tags:

• mteb
• sentence-similarity
• sentence-transformers
• Sentence Transformers model-index:
• name: gte-large results:
  • task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics:
    • type: accuracy value: 72.62686567164178
    • type: ap value: 34.46944126809772
    • type: f1 value: 66.23684353950857
  • task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics:
    • type: accuracy value: 92.51805
    • type: ap value: 89.49842783330848
    • type: f1 value: 92.51112169431808
  • task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
    • type: accuracy value: 49.074
    • type: f1 value: 48.44785682572955
  • task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics:
    • type: map_at_1 value: 32.077
    • type: map_at_10 value: 48.153
    • type: map_at_100 value: 48.963
    • type: map_at_1000 value: 48.966
    • type: map_at_3 value: 43.184
    • type: map_at_5 value: 46.072
    • type: mrr_at_1 value: 33.073
    • type: mrr_at_10 value: 48.54
    • type: mrr_at_100 value: 49.335
    • type: mrr_at_1000 value: 49.338
    • type: mrr_at_3 value: 43.563
    • type: mrr_at_5 value: 46.383
    • type: ndcg_at_1 value: 32.077
    • type: ndcg_at_10 value: 57.158
    • type: ndcg_at_100 value: 60.324999999999996
    • type: ndcg_at_1000 value: 60.402
    • type: ndcg_at_3 value: 46.934
    • type: ndcg_at_5 value: 52.158
    • type: precision_at_1 value: 32.077
    • type: precision_at_10 value: 8.591999999999999
    • type: precision_at_100 value: 0.991
    • type: precision_at_1000 value: 0.1
    • type: precision_at_3 value: 19.275000000000002
    • type: precision_at_5 value: 14.111
    • type: recall_at_1 value: 32.077
    • type: recall_at_10 value: 85.917
    • type: recall_at_100 value: 99.075
    • type: recall_at_1000 value: 99.644
    • type: recall_at_3 value: 57.824
    • type: recall_at_5 value: 70.555
  • task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics:
    • type: v_measure value: 48.619246083417295
  • task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics:
    • type: v_measure value: 43.3574067664688
  • task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics:
    • type: map value: 63.06359661829253
    • type: mrr value: 76.15596007562766
  • task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics:
    • type: cos_sim_pearson value: 90.25407547368691
    • type: cos_sim_spearman value: 88.65081514968477
    • type: euclidean_pearson value: 88.14857116664494
    • type: euclidean_spearman value: 88.50683596540692
    • type: manhattan_pearson value: 87.9654797992225
    • type: manhattan_spearman value: 88.21164851646908
  • task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics:
    • type: accuracy value: 86.05844155844157
    • type: f1 value: 86.01555597681825
  • task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics:
    • type: v_measure value: 39.10510519739522
  • task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics:
    • type: v_measure value: 36.84689960264385
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 32.800000000000004
    • type: map_at_10 value: 44.857
    • type: map_at_100 value: 46.512
    • type: map_at_1000 value: 46.635
    • type: map_at_3 value: 41.062
    • type: map_at_5 value: 43.126
    • type: mrr_at_1 value: 39.628
    • type: mrr_at_10 value: 50.879
    • type: mrr_at_100 value: 51.605000000000004
    • type: mrr_at_1000 value: 51.641000000000005
    • type: mrr_at_3 value: 48.14
    • type: mrr_at_5 value: 49.835
    • type: ndcg_at_1 value: 39.628
    • type: ndcg_at_10 value: 51.819
    • type: ndcg_at_100 value: 57.318999999999996
    • type: ndcg_at_1000 value: 58.955999999999996
    • type: ndcg_at_3 value: 46.409
    • type: ndcg_at_5 value: 48.825
    • type: precision_at_1 value: 39.628
    • type: precision_at_10 value: 10.072000000000001
    • type: precision_at_100 value: 1.625
    • type: precision_at_1000 value: 0.21
    • type: precision_at_3 value: 22.556
    • type: precision_at_5 value: 16.309
    • type: recall_at_1 value: 32.800000000000004
    • type: recall_at_10 value: 65.078
    • type: recall_at_100 value: 87.491
    • type: recall_at_1000 value: 97.514
    • type: recall_at_3 value: 49.561
    • type: recall_at_5 value: 56.135999999999996
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 32.614
    • type: map_at_10 value: 43.578
    • type: map_at_100 value: 44.897
    • type: map_at_1000 value: 45.023
    • type: map_at_3 value: 40.282000000000004
    • type: map_at_5 value: 42.117
    • type: mrr_at_1 value: 40.510000000000005
    • type: mrr_at_10 value: 49.428
    • type: mrr_at_100 value: 50.068999999999996
    • type: mrr_at_1000 value: 50.111000000000004
    • type: mrr_at_3 value: 47.176
    • type: mrr_at_5 value: 48.583999999999996
    • type: ndcg_at_1 value: 40.510000000000005
    • type: ndcg_at_10 value: 49.478
    • type: ndcg_at_100 value: 53.852
    • type: ndcg_at_1000 value: 55.782
    • type: ndcg_at_3 value: 45.091
    • type: ndcg_at_5 value: 47.19
    • type: precision_at_1 value: 40.510000000000005
    • type: precision_at_10 value: 9.363000000000001
    • type: precision_at_100 value: 1.51
    • type: precision_at_1000 value: 0.196
    • type: precision_at_3 value: 21.741
    • type: precision_at_5 value: 15.465000000000002
    • type: recall_at_1 value: 32.614
    • type: recall_at_10 value: 59.782000000000004
    • type: recall_at_100 value: 78.012
    • type: recall_at_1000 value: 90.319
    • type: recall_at_3 value: 46.825
    • type: recall_at_5 value: 52.688
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 40.266000000000005
    • type: map_at_10 value: 53.756
    • type: map_at_100 value: 54.809
    • type: map_at_1000 value: 54.855
    • type: map_at_3 value: 50.073
    • type: map_at_5 value: 52.293
    • type: mrr_at_1 value: 46.332
    • type: mrr_at_10 value: 57.116
    • type: mrr_at_100 value: 57.767
    • type: mrr_at_1000 value: 57.791000000000004
    • type: mrr_at_3 value: 54.461999999999996
    • type: mrr_at_5 value: 56.092
    • type: ndcg_at_1 value: 46.332
    • type: ndcg_at_10 value: 60.092
    • type: ndcg_at_100 value: 64.034
    • type: ndcg_at_1000 value: 64.937
    • type: ndcg_at_3 value: 54.071000000000005
    • type: ndcg_at_5 value: 57.254000000000005
    • type: precision_at_1 value: 46.332
    • type: precision_at_10 value: 9.799
    • type: precision_at_100 value: 1.278
    • type: precision_at_1000 value: 0.13899999999999998
    • type: precision_at_3 value: 24.368000000000002
    • type: precision_at_5 value: 16.89
    • type: recall_at_1 value: 40.266000000000005
    • type: recall_at_10 value: 75.41499999999999
    • type: recall_at_100 value: 92.01700000000001
    • type: recall_at_1000 value: 98.379
    • type: recall_at_3 value: 59.476
    • type: recall_at_5 value: 67.297
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 28.589
    • type: map_at_10 value: 37.755
    • type: map_at_100 value: 38.881
    • type: map_at_1000 value: 38.954
    • type: map_at_3 value: 34.759
    • type: map_at_5 value: 36.544
    • type: mrr_at_1 value: 30.734
    • type: mrr_at_10 value: 39.742
    • type: mrr_at_100 value: 40.774
    • type: mrr_at_1000 value: 40.824
    • type: mrr_at_3 value: 37.137
    • type: mrr_at_5 value: 38.719
    • type: ndcg_at_1 value: 30.734
    • type: ndcg_at_10 value: 42.978
    • type: ndcg_at_100 value: 48.309000000000005
    • type: ndcg_at_1000 value: 50.068
    • type: ndcg_at_3 value: 37.361
    • type: ndcg_at_5 value: 40.268
    • type: precision_at_1 value: 30.734
    • type: precision_at_10 value: 6.565
    • type: precision_at_100 value: 0.964
    • type: precision_at_1000 value: 0.11499999999999999
    • type: precision_at_3 value: 15.744
    • type: precision_at_5 value: 11.096
    • type: recall_at_1 value: 28.589
    • type: recall_at_10 value: 57.126999999999995
    • type: recall_at_100 value: 81.051
    • type: recall_at_1000 value: 94.027
    • type: recall_at_3 value: 42.045
    • type: recall_at_5 value: 49.019
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 18.5
    • type: map_at_10 value: 27.950999999999997
    • type: map_at_100 value: 29.186
    • type: map_at_1000 value: 29.298000000000002
    • type: map_at_3 value: 25.141000000000002
    • type: map_at_5 value: 26.848
    • type: mrr_at_1 value: 22.637
    • type: mrr_at_10 value: 32.572
    • type: mrr_at_100 value: 33.472
    • type: mrr_at_1000 value: 33.533
    • type: mrr_at_3 value: 29.747
    • type: mrr_at_5 value: 31.482
    • type: ndcg_at_1 value: 22.637
    • type: ndcg_at_10 value: 33.73
    • type: ndcg_at_100 value: 39.568
    • type: ndcg_at_1000 value: 42.201
    • type: ndcg_at_3 value: 28.505999999999997
    • type: ndcg_at_5 value: 31.255
    • type: precision_at_1 value: 22.637
    • type: precision_at_10 value: 6.281000000000001
    • type: precision_at_100 value: 1.073
    • type: precision_at_1000 value: 0.14300000000000002
    • type: precision_at_3 value: 13.847000000000001
    • type: precision_at_5 value: 10.224
    • type: recall_at_1 value: 18.5
    • type: recall_at_10 value: 46.744
    • type: recall_at_100 value: 72.072
    • type: recall_at_1000 value: 91.03999999999999
    • type: recall_at_3 value: 32.551
    • type: recall_at_5 value: 39.533
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 30.602
    • type: map_at_10 value: 42.18
    • type: map_at_100 value: 43.6
    • type: map_at_1000 value: 43.704
    • type: map_at_3 value: 38.413000000000004
    • type: map_at_5 value: 40.626
    • type: mrr_at_1 value: 37.344
    • type: mrr_at_10 value: 47.638000000000005
    • type: mrr_at_100 value: 48.485
    • type: mrr_at_1000 value: 48.52
    • type: mrr_at_3 value: 44.867000000000004
    • type: mrr_at_5 value: 46.566
    • type: ndcg_at_1 value: 37.344
    • type: ndcg_at_10 value: 48.632
    • type: ndcg_at_100 value: 54.215
    • type: ndcg_at_1000 value: 55.981
    • type: ndcg_at_3 value: 42.681999999999995
    • type: ndcg_at_5 value: 45.732
    • type: precision_at_1 value: 37.344
    • type: precision_at_10 value: 8.932
    • type: precision_at_100 value: 1.376
    • type: precision_at_1000 value: 0.17099999999999999
    • type: precision_at_3 value: 20.276
    • type: precision_at_5 value: 14.726
    • type: recall_at_1 value: 30.602
    • type: recall_at_10 value: 62.273
    • type: recall_at_100 value: 85.12100000000001
    • type: recall_at_1000 value: 96.439
    • type: recall_at_3 value: 45.848
    • type: recall_at_5 value: 53.615
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 23.952
    • type: map_at_10 value: 35.177
    • type: map_at_100 value: 36.59
    • type: map_at_1000 value: 36.703
    • type: map_at_3 value: 31.261
    • type: map_at_5 value: 33.222
    • type: mrr_at_1 value: 29.337999999999997
    • type: mrr_at_10 value: 40.152
    • type: mrr_at_100 value: 40.963
    • type: mrr_at_1000 value: 41.016999999999996
    • type: mrr_at_3 value: 36.91
    • type: mrr_at_5 value: 38.685
    • type: ndcg_at_1 value: 29.337999999999997
    • type: ndcg_at_10 value: 41.994
    • type: ndcg_at_100 value: 47.587
    • type: ndcg_at_1000 value: 49.791000000000004
    • type: ndcg_at_3 value: 35.27
    • type: ndcg_at_5 value: 38.042
    • type: precision_at_1 value: 29.337999999999997
    • type: precision_at_10 value: 8.276
    • type: precision_at_100 value: 1.276
    • type: precision_at_1000 value: 0.164
    • type: precision_at_3 value: 17.161
    • type: precision_at_5 value: 12.671
    • type: recall_at_1 value: 23.952
    • type: recall_at_10 value: 57.267
    • type: recall_at_100 value: 80.886
    • type: recall_at_1000 value: 95.611
    • type: recall_at_3 value: 38.622
    • type: recall_at_5 value: 45.811
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 27.092083333333335
    • type: map_at_10 value: 37.2925
    • type: map_at_100 value: 38.57041666666666
    • type: map_at_1000 value: 38.68141666666667
    • type: map_at_3 value: 34.080000000000005
    • type: map_at_5 value: 35.89958333333333
    • type: mrr_at_1 value: 31.94758333333333
    • type: mrr_at_10 value: 41.51049999999999
    • type: mrr_at_100 value: 42.36099999999999
    • type: mrr_at_1000 value: 42.4125
    • type: mrr_at_3 value: 38.849583333333335
    • type: mrr_at_5 value: 40.448249999999994
    • type: ndcg_at_1 value: 31.94758333333333
    • type: ndcg_at_10 value: 43.17633333333333
    • type: ndcg_at_100 value: 48.45241666666668
    • type: ndcg_at_1000 value: 50.513999999999996
    • type: ndcg_at_3 value: 37.75216666666667
    • type: ndcg_at_5 value: 40.393833333333326
    • type: precision_at_1 value: 31.94758333333333
    • type: precision_at_10 value: 7.688916666666666
    • type: precision_at_100 value: 1.2250833333333333
    • type: precision_at_1000 value: 0.1595
    • type: precision_at_3 value: 17.465999999999998
    • type: precision_at_5 value: 12.548083333333333
    • type: recall_at_1 value: 27.092083333333335
    • type: recall_at_10 value: 56.286583333333326
    • type: recall_at_100 value: 79.09033333333333
    • type: recall_at_1000 value: 93.27483333333335
    • type: recall_at_3 value: 41.35325
    • type: recall_at_5 value: 48.072750000000006
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 25.825
    • type: map_at_10 value: 33.723
    • type: map_at_100 value: 34.74
    • type: map_at_1000 value: 34.824
    • type: map_at_3 value: 31.369000000000003
    • type: map_at_5 value: 32.533
    • type: mrr_at_1 value: 29.293999999999997
    • type: mrr_at_10 value: 36.84
    • type: mrr_at_100 value: 37.681
    • type: mrr_at_1000 value: 37.742
    • type: mrr_at_3 value: 34.79
    • type: mrr_at_5 value: 35.872
    • type: ndcg_at_1 value: 29.293999999999997
    • type: ndcg_at_10 value: 38.385999999999996
    • type: ndcg_at_100 value: 43.327
    • type: ndcg_at_1000 value: 45.53
    • type: ndcg_at_3 value: 33.985
    • type: ndcg_at_5 value: 35.817
    • type: precision_at_1 value: 29.293999999999997
    • type: precision_at_10 value: 6.12
    • type: precision_at_100 value: 0.9329999999999999
    • type: precision_at_1000 value: 0.11900000000000001
    • type: precision_at_3 value: 14.621999999999998
    • type: precision_at_5 value: 10.030999999999999
    • type: recall_at_1 value: 25.825
    • type: recall_at_10 value: 49.647000000000006
    • type: recall_at_100 value: 72.32300000000001
    • type: recall_at_1000 value: 88.62400000000001
    • type: recall_at_3 value: 37.366
    • type: recall_at_5 value: 41.957
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 18.139
    • type: map_at_10 value: 26.107000000000003
    • type: map_at_100 value: 27.406999999999996
    • type: map_at_1000 value: 27.535999999999998
    • type: map_at_3 value: 23.445
    • type: map_at_5 value: 24.916
    • type: mrr_at_1 value: 21.817
    • type: mrr_at_10 value: 29.99
    • type: mrr_at_100 value: 31.052000000000003
    • type: mrr_at_1000 value: 31.128
    • type: mrr_at_3 value: 27.627000000000002
    • type: mrr_at_5 value: 29.005
    • type: ndcg_at_1 value: 21.817
    • type: ndcg_at_10 value: 31.135
    • type: ndcg_at_100 value: 37.108000000000004
    • type: ndcg_at_1000 value: 39.965
    • type: ndcg_at_3 value: 26.439
    • type: ndcg_at_5 value: 28.655
    • type: precision_at_1 value: 21.817
    • type: precision_at_10 value: 5.757000000000001
    • type: precision_at_100 value: 1.036
    • type: precision_at_1000 value: 0.147
    • type: precision_at_3 value: 12.537
    • type: precision_at_5 value: 9.229
    • type: recall_at_1 value: 18.139
    • type: recall_at_10 value: 42.272999999999996
    • type: recall_at_100 value: 68.657
    • type: recall_at_1000 value: 88.93799999999999
    • type: recall_at_3 value: 29.266
    • type: recall_at_5 value: 34.892
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 27.755000000000003
    • type: map_at_10 value: 37.384
    • type: map_at_100 value: 38.56
    • type: map_at_1000 value: 38.655
    • type: map_at_3 value: 34.214
    • type: map_at_5 value: 35.96
    • type: mrr_at_1 value: 32.369
    • type: mrr_at_10 value: 41.625
    • type: mrr_at_100 value: 42.449
    • type: mrr_at_1000 value: 42.502
    • type: mrr_at_3 value: 38.899
    • type: mrr_at_5 value: 40.489999999999995
    • type: ndcg_at_1 value: 32.369
    • type: ndcg_at_10 value: 43.287
    • type: ndcg_at_100 value: 48.504999999999995
    • type: ndcg_at_1000 value: 50.552
    • type: ndcg_at_3 value: 37.549
    • type: ndcg_at_5 value: 40.204
    • type: precision_at_1 value: 32.369
    • type: precision_at_10 value: 7.425
    • type: precision_at_100 value: 1.134
    • type: precision_at_1000 value: 0.14200000000000002
    • type: precision_at_3 value: 17.102
    • type: precision_at_5 value: 12.107999999999999
    • type: recall_at_1 value: 27.755000000000003
    • type: recall_at_10 value: 57.071000000000005
    • type: recall_at_100 value: 79.456
    • type: recall_at_1000 value: 93.54299999999999
    • type: recall_at_3 value: 41.298
    • type: recall_at_5 value: 48.037
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 24.855
    • type: map_at_10 value: 34.53
    • type: map_at_100 value: 36.167
    • type: map_at_1000 value: 36.394999999999996
    • type: map_at_3 value: 31.037
    • type: map_at_5 value: 33.119
    • type: mrr_at_1 value: 30.631999999999998
    • type: mrr_at_10 value: 39.763999999999996
    • type: mrr_at_100 value: 40.77
    • type: mrr_at_1000 value: 40.826
    • type: mrr_at_3 value: 36.495
    • type: mrr_at_5 value: 38.561
    • type: ndcg_at_1 value: 30.631999999999998
    • type: ndcg_at_10 value: 40.942
    • type: ndcg_at_100 value: 47.07
    • type: ndcg_at_1000 value: 49.363
    • type: ndcg_at_3 value: 35.038000000000004
    • type: ndcg_at_5 value: 38.161
    • type: precision_at_1 value: 30.631999999999998
    • type: precision_at_10 value: 7.983999999999999
    • type: precision_at_100 value: 1.6070000000000002
    • type: precision_at_1000 value: 0.246
    • type: precision_at_3 value: 16.206
    • type: precision_at_5 value: 12.253
    • type: recall_at_1 value: 24.855
    • type: recall_at_10 value: 53.291999999999994
    • type: recall_at_100 value: 80.283
    • type: recall_at_1000 value: 94.309
    • type: recall_at_3 value: 37.257
    • type: recall_at_5 value: 45.282
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 21.208
    • type: map_at_10 value: 30.512
    • type: map_at_100 value: 31.496000000000002
    • type: map_at_1000 value: 31.595000000000002
    • type: map_at_3 value: 27.904
    • type: map_at_5 value: 29.491
    • type: mrr_at_1 value: 22.736
    • type: mrr_at_10 value: 32.379999999999995
    • type: mrr_at_100 value: 33.245000000000005
    • type: mrr_at_1000 value: 33.315
    • type: mrr_at_3 value: 29.945
    • type: mrr_at_5 value: 31.488
    • type: ndcg_at_1 value: 22.736
    • type: ndcg_at_10 value: 35.643
    • type: ndcg_at_100 value: 40.535
    • type: ndcg_at_1000 value: 43.042
    • type: ndcg_at_3 value: 30.625000000000004
    • type: ndcg_at_5 value: 33.323
    • type: precision_at_1 value: 22.736
    • type: precision_at_10 value: 5.6930000000000005
    • type: precision_at_100 value: 0.889
    • type: precision_at_1000 value: 0.122
    • type: precision_at_3 value: 13.431999999999999
    • type: precision_at_5 value: 9.575
    • type: recall_at_1 value: 21.208
    • type: recall_at_10 value: 49.47
    • type: recall_at_100 value: 71.71499999999999
    • type: recall_at_1000 value: 90.55499999999999
    • type: recall_at_3 value: 36.124
    • type: recall_at_5 value: 42.606
  • task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics:
    • type: map_at_1 value: 11.363
    • type: map_at_10 value: 20.312
    • type: map_at_100 value: 22.225
    • type: map_at_1000 value: 22.411
    • type: map_at_3 value: 16.68
    • type: map_at_5 value: 18.608
    • type: mrr_at_1 value: 25.537
    • type: mrr_at_10 value: 37.933
    • type: mrr_at_100 value: 38.875
    • type: mrr_at_1000 value: 38.911
    • type: mrr_at_3 value: 34.387
    • type: mrr_at_5 value: 36.51
    • type: ndcg_at_1 value: 25.537
    • type: ndcg_at_10 value: 28.82
    • type: ndcg_at_100 value: 36.341
    • type: ndcg_at_1000 value: 39.615
    • type: ndcg_at_3 value: 23.01
    • type: ndcg_at_5 value: 25.269000000000002
    • type: precision_at_1 value: 25.537
    • type: precision_at_10 value: 9.153
    • type: precision_at_100 value: 1.7319999999999998
    • type: precision_at_1000 value: 0.234
    • type: precision_at_3 value: 17.22
    • type: precision_at_5 value: 13.629
    • type: recall_at_1 value: 11.363
    • type: recall_at_10 value: 35.382999999999996
    • type: recall_at_100 value: 61.367000000000004
    • type: recall_at_1000 value: 79.699
    • type: recall_at_3 value: 21.495
    • type: recall_at_5 value: 27.42
  • task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics:
    • type: map_at_1 value: 9.65
    • type: map_at_10 value: 20.742
    • type: map_at_100 value: 29.614
    • type: map_at_1000 value: 31.373
    • type: map_at_3 value: 14.667
    • type: map_at_5 value: 17.186
    • type: mrr_at_1 value: 69.75
    • type: mrr_at_10 value: 76.762
    • type: mrr_at_100 value: 77.171
    • type: mrr_at_1000 value: 77.179
    • type: mrr_at_3 value: 75.125
    • type: mrr_at_5 value: 76.287
    • type: ndcg_at_1 value: 57.62500000000001
    • type: ndcg_at_10 value: 42.370999999999995
    • type: ndcg_at_100 value: 47.897
    • type: ndcg_at_1000 value: 55.393
    • type: ndcg_at_3 value: 46.317
    • type: ndcg_at_5 value: 43.906
    • type: precision_at_1 value: 69.75
    • type: precision_at_10 value: 33.95
    • type: precision_at_100 value: 10.885
    • type: precision_at_1000 value: 2.2239999999999998
    • type: precision_at_3 value: 49.75
    • type: precision_at_5 value: 42.3
    • type: recall_at_1 value: 9.65
    • type: recall_at_10 value: 26.117
    • type: recall_at_100 value: 55.084
    • type: recall_at_1000 value: 78.62400000000001
    • type: recall_at_3 value: 15.823
    • type: recall_at_5 value: 19.652
  • task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics:
    • type: accuracy value: 47.885
    • type: f1 value: 42.99567641346983
  • task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics:
    • type: map_at_1 value: 70.97
    • type: map_at_10 value: 80.34599999999999
    • type: map_at_100 value: 80.571
    • type: map_at_1000 value: 80.584
    • type: map_at_3 value: 79.279
    • type: map_at_5 value: 79.94
    • type: mrr_at_1 value: 76.613
    • type: mrr_at_10 value: 85.15700000000001
    • type: mrr_at_100 value: 85.249
    • type: mrr_at_1000 value: 85.252
    • type: mrr_at_3 value: 84.33800000000001
    • type: mrr_at_5 value: 84.89
    • type: ndcg_at_1 value: 76.613
    • type: ndcg_at_10 value: 84.53399999999999
    • type: ndcg_at_100 value: 85.359
    • type: ndcg_at_1000 value: 85.607
    • type: ndcg_at_3 value: 82.76599999999999
    • type: ndcg_at_5 value: 83.736
    • type: precision_at_1 value: 76.613
    • type: precision_at_10 value: 10.206
    • type: precision_at_100 value: 1.083
    • type: precision_at_1000 value: 0.11199999999999999
    • type: precision_at_3 value: 31.913000000000004
    • type: precision_at_5 value: 19.769000000000002
    • type: recall_at_1 value: 70.97
    • type: recall_at_10 value: 92.674
    • type: recall_at_100 value: 95.985
    • type: recall_at_1000 value: 97.57000000000001
    • type: recall_at_3 value: 87.742
    • type: recall_at_5 value: 90.28
  • task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics:
    • type: map_at_1 value: 22.494
    • type: map_at_10 value: 36.491
    • type: map_at_100 value: 38.550000000000004
    • type: map_at_1000 value: 38.726
    • type: map_at_3 value: 31.807000000000002
    • type: map_at_5 value: 34.299
    • type: mrr_at_1 value: 44.907000000000004
    • type: mrr_at_10 value: 53.146
    • type: mrr_at_100 value: 54.013999999999996
    • type: mrr_at_1000 value: 54.044000000000004
    • type: mrr_at_3 value: 50.952
    • type: mrr_at_5 value: 52.124
    • type: ndcg_at_1 value: 44.907000000000004
    • type: ndcg_at_10 value: 44.499
    • type: ndcg_at_100 value: 51.629000000000005
    • type: ndcg_at_1000 value: 54.367
    • type: ndcg_at_3 value: 40.900999999999996
    • type: ndcg_at_5 value: 41.737
    • type: precision_at_1 value: 44.907000000000004
    • type: precision_at_10 value: 12.346
    • type: precision_at_100 value: 1.974
    • type: precision_at_1000 value: 0.246
    • type: precision_at_3 value: 27.366
    • type: precision_at_5 value: 19.846
    • type: recall_at_1 value: 22.494
    • type: recall_at_10 value: 51.156
    • type: recall_at_100 value: 77.11200000000001
    • type: recall_at_1000 value: 93.44
    • type: recall_at_3 value: 36.574
    • type: recall_at_5 value: 42.361
  • task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics:
    • type: map_at_1 value: 38.568999999999996
    • type: map_at_10 value: 58.485
    • type: map_at_100 value: 59.358999999999995
    • type: map_at_1000 value: 59.429
    • type: map_at_3 value: 55.217000000000006
    • type: map_at_5 value: 57.236
    • type: mrr_at_1 value: 77.137
    • type: mrr_at_10 value: 82.829
    • type: mrr_at_100 value: 83.04599999999999
    • type: mrr_at_1000 value: 83.05399999999999
    • type: mrr_at_3 value: 81.904
    • type: mrr_at_5 value: 82.50800000000001
    • type: ndcg_at_1 value: 77.137
    • type: ndcg_at_10 value: 67.156
    • type: ndcg_at_100 value: 70.298
    • type: ndcg_at_1000 value: 71.65700000000001
    • type: ndcg_at_3 value: 62.535
    • type: ndcg_at_5 value: 65.095
    • type: precision_at_1 value: 77.137
    • type: precision_at_10 value: 13.911999999999999
    • type: precision_at_100 value: 1.6389999999999998
    • type: precision_at_1000 value: 0.182
    • type: precision_at_3 value: 39.572
    • type: precision_at_5 value: 25.766
    • type: recall_at_1 value: 38.568999999999996
    • type: recall_at_10 value: 69.56099999999999
    • type: recall_at_100 value: 81.931
    • type: recall_at_1000 value: 90.91799999999999
    • type: recall_at_3 value: 59.358999999999995
    • type: recall_at_5 value: 64.416
  • task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics:
    • type: accuracy value: 88.45600000000002
    • type: ap value: 84.09725115338568
    • type: f1 value: 88.41874909080512
  • task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics:
    • type: map_at_1 value: 21.404999999999998
    • type: map_at_10 value: 33.921
    • type: map_at_100 value: 35.116
    • type: map_at_1000 value: 35.164
    • type: map_at_3 value: 30.043999999999997
    • type: map_at_5 value: 32.327
    • type: mrr_at_1 value: 21.977
    • type: mrr_at_10 value: 34.505
    • type: mrr_at_100 value: 35.638999999999996
    • type: mrr_at_1000 value: 35.68
    • type: mrr_at_3 value: 30.703999999999997
    • type: mrr_at_5 value: 32.96
    • type: ndcg_at_1 value: 21.963
    • type: ndcg_at_10 value: 40.859
    • type: ndcg_at_100 value: 46.614
    • type: ndcg_at_1000 value: 47.789
    • type: ndcg_at_3 value: 33.007999999999996
    • type: ndcg_at_5 value: 37.084
    • type: precision_at_1 value: 21.963
    • type: precision_at_10 value: 6.493
    • type: precision_at_100 value: 0.938
    • type: precision_at_1000 value: 0.104
    • type: precision_at_3 value: 14.155000000000001
    • type: precision_at_5 value: 10.544
    • type: recall_at_1 value: 21.404999999999998
    • type: recall_at_10 value: 62.175000000000004
    • type: recall_at_100 value: 88.786
    • type: recall_at_1000 value: 97.738
    • type: recall_at_3 value: 40.925
    • type: recall_at_5 value: 50.722
  • task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics:
    • type: accuracy value: 93.50661194710442
    • type: f1 value: 93.30311193153668
  • task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics:
    • type: accuracy value: 73.24669402644778
    • type: f1 value: 54.23122108002977
  • task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics:
    • type: accuracy value: 72.61936785474109
    • type: f1 value: 70.52644941025565
  • task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
    • type: accuracy value: 76.76529926025555
    • type: f1 value: 77.26872729322514
  • task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics:
    • type: v_measure value: 33.39450293021839
  • task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics:
    • type: v_measure value: 31.757796879839294
  • task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics:
    • type: map value: 32.62512146657428
    • type: mrr value: 33.84624322066173
  • task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics:
    • type: map_at_1 value: 6.462
    • type: map_at_10 value: 14.947
    • type: map_at_100 value: 19.344
    • type: map_at_1000 value: 20.933
    • type: map_at_3 value: 10.761999999999999
    • type: map_at_5 value: 12.744
    • type: mrr_at_1 value: 47.988
    • type: mrr_at_10 value: 57.365
    • type: mrr_at_100 value: 57.931
    • type: mrr_at_1000 value: 57.96
    • type: mrr_at_3 value: 54.85
    • type: mrr_at_5 value: 56.569
    • type: ndcg_at_1 value: 46.129999999999995
    • type: ndcg_at_10 value: 38.173
    • type: ndcg_at_100 value: 35.983
    • type: ndcg_at_1000 value: 44.507000000000005
    • type: ndcg_at_3 value: 42.495
    • type: ndcg_at_5 value: 41.019
    • type: precision_at_1 value: 47.678
    • type: precision_at_10 value: 28.731
    • type: precision_at_100 value: 9.232
    • type: precision_at_1000 value: 2.202
    • type: precision_at_3 value: 39.628
    • type: precision_at_5 value: 35.851
    • type: recall_at_1 value: 6.462
    • type: recall_at_10 value: 18.968
    • type: recall_at_100 value: 37.131
    • type: recall_at_1000 value: 67.956
    • type: recall_at_3 value: 11.905000000000001
    • type: recall_at_5 value: 15.097
  • task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics:
    • type: map_at_1 value: 30.335
    • type: map_at_10 value: 46.611999999999995
    • type: map_at_100 value: 47.632000000000005
    • type: map_at_1000 value: 47.661
    • type: map_at_3 value: 41.876999999999995
    • type: map_at_5 value: 44.799
    • type: mrr_at_1 value: 34.125
    • type: mrr_at_10 value: 49.01
    • type: mrr_at_100 value: 49.75
    • type: mrr_at_1000 value: 49.768
    • type: mrr_at_3 value: 45.153
    • type: mrr_at_5 value: 47.589999999999996
    • type: ndcg_at_1 value: 34.125
    • type: ndcg_at_10 value: 54.777
    • type: ndcg_at_100 value: 58.914
    • type: ndcg_at_1000 value: 59.521
    • type: ndcg_at_3 value: 46.015
    • type: ndcg_at_5 value: 50.861000000000004
    • type: precision_at_1 value: 34.125
    • type: precision_at_10 value: 9.166
    • type: precision_at_100 value: 1.149
    • type: precision_at_1000 value: 0.121
    • type: precision_at_3 value: 21.147
    • type: precision_at_5 value: 15.469
    • type: recall_at_1 value: 30.335
    • type: recall_at_10 value: 77.194
    • type: recall_at_100 value: 94.812
    • type: recall_at_1000 value: 99.247
    • type: recall_at_3 value: 54.681000000000004
    • type: recall_at_5 value: 65.86800000000001
  • task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 70.62
    • type: map_at_10 value: 84.536
    • type: map_at_100 value: 85.167
    • type: map_at_1000 value: 85.184
    • type: map_at_3 value: 81.607
    • type: map_at_5 value: 83.423
    • type: mrr_at_1 value: 81.36
    • type: mrr_at_10 value: 87.506
    • type: mrr_at_100 value: 87.601
    • type: mrr_at_1000 value: 87.601
    • type: mrr_at_3 value: 86.503
    • type: mrr_at_5 value: 87.179
    • type: ndcg_at_1 value: 81.36
    • type: ndcg_at_10 value: 88.319
    • type: ndcg_at_100 value: 89.517
    • type: ndcg_at_1000 value: 89.60900000000001
    • type: ndcg_at_3 value: 85.423
    • type: ndcg_at_5 value: 86.976
    • type: precision_at_1 value: 81.36
    • type: precision_at_10 value: 13.415
    • type: precision_at_100 value: 1.529
    • type: precision_at_1000 value: 0.157
    • type: precision_at_3 value: 37.342999999999996
    • type: precision_at_5 value: 24.534
    • type: recall_at_1 value: 70.62
    • type: recall_at_10 value: 95.57600000000001
    • type: recall_at_100 value: 99.624
    • type: recall_at_1000 value: 99.991
    • type: recall_at_3 value: 87.22
    • type: recall_at_5 value: 91.654
  • task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics:
    • type: v_measure value: 60.826438478212744
  • task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics:
    • type: v_measure value: 64.24027467551447
  • task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics:
    • type: map_at_1 value: 4.997999999999999
    • type: map_at_10 value: 14.267
    • type: map_at_100 value: 16.843
    • type: map_at_1000 value: 17.229
    • type: map_at_3 value: 9.834
    • type: map_at_5 value: 11.92
    • type: mrr_at_1 value: 24.7
    • type: mrr_at_10 value: 37.685
    • type: mrr_at_100 value: 38.704
    • type: mrr_at_1000 value: 38.747
    • type: mrr_at_3 value: 34.150000000000006
    • type: mrr_at_5 value: 36.075
    • type: ndcg_at_1 value: 24.7
    • type: ndcg_at_10 value: 23.44
    • type: ndcg_at_100 value: 32.617000000000004
    • type: ndcg_at_1000 value: 38.628
    • type: ndcg_at_3 value: 21.747
    • type: ndcg_at_5 value: 19.076
    • type: precision_at_1 value: 24.7
    • type: precision_at_10 value: 12.47
    • type: precision_at_100 value: 2.564
    • type: precision_at_1000 value: 0.4
    • type: precision_at_3 value: 20.767
    • type: precision_at_5 value: 17.06
    • type: recall_at_1 value: 4.997999999999999
    • type: recall_at_10 value: 25.3
    • type: recall_at_100 value: 52.048
    • type: recall_at_1000 value: 81.093
    • type: recall_at_3 value: 12.642999999999999
    • type: recall_at_5 value: 17.312
  • task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics:
    • type: cos_sim_pearson value: 85.44942006292234
    • type: cos_sim_spearman value: 79.80930790660699
    • type: euclidean_pearson value: 82.93400777494863
    • type: euclidean_spearman value: 80.04664991110705
    • type: manhattan_pearson value: 82.93551681854949
    • type: manhattan_spearman value: 80.03156736837379
  • task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics:
    • type: cos_sim_pearson value: 85.63574059135726
    • type: cos_sim_spearman value: 76.80552915288186
    • type: euclidean_pearson value: 82.46368529820518
    • type: euclidean_spearman value: 76.60338474719275
    • type: manhattan_pearson value: 82.4558617035968
    • type: manhattan_spearman value: 76.57936082895705
  • task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics:
    • type: cos_sim_pearson value: 86.24116811084211
    • type: cos_sim_spearman value: 88.10998662068769
    • type: euclidean_pearson value: 87.04961732352689
    • type: euclidean_spearman value: 88.12543945864087
    • type: manhattan_pearson value: 86.9905224528854
    • type: manhattan_spearman value: 88.07827944705546
  • task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics:
    • type: cos_sim_pearson value: 84.74847296555048
    • type: cos_sim_spearman value: 82.66200957916445
    • type: euclidean_pearson value: 84.48132256004965
    • type: euclidean_spearman value: 82.67915286000596
    • type: manhattan_pearson value: 84.44950477268334
    • type: manhattan_spearman value: 82.63327639173352
  • task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics:
    • type: cos_sim_pearson value: 87.23056258027053
    • type: cos_sim_spearman value: 88.92791680286955
    • type: euclidean_pearson value: 88.13819235461933
    • type: euclidean_spearman value: 88.87294661361716
    • type: manhattan_pearson value: 88.14212133687899
    • type: manhattan_spearman value: 88.88551854529777
  • task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics:
    • type: cos_sim_pearson value: 82.64179522732887
    • type: cos_sim_spearman value: 84.25028809903114
    • type: euclidean_pearson value: 83.40175015236979
    • type: euclidean_spearman value: 84.23369296429406
    • type: manhattan_pearson value: 83.43768174261321
    • type: manhattan_spearman value: 84.27855229214734
  • task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics:
    • type: cos_sim_pearson value: 88.20378955494732
    • type: cos_sim_spearman value: 88.46863559173111
    • type: euclidean_pearson value: 88.8249295811663
    • type: euclidean_spearman value: 88.6312737724905
    • type: manhattan_pearson value: 88.87744466378827
    • type: manhattan_spearman value: 88.82908423767314
  • 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: 69.91342028796086
    • type: cos_sim_spearman value: 69.71495021867864
    • type: euclidean_pearson value: 70.65334330405646
    • type: euclidean_spearman value: 69.4321253472211
    • type: manhattan_pearson value: 70.59743494727465
    • type: manhattan_spearman value: 69.11695509297482
  • task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics:
    • type: cos_sim_pearson value: 85.42451709766952
    • type: cos_sim_spearman value: 86.07166710670508
    • type: euclidean_pearson value: 86.12711421258899
    • type: euclidean_spearman value: 86.05232086925126
    • type: manhattan_pearson value: 86.15591089932126
    • type: manhattan_spearman value: 86.0890128623439
  • task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics:
    • type: map value: 87.1976344717285
    • type: mrr value: 96.3703145075694
  • task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics:
    • type: map_at_1 value: 59.511
    • type: map_at_10 value: 69.724
    • type: map_at_100 value: 70.208
    • type: map_at_1000 value: 70.22800000000001
    • type: map_at_3 value: 66.986
    • type: map_at_5 value: 68.529
    • type: mrr_at_1 value: 62.333000000000006
    • type: mrr_at_10 value: 70.55
    • type: mrr_at_100 value: 70.985
    • type: mrr_at_1000 value: 71.004
    • type: mrr_at_3 value: 68.611
    • type: mrr_at_5 value: 69.728
    • type: ndcg_at_1 value: 62.333000000000006
    • type: ndcg_at_10 value: 74.265
    • type: ndcg_at_100 value: 76.361
    • type: ndcg_at_1000 value: 76.82900000000001
    • type: ndcg_at_3 value: 69.772
    • type: ndcg_at_5 value: 71.94800000000001
    • type: precision_at_1 value: 62.333000000000006
    • type: precision_at_10 value: 9.9
    • type: precision_at_100 value: 1.093
    • type: precision_at_1000 value: 0.11299999999999999
    • type: precision_at_3 value: 27.444000000000003
    • type: precision_at_5 value: 18
    • type: recall_at_1 value: 59.511
    • type: recall_at_10 value: 87.156
    • type: recall_at_100 value: 96.5
    • type: recall_at_1000 value: 100
    • type: recall_at_3 value: 75.2
    • type: recall_at_5 value: 80.661
  • task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics:
    • type: cos_sim_accuracy value: 99.81683168316832
    • type: cos_sim_ap value: 95.74716566563774
    • type: cos_sim_f1 value: 90.64238745574103
    • type: cos_sim_precision value: 91.7093142272262
    • type: cos_sim_recall value: 89.60000000000001
    • type: dot_accuracy value: 99.69405940594059
    • type: dot_ap value: 91.09013507754594
    • type: dot_f1 value: 84.54227113556779
    • type: dot_precision value: 84.58458458458459
    • type: dot_recall value: 84.5
    • type: euclidean_accuracy value: 99.81782178217821
    • type: euclidean_ap value: 95.6324301072609
    • type: euclidean_f1 value: 90.58341862845445
    • type: euclidean_precision value: 92.76729559748428
    • type: euclidean_recall value: 88.5
    • type: manhattan_accuracy value: 99.81980198019802
    • type: manhattan_ap value: 95.68510494437183
    • type: manhattan_f1 value: 90.58945191313342
    • type: manhattan_precision value: 93.79014989293361
    • type: manhattan_recall value: 87.6
    • type: max_accuracy value: 99.81980198019802
    • type: max_ap value: 95.74716566563774
    • type: max_f1 value: 90.64238745574103
  • task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics:
    • type: v_measure value: 67.63761899427078
  • task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics:
    • type: v_measure value: 36.572473369697235
  • task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics:
    • type: map value: 53.63000245208579
    • type: mrr value: 54.504193722943725
  • task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics:
    • type: cos_sim_pearson value: 30.300791939416545
    • type: cos_sim_spearman value: 31.662904057924123
    • type: dot_pearson value: 26.21198530758316
    • type: dot_spearman value: 27.006921548904263
  • task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics:
    • type: map_at_1 value: 0.197
    • type: map_at_10 value: 1.752
    • type: map_at_100 value: 10.795
    • type: map_at_1000 value: 27.18
    • type: map_at_3 value: 0.5890000000000001
    • type: map_at_5 value: 0.938
    • type: mrr_at_1 value: 74
    • type: mrr_at_10 value: 85.833
    • type: mrr_at_100 value: 85.833
    • type: mrr_at_1000 value: 85.833
    • type: mrr_at_3 value: 85.333
    • type: mrr_at_5 value: 85.833
    • type: ndcg_at_1 value: 69
    • type: ndcg_at_10 value: 70.22
    • type: ndcg_at_100 value: 55.785
    • type: ndcg_at_1000 value: 52.93600000000001
    • type: ndcg_at_3 value: 72.084
    • type: ndcg_at_5 value: 71.184
    • type: precision_at_1 value: 74
    • type: precision_at_10 value: 75.2
    • type: precision_at_100 value: 57.3
    • type: precision_at_1000 value: 23.302
    • type: precision_at_3 value: 77.333
    • type: precision_at_5 value: 75.6
    • type: recall_at_1 value: 0.197
    • type: recall_at_10 value: 2.019
    • type: recall_at_100 value: 14.257
    • type: recall_at_1000 value: 50.922
    • type: recall_at_3 value: 0.642
    • type: recall_at_5 value: 1.043
  • task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics:
    • type: map_at_1 value: 2.803
    • type: map_at_10 value: 10.407
    • type: map_at_100 value: 16.948
    • type: map_at_1000 value: 18.424
    • type: map_at_3 value: 5.405
    • type: map_at_5 value: 6.908
    • type: mrr_at_1 value: 36.735
    • type: mrr_at_10 value: 50.221000000000004
    • type: mrr_at_100 value: 51.388
    • type: mrr_at_1000 value: 51.402
    • type: mrr_at_3 value: 47.278999999999996
    • type: mrr_at_5 value: 49.626
    • type: ndcg_at_1 value: 34.694
    • type: ndcg_at_10 value: 25.507
    • type: ndcg_at_100 value: 38.296
    • type: ndcg_at_1000 value: 49.492000000000004
    • type: ndcg_at_3 value: 29.006999999999998
    • type: ndcg_at_5 value: 25.979000000000003
    • type: precision_at_1 value: 36.735
    • type: precision_at_10 value: 22.041
    • type: precision_at_100 value: 8.02
    • type: precision_at_1000 value: 1.567
    • type: precision_at_3 value: 28.571
    • type: precision_at_5 value: 24.490000000000002
    • type: recall_at_1 value: 2.803
    • type: recall_at_10 value: 16.378
    • type: recall_at_100 value: 50.489
    • type: recall_at_1000 value: 85.013
    • type: recall_at_3 value: 6.505
    • type: recall_at_5 value: 9.243
  • task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics:
    • type: accuracy value: 70.55579999999999
    • type: ap value: 14.206982753316227
    • type: f1 value: 54.372142814964285
  • task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics:
    • type: accuracy value: 56.57611771363893
    • type: f1 value: 56.924172639063144
  • task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics:
    • type: v_measure value: 52.82304915719759
  • task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics:
    • type: cos_sim_accuracy value: 85.92716218632653
    • type: cos_sim_ap value: 73.73359122546046
    • type: cos_sim_f1 value: 68.42559487116262
    • type: cos_sim_precision value: 64.22124508215691
    • type: cos_sim_recall value: 73.21899736147758
    • type: dot_accuracy value: 80.38981939560112
    • type: dot_ap value: 54.61060862444974
    • type: dot_f1 value: 53.45710627400769
    • type: dot_precision value: 44.87638839125761
    • type: dot_recall value: 66.09498680738787
    • type: euclidean_accuracy value: 86.02849138701794
    • type: euclidean_ap value: 73.95673761922404
    • type: euclidean_f1 value: 68.6783042394015
    • type: euclidean_precision value: 65.1063829787234
    • type: euclidean_recall value: 72.66490765171504
    • type: manhattan_accuracy value: 85.9808070572808
    • type: manhattan_ap value: 73.9050720058029
    • type: manhattan_f1 value: 68.57560618983794
    • type: manhattan_precision value: 63.70839936608558
    • type: manhattan_recall value: 74.24802110817942
    • type: max_accuracy value: 86.02849138701794
    • type: max_ap value: 73.95673761922404
    • type: max_f1 value: 68.6783042394015
  • task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics:
    • type: cos_sim_accuracy value: 88.72783017037295
    • type: cos_sim_ap value: 85.52705223340233
    • type: cos_sim_f1 value: 77.91659078492079
    • type: cos_sim_precision value: 73.93378032764221
    • type: cos_sim_recall value: 82.35294117647058
    • type: dot_accuracy value: 85.41739434159972
    • type: dot_ap value: 77.17734818118443
    • type: dot_f1 value: 71.63473589973144
    • type: dot_precision value: 66.96123719622415
    • type: dot_recall value: 77.00954727440714
    • type: euclidean_accuracy value: 88.68125897465751
    • type: euclidean_ap value: 85.47712213906692
    • type: euclidean_f1 value: 77.81419950830664
    • type: euclidean_precision value: 75.37162649733006
    • type: euclidean_recall value: 80.42038805050817
    • type: manhattan_accuracy value: 88.67349710870494
    • type: manhattan_ap value: 85.46506475241955
    • type: manhattan_f1 value: 77.87259084890393
    • type: manhattan_precision value: 74.54929577464789
    • type: manhattan_recall value: 81.50600554357868
    • type: max_accuracy value: 88.72783017037295
    • type: max_ap value: 85.52705223340233
    • type: max_f1 value: 77.91659078492079 language:
• en license: mit