ONNX 模型库
返回模型

说明文档

tags:

  • mteb
  • transformers.js
  • transformers model-index:
  • name: mxbai-angle-large-v1 results:
    • task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics:
      • type: accuracy value: 75.044776119403
      • type: ap value: 37.7362433623053
      • type: f1 value: 68.92736573359774
    • task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics:
      • type: accuracy value: 93.84025000000001
      • type: ap value: 90.93190875404055
      • type: f1 value: 93.8297833897293
    • task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
      • type: accuracy value: 49.184
      • type: f1 value: 48.74163227751588
    • task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics:
      • type: map_at_1 value: 41.252
      • type: map_at_10 value: 57.778
      • type: map_at_100 value: 58.233000000000004
      • type: map_at_1000 value: 58.23700000000001
      • type: map_at_3 value: 53.449999999999996
      • type: map_at_5 value: 56.376000000000005
      • type: mrr_at_1 value: 41.679
      • type: mrr_at_10 value: 57.92699999999999
      • type: mrr_at_100 value: 58.389
      • type: mrr_at_1000 value: 58.391999999999996
      • type: mrr_at_3 value: 53.651
      • type: mrr_at_5 value: 56.521
      • type: ndcg_at_1 value: 41.252
      • type: ndcg_at_10 value: 66.018
      • type: ndcg_at_100 value: 67.774
      • type: ndcg_at_1000 value: 67.84400000000001
      • type: ndcg_at_3 value: 57.372
      • type: ndcg_at_5 value: 62.646
      • type: precision_at_1 value: 41.252
      • type: precision_at_10 value: 9.189
      • type: precision_at_100 value: 0.991
      • type: precision_at_1000 value: 0.1
      • type: precision_at_3 value: 22.902
      • type: precision_at_5 value: 16.302
      • type: recall_at_1 value: 41.252
      • type: recall_at_10 value: 91.892
      • type: recall_at_100 value: 99.14699999999999
      • type: recall_at_1000 value: 99.644
      • type: recall_at_3 value: 68.706
      • type: recall_at_5 value: 81.50800000000001
    • task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics:
      • type: v_measure value: 48.97294504317859
    • task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics:
      • type: v_measure value: 42.98071077674629
    • task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics:
      • type: map value: 65.16477858490782
      • type: mrr value: 78.23583080508287
    • task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics:
      • type: cos_sim_pearson value: 89.6277629421789
      • type: cos_sim_spearman value: 88.4056288400568
      • type: euclidean_pearson value: 87.94871847578163
      • type: euclidean_spearman value: 88.4056288400568
      • type: manhattan_pearson value: 87.73271254229648
      • type: manhattan_spearman value: 87.91826833762677
    • task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics:
      • type: accuracy value: 87.81818181818181
      • type: f1 value: 87.79879337316918
    • task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics:
      • type: v_measure value: 39.91773608582761
    • task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics:
      • type: v_measure value: 36.73059477462478
    • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics:
      • type: map_at_1 value: 32.745999999999995
      • type: map_at_10 value: 43.632
      • type: map_at_100 value: 45.206
      • type: map_at_1000 value: 45.341
      • type: map_at_3 value: 39.956
      • type: map_at_5 value: 42.031
      • type: mrr_at_1 value: 39.485
      • type: mrr_at_10 value: 49.537
      • type: mrr_at_100 value: 50.249
      • type: mrr_at_1000 value: 50.294000000000004
      • type: mrr_at_3 value: 46.757
      • type: mrr_at_5 value: 48.481
      • type: ndcg_at_1 value: 39.485
      • type: ndcg_at_10 value: 50.058
      • type: ndcg_at_100 value: 55.586
      • type: ndcg_at_1000 value: 57.511
      • type: ndcg_at_3 value: 44.786
      • type: ndcg_at_5 value: 47.339999999999996
      • type: precision_at_1 value: 39.485
      • type: precision_at_10 value: 9.557
      • type: precision_at_100 value: 1.552
      • type: precision_at_1000 value: 0.202
      • type: precision_at_3 value: 21.412
      • type: precision_at_5 value: 15.479000000000001
      • type: recall_at_1 value: 32.745999999999995
      • type: recall_at_10 value: 62.056
      • type: recall_at_100 value: 85.088
      • type: recall_at_1000 value: 96.952
      • type: recall_at_3 value: 46.959
      • type: recall_at_5 value: 54.06999999999999
    • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics:
      • type: map_at_1 value: 31.898
      • type: map_at_10 value: 42.142
      • type: map_at_100 value: 43.349
      • type: map_at_1000 value: 43.483
      • type: map_at_3 value: 39.18
      • type: map_at_5 value: 40.733000000000004
      • type: mrr_at_1 value: 39.617999999999995
      • type: mrr_at_10 value: 47.922
      • type: mrr_at_100 value: 48.547000000000004
      • type: mrr_at_1000 value: 48.597
      • type: mrr_at_3 value: 45.86
      • type: mrr_at_5 value: 46.949000000000005
      • type: ndcg_at_1 value: 39.617999999999995
      • type: ndcg_at_10 value: 47.739
      • type: ndcg_at_100 value: 51.934999999999995
      • type: ndcg_at_1000 value: 54.007000000000005
      • type: ndcg_at_3 value: 43.748
      • type: ndcg_at_5 value: 45.345
      • type: precision_at_1 value: 39.617999999999995
      • type: precision_at_10 value: 8.962
      • type: precision_at_100 value: 1.436
      • type: precision_at_1000 value: 0.192
      • type: precision_at_3 value: 21.083
      • type: precision_at_5 value: 14.752
      • type: recall_at_1 value: 31.898
      • type: recall_at_10 value: 57.587999999999994
      • type: recall_at_100 value: 75.323
      • type: recall_at_1000 value: 88.304
      • type: recall_at_3 value: 45.275
      • type: recall_at_5 value: 49.99
    • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics:
      • type: map_at_1 value: 40.458
      • type: map_at_10 value: 52.942
      • type: map_at_100 value: 53.974
      • type: map_at_1000 value: 54.031
      • type: map_at_3 value: 49.559999999999995
      • type: map_at_5 value: 51.408
      • type: mrr_at_1 value: 46.27
      • type: mrr_at_10 value: 56.31699999999999
      • type: mrr_at_100 value: 56.95099999999999
      • type: mrr_at_1000 value: 56.98
      • type: mrr_at_3 value: 53.835
      • type: mrr_at_5 value: 55.252
      • type: ndcg_at_1 value: 46.27
      • type: ndcg_at_10 value: 58.964000000000006
      • type: ndcg_at_100 value: 62.875
      • type: ndcg_at_1000 value: 63.969
      • type: ndcg_at_3 value: 53.297000000000004
      • type: ndcg_at_5 value: 55.938
      • type: precision_at_1 value: 46.27
      • type: precision_at_10 value: 9.549000000000001
      • type: precision_at_100 value: 1.2409999999999999
      • type: precision_at_1000 value: 0.13799999999999998
      • type: precision_at_3 value: 23.762
      • type: precision_at_5 value: 16.262999999999998
      • type: recall_at_1 value: 40.458
      • type: recall_at_10 value: 73.446
      • type: recall_at_100 value: 90.12400000000001
      • type: recall_at_1000 value: 97.795
      • type: recall_at_3 value: 58.123000000000005
      • type: recall_at_5 value: 64.68
    • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics:
      • type: map_at_1 value: 27.443
      • type: map_at_10 value: 36.081
      • type: map_at_100 value: 37.163000000000004
      • type: map_at_1000 value: 37.232
      • type: map_at_3 value: 33.308
      • type: map_at_5 value: 34.724
      • type: mrr_at_1 value: 29.492
      • type: mrr_at_10 value: 38.138
      • type: mrr_at_100 value: 39.065
      • type: mrr_at_1000 value: 39.119
      • type: mrr_at_3 value: 35.593
      • type: mrr_at_5 value: 36.785000000000004
      • type: ndcg_at_1 value: 29.492
      • type: ndcg_at_10 value: 41.134
      • type: ndcg_at_100 value: 46.300999999999995
      • type: ndcg_at_1000 value: 48.106
      • type: ndcg_at_3 value: 35.77
      • type: ndcg_at_5 value: 38.032
      • type: precision_at_1 value: 29.492
      • type: precision_at_10 value: 6.249
      • type: precision_at_100 value: 0.9299999999999999
      • type: precision_at_1000 value: 0.11199999999999999
      • type: precision_at_3 value: 15.065999999999999
      • type: precision_at_5 value: 10.373000000000001
      • type: recall_at_1 value: 27.443
      • type: recall_at_10 value: 54.80199999999999
      • type: recall_at_100 value: 78.21900000000001
      • type: recall_at_1000 value: 91.751
      • type: recall_at_3 value: 40.211000000000006
      • type: recall_at_5 value: 45.599000000000004
    • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics:
      • type: map_at_1 value: 18.731
      • type: map_at_10 value: 26.717999999999996
      • type: map_at_100 value: 27.897
      • type: map_at_1000 value: 28.029
      • type: map_at_3 value: 23.91
      • type: map_at_5 value: 25.455
      • type: mrr_at_1 value: 23.134
      • type: mrr_at_10 value: 31.769
      • type: mrr_at_100 value: 32.634
      • type: mrr_at_1000 value: 32.707
      • type: mrr_at_3 value: 28.938999999999997
      • type: mrr_at_5 value: 30.531000000000002
      • type: ndcg_at_1 value: 23.134
      • type: ndcg_at_10 value: 32.249
      • type: ndcg_at_100 value: 37.678
      • type: ndcg_at_1000 value: 40.589999999999996
      • type: ndcg_at_3 value: 26.985999999999997
      • type: ndcg_at_5 value: 29.457
      • type: precision_at_1 value: 23.134
      • type: precision_at_10 value: 5.8709999999999996
      • type: precision_at_100 value: 0.988
      • type: precision_at_1000 value: 0.13799999999999998
      • type: precision_at_3 value: 12.852
      • type: precision_at_5 value: 9.428
      • type: recall_at_1 value: 18.731
      • type: recall_at_10 value: 44.419
      • type: recall_at_100 value: 67.851
      • type: recall_at_1000 value: 88.103
      • type: recall_at_3 value: 29.919
      • type: recall_at_5 value: 36.230000000000004
    • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics:
      • type: map_at_1 value: 30.324
      • type: map_at_10 value: 41.265
      • type: map_at_100 value: 42.559000000000005
      • type: map_at_1000 value: 42.669000000000004
      • type: map_at_3 value: 38.138
      • type: map_at_5 value: 39.881
      • type: mrr_at_1 value: 36.67
      • type: mrr_at_10 value: 46.774
      • type: mrr_at_100 value: 47.554
      • type: mrr_at_1000 value: 47.593
      • type: mrr_at_3 value: 44.338
      • type: mrr_at_5 value: 45.723
      • type: ndcg_at_1 value: 36.67
      • type: ndcg_at_10 value: 47.367
      • type: ndcg_at_100 value: 52.623
      • type: ndcg_at_1000 value: 54.59
      • type: ndcg_at_3 value: 42.323
      • type: ndcg_at_5 value: 44.727
      • type: precision_at_1 value: 36.67
      • type: precision_at_10 value: 8.518
      • type: precision_at_100 value: 1.2890000000000001
      • type: precision_at_1000 value: 0.163
      • type: precision_at_3 value: 19.955000000000002
      • type: precision_at_5 value: 14.11
      • type: recall_at_1 value: 30.324
      • type: recall_at_10 value: 59.845000000000006
      • type: recall_at_100 value: 81.77499999999999
      • type: recall_at_1000 value: 94.463
      • type: recall_at_3 value: 46.019
      • type: recall_at_5 value: 52.163000000000004
    • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics:
      • type: map_at_1 value: 24.229
      • type: map_at_10 value: 35.004000000000005
      • type: map_at_100 value: 36.409000000000006
      • type: map_at_1000 value: 36.521
      • type: map_at_3 value: 31.793
      • type: map_at_5 value: 33.432
      • type: mrr_at_1 value: 30.365
      • type: mrr_at_10 value: 40.502
      • type: mrr_at_100 value: 41.372
      • type: mrr_at_1000 value: 41.435
      • type: mrr_at_3 value: 37.804
      • type: mrr_at_5 value: 39.226
      • type: ndcg_at_1 value: 30.365
      • type: ndcg_at_10 value: 41.305
      • type: ndcg_at_100 value: 47.028999999999996
      • type: ndcg_at_1000 value: 49.375
      • type: ndcg_at_3 value: 35.85
      • type: ndcg_at_5 value: 38.12
      • type: precision_at_1 value: 30.365
      • type: precision_at_10 value: 7.808
      • type: precision_at_100 value: 1.228
      • type: precision_at_1000 value: 0.161
      • type: precision_at_3 value: 17.352
      • type: precision_at_5 value: 12.42
      • type: recall_at_1 value: 24.229
      • type: recall_at_10 value: 54.673
      • type: recall_at_100 value: 78.766
      • type: recall_at_1000 value: 94.625
      • type: recall_at_3 value: 39.602
      • type: recall_at_5 value: 45.558
    • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics:
      • type: map_at_1 value: 26.695
      • type: map_at_10 value: 36.0895
      • type: map_at_100 value: 37.309416666666664
      • type: map_at_1000 value: 37.42558333333334
      • type: map_at_3 value: 33.19616666666666
      • type: map_at_5 value: 34.78641666666667
      • type: mrr_at_1 value: 31.486083333333337
      • type: mrr_at_10 value: 40.34774999999999
      • type: mrr_at_100 value: 41.17533333333333
      • type: mrr_at_1000 value: 41.231583333333326
      • type: mrr_at_3 value: 37.90075
      • type: mrr_at_5 value: 39.266999999999996
      • type: ndcg_at_1 value: 31.486083333333337
      • type: ndcg_at_10 value: 41.60433333333334
      • type: ndcg_at_100 value: 46.74525
      • type: ndcg_at_1000 value: 48.96166666666667
      • type: ndcg_at_3 value: 36.68825
      • type: ndcg_at_5 value: 38.966499999999996
      • type: precision_at_1 value: 31.486083333333337
      • type: precision_at_10 value: 7.29675
      • type: precision_at_100 value: 1.1621666666666666
      • type: precision_at_1000 value: 0.1545
      • type: precision_at_3 value: 16.8815
      • type: precision_at_5 value: 11.974583333333333
      • type: recall_at_1 value: 26.695
      • type: recall_at_10 value: 53.651916666666665
      • type: recall_at_100 value: 76.12083333333332
      • type: recall_at_1000 value: 91.31191666666668
      • type: recall_at_3 value: 40.03575
      • type: recall_at_5 value: 45.876666666666665
    • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics:
      • type: map_at_1 value: 25.668000000000003
      • type: map_at_10 value: 32.486
      • type: map_at_100 value: 33.371
      • type: map_at_1000 value: 33.458
      • type: map_at_3 value: 30.261
      • type: map_at_5 value: 31.418000000000003
      • type: mrr_at_1 value: 28.988000000000003
      • type: mrr_at_10 value: 35.414
      • type: mrr_at_100 value: 36.149
      • type: mrr_at_1000 value: 36.215
      • type: mrr_at_3 value: 33.333
      • type: mrr_at_5 value: 34.43
      • type: ndcg_at_1 value: 28.988000000000003
      • type: ndcg_at_10 value: 36.732
      • type: ndcg_at_100 value: 41.331
      • type: ndcg_at_1000 value: 43.575
      • type: ndcg_at_3 value: 32.413
      • type: ndcg_at_5 value: 34.316
      • type: precision_at_1 value: 28.988000000000003
      • type: precision_at_10 value: 5.7059999999999995
      • type: precision_at_100 value: 0.882
      • type: precision_at_1000 value: 0.11299999999999999
      • type: precision_at_3 value: 13.65
      • type: precision_at_5 value: 9.417
      • type: recall_at_1 value: 25.668000000000003
      • type: recall_at_10 value: 47.147
      • type: recall_at_100 value: 68.504
      • type: recall_at_1000 value: 85.272
      • type: recall_at_3 value: 35.19
      • type: recall_at_5 value: 39.925
    • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics:
      • type: map_at_1 value: 17.256
      • type: map_at_10 value: 24.58
      • type: map_at_100 value: 25.773000000000003
      • type: map_at_1000 value: 25.899
      • type: map_at_3 value: 22.236
      • type: map_at_5 value: 23.507
      • type: mrr_at_1 value: 20.957
      • type: mrr_at_10 value: 28.416000000000004
      • type: mrr_at_100 value: 29.447000000000003
      • type: mrr_at_1000 value: 29.524
      • type: mrr_at_3 value: 26.245
      • type: mrr_at_5 value: 27.451999999999998
      • type: ndcg_at_1 value: 20.957
      • type: ndcg_at_10 value: 29.285
      • type: ndcg_at_100 value: 35.003
      • type: ndcg_at_1000 value: 37.881
      • type: ndcg_at_3 value: 25.063000000000002
      • type: ndcg_at_5 value: 26.983
      • type: precision_at_1 value: 20.957
      • type: precision_at_10 value: 5.344
      • type: precision_at_100 value: 0.958
      • type: precision_at_1000 value: 0.13799999999999998
      • type: precision_at_3 value: 11.918
      • type: precision_at_5 value: 8.596
      • type: recall_at_1 value: 17.256
      • type: recall_at_10 value: 39.644
      • type: recall_at_100 value: 65.279
      • type: recall_at_1000 value: 85.693
      • type: recall_at_3 value: 27.825
      • type: recall_at_5 value: 32.792
    • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics:
      • type: map_at_1 value: 26.700000000000003
      • type: map_at_10 value: 36.205999999999996
      • type: map_at_100 value: 37.316
      • type: map_at_1000 value: 37.425000000000004
      • type: map_at_3 value: 33.166000000000004
      • type: map_at_5 value: 35.032999999999994
      • type: mrr_at_1 value: 31.436999999999998
      • type: mrr_at_10 value: 40.61
      • type: mrr_at_100 value: 41.415
      • type: mrr_at_1000 value: 41.48
      • type: mrr_at_3 value: 37.966
      • type: mrr_at_5 value: 39.599000000000004
      • type: ndcg_at_1 value: 31.436999999999998
      • type: ndcg_at_10 value: 41.771
      • type: ndcg_at_100 value: 46.784
      • type: ndcg_at_1000 value: 49.183
      • type: ndcg_at_3 value: 36.437000000000005
      • type: ndcg_at_5 value: 39.291
      • type: precision_at_1 value: 31.436999999999998
      • type: precision_at_10 value: 6.987
      • type: precision_at_100 value: 1.072
      • type: precision_at_1000 value: 0.13899999999999998
      • type: precision_at_3 value: 16.448999999999998
      • type: precision_at_5 value: 11.866
      • type: recall_at_1 value: 26.700000000000003
      • type: recall_at_10 value: 54.301
      • type: recall_at_100 value: 75.871
      • type: recall_at_1000 value: 92.529
      • type: recall_at_3 value: 40.201
      • type: recall_at_5 value: 47.208
    • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics:
      • type: map_at_1 value: 24.296
      • type: map_at_10 value: 33.116
      • type: map_at_100 value: 34.81
      • type: map_at_1000 value: 35.032000000000004
      • type: map_at_3 value: 30.105999999999998
      • type: map_at_5 value: 31.839000000000002
      • type: mrr_at_1 value: 29.051
      • type: mrr_at_10 value: 37.803
      • type: mrr_at_100 value: 38.856
      • type: mrr_at_1000 value: 38.903999999999996
      • type: mrr_at_3 value: 35.211
      • type: mrr_at_5 value: 36.545
      • type: ndcg_at_1 value: 29.051
      • type: ndcg_at_10 value: 39.007
      • type: ndcg_at_100 value: 45.321
      • type: ndcg_at_1000 value: 47.665
      • type: ndcg_at_3 value: 34.1
      • type: ndcg_at_5 value: 36.437000000000005
      • type: precision_at_1 value: 29.051
      • type: precision_at_10 value: 7.668
      • type: precision_at_100 value: 1.542
      • type: precision_at_1000 value: 0.24
      • type: precision_at_3 value: 16.14
      • type: precision_at_5 value: 11.897
      • type: recall_at_1 value: 24.296
      • type: recall_at_10 value: 49.85
      • type: recall_at_100 value: 78.457
      • type: recall_at_1000 value: 92.618
      • type: recall_at_3 value: 36.138999999999996
      • type: recall_at_5 value: 42.223
    • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics:
      • type: map_at_1 value: 20.591
      • type: map_at_10 value: 28.902
      • type: map_at_100 value: 29.886000000000003
      • type: map_at_1000 value: 29.987000000000002
      • type: map_at_3 value: 26.740000000000002
      • type: map_at_5 value: 27.976
      • type: mrr_at_1 value: 22.366
      • type: mrr_at_10 value: 30.971
      • type: mrr_at_100 value: 31.865
      • type: mrr_at_1000 value: 31.930999999999997
      • type: mrr_at_3 value: 28.927999999999997
      • type: mrr_at_5 value: 30.231
      • type: ndcg_at_1 value: 22.366
      • type: ndcg_at_10 value: 33.641
      • type: ndcg_at_100 value: 38.477
      • type: ndcg_at_1000 value: 41.088
      • type: ndcg_at_3 value: 29.486
      • type: ndcg_at_5 value: 31.612000000000002
      • type: precision_at_1 value: 22.366
      • type: precision_at_10 value: 5.3420000000000005
      • type: precision_at_100 value: 0.828
      • type: precision_at_1000 value: 0.11800000000000001
      • type: precision_at_3 value: 12.939
      • type: precision_at_5 value: 9.094
      • type: recall_at_1 value: 20.591
      • type: recall_at_10 value: 46.052
      • type: recall_at_100 value: 68.193
      • type: recall_at_1000 value: 87.638
      • type: recall_at_3 value: 34.966
      • type: recall_at_5 value: 40.082
    • task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics:
      • type: map_at_1 value: 15.091
      • type: map_at_10 value: 26.38
      • type: map_at_100 value: 28.421999999999997
      • type: map_at_1000 value: 28.621999999999996
      • type: map_at_3 value: 21.597
      • type: map_at_5 value: 24.12
      • type: mrr_at_1 value: 34.266999999999996
      • type: mrr_at_10 value: 46.864
      • type: mrr_at_100 value: 47.617
      • type: mrr_at_1000 value: 47.644
      • type: mrr_at_3 value: 43.312
      • type: mrr_at_5 value: 45.501000000000005
      • type: ndcg_at_1 value: 34.266999999999996
      • type: ndcg_at_10 value: 36.095
      • type: ndcg_at_100 value: 43.447
      • type: ndcg_at_1000 value: 46.661
      • type: ndcg_at_3 value: 29.337999999999997
      • type: ndcg_at_5 value: 31.824
      • type: precision_at_1 value: 34.266999999999996
      • type: precision_at_10 value: 11.472
      • type: precision_at_100 value: 1.944
      • type: precision_at_1000 value: 0.255
      • type: precision_at_3 value: 21.933
      • type: precision_at_5 value: 17.224999999999998
      • type: recall_at_1 value: 15.091
      • type: recall_at_10 value: 43.022
      • type: recall_at_100 value: 68.075
      • type: recall_at_1000 value: 85.76
      • type: recall_at_3 value: 26.564
      • type: recall_at_5 value: 33.594
    • task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics:
      • type: map_at_1 value: 9.252
      • type: map_at_10 value: 20.923
      • type: map_at_100 value: 30.741000000000003
      • type: map_at_1000 value: 32.542
      • type: map_at_3 value: 14.442
      • type: map_at_5 value: 17.399
      • type: mrr_at_1 value: 70.25
      • type: mrr_at_10 value: 78.17
      • type: mrr_at_100 value: 78.444
      • type: mrr_at_1000 value: 78.45100000000001
      • type: mrr_at_3 value: 76.958
      • type: mrr_at_5 value: 77.571
      • type: ndcg_at_1 value: 58.375
      • type: ndcg_at_10 value: 44.509
      • type: ndcg_at_100 value: 49.897999999999996
      • type: ndcg_at_1000 value: 57.269999999999996
      • type: ndcg_at_3 value: 48.64
      • type: ndcg_at_5 value: 46.697
      • type: precision_at_1 value: 70.25
      • type: precision_at_10 value: 36.05
      • type: precision_at_100 value: 11.848
      • type: precision_at_1000 value: 2.213
      • type: precision_at_3 value: 52.917
      • type: precision_at_5 value: 45.7
      • type: recall_at_1 value: 9.252
      • type: recall_at_10 value: 27.006999999999998
      • type: recall_at_100 value: 57.008
      • type: recall_at_1000 value: 80.697
      • type: recall_at_3 value: 15.798000000000002
      • type: recall_at_5 value: 20.4
    • task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics:
      • type: accuracy value: 50.88
      • type: f1 value: 45.545495028653384
    • task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics:
      • type: map_at_1 value: 75.424
      • type: map_at_10 value: 83.435
      • type: map_at_100 value: 83.66900000000001
      • type: map_at_1000 value: 83.685
      • type: map_at_3 value: 82.39800000000001
      • type: map_at_5 value: 83.07
      • type: mrr_at_1 value: 81.113
      • type: mrr_at_10 value: 87.77199999999999
      • type: mrr_at_100 value: 87.862
      • type: mrr_at_1000 value: 87.86500000000001
      • type: mrr_at_3 value: 87.17099999999999
      • type: mrr_at_5 value: 87.616
      • type: ndcg_at_1 value: 81.113
      • type: ndcg_at_10 value: 86.909
      • type: ndcg_at_100 value: 87.746
      • type: ndcg_at_1000 value: 88.017
      • type: ndcg_at_3 value: 85.368
      • type: ndcg_at_5 value: 86.28099999999999
      • type: precision_at_1 value: 81.113
      • type: precision_at_10 value: 10.363
      • type: precision_at_100 value: 1.102
      • type: precision_at_1000 value: 0.11399999999999999
      • type: precision_at_3 value: 32.507999999999996
      • type: precision_at_5 value: 20.138
      • type: recall_at_1 value: 75.424
      • type: recall_at_10 value: 93.258
      • type: recall_at_100 value: 96.545
      • type: recall_at_1000 value: 98.284
      • type: recall_at_3 value: 89.083
      • type: recall_at_5 value: 91.445
    • task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics:
      • type: map_at_1 value: 22.532
      • type: map_at_10 value: 37.141999999999996
      • type: map_at_100 value: 39.162
      • type: map_at_1000 value: 39.322
      • type: map_at_3 value: 32.885
      • type: map_at_5 value: 35.093999999999994
      • type: mrr_at_1 value: 44.29
      • type: mrr_at_10 value: 53.516
      • type: mrr_at_100 value: 54.24
      • type: mrr_at_1000 value: 54.273
      • type: mrr_at_3 value: 51.286
      • type: mrr_at_5 value: 52.413
      • type: ndcg_at_1 value: 44.29
      • type: ndcg_at_10 value: 45.268
      • type: ndcg_at_100 value: 52.125
      • type: ndcg_at_1000 value: 54.778000000000006
      • type: ndcg_at_3 value: 41.829
      • type: ndcg_at_5 value: 42.525
      • type: precision_at_1 value: 44.29
      • type: precision_at_10 value: 12.5
      • type: precision_at_100 value: 1.9720000000000002
      • type: precision_at_1000 value: 0.245
      • type: precision_at_3 value: 28.035
      • type: precision_at_5 value: 20.093
      • type: recall_at_1 value: 22.532
      • type: recall_at_10 value: 52.419000000000004
      • type: recall_at_100 value: 77.43299999999999
      • type: recall_at_1000 value: 93.379
      • type: recall_at_3 value: 38.629000000000005
      • type: recall_at_5 value: 43.858000000000004
    • task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics:
      • type: map_at_1 value: 39.359
      • type: map_at_10 value: 63.966
      • type: map_at_100 value: 64.87
      • type: map_at_1000 value: 64.92599999999999
      • type: map_at_3 value: 60.409
      • type: map_at_5 value: 62.627
      • type: mrr_at_1 value: 78.717
      • type: mrr_at_10 value: 84.468
      • type: mrr_at_100 value: 84.655
      • type: mrr_at_1000 value: 84.661
      • type: mrr_at_3 value: 83.554
      • type: mrr_at_5 value: 84.133
      • type: ndcg_at_1 value: 78.717
      • type: ndcg_at_10 value: 72.03399999999999
      • type: ndcg_at_100 value: 75.158
      • type: ndcg_at_1000 value: 76.197
      • type: ndcg_at_3 value: 67.049
      • type: ndcg_at_5 value: 69.808
      • type: precision_at_1 value: 78.717
      • type: precision_at_10 value: 15.201
      • type: precision_at_100 value: 1.764
      • type: precision_at_1000 value: 0.19
      • type: precision_at_3 value: 43.313
      • type: precision_at_5 value: 28.165000000000003
      • type: recall_at_1 value: 39.359
      • type: recall_at_10 value: 76.003
      • type: recall_at_100 value: 88.197
      • type: recall_at_1000 value: 95.003
      • type: recall_at_3 value: 64.97
      • type: recall_at_5 value: 70.41199999999999
    • task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics:
      • type: accuracy value: 92.83200000000001
      • type: ap value: 89.33560571859861
      • type: f1 value: 92.82322915005167
    • task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics:
      • type: map_at_1 value: 21.983
      • type: map_at_10 value: 34.259
      • type: map_at_100 value: 35.432
      • type: map_at_1000 value: 35.482
      • type: map_at_3 value: 30.275999999999996
      • type: map_at_5 value: 32.566
      • type: mrr_at_1 value: 22.579
      • type: mrr_at_10 value: 34.882999999999996
      • type: mrr_at_100 value: 35.984
      • type: mrr_at_1000 value: 36.028
      • type: mrr_at_3 value: 30.964999999999996
      • type: mrr_at_5 value: 33.245000000000005
      • type: ndcg_at_1 value: 22.564
      • type: ndcg_at_10 value: 41.258
      • type: ndcg_at_100 value: 46.824
      • type: ndcg_at_1000 value: 48.037
      • type: ndcg_at_3 value: 33.17
      • type: ndcg_at_5 value: 37.263000000000005
      • type: precision_at_1 value: 22.564
      • type: precision_at_10 value: 6.572
      • type: precision_at_100 value: 0.935
      • type: precision_at_1000 value: 0.104
      • type: precision_at_3 value: 14.130999999999998
      • type: precision_at_5 value: 10.544
      • type: recall_at_1 value: 21.983
      • type: recall_at_10 value: 62.775000000000006
      • type: recall_at_100 value: 88.389
      • type: recall_at_1000 value: 97.603
      • type: recall_at_3 value: 40.878
      • type: recall_at_5 value: 50.690000000000005
    • task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics:
      • type: accuracy value: 93.95120839033288
      • type: f1 value: 93.73824125055208
    • task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics:
      • type: accuracy value: 76.78978568171455
      • type: f1 value: 57.50180552858304
    • task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics:
      • type: accuracy value: 76.24411566913248
      • type: f1 value: 74.37851403532832
    • task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
      • type: accuracy value: 79.94620040349699
      • type: f1 value: 80.21293397970435
    • task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics:
      • type: v_measure value: 33.44403096245675
    • task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics:
      • type: v_measure value: 31.659594631336812
    • task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics:
      • type: map value: 32.53833075108798
      • type: mrr value: 33.78840823218308
    • task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics:
      • type: map_at_1 value: 7.185999999999999
      • type: map_at_10 value: 15.193999999999999
      • type: map_at_100 value: 19.538
      • type: map_at_1000 value: 21.178
      • type: map_at_3 value: 11.208
      • type: map_at_5 value: 12.745999999999999
      • type: mrr_at_1 value: 48.916
      • type: mrr_at_10 value: 58.141
      • type: mrr_at_100 value: 58.656
      • type: mrr_at_1000 value: 58.684999999999995
      • type: mrr_at_3 value: 55.521
      • type: mrr_at_5 value: 57.239
      • type: ndcg_at_1 value: 47.059
      • type: ndcg_at_10 value: 38.644
      • type: ndcg_at_100 value: 36.272999999999996
      • type: ndcg_at_1000 value: 44.996
      • type: ndcg_at_3 value: 43.293
      • type: ndcg_at_5 value: 40.819
      • type: precision_at_1 value: 48.916
      • type: precision_at_10 value: 28.607
      • type: precision_at_100 value: 9.195
      • type: precision_at_1000 value: 2.225
      • type: precision_at_3 value: 40.454
      • type: precision_at_5 value: 34.985
      • type: recall_at_1 value: 7.185999999999999
      • type: recall_at_10 value: 19.654
      • type: recall_at_100 value: 37.224000000000004
      • type: recall_at_1000 value: 68.663
      • type: recall_at_3 value: 12.158
      • type: recall_at_5 value: 14.674999999999999
    • task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics:
      • type: map_at_1 value: 31.552000000000003
      • type: map_at_10 value: 47.75
      • type: map_at_100 value: 48.728
      • type: map_at_1000 value: 48.754
      • type: map_at_3 value: 43.156
      • type: map_at_5 value: 45.883
      • type: mrr_at_1 value: 35.66
      • type: mrr_at_10 value: 50.269
      • type: mrr_at_100 value: 50.974
      • type: mrr_at_1000 value: 50.991
      • type: mrr_at_3 value: 46.519
      • type: mrr_at_5 value: 48.764
      • type: ndcg_at_1 value: 35.632000000000005
      • type: ndcg_at_10 value: 55.786
      • type: ndcg_at_100 value: 59.748999999999995
      • type: ndcg_at_1000 value: 60.339
      • type: ndcg_at_3 value: 47.292
      • type: ndcg_at_5 value: 51.766999999999996
      • type: precision_at_1 value: 35.632000000000005
      • type: precision_at_10 value: 9.267
      • type: precision_at_100 value: 1.149
      • type: precision_at_1000 value: 0.12
      • type: precision_at_3 value: 21.601
      • type: precision_at_5 value: 15.539
      • type: recall_at_1 value: 31.552000000000003
      • type: recall_at_10 value: 77.62400000000001
      • type: recall_at_100 value: 94.527
      • type: recall_at_1000 value: 98.919
      • type: recall_at_3 value: 55.898
      • type: recall_at_5 value: 66.121
    • task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics:
      • type: map_at_1 value: 71.414
      • type: map_at_10 value: 85.37400000000001
      • type: map_at_100 value: 86.01100000000001
      • type: map_at_1000 value: 86.027
      • type: map_at_3 value: 82.562
      • type: map_at_5 value: 84.284
      • type: mrr_at_1 value: 82.24000000000001
      • type: mrr_at_10 value: 88.225
      • type: mrr_at_100 value: 88.324
      • type: mrr_at_1000 value: 88.325
      • type: mrr_at_3 value: 87.348
      • type: mrr_at_5 value: 87.938
      • type: ndcg_at_1 value: 82.24000000000001
      • type: ndcg_at_10 value: 88.97699999999999
      • type: ndcg_at_100 value: 90.16
      • type: ndcg_at_1000 value: 90.236
      • type: ndcg_at_3 value: 86.371
      • type: ndcg_at_5 value: 87.746
      • type: precision_at_1 value: 82.24000000000001
      • type: precision_at_10 value: 13.481000000000002
      • type: precision_at_100 value: 1.534
      • type: precision_at_1000 value: 0.157
      • type: precision_at_3 value: 37.86
      • type: precision_at_5 value: 24.738
      • type: recall_at_1 value: 71.414
      • type: recall_at_10 value: 95.735
      • type: recall_at_100 value: 99.696
      • type: recall_at_1000 value: 99.979
      • type: recall_at_3 value: 88.105
      • type: recall_at_5 value: 92.17999999999999
    • task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics:
      • type: v_measure value: 60.22146692057259
    • task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics:
      • type: v_measure value: 65.29273320614578
    • task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics:
      • type: map_at_1 value: 5.023
      • type: map_at_10 value: 14.161000000000001
      • type: map_at_100 value: 16.68
      • type: map_at_1000 value: 17.072000000000003
      • type: map_at_3 value: 9.763
      • type: map_at_5 value: 11.977
      • type: mrr_at_1 value: 24.8
      • type: mrr_at_10 value: 37.602999999999994
      • type: mrr_at_100 value: 38.618
      • type: mrr_at_1000 value: 38.659
      • type: mrr_at_3 value: 34.117
      • type: mrr_at_5 value: 36.082
      • type: ndcg_at_1 value: 24.8
      • type: ndcg_at_10 value: 23.316
      • type: ndcg_at_100 value: 32.613
      • type: ndcg_at_1000 value: 38.609
      • type: ndcg_at_3 value: 21.697
      • type: ndcg_at_5 value: 19.241
      • type: precision_at_1 value: 24.8
      • type: precision_at_10 value: 12.36
      • type: precision_at_100 value: 2.593
      • type: precision_at_1000 value: 0.402
      • type: precision_at_3 value: 20.767
      • type: precision_at_5 value: 17.34
      • type: recall_at_1 value: 5.023
      • type: recall_at_10 value: 25.069999999999997
      • type: recall_at_100 value: 52.563
      • type: recall_at_1000 value: 81.525
      • type: recall_at_3 value: 12.613
      • type: recall_at_5 value: 17.583
    • task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics:
      • type: cos_sim_pearson value: 87.71506247604255
      • type: cos_sim_spearman value: 82.91813463738802
      • type: euclidean_pearson value: 85.5154616194479
      • type: euclidean_spearman value: 82.91815254466314
      • type: manhattan_pearson value: 85.5280917850374
      • type: manhattan_spearman value: 82.92276537286398
    • task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics:
      • type: cos_sim_pearson value: 87.43772054228462
      • type: cos_sim_spearman value: 78.75750601716682
      • type: euclidean_pearson value: 85.76074482955764
      • type: euclidean_spearman value: 78.75651057223058
      • type: manhattan_pearson value: 85.73390291701668
      • type: manhattan_spearman value: 78.72699385957797
    • task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics:
      • type: cos_sim_pearson value: 89.58144067172472
      • type: cos_sim_spearman value: 90.3524512966946
      • type: euclidean_pearson value: 89.71365391594237
      • type: euclidean_spearman value: 90.35239632843408
      • type: manhattan_pearson value: 89.66905421746478
      • type: manhattan_spearman value: 90.31508211683513
    • task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics:
      • type: cos_sim_pearson value: 87.77692637102102
      • type: cos_sim_spearman value: 85.45710562643485
      • type: euclidean_pearson value: 87.42456979928723
      • type: euclidean_spearman value: 85.45709386240908
      • type: manhattan_pearson value: 87.40754529526272
      • type: manhattan_spearman value: 85.44834854173303
    • task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics:
      • type: cos_sim_pearson value: 88.28491331695997
      • type: cos_sim_spearman value: 89.62037029566964
      • type: euclidean_pearson value: 89.02479391362826
      • type: euclidean_spearman value: 89.62036733618466
      • type: manhattan_pearson value: 89.00394756040342
      • type: manhattan_spearman value: 89.60867744215236
    • task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics:
      • type: cos_sim_pearson value: 85.08911381280191
      • type: cos_sim_spearman value: 86.5791780765767
      • type: euclidean_pearson value: 86.16063473577861
      • type: euclidean_spearman value: 86.57917745378766
      • type: manhattan_pearson value: 86.13677924604175
      • type: manhattan_spearman value: 86.56115615768685
    • 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: 89.58029496205235
      • type: cos_sim_spearman value: 89.49551253826998
      • type: euclidean_pearson value: 90.13714840963748
      • type: euclidean_spearman value: 89.49551253826998
      • type: manhattan_pearson value: 90.13039633601363
      • type: manhattan_spearman value: 89.4513453745516
    • 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: 69.01546399666435
      • type: cos_sim_spearman value: 69.33824484595624
      • type: euclidean_pearson value: 70.76511642998874
      • type: euclidean_spearman value: 69.33824484595624
      • type: manhattan_pearson value: 70.84320785047453
      • type: manhattan_spearman value: 69.54233632223537
    • task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics:
      • type: cos_sim_pearson value: 87.26389196390119
      • type: cos_sim_spearman value: 89.09721478341385
      • type: euclidean_pearson value: 88.97208685922517
      • type: euclidean_spearman value: 89.09720927308881
      • type: manhattan_pearson value: 88.97513670502573
      • type: manhattan_spearman value: 89.07647853984004
    • task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics:
      • type: map value: 87.53075025771936
      • type: mrr value: 96.24327651288436
    • task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics:
      • type: map_at_1 value: 60.428000000000004
      • type: map_at_10 value: 70.088
      • type: map_at_100 value: 70.589
      • type: map_at_1000 value: 70.614
      • type: map_at_3 value: 67.191
      • type: map_at_5 value: 68.515
      • type: mrr_at_1 value: 63.333
      • type: mrr_at_10 value: 71.13000000000001
      • type: mrr_at_100 value: 71.545
      • type: mrr_at_1000 value: 71.569
      • type: mrr_at_3 value: 68.944
      • type: mrr_at_5 value: 70.078
      • type: ndcg_at_1 value: 63.333
      • type: ndcg_at_10 value: 74.72800000000001
      • type: ndcg_at_100 value: 76.64999999999999
      • type: ndcg_at_1000 value: 77.176
      • type: ndcg_at_3 value: 69.659
      • type: ndcg_at_5 value: 71.626
      • type: precision_at_1 value: 63.333
      • type: precision_at_10 value: 10
      • type: precision_at_100 value: 1.09
      • type: precision_at_1000 value: 0.11299999999999999
      • type: precision_at_3 value: 27.111
      • type: precision_at_5 value: 17.666999999999998
      • type: recall_at_1 value: 60.428000000000004
      • type: recall_at_10 value: 87.98899999999999
      • type: recall_at_100 value: 96.167
      • type: recall_at_1000 value: 100
      • type: recall_at_3 value: 74.006
      • type: recall_at_5 value: 79.05
    • task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics:
      • type: cos_sim_accuracy value: 99.87326732673267
      • type: cos_sim_ap value: 96.81770773701805
      • type: cos_sim_f1 value: 93.6318407960199
      • type: cos_sim_precision value: 93.16831683168317
      • type: cos_sim_recall value: 94.1
      • type: dot_accuracy value: 99.87326732673267
      • type: dot_ap value: 96.8174218946665
      • type: dot_f1 value: 93.6318407960199
      • type: dot_precision value: 93.16831683168317
      • type: dot_recall value: 94.1
      • type: euclidean_accuracy value: 99.87326732673267
      • type: euclidean_ap value: 96.81770773701807
      • type: euclidean_f1 value: 93.6318407960199
      • type: euclidean_precision value: 93.16831683168317
      • type: euclidean_recall value: 94.1
      • type: manhattan_accuracy value: 99.87227722772278
      • type: manhattan_ap value: 96.83164126821747
      • type: manhattan_f1 value: 93.54677338669335
      • type: manhattan_precision value: 93.5935935935936
      • type: manhattan_recall value: 93.5
      • type: max_accuracy value: 99.87326732673267
      • type: max_ap value: 96.83164126821747
      • type: max_f1 value: 93.6318407960199
    • task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics:
      • type: v_measure value: 65.6212042420246
    • task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics:
      • type: v_measure value: 35.779230635982564
    • task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics:
      • type: map value: 55.217701909036286
      • type: mrr value: 56.17658995416349
    • task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics:
      • type: cos_sim_pearson value: 30.954206018888453
      • type: cos_sim_spearman value: 32.71062599450096
      • type: dot_pearson value: 30.95420929056943
      • type: dot_spearman value: 32.71062599450096
    • task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics:
      • type: map_at_1 value: 0.22699999999999998
      • type: map_at_10 value: 1.924
      • type: map_at_100 value: 10.525
      • type: map_at_1000 value: 24.973
      • type: map_at_3 value: 0.638
      • type: map_at_5 value: 1.0659999999999998
      • type: mrr_at_1 value: 84
      • type: mrr_at_10 value: 91.067
      • type: mrr_at_100 value: 91.067
      • type: mrr_at_1000 value: 91.067
      • type: mrr_at_3 value: 90.667
      • type: mrr_at_5 value: 91.067
      • type: ndcg_at_1 value: 81
      • type: ndcg_at_10 value: 75.566
      • type: ndcg_at_100 value: 56.387
      • type: ndcg_at_1000 value: 49.834
      • type: ndcg_at_3 value: 80.899
      • type: ndcg_at_5 value: 80.75099999999999
      • type: precision_at_1 value: 84
      • type: precision_at_10 value: 79
      • type: precision_at_100 value: 57.56
      • type: precision_at_1000 value: 21.8
      • type: precision_at_3 value: 84.667
      • type: precision_at_5 value: 85.2
      • type: recall_at_1 value: 0.22699999999999998
      • type: recall_at_10 value: 2.136
      • type: recall_at_100 value: 13.861
      • type: recall_at_1000 value: 46.299
      • type: recall_at_3 value: 0.6649999999999999
      • type: recall_at_5 value: 1.145
    • task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics:
      • type: map_at_1 value: 2.752
      • type: map_at_10 value: 9.951
      • type: map_at_100 value: 16.794999999999998
      • type: map_at_1000 value: 18.251
      • type: map_at_3 value: 5.288
      • type: map_at_5 value: 6.954000000000001
      • type: mrr_at_1 value: 38.775999999999996
      • type: mrr_at_10 value: 50.458000000000006
      • type: mrr_at_100 value: 51.324999999999996
      • type: mrr_at_1000 value: 51.339999999999996
      • type: mrr_at_3 value: 46.939
      • type: mrr_at_5 value: 47.857
      • type: ndcg_at_1 value: 36.735
      • type: ndcg_at_10 value: 25.198999999999998
      • type: ndcg_at_100 value: 37.938
      • type: ndcg_at_1000 value: 49.145
      • type: ndcg_at_3 value: 29.348000000000003
      • type: ndcg_at_5 value: 25.804
      • type: precision_at_1 value: 38.775999999999996
      • type: precision_at_10 value: 22.041
      • type: precision_at_100 value: 7.939
      • type: precision_at_1000 value: 1.555
      • type: precision_at_3 value: 29.932
      • type: precision_at_5 value: 24.490000000000002
      • type: recall_at_1 value: 2.752
      • type: recall_at_10 value: 16.197
      • type: recall_at_100 value: 49.166
      • type: recall_at_1000 value: 84.18900000000001
      • type: recall_at_3 value: 6.438000000000001
      • type: recall_at_5 value: 9.093
    • task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics:
      • type: accuracy value: 71.47980000000001
      • type: ap value: 14.605194452178754
      • type: f1 value: 55.07362924988948
    • task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics:
      • type: accuracy value: 59.708545557441994
      • type: f1 value: 60.04751270975683
    • task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics:
      • type: v_measure value: 53.21105960597211
    • task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics:
      • type: cos_sim_accuracy value: 87.58419264469214
      • type: cos_sim_ap value: 78.55300004517404
      • type: cos_sim_f1 value: 71.49673530889001
      • type: cos_sim_precision value: 68.20795400095831
      • type: cos_sim_recall value: 75.11873350923483
      • type: dot_accuracy value: 87.58419264469214
      • type: dot_ap value: 78.55297659559511
      • type: dot_f1 value: 71.49673530889001
      • type: dot_precision value: 68.20795400095831
      • type: dot_recall value: 75.11873350923483
      • type: euclidean_accuracy value: 87.58419264469214
      • type: euclidean_ap value: 78.55300477331477
      • type: euclidean_f1 value: 71.49673530889001
      • type: euclidean_precision value: 68.20795400095831
      • type: euclidean_recall value: 75.11873350923483
      • type: manhattan_accuracy value: 87.5663110210407
      • type: manhattan_ap value: 78.49982050876562
      • type: manhattan_f1 value: 71.35488740722104
      • type: manhattan_precision value: 68.18946862226497
      • type: manhattan_recall value: 74.82849604221636
      • type: max_accuracy value: 87.58419264469214
      • type: max_ap value: 78.55300477331477
      • type: max_f1 value: 71.49673530889001
    • task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics:
      • type: cos_sim_accuracy value: 89.09069740365584
      • type: cos_sim_ap value: 86.22749303724757
      • type: cos_sim_f1 value: 78.36863452005407
      • type: cos_sim_precision value: 76.49560117302053
      • type: cos_sim_recall value: 80.33569448721897
      • type: dot_accuracy value: 89.09069740365584
      • type: dot_ap value: 86.22750233655673
      • type: dot_f1 value: 78.36863452005407
      • type: dot_precision value: 76.49560117302053
      • type: dot_recall value: 80.33569448721897
      • type: euclidean_accuracy value: 89.09069740365584
      • type: euclidean_ap value: 86.22749355597347
      • type: euclidean_f1 value: 78.36863452005407
      • type: euclidean_precision value: 76.49560117302053
      • type: euclidean_recall value: 80.33569448721897
      • type: manhattan_accuracy value: 89.08293553770326
      • type: manhattan_ap value: 86.21913616084771
      • type: manhattan_f1 value: 78.3907031479847
      • type: manhattan_precision value: 75.0352013517319
      • type: manhattan_recall value: 82.06036341238065
      • type: max_accuracy value: 89.09069740365584
      • type: max_ap value: 86.22750233655673
      • type: max_f1 value: 78.3907031479847 license: apache-2.0 language:
  • en library_name: sentence-transformers pipeline_tag: feature-extraction

tomaarsen/mxbai-embed-large-v1-exported

作者 tomaarsen

feature-extraction sentence-transformers
↓ 34 ♥ 0

创建时间: 2024-10-15 12:14:41+00:00

更新时间: 2024-10-15 12:27:05+00:00

在 Hugging Face 上查看

文件 (20)

.gitattributes
1_Pooling/config.json
README.md
config.json
config_sentence_transformers.json
modules.json
onnx/model.onnx ONNX
onnx/model_O1.onnx ONNX
onnx/model_O2.onnx ONNX
onnx/model_O3.onnx ONNX
onnx/model_O4.onnx ONNX
onnx/model_qint8_arm64.onnx ONNX
onnx/model_qint8_avx512.onnx ONNX
onnx/model_qint8_avx512_vnni.onnx ONNX
onnx/model_quint8_avx2.onnx ONNX
sentence_bert_config.json
special_tokens_map.json
tokenizer.json
tokenizer_config.json
vocab.txt