README.md

---
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
---

<br><br>

<p align="center">
<svg xmlns="http://www.w3.org/2000/svg" xml:space="preserve" viewBox="0 0 2020 1130" width="150" height="150" aria-hidden="true"><path fill="#e95a0f" d="M398.167 621.992c-1.387-20.362-4.092-40.739-3.851-61.081.355-30.085 6.873-59.139 21.253-85.976 10.487-19.573 24.09-36.822 40.662-51.515 16.394-14.535 34.338-27.046 54.336-36.182 15.224-6.955 31.006-12.609 47.829-14.168 11.809-1.094 23.753-2.514 35.524-1.836 23.033 1.327 45.131 7.255 66.255 16.75 16.24 7.3 31.497 16.165 45.651 26.969 12.997 9.921 24.412 21.37 34.158 34.509 11.733 15.817 20.849 33.037 25.987 52.018 3.468 12.81 6.438 25.928 7.779 39.097 1.722 16.908 1.642 34.003 2.235 51.021.427 12.253.224 24.547 1.117 36.762 1.677 22.93 4.062 45.764 11.8 67.7 5.376 15.239 12.499 29.55 20.846 43.681l-18.282 20.328c-1.536 1.71-2.795 3.665-4.254 5.448l-19.323 23.533c-13.859-5.449-27.446-11.803-41.657-16.086-13.622-4.106-27.793-6.765-41.905-8.775-15.256-2.173-30.701-3.475-46.105-4.049-23.571-.879-47.178-1.056-70.769-1.029-10.858.013-21.723 1.116-32.57 1.926-5.362.4-10.69 1.255-16.464 1.477-2.758-7.675-5.284-14.865-7.367-22.181-3.108-10.92-4.325-22.554-13.16-31.095-2.598-2.512-5.069-5.341-6.883-8.443-6.366-10.884-12.48-21.917-18.571-32.959-4.178-7.573-8.411-14.375-17.016-18.559-10.34-5.028-19.538-12.387-29.311-18.611-3.173-2.021-6.414-4.312-9.952-5.297-5.857-1.63-11.98-2.301-17.991-3.376z"></path><path fill="#ed6d7b" d="M1478.998 758.842c-12.025.042-24.05.085-36.537-.373-.14-8.536.231-16.569.453-24.607.033-1.179-.315-2.986-1.081-3.4-.805-.434-2.376.338-3.518.81-.856.354-1.562 1.069-3.589 2.521-.239-3.308-.664-5.586-.519-7.827.488-7.544 2.212-15.166 1.554-22.589-1.016-11.451 1.397-14.592-12.332-14.419-3.793.048-3.617-2.803-3.332-5.331.499-4.422 1.45-8.803 1.77-13.233.311-4.316.068-8.672.068-12.861-2.554-.464-4.326-.86-6.12-1.098-4.415-.586-6.051-2.251-5.065-7.31 1.224-6.279.848-12.862 1.276-19.306.19-2.86-.971-4.473-3.794-4.753-4.113-.407-8.242-1.057-12.352-.975-4.663.093-5.192-2.272-4.751-6.012.733-6.229 1.252-12.483 1.875-18.726l1.102-10.495c-5.905-.309-11.146-.805-16.385-.778-3.32.017-5.174-1.4-5.566-4.4-1.172-8.968-2.479-17.944-3.001-26.96-.26-4.484-1.936-5.705-6.005-5.774-9.284-.158-18.563-.594-27.843-.953-7.241-.28-10.137-2.764-11.3-9.899-.746-4.576-2.715-7.801-7.777-8.207-7.739-.621-15.511-.992-23.207-1.961-7.327-.923-14.587-2.415-21.853-3.777-5.021-.941-10.003-2.086-15.003-3.14 4.515-22.952 13.122-44.382 26.284-63.587 18.054-26.344 41.439-47.239 69.102-63.294 15.847-9.197 32.541-16.277 50.376-20.599 16.655-4.036 33.617-5.715 50.622-4.385 33.334 2.606 63.836 13.955 92.415 31.15 15.864 9.545 30.241 20.86 42.269 34.758 8.113 9.374 15.201 19.78 21.718 30.359 10.772 17.484 16.846 36.922 20.611 56.991 1.783 9.503 2.815 19.214 3.318 28.876.758 14.578.755 29.196.65 44.311l-51.545 20.013c-7.779 3.059-15.847 5.376-21.753 12.365-4.73 5.598-10.658 10.316-16.547 14.774-9.9 7.496-18.437 15.988-25.083 26.631-3.333 5.337-7.901 10.381-12.999 14.038-11.355 8.144-17.397 18.973-19.615 32.423l-6.988 41.011z"></path><path fill="#ec663e" d="M318.11 923.047c-.702 17.693-.832 35.433-2.255 53.068-1.699 21.052-6.293 41.512-14.793 61.072-9.001 20.711-21.692 38.693-38.496 53.583-16.077 14.245-34.602 24.163-55.333 30.438-21.691 6.565-43.814 8.127-66.013 6.532-22.771-1.636-43.88-9.318-62.74-22.705-20.223-14.355-35.542-32.917-48.075-54.096-9.588-16.203-16.104-33.55-19.201-52.015-2.339-13.944-2.307-28.011-.403-42.182 2.627-19.545 9.021-37.699 17.963-55.067 11.617-22.564 27.317-41.817 48.382-56.118 15.819-10.74 33.452-17.679 52.444-20.455 8.77-1.282 17.696-1.646 26.568-2.055 11.755-.542 23.534-.562 35.289-1.11 8.545-.399 17.067-1.291 26.193-1.675 1.349 1.77 2.24 3.199 2.835 4.742 4.727 12.261 10.575 23.865 18.636 34.358 7.747 10.084 14.83 20.684 22.699 30.666 3.919 4.972 8.37 9.96 13.609 13.352 7.711 4.994 16.238 8.792 24.617 12.668 5.852 2.707 12.037 4.691 18.074 6.998z"></path><path fill="#ea580e" d="M1285.167 162.995c3.796-29.75 13.825-56.841 32.74-80.577 16.339-20.505 36.013-36.502 59.696-47.614 14.666-6.881 29.971-11.669 46.208-12.749 10.068-.669 20.239-1.582 30.255-.863 16.6 1.191 32.646 5.412 47.9 12.273 19.39 8.722 36.44 20.771 50.582 36.655 15.281 17.162 25.313 37.179 31.49 59.286 5.405 19.343 6.31 39.161 4.705 58.825-2.37 29.045-11.836 55.923-30.451 78.885-10.511 12.965-22.483 24.486-37.181 33.649-5.272-5.613-10.008-11.148-14.539-16.846-5.661-7.118-10.958-14.533-16.78-21.513-4.569-5.478-9.548-10.639-14.624-15.658-3.589-3.549-7.411-6.963-11.551-9.827-5.038-3.485-10.565-6.254-15.798-9.468-8.459-5.195-17.011-9.669-26.988-11.898-12.173-2.72-24.838-4.579-35.622-11.834-1.437-.967-3.433-1.192-5.213-1.542-12.871-2.529-25.454-5.639-36.968-12.471-5.21-3.091-11.564-4.195-17.011-6.965-4.808-2.445-8.775-6.605-13.646-8.851-8.859-4.085-18.114-7.311-27.204-10.896z"></path><path fill="#f8ab00" d="M524.963 311.12c-9.461-5.684-19.513-10.592-28.243-17.236-12.877-9.801-24.031-21.578-32.711-35.412-11.272-17.965-19.605-37.147-21.902-58.403-1.291-11.951-2.434-24.073-1.87-36.034.823-17.452 4.909-34.363 11.581-50.703 8.82-21.603 22.25-39.792 39.568-55.065 18.022-15.894 39.162-26.07 62.351-32.332 19.22-5.19 38.842-6.177 58.37-4.674 23.803 1.831 45.56 10.663 65.062 24.496 17.193 12.195 31.688 27.086 42.894 45.622-11.403 8.296-22.633 16.117-34.092 23.586-17.094 11.142-34.262 22.106-48.036 37.528-8.796 9.848-17.201 20.246-27.131 28.837-16.859 14.585-27.745 33.801-41.054 51.019-11.865 15.349-20.663 33.117-30.354 50.08-5.303 9.283-9.654 19.11-14.434 28.692z"></path><path fill="#ea5227" d="M1060.11 1122.049c-7.377 1.649-14.683 4.093-22.147 4.763-11.519 1.033-23.166 1.441-34.723 1.054-19.343-.647-38.002-4.7-55.839-12.65-15.078-6.72-28.606-15.471-40.571-26.836-24.013-22.81-42.053-49.217-49.518-81.936-1.446-6.337-1.958-12.958-2.235-19.477-.591-13.926-.219-27.909-1.237-41.795-.916-12.5-3.16-24.904-4.408-37.805 1.555-1.381 3.134-2.074 3.778-3.27 4.729-8.79 12.141-15.159 19.083-22.03 5.879-5.818 10.688-12.76 16.796-18.293 6.993-6.335 11.86-13.596 14.364-22.612l8.542-29.993c8.015 1.785 15.984 3.821 24.057 5.286 8.145 1.478 16.371 2.59 24.602 3.493 8.453.927 16.956 1.408 25.891 2.609 1.119 16.09 1.569 31.667 2.521 47.214.676 11.045 1.396 22.154 3.234 33.043 2.418 14.329 5.708 28.527 9.075 42.674 3.499 14.705 4.028 29.929 10.415 44.188 10.157 22.674 18.29 46.25 28.281 69.004 7.175 16.341 12.491 32.973 15.078 50.615.645 4.4 3.256 8.511 4.963 12.755z"></path><path fill="#ea5330" d="M1060.512 1122.031c-2.109-4.226-4.72-8.337-5.365-12.737-2.587-17.642-7.904-34.274-15.078-50.615-9.991-22.755-18.124-46.33-28.281-69.004-6.387-14.259-6.916-29.482-10.415-44.188-3.366-14.147-6.656-28.346-9.075-42.674-1.838-10.889-2.558-21.999-3.234-33.043-.951-15.547-1.401-31.124-2.068-47.146 8.568-.18 17.146.487 25.704.286l41.868-1.4c.907 3.746 1.245 7.04 1.881 10.276l8.651 42.704c.903 4.108 2.334 8.422 4.696 11.829 7.165 10.338 14.809 20.351 22.456 30.345 4.218 5.512 8.291 11.304 13.361 15.955 8.641 7.927 18.065 14.995 27.071 22.532 12.011 10.052 24.452 19.302 40.151 22.854-1.656 11.102-2.391 22.44-5.172 33.253-4.792 18.637-12.38 36.209-23.412 52.216-13.053 18.94-29.086 34.662-49.627 45.055-10.757 5.443-22.443 9.048-34.111 13.501z"></path><path fill="#f8aa05" d="M1989.106 883.951c5.198 8.794 11.46 17.148 15.337 26.491 5.325 12.833 9.744 26.207 12.873 39.737 2.95 12.757 3.224 25.908 1.987 39.219-1.391 14.973-4.643 29.268-10.349 43.034-5.775 13.932-13.477 26.707-23.149 38.405-14.141 17.104-31.215 30.458-50.807 40.488-14.361 7.352-29.574 12.797-45.741 14.594-10.297 1.144-20.732 2.361-31.031 1.894-24.275-1.1-47.248-7.445-68.132-20.263-6.096-3.741-11.925-7.917-17.731-12.342 5.319-5.579 10.361-10.852 15.694-15.811l37.072-34.009c.975-.892 2.113-1.606 3.08-2.505 6.936-6.448 14.765-12.2 20.553-19.556 8.88-11.285 20.064-19.639 31.144-28.292 4.306-3.363 9.06-6.353 12.673-10.358 5.868-6.504 10.832-13.814 16.422-20.582 6.826-8.264 13.727-16.481 20.943-24.401 4.065-4.461 8.995-8.121 13.249-12.424 14.802-14.975 28.77-30.825 45.913-43.317z"></path><path fill="#ed6876" d="M1256.099 523.419c5.065.642 10.047 1.787 15.068 2.728 7.267 1.362 14.526 2.854 21.853 3.777 7.696.97 15.468 1.34 23.207 1.961 5.062.406 7.031 3.631 7.777 8.207 1.163 7.135 4.059 9.62 11.3 9.899l27.843.953c4.069.069 5.745 1.291 6.005 5.774.522 9.016 1.829 17.992 3.001 26.96.392 3 2.246 4.417 5.566 4.4 5.239-.026 10.48.469 16.385.778l-1.102 10.495-1.875 18.726c-.44 3.74.088 6.105 4.751 6.012 4.11-.082 8.239.568 12.352.975 2.823.28 3.984 1.892 3.794 4.753-.428 6.444-.052 13.028-1.276 19.306-.986 5.059.651 6.724 5.065 7.31 1.793.238 3.566.634 6.12 1.098 0 4.189.243 8.545-.068 12.861-.319 4.43-1.27 8.811-1.77 13.233-.285 2.528-.461 5.379 3.332 5.331 13.729-.173 11.316 2.968 12.332 14.419.658 7.423-1.066 15.045-1.554 22.589-.145 2.241.28 4.519.519 7.827 2.026-1.452 2.733-2.167 3.589-2.521 1.142-.472 2.713-1.244 3.518-.81.767.414 1.114 2.221 1.081 3.4l-.917 24.539c-11.215.82-22.45.899-33.636 1.674l-43.952 3.436c-1.086-3.01-2.319-5.571-2.296-8.121.084-9.297-4.468-16.583-9.091-24.116-3.872-6.308-8.764-13.052-9.479-19.987-1.071-10.392-5.716-15.936-14.889-18.979-1.097-.364-2.16-.844-3.214-1.327-7.478-3.428-15.548-5.918-19.059-14.735-.904-2.27-3.657-3.775-5.461-5.723-2.437-2.632-4.615-5.525-7.207-7.987-2.648-2.515-5.352-5.346-8.589-6.777-4.799-2.121-10.074-3.185-15.175-4.596l-15.785-4.155c.274-12.896 1.722-25.901.54-38.662-1.647-17.783-3.457-35.526-2.554-53.352.528-10.426 2.539-20.777 3.948-31.574z"></path><path fill="#f6a200" d="M525.146 311.436c4.597-9.898 8.947-19.725 14.251-29.008 9.691-16.963 18.49-34.73 30.354-50.08 13.309-17.218 24.195-36.434 41.054-51.019 9.93-8.591 18.335-18.989 27.131-28.837 13.774-15.422 30.943-26.386 48.036-37.528 11.459-7.469 22.688-15.29 34.243-23.286 11.705 16.744 19.716 35.424 22.534 55.717 2.231 16.066 2.236 32.441 2.753 49.143-4.756 1.62-9.284 2.234-13.259 4.056-6.43 2.948-12.193 7.513-18.774 9.942-19.863 7.331-33.806 22.349-47.926 36.784-7.86 8.035-13.511 18.275-19.886 27.705-4.434 6.558-9.345 13.037-12.358 20.254-4.249 10.177-6.94 21.004-10.296 31.553-12.33.053-24.741 1.027-36.971-.049-20.259-1.783-40.227-5.567-58.755-14.69-.568-.28-1.295-.235-2.132-.658z"></path><path fill="#f7a80d" d="M1989.057 883.598c-17.093 12.845-31.061 28.695-45.863 43.67-4.254 4.304-9.184 7.963-13.249 12.424-7.216 7.92-14.117 16.137-20.943 24.401-5.59 6.768-10.554 14.078-16.422 20.582-3.614 4.005-8.367 6.995-12.673 10.358-11.08 8.653-22.264 17.007-31.144 28.292-5.788 7.356-13.617 13.108-20.553 19.556-.967.899-2.105 1.614-3.08 2.505l-37.072 34.009c-5.333 4.96-10.375 10.232-15.859 15.505-21.401-17.218-37.461-38.439-48.623-63.592 3.503-1.781 7.117-2.604 9.823-4.637 8.696-6.536 20.392-8.406 27.297-17.714.933-1.258 2.646-1.973 4.065-2.828 17.878-10.784 36.338-20.728 53.441-32.624 10.304-7.167 18.637-17.23 27.583-26.261 3.819-3.855 7.436-8.091 10.3-12.681 12.283-19.68 24.43-39.446 40.382-56.471 12.224-13.047 17.258-29.524 22.539-45.927 15.85 4.193 29.819 12.129 42.632 22.08 10.583 8.219 19.782 17.883 27.42 29.351z"></path><path fill="#ef7a72" d="M1479.461 758.907c1.872-13.734 4.268-27.394 6.525-41.076 2.218-13.45 8.26-24.279 19.615-32.423 5.099-3.657 9.667-8.701 12.999-14.038 6.646-10.643 15.183-19.135 25.083-26.631 5.888-4.459 11.817-9.176 16.547-14.774 5.906-6.99 13.974-9.306 21.753-12.365l51.48-19.549c.753 11.848.658 23.787 1.641 35.637 1.771 21.353 4.075 42.672 11.748 62.955.17.449.107.985-.019 2.158-6.945 4.134-13.865 7.337-20.437 11.143-3.935 2.279-7.752 5.096-10.869 8.384-6.011 6.343-11.063 13.624-17.286 19.727-9.096 8.92-12.791 20.684-18.181 31.587-.202.409-.072.984-.096 1.481-8.488-1.72-16.937-3.682-25.476-5.094-9.689-1.602-19.426-3.084-29.201-3.949-15.095-1.335-30.241-2.1-45.828-3.172z"></path><path fill="#e94e3b" d="M957.995 766.838c-20.337-5.467-38.791-14.947-55.703-27.254-8.2-5.967-15.451-13.238-22.958-20.37 2.969-3.504 5.564-6.772 8.598-9.563 7.085-6.518 11.283-14.914 15.8-23.153 4.933-8.996 10.345-17.743 14.966-26.892 2.642-5.231 5.547-11.01 5.691-16.611.12-4.651.194-8.932 2.577-12.742 8.52-13.621 15.483-28.026 18.775-43.704 2.11-10.049 7.888-18.774 7.81-29.825-.064-9.089 4.291-18.215 6.73-27.313 3.212-11.983 7.369-23.797 9.492-35.968 3.202-18.358 5.133-36.945 7.346-55.466l4.879-45.8c6.693.288 13.386.575 20.54 1.365.13 3.458-.41 6.407-.496 9.37l-1.136 42.595c-.597 11.552-2.067 23.058-3.084 34.59l-3.845 44.478c-.939 10.202-1.779 20.432-3.283 30.557-.96 6.464-4.46 12.646-1.136 19.383.348.706-.426 1.894-.448 2.864-.224 9.918-5.99 19.428-2.196 29.646.103.279-.033.657-.092.983l-8.446 46.205c-1.231 6.469-2.936 12.846-4.364 19.279-1.5 6.757-2.602 13.621-4.456 20.277-3.601 12.93-10.657 25.3-5.627 39.47.368 1.036.234 2.352.017 3.476l-5.949 30.123z"></path><path fill="#ea5043" d="M958.343 767.017c1.645-10.218 3.659-20.253 5.602-30.302.217-1.124.351-2.44-.017-3.476-5.03-14.17 2.026-26.539 5.627-39.47 1.854-6.656 2.956-13.52 4.456-20.277 1.428-6.433 3.133-12.81 4.364-19.279l8.446-46.205c.059-.326.196-.705.092-.983-3.794-10.218 1.972-19.728 2.196-29.646.022-.97.796-2.158.448-2.864-3.324-6.737.176-12.919 1.136-19.383 1.504-10.125 2.344-20.355 3.283-30.557l3.845-44.478c1.017-11.532 2.488-23.038 3.084-34.59.733-14.18.722-28.397 1.136-42.595.086-2.963.626-5.912.956-9.301 5.356-.48 10.714-.527 16.536-.081 2.224 15.098 1.855 29.734 1.625 44.408-.157 10.064 1.439 20.142 1.768 30.23.334 10.235-.035 20.49.116 30.733.084 5.713.789 11.418.861 17.13.054 4.289-.469 8.585-.702 12.879-.072 1.323-.138 2.659-.031 3.975l2.534 34.405-1.707 36.293-1.908 48.69c-.182 8.103.993 16.237.811 24.34-.271 12.076-1.275 24.133-1.787 36.207-.102 2.414-.101 5.283 1.06 7.219 4.327 7.22 4.463 15.215 4.736 23.103.365 10.553.088 21.128.086 31.693-11.44 2.602-22.84.688-34.106-.916-11.486-1.635-22.806-4.434-34.546-6.903z"></path><path fill="#eb5d19" d="M398.091 622.45c6.086.617 12.21 1.288 18.067 2.918 3.539.985 6.779 3.277 9.952 5.297 9.773 6.224 18.971 13.583 29.311 18.611 8.606 4.184 12.839 10.986 17.016 18.559l18.571 32.959c1.814 3.102 4.285 5.931 6.883 8.443 8.835 8.542 10.052 20.175 13.16 31.095 2.082 7.317 4.609 14.507 6.946 22.127-29.472 3.021-58.969 5.582-87.584 15.222-1.185-2.302-1.795-4.362-2.769-6.233-4.398-8.449-6.703-18.174-14.942-24.299-2.511-1.866-5.103-3.814-7.047-6.218-8.358-10.332-17.028-20.276-28.772-26.973 4.423-11.478 9.299-22.806 13.151-34.473 4.406-13.348 6.724-27.18 6.998-41.313.098-5.093.643-10.176 1.06-15.722z"></path><path fill="#e94c32" d="M981.557 392.109c-1.172 15.337-2.617 30.625-4.438 45.869-2.213 18.521-4.144 37.108-7.346 55.466-2.123 12.171-6.28 23.985-9.492 35.968-2.439 9.098-6.794 18.224-6.73 27.313.078 11.051-5.7 19.776-7.81 29.825-3.292 15.677-10.255 30.082-18.775 43.704-2.383 3.81-2.458 8.091-2.577 12.742-.144 5.6-3.049 11.38-5.691 16.611-4.621 9.149-10.033 17.896-14.966 26.892-4.517 8.239-8.715 16.635-15.8 23.153-3.034 2.791-5.629 6.06-8.735 9.255-12.197-10.595-21.071-23.644-29.301-37.24-7.608-12.569-13.282-25.962-17.637-40.37 13.303-6.889 25.873-13.878 35.311-25.315.717-.869 1.934-1.312 2.71-2.147 5.025-5.405 10.515-10.481 14.854-16.397 6.141-8.374 10.861-17.813 17.206-26.008 8.22-10.618 13.657-22.643 20.024-34.466 4.448-.626 6.729-3.21 8.114-6.89 1.455-3.866 2.644-7.895 4.609-11.492 4.397-8.05 9.641-15.659 13.708-23.86 3.354-6.761 5.511-14.116 8.203-21.206 5.727-15.082 7.277-31.248 12.521-46.578 3.704-10.828 3.138-23.116 4.478-34.753l7.56-.073z"></path><path fill="#f7a617" d="M1918.661 831.99c-4.937 16.58-9.971 33.057-22.196 46.104-15.952 17.025-28.099 36.791-40.382 56.471-2.864 4.59-6.481 8.825-10.3 12.681-8.947 9.031-17.279 19.094-27.583 26.261-17.103 11.896-35.564 21.84-53.441 32.624-1.419.856-3.132 1.571-4.065 2.828-6.904 9.308-18.6 11.178-27.297 17.714-2.705 2.033-6.319 2.856-9.874 4.281-3.413-9.821-6.916-19.583-9.36-29.602-1.533-6.284-1.474-12.957-1.665-19.913 1.913-.78 3.374-1.057 4.81-1.431 15.822-4.121 31.491-8.029 43.818-20.323 9.452-9.426 20.371-17.372 30.534-26.097 6.146-5.277 13.024-10.052 17.954-16.326 14.812-18.848 28.876-38.285 43.112-57.581 2.624-3.557 5.506-7.264 6.83-11.367 2.681-8.311 4.375-16.94 6.476-25.438 17.89.279 35.333 3.179 52.629 9.113z"></path><path fill="#ea553a" d="M1172.91 977.582c-15.775-3.127-28.215-12.377-40.227-22.43-9.005-7.537-18.43-14.605-27.071-22.532-5.07-4.651-9.143-10.443-13.361-15.955-7.647-9.994-15.291-20.007-22.456-30.345-2.361-3.407-3.792-7.72-4.696-11.829-3.119-14.183-5.848-28.453-8.651-42.704-.636-3.236-.974-6.53-1.452-10.209 15.234-2.19 30.471-3.969 46.408-5.622 2.692 5.705 4.882 11.222 6.63 16.876 2.9 9.381 7.776 17.194 15.035 24.049 7.056 6.662 13.305 14.311 19.146 22.099 9.509 12.677 23.01 19.061 36.907 25.054-1.048 7.441-2.425 14.854-3.066 22.33-.956 11.162-1.393 22.369-2.052 33.557l-1.096 17.661z"></path><path fill="#ea5453" d="M1163.123 704.036c-4.005 5.116-7.685 10.531-12.075 15.293-12.842 13.933-27.653 25.447-44.902 34.538-3.166-5.708-5.656-11.287-8.189-17.251-3.321-12.857-6.259-25.431-9.963-37.775-4.6-15.329-10.6-30.188-11.349-46.562-.314-6.871-1.275-14.287-7.114-19.644-1.047-.961-1.292-3.053-1.465-4.67l-4.092-39.927c-.554-5.245-.383-10.829-2.21-15.623-3.622-9.503-4.546-19.253-4.688-29.163-.088-6.111 1.068-12.256.782-18.344-.67-14.281-1.76-28.546-2.9-42.8-.657-8.222-1.951-16.395-2.564-24.62-.458-6.137-.285-12.322-.104-18.21.959 5.831 1.076 11.525 2.429 16.909 2.007 7.986 5.225 15.664 7.324 23.632 3.222 12.23 1.547 25.219 6.728 37.355 4.311 10.099 6.389 21.136 9.732 31.669 2.228 7.02 6.167 13.722 7.121 20.863 1.119 8.376 6.1 13.974 10.376 20.716l2.026 10.576c1.711 9.216 3.149 18.283 8.494 26.599 6.393 9.946 11.348 20.815 16.943 31.276 4.021 7.519 6.199 16.075 12.925 22.065l24.462 22.26c.556.503 1.507.571 2.274.841z"></path><path fill="#ea5b15" d="M1285.092 163.432c9.165 3.148 18.419 6.374 27.279 10.459 4.871 2.246 8.838 6.406 13.646 8.851 5.446 2.77 11.801 3.874 17.011 6.965 11.514 6.831 24.097 9.942 36.968 12.471 1.78.35 3.777.576 5.213 1.542 10.784 7.255 23.448 9.114 35.622 11.834 9.977 2.23 18.529 6.703 26.988 11.898 5.233 3.214 10.76 5.983 15.798 9.468 4.14 2.864 7.962 6.279 11.551 9.827 5.076 5.02 10.056 10.181 14.624 15.658 5.822 6.98 11.119 14.395 16.78 21.513 4.531 5.698 9.267 11.233 14.222 16.987-10.005 5.806-20.07 12.004-30.719 16.943-7.694 3.569-16.163 5.464-24.688 7.669-2.878-7.088-5.352-13.741-7.833-20.392-.802-2.15-1.244-4.55-2.498-6.396-4.548-6.7-9.712-12.999-14.011-19.847-6.672-10.627-15.34-18.93-26.063-25.376-9.357-5.625-18.367-11.824-27.644-17.587-6.436-3.997-12.902-8.006-19.659-11.405-5.123-2.577-11.107-3.536-16.046-6.37-17.187-9.863-35.13-17.887-54.031-23.767-4.403-1.37-8.953-2.267-13.436-3.382l.926-27.565z"></path><path fill="#ea504b" d="M1098 737l7.789 16.893c-15.04 9.272-31.679 15.004-49.184 17.995-9.464 1.617-19.122 2.097-29.151 3.019-.457-10.636-.18-21.211-.544-31.764-.273-7.888-.409-15.883-4.736-23.103-1.16-1.936-1.162-4.805-1.06-7.219l1.787-36.207c.182-8.103-.993-16.237-.811-24.34.365-16.236 1.253-32.461 1.908-48.69.484-12 .942-24.001 1.98-36.069 5.57 10.19 10.632 20.42 15.528 30.728 1.122 2.362 2.587 5.09 2.339 7.488-1.536 14.819 5.881 26.839 12.962 38.33 10.008 16.241 16.417 33.54 20.331 51.964 2.285 10.756 4.729 21.394 11.958 30.165L1098 737z"></path><path fill="#f6a320" d="M1865.78 822.529c-1.849 8.846-3.544 17.475-6.224 25.786-1.323 4.102-4.206 7.81-6.83 11.367l-43.112 57.581c-4.93 6.273-11.808 11.049-17.954 16.326-10.162 8.725-21.082 16.671-30.534 26.097-12.327 12.294-27.997 16.202-43.818 20.323-1.436.374-2.897.651-4.744.986-1.107-17.032-1.816-34.076-2.079-51.556 1.265-.535 2.183-.428 2.888-.766 10.596-5.072 20.8-11.059 32.586-13.273 1.69-.317 3.307-1.558 4.732-2.662l26.908-21.114c4.992-4.003 11.214-7.393 14.381-12.585 11.286-18.5 22.363-37.263 27.027-58.87l36.046 1.811c3.487.165 6.983.14 10.727.549z"></path><path fill="#ec6333" d="M318.448 922.814c-6.374-2.074-12.56-4.058-18.412-6.765-8.379-3.876-16.906-7.675-24.617-12.668-5.239-3.392-9.69-8.381-13.609-13.352-7.87-9.983-14.953-20.582-22.699-30.666-8.061-10.493-13.909-22.097-18.636-34.358-.595-1.543-1.486-2.972-2.382-4.783 6.84-1.598 13.797-3.023 20.807-4.106 18.852-2.912 36.433-9.493 53.737-17.819.697.888.889 1.555 1.292 2.051l17.921 21.896c4.14 4.939 8.06 10.191 12.862 14.412 5.67 4.984 12.185 9.007 18.334 13.447-8.937 16.282-16.422 33.178-20.696 51.31-1.638 6.951-2.402 14.107-3.903 21.403z"></path><path fill="#f49700" d="M623.467 326.903c2.893-10.618 5.584-21.446 9.833-31.623 3.013-7.217 7.924-13.696 12.358-20.254 6.375-9.43 12.026-19.67 19.886-27.705 14.12-14.434 28.063-29.453 47.926-36.784 6.581-2.429 12.344-6.994 18.774-9.942 3.975-1.822 8.503-2.436 13.186-3.592 1.947 18.557 3.248 37.15 8.307 55.686-15.453 7.931-28.853 18.092-40.46 29.996-10.417 10.683-19.109 23.111-28.013 35.175-3.238 4.388-4.888 9.948-7.262 14.973-17.803-3.987-35.767-6.498-54.535-5.931z"></path><path fill="#ea544c" d="M1097.956 736.615c-2.925-3.218-5.893-6.822-8.862-10.425-7.229-8.771-9.672-19.409-11.958-30.165-3.914-18.424-10.323-35.722-20.331-51.964-7.081-11.491-14.498-23.511-12.962-38.33.249-2.398-1.217-5.126-2.339-7.488l-15.232-31.019-3.103-34.338c-.107-1.316-.041-2.653.031-3.975.233-4.294.756-8.59.702-12.879-.072-5.713-.776-11.417-.861-17.13l-.116-30.733c-.329-10.088-1.926-20.166-1.768-30.23.23-14.674.599-29.31-1.162-44.341 9.369-.803 18.741-1.179 28.558-1.074 1.446 15.814 2.446 31.146 3.446 46.478.108 6.163-.064 12.348.393 18.485.613 8.225 1.907 16.397 2.564 24.62l2.9 42.8c.286 6.088-.869 12.234-.782 18.344.142 9.91 1.066 19.661 4.688 29.163 1.827 4.794 1.657 10.377 2.21 15.623l4.092 39.927c.172 1.617.417 3.71 1.465 4.67 5.839 5.357 6.8 12.773 7.114 19.644.749 16.374 6.749 31.233 11.349 46.562 3.704 12.344 6.642 24.918 9.963 37.775z"></path><path fill="#ec5c61" d="M1204.835 568.008c1.254 25.351-1.675 50.16-10.168 74.61-8.598-4.883-18.177-8.709-24.354-15.59-7.44-8.289-13.929-17.442-21.675-25.711-8.498-9.072-16.731-18.928-21.084-31.113-.54-1.513-1.691-2.807-2.594-4.564-4.605-9.247-7.706-18.544-7.96-29.09-.835-7.149-1.214-13.944-2.609-20.523-2.215-10.454-5.626-20.496-7.101-31.302-2.513-18.419-7.207-36.512-5.347-55.352.24-2.43-.17-4.949-.477-7.402l-4.468-34.792c2.723-.379 5.446-.757 8.585-.667 1.749 8.781 2.952 17.116 4.448 25.399 1.813 10.037 3.64 20.084 5.934 30.017 1.036 4.482 3.953 8.573 4.73 13.064 1.794 10.377 4.73 20.253 9.272 29.771 2.914 6.105 4.761 12.711 7.496 18.912 2.865 6.496 6.264 12.755 9.35 19.156 3.764 7.805 7.667 15.013 16.1 19.441 7.527 3.952 13.713 10.376 20.983 14.924 6.636 4.152 13.932 7.25 20.937 10.813z"></path><path fill="#ed676f" d="M1140.75 379.231c18.38-4.858 36.222-11.21 53.979-18.971 3.222 3.368 5.693 6.744 8.719 9.512 2.333 2.134 5.451 5.07 8.067 4.923 7.623-.429 12.363 2.688 17.309 8.215 5.531 6.18 12.744 10.854 19.224 16.184-5.121 7.193-10.461 14.241-15.323 21.606-13.691 20.739-22.99 43.255-26.782 67.926-.543 3.536-1.281 7.043-2.366 10.925-14.258-6.419-26.411-14.959-32.731-29.803-1.087-2.553-2.596-4.93-3.969-7.355-1.694-2.993-3.569-5.89-5.143-8.943-1.578-3.062-2.922-6.249-4.295-9.413-1.57-3.621-3.505-7.163-4.47-10.946-1.257-4.93-.636-10.572-2.725-15.013-5.831-12.397-7.467-25.628-9.497-38.847z"></path><path fill="#ed656e" d="M1254.103 647.439c5.325.947 10.603 2.272 15.847 3.722 5.101 1.41 10.376 2.475 15.175 4.596 3.237 1.431 5.942 4.262 8.589 6.777 2.592 2.462 4.77 5.355 7.207 7.987 1.804 1.948 4.557 3.453 5.461 5.723 3.51 8.817 11.581 11.307 19.059 14.735 1.053.483 2.116.963 3.214 1.327 9.172 3.043 13.818 8.587 14.889 18.979.715 6.935 5.607 13.679 9.479 19.987 4.623 7.533 9.175 14.819 9.091 24.116-.023 2.55 1.21 5.111 1.874 8.055-19.861 2.555-39.795 4.296-59.597 9.09l-11.596-23.203c-1.107-2.169-2.526-4.353-4.307-5.975-7.349-6.694-14.863-13.209-22.373-19.723l-17.313-14.669c-2.776-2.245-5.935-4.017-8.92-6.003l11.609-38.185c1.508-5.453 1.739-11.258 2.613-17.336z"></path><path fill="#ec6168" d="M1140.315 379.223c2.464 13.227 4.101 26.459 9.931 38.856 2.089 4.441 1.468 10.083 2.725 15.013.965 3.783 2.9 7.325 4.47 10.946 1.372 3.164 2.716 6.351 4.295 9.413 1.574 3.053 3.449 5.95 5.143 8.943 1.372 2.425 2.882 4.803 3.969 7.355 6.319 14.844 18.473 23.384 32.641 30.212.067 5.121-.501 10.201-.435 15.271l.985 38.117c.151 4.586.616 9.162.868 14.201-7.075-3.104-14.371-6.202-21.007-10.354-7.269-4.548-13.456-10.972-20.983-14.924-8.434-4.428-12.337-11.637-16.1-19.441-3.087-6.401-6.485-12.66-9.35-19.156-2.735-6.201-4.583-12.807-7.496-18.912-4.542-9.518-7.477-19.394-9.272-29.771-.777-4.491-3.694-8.581-4.73-13.064-2.294-9.933-4.121-19.98-5.934-30.017-1.496-8.283-2.699-16.618-4.036-25.335 10.349-2.461 20.704-4.511 31.054-6.582.957-.191 1.887-.515 3.264-.769z"></path><path fill="#e94c28" d="M922 537c-6.003 11.784-11.44 23.81-19.66 34.428-6.345 8.196-11.065 17.635-17.206 26.008-4.339 5.916-9.828 10.992-14.854 16.397-.776.835-1.993 1.279-2.71 2.147-9.439 11.437-22.008 18.427-35.357 24.929-4.219-10.885-6.942-22.155-7.205-33.905l-.514-49.542c7.441-2.893 14.452-5.197 21.334-7.841 1.749-.672 3.101-2.401 4.604-3.681 6.749-5.745 12.845-12.627 20.407-16.944 7.719-4.406 14.391-9.101 18.741-16.889.626-1.122 1.689-2.077 2.729-2.877 7.197-5.533 12.583-12.51 16.906-20.439.68-1.247 2.495-1.876 4.105-2.651 2.835 1.408 5.267 2.892 7.884 3.892 3.904 1.491 4.392 3.922 2.833 7.439-1.47 3.318-2.668 6.756-4.069 10.106-1.247 2.981-.435 5.242 2.413 6.544 2.805 1.282 3.125 3.14 1.813 5.601l-6.907 12.799L922 537z"></path><path fill="#eb5659" d="M1124.995 566c.868 1.396 2.018 2.691 2.559 4.203 4.353 12.185 12.586 22.041 21.084 31.113 7.746 8.269 14.235 17.422 21.675 25.711 6.176 6.881 15.756 10.707 24.174 15.932-6.073 22.316-16.675 42.446-31.058 60.937-1.074-.131-2.025-.199-2.581-.702l-24.462-22.26c-6.726-5.99-8.904-14.546-12.925-22.065-5.594-10.461-10.55-21.33-16.943-31.276-5.345-8.315-6.783-17.383-8.494-26.599-.63-3.394-1.348-6.772-1.738-10.848-.371-6.313-1.029-11.934-1.745-18.052l6.34 4.04 1.288-.675-2.143-15.385 9.454 1.208v-8.545L1124.995 566z"></path><path fill="#f5a02d" d="M1818.568 820.096c-4.224 21.679-15.302 40.442-26.587 58.942-3.167 5.192-9.389 8.582-14.381 12.585l-26.908 21.114c-1.425 1.104-3.042 2.345-4.732 2.662-11.786 2.214-21.99 8.201-32.586 13.273-.705.338-1.624.231-2.824.334a824.35 824.35 0 0 1-8.262-42.708c4.646-2.14 9.353-3.139 13.269-5.47 5.582-3.323 11.318-6.942 15.671-11.652 7.949-8.6 14.423-18.572 22.456-27.081 8.539-9.046 13.867-19.641 18.325-30.922l46.559 8.922z"></path><path fill="#eb5a57" d="M1124.96 565.639c-5.086-4.017-10.208-8.395-15.478-12.901v8.545l-9.454-1.208 2.143 15.385-1.288.675-6.34-4.04c.716 6.118 1.375 11.74 1.745 17.633-4.564-6.051-9.544-11.649-10.663-20.025-.954-7.141-4.892-13.843-7.121-20.863-3.344-10.533-5.421-21.57-9.732-31.669-5.181-12.135-3.506-25.125-6.728-37.355-2.099-7.968-5.317-15.646-7.324-23.632-1.353-5.384-1.47-11.078-2.429-16.909l-3.294-46.689a278.63 278.63 0 0 1 27.57-2.084c2.114 12.378 3.647 24.309 5.479 36.195 1.25 8.111 2.832 16.175 4.422 24.23 1.402 7.103 2.991 14.169 4.55 21.241 1.478 6.706.273 14.002 4.6 20.088 5.401 7.597 7.176 16.518 9.467 25.337 1.953 7.515 5.804 14.253 11.917 19.406.254 10.095 3.355 19.392 7.96 28.639z"></path><path fill="#ea541c" d="M911.651 810.999c-2.511 10.165-5.419 20.146-8.2 30.162-2.503 9.015-7.37 16.277-14.364 22.612-6.108 5.533-10.917 12.475-16.796 18.293-6.942 6.871-14.354 13.24-19.083 22.03-.644 1.196-2.222 1.889-3.705 2.857-2.39-7.921-4.101-15.991-6.566-23.823-5.451-17.323-12.404-33.976-23.414-48.835l21.627-21.095c3.182-3.29 5.532-7.382 8.295-11.083l10.663-14.163c9.528 4.78 18.925 9.848 28.625 14.247 7.324 3.321 15.036 5.785 22.917 8.799z"></path><path fill="#eb5d19" d="M1284.092 191.421c4.557.69 9.107 1.587 13.51 2.957 18.901 5.881 36.844 13.904 54.031 23.767 4.938 2.834 10.923 3.792 16.046 6.37 6.757 3.399 13.224 7.408 19.659 11.405l27.644 17.587c10.723 6.446 19.392 14.748 26.063 25.376 4.299 6.848 9.463 13.147 14.011 19.847 1.254 1.847 1.696 4.246 2.498 6.396l7.441 20.332c-11.685 1.754-23.379 3.133-35.533 4.037-.737-2.093-.995-3.716-1.294-5.33-3.157-17.057-14.048-30.161-23.034-44.146-3.027-4.71-7.786-8.529-12.334-11.993-9.346-7.116-19.004-13.834-28.688-20.491-6.653-4.573-13.311-9.251-20.431-13.002-8.048-4.24-16.479-7.85-24.989-11.091-11.722-4.465-23.673-8.328-35.527-12.449l.927-19.572z"></path><path fill="#eb5e24" d="M1283.09 211.415c11.928 3.699 23.88 7.562 35.602 12.027 8.509 3.241 16.941 6.852 24.989 11.091 7.12 3.751 13.778 8.429 20.431 13.002 9.684 6.657 19.342 13.375 28.688 20.491 4.548 3.463 9.307 7.283 12.334 11.993 8.986 13.985 19.877 27.089 23.034 44.146.299 1.615.557 3.237.836 5.263-13.373-.216-26.749-.839-40.564-1.923-2.935-9.681-4.597-18.92-12.286-26.152-15.577-14.651-30.4-30.102-45.564-45.193-.686-.683-1.626-1.156-2.516-1.584l-47.187-22.615 2.203-20.546z"></path><path fill="#e9511f" d="M913 486.001c-1.29.915-3.105 1.543-3.785 2.791-4.323 7.929-9.709 14.906-16.906 20.439-1.04.8-2.103 1.755-2.729 2.877-4.35 7.788-11.022 12.482-18.741 16.889-7.562 4.317-13.658 11.199-20.407 16.944-1.503 1.28-2.856 3.009-4.604 3.681-6.881 2.643-13.893 4.948-21.262 7.377-.128-11.151.202-22.302.378-33.454.03-1.892-.6-3.795-.456-6.12 13.727-1.755 23.588-9.527 33.278-17.663 2.784-2.337 6.074-4.161 8.529-6.784l29.057-31.86c1.545-1.71 3.418-3.401 4.221-5.459 5.665-14.509 11.49-28.977 16.436-43.736 2.817-8.407 4.074-17.338 6.033-26.032 5.039.714 10.078 1.427 15.536 2.629-.909 8.969-2.31 17.438-3.546 25.931-2.41 16.551-5.84 32.839-11.991 48.461L913 486.001z"></path><path fill="#ea5741" d="M1179.451 903.828c-14.224-5.787-27.726-12.171-37.235-24.849-5.841-7.787-12.09-15.436-19.146-22.099-7.259-6.854-12.136-14.667-15.035-24.049-1.748-5.654-3.938-11.171-6.254-17.033 15.099-4.009 30.213-8.629 44.958-15.533l28.367 36.36c6.09 8.015 13.124 14.75 22.72 18.375-7.404 14.472-13.599 29.412-17.48 45.244-.271 1.106-.382 2.25-.895 3.583z"></path><path fill="#ea522a" d="M913.32 486.141c2.693-7.837 5.694-15.539 8.722-23.231 6.151-15.622 9.581-31.91 11.991-48.461l3.963-25.861c7.582.317 15.168 1.031 22.748 1.797 4.171.421 8.333.928 12.877 1.596-.963 11.836-.398 24.125-4.102 34.953-5.244 15.33-6.794 31.496-12.521 46.578-2.692 7.09-4.849 14.445-8.203 21.206-4.068 8.201-9.311 15.81-13.708 23.86-1.965 3.597-3.154 7.627-4.609 11.492-1.385 3.68-3.666 6.265-8.114 6.89-1.994-1.511-3.624-3.059-5.077-4.44l6.907-12.799c1.313-2.461.993-4.318-1.813-5.601-2.849-1.302-3.66-3.563-2.413-6.544 1.401-3.35 2.599-6.788 4.069-10.106 1.558-3.517 1.071-5.948-2.833-7.439-2.617-1-5.049-2.484-7.884-3.892z"></path><path fill="#eb5e24" d="M376.574 714.118c12.053 6.538 20.723 16.481 29.081 26.814 1.945 2.404 4.537 4.352 7.047 6.218 8.24 6.125 10.544 15.85 14.942 24.299.974 1.871 1.584 3.931 2.376 6.29-7.145 3.719-14.633 6.501-21.386 10.517-9.606 5.713-18.673 12.334-28.425 18.399-3.407-3.73-6.231-7.409-9.335-10.834l-30.989-33.862c11.858-11.593 22.368-24.28 31.055-38.431 1.86-3.031 3.553-6.164 5.632-9.409z"></path><path fill="#e95514" d="M859.962 787.636c-3.409 5.037-6.981 9.745-10.516 14.481-2.763 3.701-5.113 7.792-8.295 11.083-6.885 7.118-14.186 13.834-21.65 20.755-13.222-17.677-29.417-31.711-48.178-42.878-.969-.576-2.068-.934-3.27-1.709 6.28-8.159 12.733-15.993 19.16-23.849 1.459-1.783 2.718-3.738 4.254-5.448l18.336-19.969c4.909 5.34 9.619 10.738 14.081 16.333 9.72 12.19 21.813 21.566 34.847 29.867.411.262.725.674 1.231 1.334z"></path><path fill="#eb5f2d" d="M339.582 762.088l31.293 33.733c3.104 3.425 5.928 7.104 9.024 10.979-12.885 11.619-24.548 24.139-33.899 38.704-.872 1.359-1.56 2.837-2.644 4.428-6.459-4.271-12.974-8.294-18.644-13.278-4.802-4.221-8.722-9.473-12.862-14.412l-17.921-21.896c-.403-.496-.595-1.163-.926-2.105 16.738-10.504 32.58-21.87 46.578-36.154z"></path><path fill="#f28d00" d="M678.388 332.912c1.989-5.104 3.638-10.664 6.876-15.051 8.903-12.064 17.596-24.492 28.013-35.175 11.607-11.904 25.007-22.064 40.507-29.592 4.873 11.636 9.419 23.412 13.67 35.592-5.759 4.084-11.517 7.403-16.594 11.553-4.413 3.607-8.124 8.092-12.023 12.301-5.346 5.772-10.82 11.454-15.782 17.547-3.929 4.824-7.17 10.208-10.716 15.344l-33.95-12.518z"></path><path fill="#f08369" d="M1580.181 771.427c-.191-.803-.322-1.377-.119-1.786 5.389-10.903 9.084-22.666 18.181-31.587 6.223-6.103 11.276-13.385 17.286-19.727 3.117-3.289 6.933-6.105 10.869-8.384 6.572-3.806 13.492-7.009 20.461-10.752 1.773 3.23 3.236 6.803 4.951 10.251l12.234 24.993c-1.367 1.966-2.596 3.293-3.935 4.499-7.845 7.07-16.315 13.564-23.407 21.32-6.971 7.623-12.552 16.517-18.743 24.854l-37.777-13.68z"></path><path fill="#f18b5e" d="M1618.142 785.4c6.007-8.63 11.588-17.524 18.559-25.147 7.092-7.755 15.562-14.249 23.407-21.32 1.338-1.206 2.568-2.534 3.997-4.162l28.996 33.733c1.896 2.205 4.424 3.867 6.66 6.394-6.471 7.492-12.967 14.346-19.403 21.255l-18.407 19.953c-12.958-12.409-27.485-22.567-43.809-30.706z"></path><path fill="#f49c3a" d="M1771.617 811.1c-4.066 11.354-9.394 21.949-17.933 30.995-8.032 8.509-14.507 18.481-22.456 27.081-4.353 4.71-10.089 8.329-15.671 11.652-3.915 2.331-8.623 3.331-13.318 5.069-4.298-9.927-8.255-19.998-12.1-30.743 4.741-4.381 9.924-7.582 13.882-11.904 7.345-8.021 14.094-16.603 20.864-25.131 4.897-6.168 9.428-12.626 14.123-18.955l32.61 11.936z"></path><path fill="#f08000" d="M712.601 345.675c3.283-5.381 6.524-10.765 10.453-15.589 4.962-6.093 10.435-11.774 15.782-17.547 3.899-4.21 7.61-8.695 12.023-12.301 5.078-4.15 10.836-7.469 16.636-11.19a934.12 934.12 0 0 1 23.286 35.848c-4.873 6.234-9.676 11.895-14.63 17.421l-25.195 27.801c-11.713-9.615-24.433-17.645-38.355-24.443z"></path><path fill="#ed6e04" d="M751.11 370.42c8.249-9.565 16.693-18.791 25.041-28.103 4.954-5.526 9.757-11.187 14.765-17.106 7.129 6.226 13.892 13.041 21.189 19.225 5.389 4.567 11.475 8.312 17.53 12.92-5.51 7.863-10.622 15.919-17.254 22.427-8.881 8.716-18.938 16.233-28.49 24.264-5.703-6.587-11.146-13.427-17.193-19.682-4.758-4.921-10.261-9.121-15.587-13.944z"></path><path fill="#ea541c" d="M921.823 385.544c-1.739 9.04-2.995 17.971-5.813 26.378-4.946 14.759-10.771 29.227-16.436 43.736-.804 2.058-2.676 3.749-4.221 5.459l-29.057 31.86c-2.455 2.623-5.745 4.447-8.529 6.784-9.69 8.135-19.551 15.908-33.208 17.237-1.773-9.728-3.147-19.457-4.091-29.6l36.13-16.763c.581-.267 1.046-.812 1.525-1.269 8.033-7.688 16.258-15.19 24.011-23.152 4.35-4.467 9.202-9.144 11.588-14.69 6.638-15.425 15.047-30.299 17.274-47.358 3.536.344 7.072.688 10.829 1.377z"></path><path fill="#f3944d" d="M1738.688 798.998c-4.375 6.495-8.906 12.953-13.803 19.121-6.771 8.528-13.519 17.11-20.864 25.131-3.958 4.322-9.141 7.523-13.925 11.54-8.036-13.464-16.465-26.844-27.999-38.387 5.988-6.951 12.094-13.629 18.261-20.25l19.547-20.95 38.783 23.794z"></path><path fill="#ec6168" d="M1239.583 703.142c3.282 1.805 6.441 3.576 9.217 5.821 5.88 4.755 11.599 9.713 17.313 14.669l22.373 19.723c1.781 1.622 3.2 3.806 4.307 5.975 3.843 7.532 7.477 15.171 11.194 23.136-10.764 4.67-21.532 8.973-32.69 12.982l-22.733-27.366c-2.003-2.416-4.096-4.758-6.194-7.093-3.539-3.94-6.927-8.044-10.74-11.701-2.57-2.465-5.762-4.283-8.675-6.39l16.627-29.755z"></path><path fill="#ec663e" d="M1351.006 332.839l-28.499 10.33c-.294.107-.533.367-1.194.264-11.067-19.018-27.026-32.559-44.225-44.855-4.267-3.051-8.753-5.796-13.138-8.682l9.505-24.505c10.055 4.069 19.821 8.227 29.211 13.108 3.998 2.078 7.299 5.565 10.753 8.598 3.077 2.701 5.743 5.891 8.926 8.447 4.116 3.304 9.787 5.345 12.62 9.432 6.083 8.777 10.778 18.517 16.041 27.863z"></path><path fill="#eb5e5b" d="M1222.647 733.051c3.223 1.954 6.415 3.771 8.985 6.237 3.813 3.658 7.201 7.761 10.74 11.701l6.194 7.093 22.384 27.409c-13.056 6.836-25.309 14.613-36.736 24.161l-39.323-44.7 24.494-27.846c1.072-1.224 1.974-2.598 3.264-4.056z"></path><path fill="#ea580e" d="M876.001 376.171c5.874 1.347 11.748 2.694 17.812 4.789-.81 5.265-2.687 9.791-2.639 14.296.124 11.469-4.458 20.383-12.73 27.863-2.075 1.877-3.659 4.286-5.668 6.248l-22.808 21.967c-.442.422-1.212.488-1.813.757l-23.113 10.389-9.875 4.514c-2.305-6.09-4.609-12.181-6.614-18.676 7.64-4.837 15.567-8.54 22.18-13.873 9.697-7.821 18.931-16.361 27.443-25.455 5.613-5.998 12.679-11.331 14.201-20.475.699-4.2 2.384-8.235 3.623-12.345z"></path><path fill="#e95514" d="M815.103 467.384c3.356-1.894 6.641-3.415 9.94-4.903l23.113-10.389c.6-.269 1.371-.335 1.813-.757l22.808-21.967c2.008-1.962 3.593-4.371 5.668-6.248 8.272-7.48 12.854-16.394 12.73-27.863-.049-4.505 1.828-9.031 2.847-13.956 5.427.559 10.836 1.526 16.609 2.68-1.863 17.245-10.272 32.119-16.91 47.544-2.387 5.546-7.239 10.223-11.588 14.69-7.753 7.962-15.978 15.464-24.011 23.152-.478.458-.944 1.002-1.525 1.269l-36.069 16.355c-2.076-6.402-3.783-12.81-5.425-19.607z"></path><path fill="#eb620b" d="M783.944 404.402c9.499-8.388 19.556-15.905 28.437-24.621 6.631-6.508 11.744-14.564 17.575-22.273 9.271 4.016 18.501 8.375 27.893 13.43-4.134 7.07-8.017 13.778-12.833 19.731-5.785 7.15-12.109 13.917-18.666 20.376-7.99 7.869-16.466 15.244-24.731 22.832l-17.674-29.475z"></path><path fill="#ea544c" d="M1197.986 854.686c-9.756-3.309-16.79-10.044-22.88-18.059l-28.001-36.417c8.601-5.939 17.348-11.563 26.758-17.075 1.615 1.026 2.639 1.876 3.505 2.865l26.664 30.44c3.723 4.139 7.995 7.785 12.017 11.656l-18.064 26.591z"></path><path fill="#ec6333" d="M1351.41 332.903c-5.667-9.409-10.361-19.149-16.445-27.926-2.833-4.087-8.504-6.128-12.62-9.432-3.184-2.555-5.849-5.745-8.926-8.447-3.454-3.033-6.756-6.52-10.753-8.598-9.391-4.88-19.157-9.039-29.138-13.499 1.18-5.441 2.727-10.873 4.81-16.607 11.918 4.674 24.209 8.261 34.464 14.962 14.239 9.304 29.011 18.453 39.595 32.464 2.386 3.159 5.121 6.077 7.884 8.923 6.564 6.764 10.148 14.927 11.723 24.093l-20.594 4.067z"></path><path fill="#eb5e5b" d="M1117 536.549c-6.113-4.702-9.965-11.44-11.917-18.955-2.292-8.819-4.066-17.74-9.467-25.337-4.327-6.085-3.122-13.382-4.6-20.088l-4.55-21.241c-1.59-8.054-3.172-16.118-4.422-24.23l-5.037-36.129c6.382-1.43 12.777-2.462 19.582-3.443 1.906 11.646 3.426 23.24 4.878 34.842.307 2.453.717 4.973.477 7.402-1.86 18.84 2.834 36.934 5.347 55.352 1.474 10.806 4.885 20.848 7.101 31.302 1.394 6.579 1.774 13.374 2.609 20.523z"></path><path fill="#ec644b" d="M1263.638 290.071c4.697 2.713 9.183 5.458 13.45 8.509 17.199 12.295 33.158 25.836 43.873 44.907-8.026 4.725-16.095 9.106-24.83 13.372-11.633-15.937-25.648-28.515-41.888-38.689-1.609-1.008-3.555-1.48-5.344-2.2 2.329-3.852 4.766-7.645 6.959-11.573l7.78-14.326z"></path><path fill="#eb5f2d" d="M1372.453 328.903c-2.025-9.233-5.608-17.396-12.172-24.16-2.762-2.846-5.498-5.764-7.884-8.923-10.584-14.01-25.356-23.16-39.595-32.464-10.256-6.701-22.546-10.289-34.284-15.312.325-5.246 1.005-10.444 2.027-15.863l47.529 22.394c.89.428 1.83.901 2.516 1.584l45.564 45.193c7.69 7.233 9.352 16.472 11.849 26.084-5.032.773-10.066 1.154-15.55 1.466z"></path><path fill="#e95a0f" d="M801.776 434.171c8.108-7.882 16.584-15.257 24.573-23.126 6.558-6.459 12.881-13.226 18.666-20.376 4.817-5.953 8.7-12.661 13.011-19.409 5.739 1.338 11.463 3.051 17.581 4.838-.845 4.183-2.53 8.219-3.229 12.418-1.522 9.144-8.588 14.477-14.201 20.475-8.512 9.094-17.745 17.635-27.443 25.455-6.613 5.333-14.54 9.036-22.223 13.51-2.422-4.469-4.499-8.98-6.735-13.786z"></path><path fill="#eb5e5b" d="M1248.533 316.002c2.155.688 4.101 1.159 5.71 2.168 16.24 10.174 30.255 22.752 41.532 38.727-7.166 5.736-14.641 11.319-22.562 16.731-1.16-1.277-1.684-2.585-2.615-3.46l-38.694-36.2 14.203-15.029c.803-.86 1.38-1.93 2.427-2.936z"></path><path fill="#eb5a57" d="M1216.359 827.958c-4.331-3.733-8.603-7.379-12.326-11.518l-26.664-30.44c-.866-.989-1.89-1.839-3.152-2.902 6.483-6.054 13.276-11.959 20.371-18.005l39.315 44.704c-5.648 6.216-11.441 12.12-17.544 18.161z"></path><path fill="#ec6168" d="M1231.598 334.101l38.999 36.066c.931.876 1.456 2.183 2.303 3.608-4.283 4.279-8.7 8.24-13.769 12.091-4.2-3.051-7.512-6.349-11.338-8.867-12.36-8.136-22.893-18.27-32.841-29.093l16.646-13.805z"></path><path fill="#ed656e" d="M1214.597 347.955c10.303 10.775 20.836 20.908 33.196 29.044 3.825 2.518 7.137 5.816 10.992 8.903-3.171 4.397-6.65 8.648-10.432 13.046-6.785-5.184-13.998-9.858-19.529-16.038-4.946-5.527-9.687-8.644-17.309-8.215-2.616.147-5.734-2.788-8.067-4.923-3.026-2.769-5.497-6.144-8.35-9.568 6.286-4.273 12.715-8.237 19.499-12.25z"></path></svg>
</p>

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