
            value: 51.971999999999994
          - type: precision_at_1
            value: 64.551
          - type: precision_at_10
            value: 11.110000000000001
          - type: precision_at_100
            value: 1.388
          - type: precision_at_1000
            value: 0.161
          - type: precision_at_3
            value: 30.822
          - type: precision_at_5
            value: 20.273
          - type: recall_at_1
            value: 32.275
          - type: recall_at_10
            value: 55.55
          - type: recall_at_100
            value: 69.38600000000001
          - type: recall_at_1000
            value: 80.35799999999999
          - type: recall_at_3
            value: 46.232
          - type: recall_at_5
            value: 50.682
      - task:
          type: Classification
        dataset:
          type: mteb/imdb
          name: MTEB ImdbClassification
          config: default
          split: test
          revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
        metrics:
          - type: accuracy
            value: 76.4604
          - type: ap
            value: 70.40498168422701
          - type: f1
            value: 76.38572688476046
      - task:
          type: Retrieval
        dataset:
          type: msmarco
          name: MTEB MSMARCO
          config: default
          split: dev
          revision: None
        metrics:
          - type: map_at_1
            value: 15.065999999999999
          - type: map_at_10
            value: 25.058000000000003
          - type: map_at_100
            value: 26.268
          - type: map_at_1000
            value: 26.344
          - type: map_at_3
            value: 21.626
          - type: map_at_5
            value: 23.513
          - type: mrr_at_1
            value: 15.501000000000001
          - type: mrr_at_10
            value: 25.548
          - type: mrr_at_100
            value: 26.723000000000003
          - type: mrr_at_1000
            value: 26.793
          - type: mrr_at_3
            value: 22.142
          - type: mrr_at_5
            value: 24.024
          - type: ndcg_at_1
            value: 15.501000000000001
          - type: ndcg_at_10
            value: 31.008000000000003
          - type: ndcg_at_100
            value: 37.08
          - type: ndcg_at_1000
            value: 39.102
          - type: ndcg_at_3
            value: 23.921999999999997
          - type: ndcg_at_5
            value: 27.307
          - type: precision_at_1
            value: 15.501000000000001
          - type: precision_at_10
            value: 5.155
          - type: precision_at_100
            value: 0.822
          - type: precision_at_1000
            value: 0.099
          - type: precision_at_3
            value: 10.363
          - type: precision_at_5
            value: 7.917000000000001
          - type: recall_at_1
            value: 15.065999999999999
          - type: recall_at_10
            value: 49.507
          - type: recall_at_100
            value: 78.118
          - type: recall_at_1000
            value: 93.881
          - type: recall_at_3
            value: 30.075000000000003
          - type: recall_at_5
            value: 38.222
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_domain
          name: MTEB MTOPDomainClassification (en)
          config: en
          split: test
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
        metrics:
          - type: accuracy
            value: 90.6703146374829
          - type: f1
            value: 90.1258004293966
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_intent
          name: MTEB MTOPIntentClassification (en)
          config: en
          split: test
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
        metrics:
          - type: accuracy
            value: 68.29229366165072
          - type: f1
            value: 50.016194478997875
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (en)
          config: en
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 68.57767316745124
          - type: f1
            value: 67.16194062146954
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_scenario
          name: MTEB MassiveScenarioClassification (en)
          config: en
          split: test
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
        metrics:
          - type: accuracy
            value: 73.92064559515804
          - type: f1
            value: 73.6680729569968
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-p2p
          name: MTEB MedrxivClusteringP2P
          config: default
          split: test
          revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
        metrics:
          - type: v_measure
            value: 31.56335607367883
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-s2s
          name: MTEB MedrxivClusteringS2S
          config: default
          split: test
          revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
        metrics:
          - type: v_measure
            value: 28.131807833734268
      - task:
          type: Reranking
        dataset:
          type: mteb/mind_small
          name: MTEB MindSmallReranking
          config: default
          split: test
          revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
        metrics:
          - type: map
            value: 31.07390328719844
          - type: mrr
            value: 32.117370992867905
      - task:
          type: Retrieval
        dataset:
          type: nfcorpus
          name: MTEB NFCorpus
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 5.274
          - type: map_at_10
            value: 11.489
          - type: map_at_100
            value: 14.518
          - type: map_at_1000
            value: 15.914
          - type: map_at_3
            value: 8.399
          - type: map_at_5
            value: 9.889000000000001
          - type: mrr_at_1
            value: 42.724000000000004
          - type: mrr_at_10
            value: 51.486
          - type: mrr_at_100
            value: 51.941
          - type: mrr_at_1000
            value: 51.99
          - type: mrr_at_3
            value: 49.278
          - type: mrr_at_5
            value: 50.485
          - type: ndcg_at_1
            value: 39.938
          - type: ndcg_at_10
            value: 31.862000000000002
          - type: ndcg_at_100
            value: 29.235
          - type: ndcg_at_1000
            value: 37.802
          - type: ndcg_at_3
            value: 35.754999999999995
          - type: ndcg_at_5
            value: 34.447
          - type: precision_at_1
            value: 42.105
          - type: precision_at_10
            value: 23.901
          - type: precision_at_100
            value: 7.715
          - type: precision_at_1000
            value: 2.045
          - type: precision_at_3
            value: 33.437
          - type: precision_at_5
            value: 29.782999999999998
          - type: recall_at_1
            value: 5.274
          - type: recall_at_10
            value: 15.351
          - type: recall_at_100
            value: 29.791
          - type: recall_at_1000
            value: 60.722
          - type: recall_at_3
            value: 9.411
          - type: recall_at_5
            value: 12.171999999999999
      - task:
          type: Retrieval
        dataset:
          type: nq
          name: MTEB NQ
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 16.099
          - type: map_at_10
            value: 27.913
          - type: map_at_100
            value: 29.281000000000002
          - type: map_at_1000
            value: 29.343999999999998
          - type: map_at_3
            value: 23.791
          - type: map_at_5
            value: 26.049
          - type: mrr_at_1
            value: 18.337
          - type: mrr_at_10
            value: 29.953999999999997
          - type: mrr_at_100
            value: 31.080999999999996
          - type: mrr_at_1000
            value: 31.130000000000003
          - type: mrr_at_3
            value: 26.168000000000003
          - type: mrr_at_5
            value: 28.277
          - type: ndcg_at_1
            value: 18.308
          - type: ndcg_at_10
            value: 34.938
          - type: ndcg_at_100
            value: 41.125
          - type: ndcg_at_1000
            value: 42.708
          - type: ndcg_at_3
            value: 26.805
          - type: ndcg_at_5
            value: 30.686999999999998
          - type: precision_at_1
            value: 18.308
          - type: precision_at_10
            value: 6.476999999999999
          - type: precision_at_100
            value: 0.9939999999999999
          - type: precision_at_1000
            value: 0.11399999999999999
          - type: precision_at_3
            value: 12.784999999999998
          - type: precision_at_5
            value: 9.878
          - type: recall_at_1
            value: 16.099
          - type: recall_at_10
            value: 54.63
          - type: recall_at_100
            value: 82.24900000000001
          - type: recall_at_1000
            value: 94.242
          - type: recall_at_3
            value: 33.174
          - type: recall_at_5
            value: 42.164
      - task:
          type: Retrieval
        dataset:
          type: quora
          name: MTEB QuoraRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 67.947
          - type: map_at_10
            value: 81.499
          - type: map_at_100
            value: 82.17
          - type: map_at_1000
            value: 82.194
          - type: map_at_3
            value: 78.567
          - type: map_at_5
            value: 80.34400000000001
          - type: mrr_at_1
            value: 78.18
          - type: mrr_at_10
            value: 85.05
          - type: mrr_at_100
            value: 85.179
          - type: mrr_at_1000
            value: 85.181
          - type: mrr_at_3
            value: 83.91
          - type: mrr_at_5
            value: 84.638
          - type: ndcg_at_1
            value: 78.2
          - type: ndcg_at_10
            value: 85.715
          - type: ndcg_at_100
            value: 87.2
          - type: ndcg_at_1000
            value: 87.39
          - type: ndcg_at_3
            value: 82.572
          - type: ndcg_at_5
            value: 84.176
          - type: precision_at_1
            value: 78.2
          - type: precision_at_10
            value: 12.973
          - type: precision_at_100
            value: 1.5010000000000001
          - type: precision_at_1000
            value: 0.156
          - type: precision_at_3
            value: 35.949999999999996
          - type: precision_at_5
            value: 23.62
          - type: recall_at_1
            value: 67.947
          - type: recall_at_10
            value: 93.804
          - type: recall_at_100
            value: 98.971
          - type: recall_at_1000
            value: 99.91600000000001
          - type: recall_at_3
            value: 84.75399999999999
          - type: recall_at_5
            value: 89.32
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering
          name: MTEB RedditClustering
          config: default
          split: test
          revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
        metrics:
          - type: v_measure
            value: 45.457201684255104
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering-p2p
          name: MTEB RedditClusteringP2P
          config: default
          split: test
          revision: 282350215ef01743dc01b456c7f5241fa8937f16
        metrics:
          - type: v_measure
            value: 55.162226937477875
      - task:
          type: Retrieval
        dataset:
          type: scidocs
          name: MTEB SCIDOCS
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 4.173
          - type: map_at_10
            value: 10.463000000000001
          - type: map_at_100
            value: 12.278
          - type: map_at_1000
            value: 12.572
          - type: map_at_3
            value: 7.528
          - type: map_at_5
            value: 8.863
          - type: mrr_at_1
            value: 20.599999999999998
          - type: mrr_at_10
            value: 30.422
          - type: mrr_at_100
            value: 31.6
          - type: mrr_at_1000
            value: 31.663000000000004
          - type: mrr_at_3
            value: 27.400000000000002
          - type: mrr_at_5
            value: 29.065
          - type: ndcg_at_1
            value: 20.599999999999998
          - type: ndcg_at_10
            value: 17.687
          - type: ndcg_at_100
            value: 25.172
          - type: ndcg_at_1000
            value: 30.617
          - type: ndcg_at_3
            value: 16.81
          - type: ndcg_at_5
            value: 14.499
          - type: precision_at_1
            value: 20.599999999999998
          - type: precision_at_10
            value: 9.17
          - type: precision_at_100
            value: 2.004
          - type: precision_at_1000
            value: 0.332
          - type: precision_at_3
            value: 15.6
          - type: precision_at_5
            value: 12.58
          - type: recall_at_1
            value: 4.173
          - type: recall_at_10
            value: 18.575
          - type: recall_at_100
            value: 40.692
          - type: recall_at_1000
            value: 67.467
          - type: recall_at_3
            value: 9.488000000000001
          - type: recall_at_5
            value: 12.738
      - task:
          type: STS
        dataset:
          type: mteb/sickr-sts
          name: MTEB SICK-R
          config: default
          split: test
          revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
        metrics:
          - type: cos_sim_pearson
            value: 81.12603499315416
          - type: cos_sim_spearman
            value: 73.62060290948378
          - type: euclidean_pearson
            value: 78.14083565781135
          - type: euclidean_spearman
            value: 73.16840437541543
          - type: manhattan_pearson
            value: 77.92017261109734
          - type: manhattan_spearman
            value: 72.8805059949965
      - task:
          type: STS
        dataset:
          type: mteb/sts12-sts
          name: MTEB STS12
          config: default
          split: test
          revision: a0d554a64d88156834ff5ae9920b964011b16384
        metrics:
          - type: cos_sim_pearson
            value: 79.75955377133172
          - type: cos_sim_spearman
            value: 71.8872633964069
          - type: euclidean_pearson
            value: 76.31922068538256
          - type: euclidean_spearman
            value: 70.86449661855376
          - type: manhattan_pearson
            value: 76.47852229730407
          - type: manhattan_spearman
            value: 70.99367421984789
      - task:
          type: STS
        dataset:
          type: mteb/sts13-sts
          name: MTEB STS13
          config: default
          split: test
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
        metrics:
          - type: cos_sim_pearson
            value: 78.80762722908158
          - type: cos_sim_spearman
            value: 79.84588978756372
          - type: euclidean_pearson
            value: 79.8216849781164
          - type: euclidean_spearman
            value: 80.22647061695481
          - type: manhattan_pearson
            value: 79.56604194112572
          - type: manhattan_spearman
            value: 79.96495189862462
      - task:
          type: STS
        dataset:
          type: mteb/sts14-sts
          name: MTEB STS14
          config: default
          split: test
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
        metrics:
          - type: cos_sim_pearson
            value: 80.1012718092742
          - type: cos_sim_spearman
            value: 76.86011381793661
          - type: euclidean_pearson
            value: 79.94426039862019
          - type: euclidean_spearman
            value: 77.36751135465131
          - type: manhattan_pearson
            value: 79.87959373304288
          - type: manhattan_spearman
            value: 77.37717129004746
      - task:
          type: STS
        dataset:
          type: mteb/sts15-sts
          name: MTEB STS15
          config: default
          split: test
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
        metrics:
          - type: cos_sim_pearson
            value: 83.90618420346104
          - type: cos_sim_spearman
            value: 84.77290791243722
          - type: euclidean_pearson
            value: 84.64732258073293
          - type: euclidean_spearman
            value: 85.21053649543357
          - type: manhattan_pearson
            value: 84.61616883522647
          - type: manhattan_spearman
            value: 85.19803126766931
      - task:
          type: STS
        dataset:
          type: mteb/sts16-sts
          name: MTEB STS16
          config: default
          split: test
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
        metrics:
          - type: cos_sim_pearson
            value: 80.52192114059063
          - type: cos_sim_spearman
            value: 81.9103244827937
          - type: euclidean_pearson
            value: 80.99375176138985
          - type: euclidean_spearman
            value: 81.540250641079
          - type: manhattan_pearson
            value: 80.84979573396426
          - type: manhattan_spearman
            value: 81.3742591621492
      - 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: 85.82166001234197
          - type: cos_sim_spearman
            value: 86.81857495659123
          - type: euclidean_pearson
            value: 85.72798403202849
          - type: euclidean_spearman
            value: 85.70482438950965
          - type: manhattan_pearson
            value: 85.51579093130357
          - type: manhattan_spearman
            value: 85.41233705379751
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (en)
          config: en
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 64.48071151079803
          - type: cos_sim_spearman
            value: 65.37838108084044
          - type: euclidean_pearson
            value: 64.67378947096257
          - type: euclidean_spearman
            value: 65.39187147219869
          - type: manhattan_pearson
            value: 65.35487466133208
          - type: manhattan_spearman
            value: 65.51328499442272
      - task:
          type: STS
        dataset:
          type: mteb/stsbenchmark-sts
          name: MTEB STSBenchmark
          config: default
          split: test
          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
        metrics:
          - type: cos_sim_pearson
            value: 82.64702367823314
          - type: cos_sim_spearman
            value: 82.49732953181818
          - type: euclidean_pearson
            value: 83.05996062475664
          - type: euclidean_spearman
            value: 82.28159546751176
          - type: manhattan_pearson
            value: 82.98305503664952
          - type: manhattan_spearman
            value: 82.18405771943928
      - task:
          type: Reranking
        dataset:
          type: mteb/scidocs-reranking
          name: MTEB SciDocsRR
          config: default
          split: test
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
        metrics:
          - type: map
            value: 78.5744649318696
          - type: mrr
            value: 93.35386291268645
      - task:
          type: Retrieval
        dataset:
          type: scifact
          name: MTEB SciFact
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 52.093999999999994
          - type: map_at_10
            value: 61.646
          - type: map_at_100
            value: 62.197
          - type: map_at_1000
            value: 62.22800000000001
          - type: map_at_3
            value: 58.411
          - type: map_at_5
            value: 60.585
          - type: mrr_at_1
            value: 55.00000000000001
          - type: mrr_at_10
            value: 62.690999999999995
          - type: mrr_at_100
            value: 63.139
          - type: mrr_at_1000
            value: 63.166999999999994
          - type: mrr_at_3
            value: 60.111000000000004
          - type: mrr_at_5
            value: 61.778
          - type: ndcg_at_1
            value: 55.00000000000001
          - type: ndcg_at_10
            value: 66.271
          - type: ndcg_at_100
            value: 68.879
          - type: ndcg_at_1000
            value: 69.722
          - type: ndcg_at_3
            value: 60.672000000000004
          - type: ndcg_at_5
            value: 63.929
          - type: precision_at_1
            value: 55.00000000000001
          - type: precision_at_10
            value: 9
          - type: precision_at_100
            value: 1.043
          - type: precision_at_1000
            value: 0.11100000000000002
          - type: precision_at_3
            value: 23.555999999999997
          - type: precision_at_5
            value: 16.2
          - type: recall_at_1
            value: 52.093999999999994
          - type: recall_at_10
            value: 79.567
          - type: recall_at_100
            value: 91.60000000000001
          - type: recall_at_1000
            value: 98.333
          - type: recall_at_3
            value: 64.633
          - type: recall_at_5
            value: 72.68299999999999
      - task:
          type: PairClassification
        dataset:
          type: mteb/sprintduplicatequestions-pairclassification
          name: MTEB SprintDuplicateQuestions
          config: default
          split: test
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
        metrics:
          - type: cos_sim_accuracy
            value: 99.83267326732673
          - type: cos_sim_ap
            value: 95.77995366495178
          - type: cos_sim_f1
            value: 91.51180311401306
          - type: cos_sim_precision
            value: 91.92734611503532
          - type: cos_sim_recall
            value: 91.10000000000001
          - type: dot_accuracy
            value: 99.63366336633663
          - type: dot_ap
            value: 88.53996286967461
          - type: dot_f1
            value: 81.06537530266343
          - type: dot_precision
            value: 78.59154929577464
          - type: dot_recall
            value: 83.7
          - type: euclidean_accuracy
            value: 99.82376237623762
          - type: euclidean_ap
            value: 95.53192209281187
          - type: euclidean_f1
            value: 91.19683481701286
          - type: euclidean_precision
            value: 90.21526418786692
          - type: euclidean_recall
            value: 92.2
          - type: manhattan_accuracy
            value: 99.82376237623762
          - type: manhattan_ap
            value: 95.55642082191741
          - type: manhattan_f1
            value: 91.16186693147964
          - type: manhattan_precision
            value: 90.53254437869822
          - type: manhattan_recall
            value: 91.8
          - type: max_accuracy
            value: 99.83267326732673
          - type: max_ap
            value: 95.77995366495178
          - type: max_f1
            value: 91.51180311401306
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering
          name: MTEB StackExchangeClustering
          config: default
          split: test
          revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
        metrics:
          - type: v_measure
            value: 54.508462134213474
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering-p2p
          name: MTEB StackExchangeClusteringP2P
          config: default
          split: test
          revision: 815ca46b2622cec33ccafc3735d572c266efdb44
        metrics:
          - type: v_measure
            value: 34.06549765184959
      - task:
          type: Reranking
        dataset:
          type: mteb/stackoverflowdupquestions-reranking
          name: MTEB StackOverflowDupQuestions
          config: default
          split: test
          revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
        metrics:
          - type: map
            value: 49.43129549466616
          - type: mrr
            value: 50.20613169510227
      - task:
          type: Summarization
        dataset:
          type: mteb/summeval
          name: MTEB SummEval
          config: default
          split: test
          revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
        metrics:
          - type: cos_sim_pearson
            value: 30.069516173193044
          - type: cos_sim_spearman
            value: 29.872498354017353
          - type: dot_pearson
            value: 28.80761257516063
          - type: dot_spearman
            value: 28.397422678527708
      - task:
          type: Retrieval
        dataset:
          type: trec-covid
          name: MTEB TRECCOVID
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 0.169
          - type: map_at_10
            value: 1.208
          - type: map_at_100
            value: 5.925
          - type: map_at_1000
            value: 14.427000000000001
          - type: map_at_3
            value: 0.457
          - type: map_at_5
            value: 0.716
          - type: mrr_at_1
            value: 64
          - type: mrr_at_10
            value: 74.075
          - type: mrr_at_100
            value: 74.303
          - type: mrr_at_1000
            value: 74.303
          - type: mrr_at_3
            value: 71
          - type: mrr_at_5
            value: 72.89999999999999
          - type: ndcg_at_1
            value: 57.99999999999999
          - type: ndcg_at_10
            value: 50.376
          - type: ndcg_at_100
            value: 38.582
          - type: ndcg_at_1000
            value: 35.663
          - type: ndcg_at_3
            value: 55.592
          - type: ndcg_at_5
            value: 53.647999999999996
          - type: precision_at_1
            value: 64
          - type: precision_at_10
            value: 53.2
          - type: precision_at_100
            value: 39.6
          - type: precision_at_1000
            value: 16.218
          - type: precision_at_3
            value: 59.333000000000006
          - type: precision_at_5
            value: 57.599999999999994
          - type: recall_at_1
            value: 0.169
          - type: recall_at_10
            value: 1.423
          - type: recall_at_100
            value: 9.049999999999999
          - type: recall_at_1000
            value: 34.056999999999995
          - type: recall_at_3
            value: 0.48700000000000004
          - type: recall_at_5
            value: 0.792
      - task:
          type: Retrieval
        dataset:
          type: webis-touche2020
          name: MTEB Touche2020
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 1.319
          - type: map_at_10
            value: 7.112
          - type: map_at_100
            value: 12.588
          - type: map_at_1000
            value: 14.056
          - type: map_at_3
            value: 2.8049999999999997
          - type: map_at_5
            value: 4.68
          - type: mrr_at_1
            value: 18.367
          - type: mrr_at_10
            value: 33.94
          - type: mrr_at_100
            value: 35.193000000000005
          - type: mrr_at_1000
            value: 35.193000000000005
          - type: mrr_at_3
            value: 29.932
          - type: mrr_at_5
            value: 32.279
          - type: ndcg_at_1
            value: 15.306000000000001
          - type: ndcg_at_10
            value: 18.096
          - type: ndcg_at_100
            value: 30.512
          - type: ndcg_at_1000
            value: 42.148
          - type: ndcg_at_3
            value: 17.034
          - type: ndcg_at_5
            value: 18.509
          - type: precision_at_1
            value: 18.367
          - type: precision_at_10
            value: 18.776
          - type: precision_at_100
            value: 7.02
          - type: precision_at_1000
            value: 1.467
          - type: precision_at_3
            value: 19.048000000000002
          - type: precision_at_5
            value: 22.041
          - type: recall_at_1
            value: 1.319
          - type: recall_at_10
            value: 13.748
          - type: recall_at_100
            value: 43.972
          - type: recall_at_1000
            value: 79.557
          - type: recall_at_3
            value: 4.042
          - type: recall_at_5
            value: 7.742
      - task:
          type: Classification
        dataset:
          type: mteb/toxic_conversations_50k
          name: MTEB ToxicConversationsClassification
          config: default
          split: test
          revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
        metrics:
          - type: accuracy
            value: 70.2282
          - type: ap
            value: 13.995763859570426
          - type: f1
            value: 54.08126256731344
      - task:
          type: Classification
        dataset:
          type: mteb/tweet_sentiment_extraction
          name: MTEB TweetSentimentExtractionClassification
          config: default
          split: test
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
        metrics:
          - type: accuracy
            value: 57.64006791171477
          - type: f1
            value: 57.95841320748957
      - task:
          type: Clustering
        dataset:
          type: mteb/twentynewsgroups-clustering
          name: MTEB TwentyNewsgroupsClustering
          config: default
          split: test
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
        metrics:
          - type: v_measure
            value: 40.19267841788564
      - task:
          type: PairClassification
        dataset:
          type: mteb/twittersemeval2015-pairclassification
          name: MTEB TwitterSemEval2015
          config: default
          split: test
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
        metrics:
          - type: cos_sim_accuracy
            value: 83.96614412588663
          - type: cos_sim_ap
            value: 67.75985678572738
          - type: cos_sim_f1
            value: 64.04661542276222
          - type: cos_sim_precision
            value: 60.406922357343305
          - type: cos_sim_recall
            value: 68.15303430079156
          - type: dot_accuracy
            value: 79.5732252488526
          - type: dot_ap
            value: 51.30562107572645
          - type: dot_f1
            value: 53.120759837177744
          - type: dot_precision
            value: 46.478037198258804
          - type: dot_recall
            value: 61.97889182058047
          - type: euclidean_accuracy
            value: 84.00786791440663
          - type: euclidean_ap
            value: 67.58930214486998
          - type: euclidean_f1
            value: 64.424821579775
          - type: euclidean_precision
            value: 59.4817958454322
          - type: euclidean_recall
            value: 70.26385224274406
          - type: manhattan_accuracy
            value: 83.87673600762949
          - type: manhattan_ap
            value: 67.4250981523309
          - type: manhattan_f1
            value: 64.10286658015808
          - type: manhattan_precision
            value: 57.96885001066781
          - type: manhattan_recall
            value: 71.68865435356201
          - type: max_accuracy
            value: 84.00786791440663
          - type: max_ap
            value: 67.75985678572738
          - type: max_f1
            value: 64.424821579775
      - task:
          type: PairClassification
        dataset:
          type: mteb/twitterurlcorpus-pairclassification
          name: MTEB TwitterURLCorpus
          config: default
          split: test
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
        metrics:
          - type: cos_sim_accuracy
            value: 88.41347459929368
          - type: cos_sim_ap
            value: 84.89261930113058
          - type: cos_sim_f1
            value: 77.13677607258877
          - type: cos_sim_precision
            value: 74.88581164358733
          - type: cos_sim_recall
            value: 79.52725592854944
          - type: dot_accuracy
            value: 86.32359219156285
          - type: dot_ap
            value: 79.29794992131094
          - type: dot_f1
            value: 72.84356337679777
          - type: dot_precision
            value: 67.31761478675462
          - type: dot_recall
            value: 79.35786880197105
          - type: euclidean_accuracy
            value: 88.33585593976791
          - type: euclidean_ap
            value: 84.73257641312746
          - type: euclidean_f1
            value: 76.83529582788195
          - type: euclidean_precision
            value: 72.76294052863436
          - type: euclidean_recall
            value: 81.3905143209116
          - type: manhattan_accuracy
            value: 88.3086894089339
          - type: manhattan_ap
            value: 84.66304891729399
          - type: manhattan_f1
            value: 76.8181650632165
          - type: manhattan_precision
            value: 73.6864436744219
          - type: manhattan_recall
            value: 80.22790267939637
          - type: max_accuracy
            value: 88.41347459929368
          - type: max_ap
            value: 84.89261930113058
          - type: max_f1
            value: 77.13677607258877
license: mit

bge-micro-v2

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Distilled in a 2-step training process (bge-micro was step 1) from BAAI/bge-small-en-v1.5.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

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, 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.

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

For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net

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