TaylorAI_bge-micro-v2

---

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