mixedbread-ai_mxbai-embed-large-v1

---

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

---

The crispy sentence embedding family from Mixedbread.

^{🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. Get in touch for access.}

mixedbread-ai/mxbai-embed-large-v1

Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt Represent this sentence for searching relevant passages: for query if you want to use it for retrieval. Besides that you don't need any prompt. Our model also supports Matryoshka Representation Learning and binary quantization.

Quickstart

Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt Represent this sentence for searching relevant passages: for query if you want to use it for retrieval. Besides that you don't need any prompt.

sentence-transformers

python -m pip install -U sentence-transformers

from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
from sentence_transformers.quantization import quantize_embeddings

# 1. Specify preffered dimensions
dimensions = 512

# 2. load model
model = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1", truncate_dim=dimensions)

# The prompt used for query retrieval tasks:
# query_prompt = 'Represent this sentence for searching relevant passages: '

query = "A man is eating a piece of bread"
docs = [
    "A man is eating food.",
    "A man is eating pasta.",
    "The girl is carrying a baby.",
    "A man is riding a horse.",
]

# 2. Encode
query_embedding = model.encode(query, prompt_name="query")
# Equivalent Alternatives:
# query_embedding = model.encode(query_prompt + query)
# query_embedding = model.encode(query, prompt=query_prompt)

docs_embeddings = model.encode(docs)

# Optional: Quantize the embeddings
binary_query_embedding = quantize_embeddings(query_embedding, precision="ubinary")
binary_docs_embeddings = quantize_embeddings(docs_embeddings, precision="ubinary")

similarities = cos_sim(query_embedding, docs_embeddings)
print('similarities:', similarities)

Transformers

from typing import Dict

import torch
import numpy as np
from transformers import AutoModel, AutoTokenizer
from sentence_transformers.util import cos_sim

# For retrieval you need to pass this prompt. Please find our more in our blog post.
def transform_query(query: str) -> str:
    """ For retrieval, add the prompt for query (not for documents).
    """
    return f'Represent this sentence for searching relevant passages: {query}'

# The model works really well with cls pooling (default) but also with mean pooling.
def pooling(outputs: torch.Tensor, inputs: Dict,  strategy: str = 'cls') -> np.ndarray:
    if strategy == 'cls':
        outputs = outputs[:, 0]
    elif strategy == 'mean':
        outputs = torch.sum(
            outputs * inputs["attention_mask"][:, :, None], dim=1) / torch.sum(inputs["attention_mask"], dim=1, keepdim=True)
    else:
        raise NotImplementedError
    return outputs.detach().cpu().numpy()

# 1. load model
model_id = 'mixedbread-ai/mxbai-embed-large-v1'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModel.from_pretrained(model_id).cuda()


docs = [
    transform_query('A man is eating a piece of bread'),
    "A man is eating food.",
    "A man is eating pasta.",
    "The girl is carrying a baby.",
    "A man is riding a horse.",
]

# 2. encode
inputs = tokenizer(docs, padding=True, return_tensors='pt')
for k, v in inputs.items():
    inputs[k] = v.cuda()
outputs = model(**inputs).last_hidden_state
embeddings = pooling(outputs, inputs, 'cls')

similarities = cos_sim(embeddings[0], embeddings[1:])
print('similarities:', similarities)

Transformers.js

If you haven't already, you can install the Transformers.js JavaScript library from NPM using:

npm i @huggingface/transformers

You can then use the model to compute embeddings like this:

import { pipeline, cos_sim } from "@huggingface/transformers";

// Create a feature extraction pipeline
const extractor = await pipeline("feature-extraction", "mixedbread-ai/mxbai-embed-large-v1", {
    dtype: "fp32", // Options: "fp32", "fp16", "q8"
});

// Generate sentence embeddings
const docs = [
    "Represent this sentence for searching relevant passages: A man is eating a piece of bread",
    "A man is eating food.",
    "A man is eating pasta.",
    "The girl is carrying a baby.",
    "A man is riding a horse.",
]
const output = await extractor(docs, { pooling: "cls" });

// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => cos_sim(source_embeddings, x));
console.log(similarities); // [0.7919578577247139, 0.6369278664248345, 0.16512018371357193, 0.3620778366720027]

Using API

You can use the model via our API as follows:

from mixedbread_ai.client import MixedbreadAI, EncodingFormat
from sklearn.metrics.pairwise import cosine_similarity
import os

mxbai = MixedbreadAI(api_key="{MIXEDBREAD_API_KEY}")

english_sentences = [
    'What is the capital of Australia?',
    'Canberra is the capital of Australia.'
] 

res = mxbai.embeddings(
     input=english_sentences,
     model="mixedbread-ai/mxbai-embed-large-v1",
     normalized=True,
     encoding_format=[EncodingFormat.FLOAT, EncodingFormat.UBINARY, EncodingFormat.INT_8],
     dimensions=512
)

encoded_embeddings = res.data[0].embedding
print(res.dimensions, encoded_embeddings.ubinary, encoded_embeddings.float_, encoded_embeddings.int_8)

The API comes with native int8 and binary quantization support! Check out the docs for more information.

Infinity

docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
michaelf34/infinity:0.0.68 \
v2 --model-id mixedbread-ai/mxbai-embed-large-v1 --revision "main" --dtype float16 --engine torch --port 7997

Evaluation

As of March 2024, our model archives SOTA performance for Bert-large sized models on the MTEB. It ourperforms commercial models like OpenAIs text-embedding-3-large and matches the performance of model 20x it's size like the echo-mistral-7b. Our model was trained with no overlap of the MTEB data, which indicates that our model generalizes well across several domains, tasks and text length. We know there are some limitations with this model, which will be fixed in v2.

Model	Avg (56 datasets)	Classification (12 datasets)	Clustering (11 datasets)	PairClassification (3 datasets)	Reranking (4 datasets)	Retrieval (15 datasets)	STS (10 datasets)	Summarization (1 dataset)
mxbai-embed-large-v1	64.68	75.64	46.71	87.2	60.11	54.39	85.00	32.71
bge-large-en-v1.5	64.23	75.97	46.08	87.12	60.03	54.29	83.11	31.61
mxbai-embed-2d-large-v1	63.25	74.14	46.07	85.89	58.94	51.42	84.9	31.55
nomic-embed-text-v1	62.39	74.12	43.91	85.15	55.69	52.81	82.06	30.08
jina-embeddings-v2-base-en	60.38	73.45	41.73	85.38	56.98	47.87	80.7	31.6
Proprietary Models
OpenAI text-embedding-3-large	64.58	75.45	49.01	85.72	59.16	55.44	81.73	29.92
Cohere embed-english-v3.0	64.47	76.49	47.43	85.84	58.01	55.00	82.62	30.18
OpenAI text-embedding-ada-002	60.99	70.93	45.90	84.89	56.32	49.25	80.97	30.80

Please find more information in our blog post.

Matryoshka and Binary Quantization

Embeddings in their commonly used form (float arrays) have a high memory footprint when used at scale. Two approaches to solve this problem are Matryoshka Representation Learning (MRL) and (Binary) Quantization. While MRL reduces the number of dimensions of an embedding, binary quantization transforms the value of each dimension from a float32 into a lower precision (int8 or even binary). The model supports both approaches!

You can also take it one step further, and combine both MRL and quantization. This combination of binary quantization and MRL allows you to reduce the memory usage of your embeddings significantly. This leads to much lower costs when using a vector database in particular. You can read more about the technology and its advantages in our blog post.

Community

Please join our Discord Community and share your feedback and thoughts! We are here to help and also always happy to chat.

License

Apache 2.0

Citation

@online{emb2024mxbai,
  title={Open Source Strikes Bread - New Fluffy Embeddings Model},
  author={Sean Lee and Aamir Shakir and Darius Koenig and Julius Lipp},
  year={2024},
  url={https://www.mixedbread.ai/blog/mxbai-embed-large-v1},
}

@article{li2023angle,
  title={AnglE-optimized Text Embeddings},
  author={Li, Xianming and Li, Jing},
  journal={arXiv preprint arXiv:2309.12871},
  year={2023}
}