sentence-transformers onnx safetensors openvino gguf bert feature-extraction mteb transformers.js transformers en arxiv:2309.12871 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
language:
- en
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: feature-extraction
tags:
- mteb
- transformers.js
- transformers
model-index:
- name: mxbai-angle-large-v1
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      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:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      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:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 49.184
    - type: f1
      value: 48.74163227751588
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: 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:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 48.97294504317859
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 42.98071077674629
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 65.16477858490782
    - type: mrr
      value: 78.23583080508287
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      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:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 87.81818181818181
    - type: f1
      value: 87.79879337316918
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 39.91773608582761
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 36.73059477462478
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval
      type: BeIR/cqadupstack
      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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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:
      name: MTEB ClimateFEVER
      type: climate-fever
      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:
      name: MTEB DBPedia
      type: dbpedia-entity
      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:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 50.88
    - type: f1
      value: 45.545495028653384
  - task:
      type: Retrieval
    dataset:
      name: MTEB FEVER
      type: 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:
      name: MTEB FiQA2018
      type: fiqa
      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:
      name: MTEB HotpotQA
      type: 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:
      name: MTEB ImdbClassification
      type: mteb/imdb
      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:
      name: MTEB MSMARCO
      type: 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:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 93.95120839033288
    - type: f1
      value: 93.73824125055208
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 76.78978568171455
    - type: f1
      value: 57.50180552858304
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 76.24411566913248
    - type: f1
      value: 74.37851403532832
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 79.94620040349699
    - type: f1
      value: 80.21293397970435
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 33.44403096245675
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 31.659594631336812
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 32.53833075108798
    - type: mrr
      value: 33.78840823218308
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: 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:
      name: MTEB NQ
      type: 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:
      name: MTEB QuoraRetrieval
      type: quora
      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:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 60.22146692057259
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 65.29273320614578
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS
      type: 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:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      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:
      name: MTEB STS12
      type: mteb/sts12-sts
      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:
      name: MTEB STS13
      type: mteb/sts13-sts
      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:
      name: MTEB STS14
      type: mteb/sts14-sts
      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:
      name: MTEB STS15
      type: mteb/sts15-sts
      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:
      name: MTEB STS16
      type: mteb/sts16-sts
      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:
      name: MTEB STS17 (en-en)
      type: mteb/sts17-crosslingual-sts
      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:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      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:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      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:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 87.53075025771936
    - type: mrr
      value: 96.24327651288436
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: 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:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      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:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 65.6212042420246
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 35.779230635982564
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 55.217701909036286
    - type: mrr
      value: 56.17658995416349
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: 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:
      name: MTEB TRECCOVID
      type: trec-covid
      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:
      name: MTEB Touche2020
      type: webis-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:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      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:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 59.708545557441994
    - type: f1
      value: 60.04751270975683
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 53.21105960597211
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      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:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      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
---

<br><br>

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</p>

<p align="center">
<b>The crispy sentence embedding family from <a href="https://mixedbread.com"><b>Mixedbread</b></a>.</b>
</p>

<p align="center">
<sup> 🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. <a href="https://mixedbread.com"><b>Get in touch for access.</a> </sup>
</p>


# 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](https://www.mixedbread.ai/blog/binary-mrl).

## 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
```

```python
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

```python
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](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:

```
npm i @xenova/transformers
```

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

```javascript
import { pipeline, cos_sim } from '@xenova/transformers';

// Create a feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'mixedbread-ai/mxbai-embed-large-v1', {
    quantized: false, // Comment out this line to use the quantized version
});

// 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:

```python
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](https://mixedbread.ai/docs) for more information.

### Infinity
```bash
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](https://huggingface.co/spaces/mteb/leaderboard). 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](https://huggingface.co/jspringer/echo-mistral-7b-instruct-lasttoken). 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](https://huggingface.co/BAAI/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](https://huggingface.co/mixedbread-ai/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](https://huggingface.co/nomic-ai/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](https://huggingface.co/jinaai/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](https://openai.com/blog/new-embedding-models-and-api-updates) | 64.58             | 75.45                        | 49.01                    | 85.72                           | 59.16                  | 55.44                   | 81.73             | 29.92                     |
| [Cohere embed-english-v3.0](https://txt.cohere.com/introducing-embed-v3/)                     | 64.47             | 76.49                        | 47.43                    | 85.84                           | 58.01                  | 55.00                   | 82.62             | 30.18                     |
| [OpenAI text-embedding-ada-002](https://openai.com/blog/new-and-improved-embedding-model)     | 60.99             | 70.93                        | 45.90                    | 84.89                           | 56.32                  | 49.25                   | 80.97             | 30.80                     |


Please find more information in our [blog post](https://mixedbread.ai/blog/mxbai-embed-large-v1). 

## 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). <b> The model supports both approaches! </b>

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](https://www.mixedbread.ai/blog/binary-mrl).

## Community
Please join our [Discord Community](https://discord.gg/jDfMHzAVfU) and share your feedback and thoughts! We are here to help and also always happy to chat.

## License
Apache 2.0

## Citation

```bibtex
@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}
}
```
