region:us
---
{}
---
---
tags:
- mteb
model-index:
- name: pythia-14m_mean
  results:
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification (en)
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 70.73134328358208
    - type: ap
      value: 32.35996836729783
    - type: f1
      value: 64.2137087561157
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification (de)
      config: de
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 62.291220556745174
    - type: ap
      value: 76.5427302441011
    - type: f1
      value: 60.37703210343267
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification (en-ext)
      config: en-ext
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 67.57871064467767
    - type: ap
      value: 17.03033311712744
    - type: f1
      value: 54.821750631894986
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification (ja)
      config: ja
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 62.51605995717344
    - type: ap
      value: 14.367489440317666
    - type: f1
      value: 50.48473578289779
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_polarity
      name: MTEB AmazonPolarityClassification
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 57.567425000000014
    - type: ap
      value: 54.53026421737829
    - type: f1
      value: 56.60093061259046
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (en)
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 29.172000000000004
    - type: f1
      value: 28.264998641170465
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (de)
      config: de
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 25.157999999999998
    - type: f1
      value: 23.033533062569987
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (es)
      config: es
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 26.840000000000003
    - type: f1
      value: 25.693413738086402
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (fr)
      config: fr
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 26.491999999999997
    - type: f1
      value: 25.6252880863665
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (ja)
      config: ja
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 24.448000000000004
    - type: f1
      value: 23.86460242225935
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (zh)
      config: zh
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 26.412000000000003
    - type: f1
      value: 25.779710231390755
  - task:
      type: Retrieval
    dataset:
      type: arguana
      name: MTEB ArguAna
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 5.761
    - type: map_at_10
      value: 10.267
    - type: map_at_100
      value: 11.065999999999999
    - type: map_at_1000
      value: 11.16
    - type: map_at_3
      value: 8.642
    - type: map_at_5
      value: 9.474
    - type: mrr_at_1
      value: 6.046
    - type: mrr_at_10
      value: 10.365
    - type: mrr_at_100
      value: 11.178
    - type: mrr_at_1000
      value: 11.272
    - type: mrr_at_3
      value: 8.713
    - type: mrr_at_5
      value: 9.587
    - type: ndcg_at_1
      value: 5.761
    - type: ndcg_at_10
      value: 13.055
    - type: ndcg_at_100
      value: 17.526
    - type: ndcg_at_1000
      value: 20.578
    - type: ndcg_at_3
      value: 9.616
    - type: ndcg_at_5
      value: 11.128
    - type: precision_at_1
      value: 5.761
    - type: precision_at_10
      value: 2.212
    - type: precision_at_100
      value: 0.44400000000000006
    - type: precision_at_1000
      value: 0.06999999999999999
    - type: precision_at_3
      value: 4.149
    - type: precision_at_5
      value: 3.229
    - type: recall_at_1
      value: 5.761
    - type: recall_at_10
      value: 22.119
    - type: recall_at_100
      value: 44.381
    - type: recall_at_1000
      value: 69.70100000000001
    - type: recall_at_3
      value: 12.447
    - type: recall_at_5
      value: 16.145
  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-p2p
      name: MTEB ArxivClusteringP2P
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 25.92658946113241
  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-s2s
      name: MTEB ArxivClusteringS2S
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 13.902183567893395
  - task:
      type: Reranking
    dataset:
      type: mteb/askubuntudupquestions-reranking
      name: MTEB AskUbuntuDupQuestions
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 47.93210378051478
    - type: mrr
      value: 60.70318339708921
  - task:
      type: STS
    dataset:
      type: mteb/biosses-sts
      name: MTEB BIOSSES
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 49.57650220181508
    - type: cos_sim_spearman
      value: 51.842145113866636
    - type: euclidean_pearson
      value: 41.2188173176347
    - type: euclidean_spearman
      value: 41.16840792962046
    - type: manhattan_pearson
      value: 42.73893519020435
    - type: manhattan_spearman
      value: 44.384746276312534
  - task:
      type: Classification
    dataset:
      type: mteb/banking77
      name: MTEB Banking77Classification
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 46.03896103896104
    - type: f1
      value: 44.54083818845286
  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-p2p
      name: MTEB BiorxivClusteringP2P
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 23.113393015706908
  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-s2s
      name: MTEB BiorxivClusteringS2S
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 12.624675113307488
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackAndroidRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 10.105
    - type: map_at_10
      value: 13.364
    - type: map_at_100
      value: 13.987
    - type: map_at_1000
      value: 14.08
    - type: map_at_3
      value: 12.447
    - type: map_at_5
      value: 12.992999999999999
    - type: mrr_at_1
      value: 12.876000000000001
    - type: mrr_at_10
      value: 16.252
    - type: mrr_at_100
      value: 16.926
    - type: mrr_at_1000
      value: 17.004
    - type: mrr_at_3
      value: 15.235999999999999
    - type: mrr_at_5
      value: 15.744
    - type: ndcg_at_1
      value: 12.876000000000001
    - type: ndcg_at_10
      value: 15.634999999999998
    - type: ndcg_at_100
      value: 19.173000000000002
    - type: ndcg_at_1000
      value: 22.168
    - type: ndcg_at_3
      value: 14.116999999999999
    - type: ndcg_at_5
      value: 14.767
    - type: precision_at_1
      value: 12.876000000000001
    - type: precision_at_10
      value: 2.761
    - type: precision_at_100
      value: 0.5579999999999999
    - type: precision_at_1000
      value: 0.101
    - type: precision_at_3
      value: 6.676
    - type: precision_at_5
      value: 4.635
    - type: recall_at_1
      value: 10.105
    - type: recall_at_10
      value: 19.767000000000003
    - type: recall_at_100
      value: 36.448
    - type: recall_at_1000
      value: 58.623000000000005
    - type: recall_at_3
      value: 15.087
    - type: recall_at_5
      value: 17.076
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackEnglishRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 7.249999999999999
    - type: map_at_10
      value: 9.41
    - type: map_at_100
      value: 9.903
    - type: map_at_1000
      value: 9.993
    - type: map_at_3
      value: 8.693
    - type: map_at_5
      value: 9.052
    - type: mrr_at_1
      value: 9.299
    - type: mrr_at_10
      value: 11.907
    - type: mrr_at_100
      value: 12.424
    - type: mrr_at_1000
      value: 12.503
    - type: mrr_at_3
      value: 10.945
    - type: mrr_at_5
      value: 11.413
    - type: ndcg_at_1
      value: 9.299
    - type: ndcg_at_10
      value: 11.278
    - type: ndcg_at_100
      value: 13.904
    - type: ndcg_at_1000
      value: 16.642000000000003
    - type: ndcg_at_3
      value: 9.956
    - type: ndcg_at_5
      value: 10.488
    - type: precision_at_1
      value: 9.299
    - type: precision_at_10
      value: 2.166
    - type: precision_at_100
      value: 0.45399999999999996
    - type: precision_at_1000
      value: 0.089
    - type: precision_at_3
      value: 4.798
    - type: precision_at_5
      value: 3.427
    - type: recall_at_1
      value: 7.249999999999999
    - type: recall_at_10
      value: 14.285
    - type: recall_at_100
      value: 26.588
    - type: recall_at_1000
      value: 46.488
    - type: recall_at_3
      value: 10.309
    - type: recall_at_5
      value: 11.756
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackGamingRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 11.57
    - type: map_at_10
      value: 15.497
    - type: map_at_100
      value: 16.036
    - type: map_at_1000
      value: 16.122
    - type: map_at_3
      value: 14.309
    - type: map_at_5
      value: 14.895
    - type: mrr_at_1
      value: 13.354
    - type: mrr_at_10
      value: 17.408
    - type: mrr_at_100
      value: 17.936
    - type: mrr_at_1000
      value: 18.015
    - type: mrr_at_3
      value: 16.123
    - type: mrr_at_5
      value: 16.735
    - type: ndcg_at_1
      value: 13.354
    - type: ndcg_at_10
      value: 18.071
    - type: ndcg_at_100
      value: 21.017
    - type: ndcg_at_1000
      value: 23.669999999999998
    - type: ndcg_at_3
      value: 15.644
    - type: ndcg_at_5
      value: 16.618
    - type: precision_at_1
      value: 13.354
    - type: precision_at_10
      value: 2.94
    - type: precision_at_100
      value: 0.481
    - type: precision_at_1000
      value: 0.076
    - type: precision_at_3
      value: 7.001
    - type: precision_at_5
      value: 4.765
    - type: recall_at_1
      value: 11.57
    - type: recall_at_10
      value: 24.147
    - type: recall_at_100
      value: 38.045
    - type: recall_at_1000
      value: 58.648
    - type: recall_at_3
      value: 17.419999999999998
    - type: recall_at_5
      value: 19.875999999999998
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackGisRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 4.463
    - type: map_at_10
      value: 6.091
    - type: map_at_100
      value: 6.548
    - type: map_at_1000
      value: 6.622
    - type: map_at_3
      value: 5.461
    - type: map_at_5
      value: 5.768
    - type: mrr_at_1
      value: 4.746
    - type: mrr_at_10
      value: 6.431000000000001
    - type: mrr_at_100
      value: 6.941
    - type: mrr_at_1000
      value: 7.016
    - type: mrr_at_3
      value: 5.763
    - type: mrr_at_5
      value: 6.101999999999999
    - type: ndcg_at_1
      value: 4.746
    - type: ndcg_at_10
      value: 7.19
    - type: ndcg_at_100
      value: 9.604
    - type: ndcg_at_1000
      value: 12.086
    - type: ndcg_at_3
      value: 5.88
    - type: ndcg_at_5
      value: 6.429
    - type: precision_at_1
      value: 4.746
    - type: precision_at_10
      value: 1.141
    - type: precision_at_100
      value: 0.249
    - type: precision_at_1000
      value: 0.049
    - type: precision_at_3
      value: 2.448
    - type: precision_at_5
      value: 1.7850000000000001
    - type: recall_at_1
      value: 4.463
    - type: recall_at_10
      value: 10.33
    - type: recall_at_100
      value: 21.578
    - type: recall_at_1000
      value: 41.404
    - type: recall_at_3
      value: 6.816999999999999
    - type: recall_at_5
      value: 8.06
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackMathematicaRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 1.521
    - type: map_at_10
      value: 2.439
    - type: map_at_100
      value: 2.785
    - type: map_at_1000
      value: 2.858
    - type: map_at_3
      value: 2.091
    - type: map_at_5
      value: 2.2560000000000002
    - type: mrr_at_1
      value: 2.114
    - type: mrr_at_10
      value: 3.216
    - type: mrr_at_100
      value: 3.6319999999999997
    - type: mrr_at_1000
      value: 3.712
    - type: mrr_at_3
      value: 2.778
    - type: mrr_at_5
      value: 2.971
    - type: ndcg_at_1
      value: 2.114
    - type: ndcg_at_10
      value: 3.1910000000000003
    - type: ndcg_at_100
      value: 5.165
    - type: ndcg_at_1000
      value: 7.607
    - type: ndcg_at_3
      value: 2.456
    - type: ndcg_at_5
      value: 2.7439999999999998
    - type: precision_at_1
      value: 2.114
    - type: precision_at_10
      value: 0.634
    - type: precision_at_100
      value: 0.189
    - type: precision_at_1000
      value: 0.049
    - type: precision_at_3
      value: 1.202
    - type: precision_at_5
      value: 0.8959999999999999
    - type: recall_at_1
      value: 1.521
    - type: recall_at_10
      value: 4.8
    - type: recall_at_100
      value: 13.877
    - type: recall_at_1000
      value: 32.1
    - type: recall_at_3
      value: 2.806
    - type: recall_at_5
      value: 3.5520000000000005
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackPhysicsRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 7.449999999999999
    - type: map_at_10
      value: 10.065
    - type: map_at_100
      value: 10.507
    - type: map_at_1000
      value: 10.599
    - type: map_at_3
      value: 9.017
    - type: map_at_5
      value: 9.603
    - type: mrr_at_1
      value: 9.336
    - type: mrr_at_10
      value: 12.589
    - type: mrr_at_100
      value: 13.086
    - type: mrr_at_1000
      value: 13.161000000000001
    - type: mrr_at_3
      value: 11.373
    - type: mrr_at_5
      value: 12.084999999999999
    - type: ndcg_at_1
      value: 9.336
    - type: ndcg_at_10
      value: 12.299
    - type: ndcg_at_100
      value: 14.780999999999999
    - type: ndcg_at_1000
      value: 17.632
    - type: ndcg_at_3
      value: 10.302
    - type: ndcg_at_5
      value: 11.247
    - type: precision_at_1
      value: 9.336
    - type: precision_at_10
      value: 2.271
    - type: precision_at_100
      value: 0.42300000000000004
    - type: precision_at_1000
      value: 0.08099999999999999
    - type: precision_at_3
      value: 4.909
    - type: precision_at_5
      value: 3.5999999999999996
    - type: recall_at_1
      value: 7.449999999999999
    - type: recall_at_10
      value: 16.891000000000002
    - type: recall_at_100
      value: 28.050000000000004
    - type: recall_at_1000
      value: 49.267
    - type: recall_at_3
      value: 11.187999999999999
    - type: recall_at_5
      value: 13.587
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackProgrammersRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 4.734
    - type: map_at_10
      value: 7.045999999999999
    - type: map_at_100
      value: 7.564
    - type: map_at_1000
      value: 7.6499999999999995
    - type: map_at_3
      value: 6.21
    - type: map_at_5
      value: 6.617000000000001
    - type: mrr_at_1
      value: 5.936
    - type: mrr_at_10
      value: 8.624
    - type: mrr_at_100
      value: 9.193
    - type: mrr_at_1000
      value: 9.28
    - type: mrr_at_3
      value: 7.725
    - type: mrr_at_5
      value: 8.147
    - type: ndcg_at_1
      value: 5.936
    - type: ndcg_at_10
      value: 8.81
    - type: ndcg_at_100
      value: 11.694
    - type: ndcg_at_1000
      value: 14.526
    - type: ndcg_at_3
      value: 7.140000000000001
    - type: ndcg_at_5
      value: 7.8020000000000005
    - type: precision_at_1
      value: 5.936
    - type: precision_at_10
      value: 1.701
    - type: precision_at_100
      value: 0.366
    - type: precision_at_1000
      value: 0.07200000000000001
    - type: precision_at_3
      value: 3.463
    - type: precision_at_5
      value: 2.557
    - type: recall_at_1
      value: 4.734
    - type: recall_at_10
      value: 12.733
    - type: recall_at_100
      value: 25.982
    - type: recall_at_1000
      value: 47.233999999999995
    - type: recall_at_3
      value: 8.018
    - type: recall_at_5
      value: 9.762
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackStatsRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 4.293
    - type: map_at_10
      value: 6.146999999999999
    - type: map_at_100
      value: 6.487
    - type: map_at_1000
      value: 6.544999999999999
    - type: map_at_3
      value: 5.6930000000000005
    - type: map_at_5
      value: 5.869
    - type: mrr_at_1
      value: 5.061
    - type: mrr_at_10
      value: 7.1690000000000005
    - type: mrr_at_100
      value: 7.542
    - type: mrr_at_1000
      value: 7.5969999999999995
    - type: mrr_at_3
      value: 6.646000000000001
    - type: mrr_at_5
      value: 6.8229999999999995
    - type: ndcg_at_1
      value: 5.061
    - type: ndcg_at_10
      value: 7.396
    - type: ndcg_at_100
      value: 9.41
    - type: ndcg_at_1000
      value: 11.386000000000001
    - type: ndcg_at_3
      value: 6.454
    - type: ndcg_at_5
      value: 6.718
    - type: precision_at_1
      value: 5.061
    - type: precision_at_10
      value: 1.319
    - type: precision_at_100
      value: 0.262
    - type: precision_at_1000
      value: 0.047
    - type: precision_at_3
      value: 3.0669999999999997
    - type: precision_at_5
      value: 1.994
    - type: recall_at_1
      value: 4.293
    - type: recall_at_10
      value: 10.221
    - type: recall_at_100
      value: 19.744999999999997
    - type: recall_at_1000
      value: 35.399
    - type: recall_at_3
      value: 7.507999999999999
    - type: recall_at_5
      value: 8.275
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackTexRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 3.519
    - type: map_at_10
      value: 4.768
    - type: map_at_100
      value: 5.034000000000001
    - type: map_at_1000
      value: 5.087
    - type: map_at_3
      value: 4.308
    - type: map_at_5
      value: 4.565
    - type: mrr_at_1
      value: 4.474
    - type: mrr_at_10
      value: 6.045
    - type: mrr_at_100
      value: 6.361999999999999
    - type: mrr_at_1000
      value: 6.417000000000001
    - type: mrr_at_3
      value: 5.483
    - type: mrr_at_5
      value: 5.81
    - type: ndcg_at_1
      value: 4.474
    - type: ndcg_at_10
      value: 5.799
    - type: ndcg_at_100
      value: 7.344
    - type: ndcg_at_1000
      value: 9.141
    - type: ndcg_at_3
      value: 4.893
    - type: ndcg_at_5
      value: 5.309
    - type: precision_at_1
      value: 4.474
    - type: precision_at_10
      value: 1.06
    - type: precision_at_100
      value: 0.217
    - type: precision_at_1000
      value: 0.045
    - type: precision_at_3
      value: 2.306
    - type: precision_at_5
      value: 1.7000000000000002
    - type: recall_at_1
      value: 3.519
    - type: recall_at_10
      value: 7.75
    - type: recall_at_100
      value: 15.049999999999999
    - type: recall_at_1000
      value: 28.779
    - type: recall_at_3
      value: 5.18
    - type: recall_at_5
      value: 6.245
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackUnixRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 6.098
    - type: map_at_10
      value: 7.918
    - type: map_at_100
      value: 8.229000000000001
    - type: map_at_1000
      value: 8.293000000000001
    - type: map_at_3
      value: 7.138999999999999
    - type: map_at_5
      value: 7.646
    - type: mrr_at_1
      value: 7.090000000000001
    - type: mrr_at_10
      value: 9.293
    - type: mrr_at_100
      value: 9.669
    - type: mrr_at_1000
      value: 9.734
    - type: mrr_at_3
      value: 8.364
    - type: mrr_at_5
      value: 8.956999999999999
    - type: ndcg_at_1
      value: 7.090000000000001
    - type: ndcg_at_10
      value: 9.411999999999999
    - type: ndcg_at_100
      value: 11.318999999999999
    - type: ndcg_at_1000
      value: 13.478000000000002
    - type: ndcg_at_3
      value: 7.837
    - type: ndcg_at_5
      value: 8.73
    - type: precision_at_1
      value: 7.090000000000001
    - type: precision_at_10
      value: 1.558
    - type: precision_at_100
      value: 0.28400000000000003
    - type: precision_at_1000
      value: 0.053
    - type: precision_at_3
      value: 3.42
    - type: precision_at_5
      value: 2.5749999999999997
    - type: recall_at_1
      value: 6.098
    - type: recall_at_10
      value: 12.764000000000001
    - type: recall_at_100
      value: 21.747
    - type: recall_at_1000
      value: 38.279999999999994
    - type: recall_at_3
      value: 8.476
    - type: recall_at_5
      value: 10.707
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackWebmastersRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 8.607
    - type: map_at_10
      value: 10.835
    - type: map_at_100
      value: 11.285
    - type: map_at_1000
      value: 11.383000000000001
    - type: map_at_3
      value: 10.111
    - type: map_at_5
      value: 10.334999999999999
    - type: mrr_at_1
      value: 10.671999999999999
    - type: mrr_at_10
      value: 13.269
    - type: mrr_at_100
      value: 13.729
    - type: mrr_at_1000
      value: 13.813
    - type: mrr_at_3
      value: 12.385
    - type: mrr_at_5
      value: 12.701
    - type: ndcg_at_1
      value: 10.671999999999999
    - type: ndcg_at_10
      value: 12.728
    - type: ndcg_at_100
      value: 15.312999999999999
    - type: ndcg_at_1000
      value: 18.160999999999998
    - type: ndcg_at_3
      value: 11.355
    - type: ndcg_at_5
      value: 11.605
    - type: precision_at_1
      value: 10.671999999999999
    - type: precision_at_10
      value: 2.154
    - type: precision_at_100
      value: 0.455
    - type: precision_at_1000
      value: 0.098
    - type: precision_at_3
      value: 4.941
    - type: precision_at_5
      value: 3.2809999999999997
    - type: recall_at_1
      value: 8.607
    - type: recall_at_10
      value: 16.398
    - type: recall_at_100
      value: 28.92
    - type: recall_at_1000
      value: 49.761
    - type: recall_at_3
      value: 11.844000000000001
    - type: recall_at_5
      value: 12.792
  - task:
      type: Retrieval
    dataset:
      type: BeIR/cqadupstack
      name: MTEB CQADupstackWordpressRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 3.826
    - type: map_at_10
      value: 5.6419999999999995
    - type: map_at_100
      value: 5.943
    - type: map_at_1000
      value: 6.005
    - type: map_at_3
      value: 5.1049999999999995
    - type: map_at_5
      value: 5.437
    - type: mrr_at_1
      value: 4.436
    - type: mrr_at_10
      value: 6.413
    - type: mrr_at_100
      value: 6.752
    - type: mrr_at_1000
      value: 6.819999999999999
    - type: mrr_at_3
      value: 5.884
    - type: mrr_at_5
      value: 6.18
    - type: ndcg_at_1
      value: 4.436
    - type: ndcg_at_10
      value: 6.7989999999999995
    - type: ndcg_at_100
      value: 8.619
    - type: ndcg_at_1000
      value: 10.842
    - type: ndcg_at_3
      value: 5.739
    - type: ndcg_at_5
      value: 6.292000000000001
    - type: precision_at_1
      value: 4.436
    - type: precision_at_10
      value: 1.109
    - type: precision_at_100
      value: 0.214
    - type: precision_at_1000
      value: 0.043
    - type: precision_at_3
      value: 2.588
    - type: precision_at_5
      value: 1.848
    - type: recall_at_1
      value: 3.826
    - type: recall_at_10
      value: 9.655
    - type: recall_at_100
      value: 18.611
    - type: recall_at_1000
      value: 36.733
    - type: recall_at_3
      value: 6.784
    - type: recall_at_5
      value: 8.17
  - task:
      type: Retrieval
    dataset:
      type: climate-fever
      name: MTEB ClimateFEVER
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 2.09
    - type: map_at_10
      value: 3.469
    - type: map_at_100
      value: 3.93
    - type: map_at_1000
      value: 4.018
    - type: map_at_3
      value: 2.8209999999999997
    - type: map_at_5
      value: 3.144
    - type: mrr_at_1
      value: 4.756
    - type: mrr_at_10
      value: 7.853000000000001
    - type: mrr_at_100
      value: 8.547
    - type: mrr_at_1000
      value: 8.631
    - type: mrr_at_3
      value: 6.569
    - type: mrr_at_5
      value: 7.249999999999999
    - type: ndcg_at_1
      value: 4.756
    - type: ndcg_at_10
      value: 5.494000000000001
    - type: ndcg_at_100
      value: 8.275
    - type: ndcg_at_1000
      value: 10.892
    - type: ndcg_at_3
      value: 4.091
    - type: ndcg_at_5
      value: 4.588
    - type: precision_at_1
      value: 4.756
    - type: precision_at_10
      value: 1.8370000000000002
    - type: precision_at_100
      value: 0.475
    - type: precision_at_1000
      value: 0.094
    - type: precision_at_3
      value: 3.018
    - type: precision_at_5
      value: 2.528
    - type: recall_at_1
      value: 2.09
    - type: recall_at_10
      value: 7.127
    - type: recall_at_100
      value: 17.483999999999998
    - type: recall_at_1000
      value: 33.353
    - type: recall_at_3
      value: 3.742
    - type: recall_at_5
      value: 5.041
  - task:
      type: Retrieval
    dataset:
      type: dbpedia-entity
      name: MTEB DBPedia
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.573
    - type: map_at_10
      value: 1.282
    - type: map_at_100
      value: 1.625
    - type: map_at_1000
      value: 1.71
    - type: map_at_3
      value: 1.0
    - type: map_at_5
      value: 1.135
    - type: mrr_at_1
      value: 7.000000000000001
    - type: mrr_at_10
      value: 11.084
    - type: mrr_at_100
      value: 11.634
    - type: mrr_at_1000
      value: 11.715
    - type: mrr_at_3
      value: 9.792
    - type: mrr_at_5
      value: 10.404
    - type: ndcg_at_1
      value: 4.375
    - type: ndcg_at_10
      value: 3.7800000000000002
    - type: ndcg_at_100
      value: 4.353
    - type: ndcg_at_1000
      value: 6.087
    - type: ndcg_at_3
      value: 4.258
    - type: ndcg_at_5
      value: 3.988
    - type: precision_at_1
      value: 7.000000000000001
    - type: precision_at_10
      value: 3.35
    - type: precision_at_100
      value: 1.057
    - type: precision_at_1000
      value: 0.243
    - type: precision_at_3
      value: 5.75
    - type: precision_at_5
      value: 4.6
    - type: recall_at_1
      value: 0.573
    - type: recall_at_10
      value: 2.464
    - type: recall_at_100
      value: 5.6770000000000005
    - type: recall_at_1000
      value: 12.516
    - type: recall_at_3
      value: 1.405
    - type: recall_at_5
      value: 1.807
  - task:
      type: Classification
    dataset:
      type: mteb/emotion
      name: MTEB EmotionClassification
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 23.279999999999998
    - type: f1
      value: 19.87865985032945
  - task:
      type: Retrieval
    dataset:
      type: fever
      name: MTEB FEVER
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 3.145
    - type: map_at_10
      value: 4.721
    - type: map_at_100
      value: 5.086
    - type: map_at_1000
      value: 5.142
    - type: map_at_3
      value: 4.107
    - type: map_at_5
      value: 4.45
    - type: mrr_at_1
      value: 3.27
    - type: mrr_at_10
      value: 4.958
    - type: mrr_at_100
      value: 5.35
    - type: mrr_at_1000
      value: 5.409
    - type: mrr_at_3
      value: 4.303
    - type: mrr_at_5
      value: 4.6739999999999995
    - type: ndcg_at_1
      value: 3.27
    - type: ndcg_at_10
      value: 5.768
    - type: ndcg_at_100
      value: 7.854
    - type: ndcg_at_1000
      value: 9.729000000000001
    - type: ndcg_at_3
      value: 4.476
    - type: ndcg_at_5
      value: 5.102
    - type: precision_at_1
      value: 3.27
    - type: precision_at_10
      value: 0.942
    - type: precision_at_100
      value: 0.20600000000000002
    - type: precision_at_1000
      value: 0.038
    - type: precision_at_3
      value: 1.8849999999999998
    - type: precision_at_5
      value: 1.455
    - type: recall_at_1
      value: 3.145
    - type: recall_at_10
      value: 8.889
    - type: recall_at_100
      value: 19.092000000000002
    - type: recall_at_1000
      value: 34.35
    - type: recall_at_3
      value: 5.353
    - type: recall_at_5
      value: 6.836
  - task:
      type: Retrieval
    dataset:
      type: fiqa
      name: MTEB FiQA2018
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 1.166
    - type: map_at_10
      value: 2.283
    - type: map_at_100
      value: 2.564
    - type: map_at_1000
      value: 2.6519999999999997
    - type: map_at_3
      value: 1.867
    - type: map_at_5
      value: 2.0500000000000003
    - type: mrr_at_1
      value: 2.932
    - type: mrr_at_10
      value: 4.852
    - type: mrr_at_100
      value: 5.306
    - type: mrr_at_1000
      value: 5.4
    - type: mrr_at_3
      value: 4.141
    - type: mrr_at_5
      value: 4.457
    - type: ndcg_at_1
      value: 2.932
    - type: ndcg_at_10
      value: 3.5709999999999997
    - type: ndcg_at_100
      value: 5.489
    - type: ndcg_at_1000
      value: 8.309999999999999
    - type: ndcg_at_3
      value: 2.773
    - type: ndcg_at_5
      value: 2.979
    - type: precision_at_1
      value: 2.932
    - type: precision_at_10
      value: 1.049
    - type: precision_at_100
      value: 0.306
    - type: precision_at_1000
      value: 0.077
    - type: precision_at_3
      value: 1.8519999999999999
    - type: precision_at_5
      value: 1.389
    - type: recall_at_1
      value: 1.166
    - type: recall_at_10
      value: 5.178
    - type: recall_at_100
      value: 13.056999999999999
    - type: recall_at_1000
      value: 31.708
    - type: recall_at_3
      value: 2.714
    - type: recall_at_5
      value: 3.4909999999999997
  - task:
      type: Retrieval
    dataset:
      type: hotpotqa
      name: MTEB HotpotQA
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 6.138
    - type: map_at_10
      value: 8.212
    - type: map_at_100
      value: 8.548
    - type: map_at_1000
      value: 8.604000000000001
    - type: map_at_3
      value: 7.555000000000001
    - type: map_at_5
      value: 7.881
    - type: mrr_at_1
      value: 12.275
    - type: mrr_at_10
      value: 15.49
    - type: mrr_at_100
      value: 15.978
    - type: mrr_at_1000
      value: 16.043
    - type: mrr_at_3
      value: 14.488000000000001
    - type: mrr_at_5
      value: 14.975
    - type: ndcg_at_1
      value: 12.275
    - type: ndcg_at_10
      value: 11.078000000000001
    - type: ndcg_at_100
      value: 13.081999999999999
    - type: ndcg_at_1000
      value: 14.906
    - type: ndcg_at_3
      value: 9.574
    - type: ndcg_at_5
      value: 10.206999999999999
    - type: precision_at_1
      value: 12.275
    - type: precision_at_10
      value: 2.488
    - type: precision_at_100
      value: 0.41200000000000003
    - type: precision_at_1000
      value: 0.066
    - type: precision_at_3
      value: 5.991
    - type: precision_at_5
      value: 4.0969999999999995
    - type: recall_at_1
      value: 6.138
    - type: recall_at_10
      value: 12.438
    - type: recall_at_100
      value: 20.601
    - type: recall_at_1000
      value: 32.984
    - type: recall_at_3
      value: 8.987
    - type: recall_at_5
      value: 10.242999999999999
  - task:
      type: Classification
    dataset:
      type: mteb/imdb
      name: MTEB ImdbClassification
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 56.96359999999999
    - type: ap
      value: 54.16760114570921
    - type: f1
      value: 56.193845361069116
  - task:
      type: Retrieval
    dataset:
      type: msmarco
      name: MTEB MSMARCO
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 1.34
    - type: map_at_10
      value: 2.2190000000000003
    - type: map_at_100
      value: 2.427
    - type: map_at_1000
      value: 2.461
    - type: map_at_3
      value: 1.8610000000000002
    - type: map_at_5
      value: 2.0340000000000003
    - type: mrr_at_1
      value: 1.375
    - type: mrr_at_10
      value: 2.284
    - type: mrr_at_100
      value: 2.5
    - type: mrr_at_1000
      value: 2.535
    - type: mrr_at_3
      value: 1.913
    - type: mrr_at_5
      value: 2.094
    - type: ndcg_at_1
      value: 1.375
    - type: ndcg_at_10
      value: 2.838
    - type: ndcg_at_100
      value: 4.043
    - type: ndcg_at_1000
      value: 5.205
    - type: ndcg_at_3
      value: 2.0629999999999997
    - type: ndcg_at_5
      value: 2.387
    - type: precision_at_1
      value: 1.375
    - type: precision_at_10
      value: 0.496
    - type: precision_at_100
      value: 0.11399999999999999
    - type: precision_at_1000
      value: 0.022000000000000002
    - type: precision_at_3
      value: 0.898
    - type: precision_at_5
      value: 0.705
    - type: recall_at_1
      value: 1.34
    - type: recall_at_10
      value: 4.787
    - type: recall_at_100
      value: 10.759
    - type: recall_at_1000
      value: 20.362
    - type: recall_at_3
      value: 2.603
    - type: recall_at_5
      value: 3.398
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (en)
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 64.39808481532147
    - type: f1
      value: 63.468270818712625
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (de)
      config: de
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 53.961679346294744
    - type: f1
      value: 51.6707117653683
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (es)
      config: es
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 57.018012008005336
    - type: f1
      value: 54.23413458037234
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (fr)
      config: fr
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 48.84434700908236
    - type: f1
      value: 46.48494180527987
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (hi)
      config: hi
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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    - type: accuracy
      value: 39.7669415561133
    - type: f1
      value: 35.50974325529877
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (th)
      config: th
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 42.589511754068724
    - type: f1
      value: 40.47244422785889
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (en)
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 34.01276789785682
    - type: f1
      value: 21.256775922291286
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (de)
      config: de
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 33.285432516201745
    - type: f1
      value: 19.841703666811565
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (es)
      config: es
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 32.121414276184126
    - type: f1
      value: 19.34706868150749
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (fr)
      config: fr
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 26.088318196053866
    - type: f1
      value: 17.22608011891254
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (hi)
      config: hi
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 15.320903549659375
    - type: f1
      value: 9.62002916015258
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (th)
      config: th
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 16.426763110307412
    - type: f1
      value: 11.023799171137183
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (af)
      config: af
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 27.347007397444518
    - type: f1
      value: 25.503551916252842
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (am)
      config: am
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 10.655682582380631
    - type: f1
      value: 9.141696317946996
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (ar)
      config: ar
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 17.347007397444518
    - type: f1
      value: 15.345346511499534
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (az)
      config: az
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 20.39004707464694
    - type: f1
      value: 21.129515472610237
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (bn)
      config: bn
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 14.082044384667114
    - type: f1
      value: 12.169922201279885
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (cy)
      config: cy
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
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    - type: f1
      value: 25.449222444030063
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (da)
      config: da
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 27.780766644250164
    - type: f1
      value: 26.96237025531764
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (de)
      config: de
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 21.768661735036986
    - type: f1
      value: 22.377462868662263
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (el)
      config: el
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 21.967047747141898
    - type: f1
      value: 22.427583602797057
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (en)
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 33.221250840618694
    - type: f1
      value: 32.627621011904495
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (es)
      config: es
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
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    - type: f1
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: fa
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 18.850033624747812
    - type: f1
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: fi
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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    - type: accuracy
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      value: 24.126974912341858
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (fr)
      config: fr
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
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    - type: f1
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (he)
      config: he
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
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    - type: f1
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: hi
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 14.277067921990588
    - type: f1
      value: 13.06156794974721
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: hu
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
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    - type: f1
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: hy
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 12.326832548755885
    - type: f1
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  - task:
      type: Classification
    dataset:
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      config: id
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      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 28.244788164088764
    - type: f1
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: is
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      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 26.0390047074647
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      value: 24.29180485465988
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: it
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: ja
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
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    - type: f1
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: jv
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: ka
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 14.159381304640215
    - type: f1
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: km
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 12.078009414929387
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: kn
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 10.144586415601884
    - type: f1
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: ko
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 23.799596503026226
    - type: f1
      value: 21.4839774342838
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: lv
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 27.047747141896433
    - type: f1
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  - task:
      type: Classification
    dataset:
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      config: ml
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 10.82380632145259
    - type: f1
      value: 9.568319030257811
  - task:
      type: Classification
    dataset:
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      config: mn
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 14.435104236718225
    - type: f1
      value: 14.861584951252121
  - task:
      type: Classification
    dataset:
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      config: ms
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
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    - type: f1
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: my
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
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    - type: f1
      value: 11.496310101617802
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: nb
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
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    - type: f1
      value: 24.098118477282508
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: nl
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
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    - type: f1
      value: 25.259998680849815
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: pl
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 24.909213180901148
    - type: f1
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  - task:
      type: Classification
    dataset:
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      config: pt
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 27.310020174848688
    - type: f1
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: ro
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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    - type: accuracy
      value: 21.930060524546064
    - type: f1
      value: 21.993435727293242
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: ru
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 25.773369199731
    - type: f1
      value: 23.857739120942632
  - task:
      type: Classification
    dataset:
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      config: sl
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 29.256893073301953
    - type: f1
      value: 26.6824726822139
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: sq
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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    - type: accuracy
      value: 17.673167451244115
    - type: f1
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: sv
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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    - type: accuracy
      value: 26.190316072629454
    - type: f1
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: sw
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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    - type: accuracy
      value: 28.903833221250842
    - type: f1
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: ta
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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    - type: accuracy
      value: 13.517148621385342
    - type: f1
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: te
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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    - type: accuracy
      value: 12.414256893073302
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: th
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 17.330195023537325
    - type: f1
      value: 17.262535319973026
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
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      config: tl
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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    - type: accuracy
      value: 22.310020174848688
    - type: f1
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (tr)
      config: tr
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 23.066577000672496
    - type: f1
      value: 22.50576183714979
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (ur)
      config: ur
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 12.501681237390722
    - type: f1
      value: 11.803223778323549
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (vi)
      config: vi
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 22.76059179556153
    - type: f1
      value: 21.55631039694901
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (zh-CN)
      config: zh-CN
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 34.92602555480834
    - type: f1
      value: 33.727974171773425
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (zh-TW)
      config: zh-TW
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 28.365837256220576
    - type: f1
      value: 28.510022067424707
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (af)
      config: af
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 36.02891728312038
    - type: f1
      value: 31.370502912090657
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (am)
      config: am
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 19.330867518493612
    - type: f1
      value: 15.90055477686407
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (ar)
      config: ar
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 23.880295897780766
    - type: f1
      value: 21.695586897546278
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (az)
      config: az
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 28.853396099529256
    - type: f1
      value: 26.709933198206503
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (bn)
      config: bn
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 18.382649630127773
    - type: f1
      value: 16.772353498005437
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (cy)
      config: cy
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 35.40685944855414
    - type: f1
      value: 31.488014251203257
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (da)
      config: da
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 34.4754539340955
    - type: f1
      value: 31.226537880607296
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (de)
      config: de
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 30.0
    - type: f1
      value: 28.08153376359271
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (el)
      config: el
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 26.879623402824482
    - type: f1
      value: 24.773267596830472
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (en)
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 43.77605917955615
    - type: f1
      value: 41.60519309254586
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (es)
      config: es
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 32.807666442501684
    - type: f1
      value: 30.250350082554473
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (fa)
      config: fa
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 24.515803631472764
    - type: f1
      value: 21.726717236406927
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (fi)
      config: fi
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 33.315400134498994
    - type: f1
      value: 29.353644700383935
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (fr)
      config: fr
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 32.42434431741762
    - type: f1
      value: 29.87668403274445
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (he)
      config: he
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 24.40147948890384
    - type: f1
      value: 21.429656257015555
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (hi)
      config: hi
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 21.435776731674512
    - type: f1
      value: 18.344740043506686
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (hu)
      config: hu
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 31.533288500336248
    - type: f1
      value: 29.185785763469298
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (hy)
      config: hy
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 19.983187626092803
    - type: f1
      value: 17.088318438817677
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (id)
      config: id
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 32.73369199731002
    - type: f1
      value: 31.37876294000571
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (is)
      config: is
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 31.503026227303295
    - type: f1
      value: 28.944584739759726
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (it)
      config: it
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 31.116341627437794
    - type: f1
      value: 29.410419968584232
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (ja)
      config: ja
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 31.136516476126424
    - type: f1
      value: 29.6719090248999
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (jv)
      config: jv
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 34.3813046402152
    - type: f1
      value: 31.481374035431443
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (ka)
      config: ka
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 18.3591123066577
    - type: f1
      value: 16.158113168004945
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (km)
      config: km
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 19.250168123739073
    - type: f1
      value: 16.728590019294526
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (kn)
      config: kn
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 16.04236718224613
    - type: f1
      value: 14.304518042661318
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (ko)
      config: ko
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 28.95427034297243
    - type: f1
      value: 26.14194694048272
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (lv)
      config: lv
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 32.286482851378615
    - type: f1
      value: 29.426003364232837
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (ml)
      config: ml
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 18.762609280430397
    - type: f1
      value: 15.764539982254005
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (mn)
      config: mn
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 21.570275722932077
    - type: f1
      value: 18.98517402487539
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (ms)
      config: ms
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 35.95158036314728
    - type: f1
      value: 33.45853327725729
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (my)
      config: my
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 20.03026227303295
    - type: f1
      value: 16.864192363299615
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (nb)
      config: nb
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 32.89172831203766
    - type: f1
      value: 29.90554132334421
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (nl)
      config: nl
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 32.36381977135171
    - type: f1
      value: 29.57501043505274
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (pl)
      config: pl
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 31.856086079354405
    - type: f1
      value: 30.242758819443548
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (pt)
      config: pt
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 31.694687289845326
    - type: f1
      value: 29.495870419371684
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (ro)
      config: ro
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 27.01412239408204
    - type: f1
      value: 26.44782204477556
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (ru)
      config: ru
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 30.554808338937463
    - type: f1
      value: 27.93696283146029
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (sl)
      config: sl
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 35.813718897108274
    - type: f1
      value: 33.021495683147286
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (sq)
      config: sq
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 22.992602555480836
    - type: f1
      value: 21.524928515996447
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (sv)
      config: sv
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 34.7074646940148
    - type: f1
      value: 31.54759971873104
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (sw)
      config: sw
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 36.240753194351036
    - type: f1
      value: 33.34397881816082
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (ta)
      config: ta
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 19.741089441829185
    - type: f1
      value: 16.129268723975766
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (te)
      config: te
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 17.54203093476799
    - type: f1
      value: 15.537381383894061
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (th)
      config: th
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 22.817753866846
    - type: f1
      value: 20.72245485990428
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (tl)
      config: tl
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 28.58439811701412
    - type: f1
      value: 26.88190194852028
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (tr)
      config: tr
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 28.60457296570275
    - type: f1
      value: 26.98989368733863
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (ur)
      config: ur
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 23.345662407531943
    - type: f1
      value: 19.75032390408514
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (vi)
      config: vi
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 29.71082716879624
    - type: f1
      value: 26.675920460240782
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (zh-CN)
      config: zh-CN
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 44.09549428379288
    - type: f1
      value: 41.275350430825675
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (zh-TW)
      config: zh-TW
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 37.24277067921991
    - type: f1
      value: 35.65629114113254
  - task:
      type: Clustering
    dataset:
      type: mteb/medrxiv-clustering-p2p
      name: MTEB MedrxivClusteringP2P
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 22.08508717069763
  - task:
      type: Clustering
    dataset:
      type: mteb/medrxiv-clustering-s2s
      name: MTEB MedrxivClusteringS2S
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 16.58582885790446
  - task:
      type: Reranking
    dataset:
      type: mteb/mind_small
      name: MTEB MindSmallReranking
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 26.730268595233923
    - type: mrr
      value: 27.065185919114704
  - task:
      type: Retrieval
    dataset:
      type: nfcorpus
      name: MTEB NFCorpus
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 1.2
    - type: map_at_10
      value: 1.6400000000000001
    - type: map_at_100
      value: 1.9789999999999999
    - type: map_at_1000
      value: 2.554
    - type: map_at_3
      value: 1.4449999999999998
    - type: map_at_5
      value: 1.533
    - type: mrr_at_1
      value: 6.811
    - type: mrr_at_10
      value: 11.068999999999999
    - type: mrr_at_100
      value: 12.454
    - type: mrr_at_1000
      value: 12.590000000000002
    - type: mrr_at_3
      value: 9.751999999999999
    - type: mrr_at_5
      value: 10.31
    - type: ndcg_at_1
      value: 6.3469999999999995
    - type: ndcg_at_10
      value: 4.941
    - type: ndcg_at_100
      value: 6.524000000000001
    - type: ndcg_at_1000
      value: 15.918
    - type: ndcg_at_3
      value: 5.959
    - type: ndcg_at_5
      value: 5.395
    - type: precision_at_1
      value: 6.811
    - type: precision_at_10
      value: 3.375
    - type: precision_at_100
      value: 2.0709999999999997
    - type: precision_at_1000
      value: 1.313
    - type: precision_at_3
      value: 5.47
    - type: precision_at_5
      value: 4.396
    - type: recall_at_1
      value: 1.2
    - type: recall_at_10
      value: 2.5909999999999997
    - type: recall_at_100
      value: 9.443999999999999
    - type: recall_at_1000
      value: 41.542
    - type: recall_at_3
      value: 1.702
    - type: recall_at_5
      value: 1.9879999999999998
  - task:
      type: Retrieval
    dataset:
      type: nq
      name: MTEB NQ
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 1.214
    - type: map_at_10
      value: 2.067
    - type: map_at_100
      value: 2.2399999999999998
    - type: map_at_1000
      value: 2.2689999999999997
    - type: map_at_3
      value: 1.691
    - type: map_at_5
      value: 1.916
    - type: mrr_at_1
      value: 1.506
    - type: mrr_at_10
      value: 2.413
    - type: mrr_at_100
      value: 2.587
    - type: mrr_at_1000
      value: 2.616
    - type: mrr_at_3
      value: 2.023
    - type: mrr_at_5
      value: 2.246
    - type: ndcg_at_1
      value: 1.506
    - type: ndcg_at_10
      value: 2.703
    - type: ndcg_at_100
      value: 3.66
    - type: ndcg_at_1000
      value: 4.6
    - type: ndcg_at_3
      value: 1.9300000000000002
    - type: ndcg_at_5
      value: 2.33
    - type: precision_at_1
      value: 1.506
    - type: precision_at_10
      value: 0.539
    - type: precision_at_100
      value: 0.11
    - type: precision_at_1000
      value: 0.02
    - type: precision_at_3
      value: 0.9369999999999999
    - type: precision_at_5
      value: 0.7939999999999999
    - type: recall_at_1
      value: 1.214
    - type: recall_at_10
      value: 4.34
    - type: recall_at_100
      value: 8.905000000000001
    - type: recall_at_1000
      value: 16.416
    - type: recall_at_3
      value: 2.3009999999999997
    - type: recall_at_5
      value: 3.2489999999999997
  - task:
      type: Retrieval
    dataset:
      type: quora
      name: MTEB QuoraRetrieval
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 45.708
    - type: map_at_10
      value: 55.131
    - type: map_at_100
      value: 55.935
    - type: map_at_1000
      value: 55.993
    - type: map_at_3
      value: 52.749
    - type: map_at_5
      value: 54.166000000000004
    - type: mrr_at_1
      value: 52.44
    - type: mrr_at_10
      value: 59.99
    - type: mrr_at_100
      value: 60.492999999999995
    - type: mrr_at_1000
      value: 60.522
    - type: mrr_at_3
      value: 58.285
    - type: mrr_at_5
      value: 59.305
    - type: ndcg_at_1
      value: 52.43
    - type: ndcg_at_10
      value: 59.873
    - type: ndcg_at_100
      value: 63.086
    - type: ndcg_at_1000
      value: 64.291
    - type: ndcg_at_3
      value: 56.291000000000004
    - type: ndcg_at_5
      value: 58.071
    - type: precision_at_1
      value: 52.43
    - type: precision_at_10
      value: 8.973
    - type: precision_at_100
      value: 1.161
    - type: precision_at_1000
      value: 0.134
    - type: precision_at_3
      value: 24.177
    - type: precision_at_5
      value: 16.073999999999998
    - type: recall_at_1
      value: 45.708
    - type: recall_at_10
      value: 69.195
    - type: recall_at_100
      value: 82.812
    - type: recall_at_1000
      value: 91.136
    - type: recall_at_3
      value: 58.938
    - type: recall_at_5
      value: 63.787000000000006
  - task:
      type: Clustering
    dataset:
      type: mteb/reddit-clustering
      name: MTEB RedditClustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 13.142048230676806
  - task:
      type: Clustering
    dataset:
      type: mteb/reddit-clustering-p2p
      name: MTEB RedditClusteringP2P
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 26.06687178917052
  - task:
      type: Retrieval
    dataset:
      type: scidocs
      name: MTEB SCIDOCS
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.46499999999999997
    - type: map_at_10
      value: 0.906
    - type: map_at_100
      value: 1.127
    - type: map_at_1000
      value: 1.203
    - type: map_at_3
      value: 0.72
    - type: map_at_5
      value: 0.814
    - type: mrr_at_1
      value: 2.3
    - type: mrr_at_10
      value: 3.733
    - type: mrr_at_100
      value: 4.295999999999999
    - type: mrr_at_1000
      value: 4.412
    - type: mrr_at_3
      value: 3.183
    - type: mrr_at_5
      value: 3.458
    - type: ndcg_at_1
      value: 2.3
    - type: ndcg_at_10
      value: 1.797
    - type: ndcg_at_100
      value: 3.376
    - type: ndcg_at_1000
      value: 6.143
    - type: ndcg_at_3
      value: 1.763
    - type: ndcg_at_5
      value: 1.5070000000000001
    - type: precision_at_1
      value: 2.3
    - type: precision_at_10
      value: 0.91
    - type: precision_at_100
      value: 0.32399999999999995
    - type: precision_at_1000
      value: 0.101
    - type: precision_at_3
      value: 1.633
    - type: precision_at_5
      value: 1.3
    - type: recall_at_1
      value: 0.46499999999999997
    - type: recall_at_10
      value: 1.8499999999999999
    - type: recall_at_100
      value: 6.625
    - type: recall_at_1000
      value: 20.587
    - type: recall_at_3
      value: 0.9900000000000001
    - type: recall_at_5
      value: 1.315
  - task:
      type: STS
    dataset:
      type: mteb/sickr-sts
      name: MTEB SICK-R
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 60.78961481918511
    - type: cos_sim_spearman
      value: 54.92014630234372
    - type: euclidean_pearson
      value: 54.91456364340953
    - type: euclidean_spearman
      value: 50.95537043206628
    - type: manhattan_pearson
      value: 55.0450005071106
    - type: manhattan_spearman
      value: 51.227579527791654
  - task:
      type: STS
    dataset:
      type: mteb/sts12-sts
      name: MTEB STS12
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 43.73124494569395
    - type: cos_sim_spearman
      value: 43.07629933550637
    - type: euclidean_pearson
      value: 37.2529484210563
    - type: euclidean_spearman
      value: 36.68421330216546
    - type: manhattan_pearson
      value: 37.41673219009712
    - type: manhattan_spearman
      value: 36.92073705702668
  - task:
      type: STS
    dataset:
      type: mteb/sts13-sts
      name: MTEB STS13
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 57.17534157059787
    - type: cos_sim_spearman
      value: 56.86679858348438
    - type: euclidean_pearson
      value: 54.51552371857776
    - type: euclidean_spearman
      value: 53.80989851917749
    - type: manhattan_pearson
      value: 54.44486043632584
    - type: manhattan_spearman
      value: 53.83487353949481
  - task:
      type: STS
    dataset:
      type: mteb/sts14-sts
      name: MTEB STS14
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 52.319034960820375
    - type: cos_sim_spearman
      value: 50.89512224974754
    - type: euclidean_pearson
      value: 49.19308209408045
    - type: euclidean_spearman
      value: 47.45736923614355
    - type: manhattan_pearson
      value: 48.82127080055118
    - type: manhattan_spearman
      value: 47.20185686489298
  - task:
      type: STS
    dataset:
      type: mteb/sts15-sts
      name: MTEB STS15
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 61.57602956458427
    - type: cos_sim_spearman
      value: 62.894640061838956
    - type: euclidean_pearson
      value: 53.86893407586029
    - type: euclidean_spearman
      value: 54.68528520514299
    - type: manhattan_pearson
      value: 53.689614981956815
    - type: manhattan_spearman
      value: 54.51172839699876
  - task:
      type: STS
    dataset:
      type: mteb/sts16-sts
      name: MTEB STS16
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 56.2305694109318
    - type: cos_sim_spearman
      value: 57.885939000786045
    - type: euclidean_pearson
      value: 50.486043353701994
    - type: euclidean_spearman
      value: 50.4463227974027
    - type: manhattan_pearson
      value: 50.73317560427465
    - type: manhattan_spearman
      value: 50.81397877006027
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (ko-ko)
      config: ko-ko
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 55.52162058025664
    - type: cos_sim_spearman
      value: 59.02220327783535
    - type: euclidean_pearson
      value: 55.66332330866701
    - type: euclidean_spearman
      value: 56.829076266662206
    - type: manhattan_pearson
      value: 55.39181385186973
    - type: manhattan_spearman
      value: 56.607432176121144
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (ar-ar)
      config: ar-ar
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 46.312186899914906
    - type: cos_sim_spearman
      value: 48.07172073934163
    - type: euclidean_pearson
      value: 46.957276350776695
    - type: euclidean_spearman
      value: 43.98800593212707
    - type: manhattan_pearson
      value: 46.910805787619914
    - type: manhattan_spearman
      value: 43.96662723946553
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (en-ar)
      config: en-ar
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 16.222172523403835
    - type: cos_sim_spearman
      value: 17.230258645779042
    - type: euclidean_pearson
      value: -6.781460243147299
    - type: euclidean_spearman
      value: -6.884123336780775
    - type: manhattan_pearson
      value: -4.369061881907372
    - type: manhattan_spearman
      value: -4.235845433380353
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (en-de)
      config: en-de
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 7.462476431657987
    - type: cos_sim_spearman
      value: 5.875270645234161
    - type: euclidean_pearson
      value: -10.79494346180473
    - type: euclidean_spearman
      value: -11.704529023304776
    - type: manhattan_pearson
      value: -11.465867974964997
    - type: manhattan_spearman
      value: -12.428424608287173
  - 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: 61.46601840758559
    - type: cos_sim_spearman
      value: 65.69667638887147
    - type: euclidean_pearson
      value: 49.531065525619866
    - type: euclidean_spearman
      value: 53.880480167479725
    - type: manhattan_pearson
      value: 50.25462221374689
    - type: manhattan_spearman
      value: 54.22205494276401
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (en-tr)
      config: en-tr
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: -12.769479370624031
    - type: cos_sim_spearman
      value: -12.161427312728382
    - type: euclidean_pearson
      value: -27.950593491756536
    - type: euclidean_spearman
      value: -24.925281959398585
    - type: manhattan_pearson
      value: -25.98778888167475
    - type: manhattan_spearman
      value: -22.861942388867234
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (es-en)
      config: es-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 2.1575763564561727
    - type: cos_sim_spearman
      value: 1.182204089411577
    - type: euclidean_pearson
      value: -10.389249806317189
    - type: euclidean_spearman
      value: -16.078659904264605
    - type: manhattan_pearson
      value: -9.674301846448607
    - type: manhattan_spearman
      value: -16.976576817518577
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (es-es)
      config: es-es
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 66.16718583059163
    - type: cos_sim_spearman
      value: 69.95156267898052
    - type: euclidean_pearson
      value: 64.93174777029739
    - type: euclidean_spearman
      value: 66.21292533974568
    - type: manhattan_pearson
      value: 65.2578109632889
    - type: manhattan_spearman
      value: 66.21830865759128
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (fr-en)
      config: fr-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 0.1540829683540524
    - type: cos_sim_spearman
      value: -2.4072834011003987
    - type: euclidean_pearson
      value: -18.951775877513473
    - type: euclidean_spearman
      value: -18.393605606817527
    - type: manhattan_pearson
      value: -19.609633839454542
    - type: manhattan_spearman
      value: -19.276064769117912
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (it-en)
      config: it-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: -4.22497246932717
    - type: cos_sim_spearman
      value: -5.747420352346977
    - type: euclidean_pearson
      value: -16.86351349130112
    - type: euclidean_spearman
      value: -16.555536618547382
    - type: manhattan_pearson
      value: -17.45445643482646
    - type: manhattan_spearman
      value: -17.97322953856309
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (nl-en)
      config: nl-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 8.559184021676034
    - type: cos_sim_spearman
      value: 5.600273352595882
    - type: euclidean_pearson
      value: -10.76482859283058
    - type: euclidean_spearman
      value: -9.575202768285926
    - type: manhattan_pearson
      value: -9.48508597350615
    - type: manhattan_spearman
      value: -9.33387861352172
  - 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: 30.260087169228978
    - type: cos_sim_spearman
      value: 43.264174903196015
    - type: euclidean_pearson
      value: 35.07785877281954
    - type: euclidean_spearman
      value: 43.41294719372452
    - type: manhattan_pearson
      value: 36.74996284702431
    - type: manhattan_spearman
      value: 43.53522851890142
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (de)
      config: de
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 5.58694979115026
    - type: cos_sim_spearman
      value: 32.80692337371332
    - type: euclidean_pearson
      value: 10.53180875461474
    - type: euclidean_spearman
      value: 31.105269938654033
    - type: manhattan_pearson
      value: 10.559778015974826
    - type: manhattan_spearman
      value: 31.452204563072044
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (es)
      config: es
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 10.593783873928478
    - type: cos_sim_spearman
      value: 50.397542574042006
    - type: euclidean_pearson
      value: 28.122179063209714
    - type: euclidean_spearman
      value: 50.72847867996529
    - type: manhattan_pearson
      value: 28.730690148465005
    - type: manhattan_spearman
      value: 51.019761292483366
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (pl)
      config: pl
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: -1.3049499265017876
    - type: cos_sim_spearman
      value: 16.347130048706084
    - type: euclidean_pearson
      value: 0.5710147274110128
    - type: euclidean_spearman
      value: 16.589843077857605
    - type: manhattan_pearson
      value: 1.1226404198336415
    - type: manhattan_spearman
      value: 16.410620108636557
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (tr)
      config: tr
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: -10.96861909019159
    - type: cos_sim_spearman
      value: 24.536979219880724
    - type: euclidean_pearson
      value: -1.3040190807315306
    - type: euclidean_spearman
      value: 25.061584673761928
    - type: manhattan_pearson
      value: -0.06525719745037804
    - type: manhattan_spearman
      value: 25.979295538386893
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (ar)
      config: ar
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 1.0599417065503314
    - type: cos_sim_spearman
      value: 52.055853787103345
    - type: euclidean_pearson
      value: 23.666828441081776
    - type: euclidean_spearman
      value: 52.38656753170069
    - type: manhattan_pearson
      value: 23.398080463967215
    - type: manhattan_spearman
      value: 52.23849717509109
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (ru)
      config: ru
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: -2.847646040977239
    - type: cos_sim_spearman
      value: 40.5826838357407
    - type: euclidean_pearson
      value: 9.242304983683113
    - type: euclidean_spearman
      value: 40.35906851022345
    - type: manhattan_pearson
      value: 9.645663412799504
    - type: manhattan_spearman
      value: 40.78106154950966
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (zh)
      config: zh
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 17.761397832130992
    - type: cos_sim_spearman
      value: 59.98756452345925
    - type: euclidean_pearson
      value: 37.03125109036693
    - type: euclidean_spearman
      value: 59.58469212715707
    - type: manhattan_pearson
      value: 36.828102137170724
    - type: manhattan_spearman
      value: 59.07036501478588
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (fr)
      config: fr
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 22.281212883400205
    - type: cos_sim_spearman
      value: 48.27687537627578
    - type: euclidean_pearson
      value: 30.531395629285324
    - type: euclidean_spearman
      value: 50.349143748970384
    - type: manhattan_pearson
      value: 30.48762081986554
    - type: manhattan_spearman
      value: 50.66037165529169
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (de-en)
      config: de-en
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 15.76679673990358
    - type: cos_sim_spearman
      value: 19.123349126370442
    - type: euclidean_pearson
      value: 19.21389203087116
    - type: euclidean_spearman
      value: 23.63276413160338
    - type: manhattan_pearson
      value: 18.789263824907053
    - type: manhattan_spearman
      value: 19.962703178974692
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (es-en)
      config: es-en
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 11.024970397289941
    - type: cos_sim_spearman
      value: 13.530951900755017
    - type: euclidean_pearson
      value: 13.473514585343645
    - type: euclidean_spearman
      value: 16.754702023734914
    - type: manhattan_pearson
      value: 13.72847275970385
    - type: manhattan_spearman
      value: 16.673001637012348
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (it)
      config: it
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 33.32761589409043
    - type: cos_sim_spearman
      value: 54.14305778960692
    - type: euclidean_pearson
      value: 45.30173241170555
    - type: euclidean_spearman
      value: 54.77422257007743
    - type: manhattan_pearson
      value: 45.41890064000217
    - type: manhattan_spearman
      value: 54.533788920795544
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (pl-en)
      config: pl-en
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 20.045210048995486
    - type: cos_sim_spearman
      value: 17.597101329633823
    - type: euclidean_pearson
      value: 32.531726142346145
    - type: euclidean_spearman
      value: 27.244772040848105
    - type: manhattan_pearson
      value: 32.74618458514601
    - type: manhattan_spearman
      value: 25.81220754539242
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (zh-en)
      config: zh-en
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: -13.832846350193021
    - type: cos_sim_spearman
      value: -8.406778050457863
    - type: euclidean_pearson
      value: -6.557254855697437
    - type: euclidean_spearman
      value: -3.5112770921588563
    - type: manhattan_pearson
      value: -6.493730738275641
    - type: manhattan_spearman
      value: -2.5922348401468365
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (es-it)
      config: es-it
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 26.357929743436664
    - type: cos_sim_spearman
      value: 37.3417709718339
    - type: euclidean_pearson
      value: 30.930792572341293
    - type: euclidean_spearman
      value: 36.061866364725795
    - type: manhattan_pearson
      value: 31.56982745863155
    - type: manhattan_spearman
      value: 37.18529502311113
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (de-fr)
      config: de-fr
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 9.310102041071547
    - type: cos_sim_spearman
      value: 10.907002693108673
    - type: euclidean_pearson
      value: 7.361793742296021
    - type: euclidean_spearman
      value: 9.53967881391466
    - type: manhattan_pearson
      value: 8.017048631719996
    - type: manhattan_spearman
      value: 13.537860190039725
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (de-pl)
      config: de-pl
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: -5.534456407419709
    - type: cos_sim_spearman
      value: 17.552638994787724
    - type: euclidean_pearson
      value: -10.136558594355556
    - type: euclidean_spearman
      value: 11.055083156366303
    - type: manhattan_pearson
      value: -11.799223055640773
    - type: manhattan_spearman
      value: 1.416528760982869
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (fr-pl)
      config: fr-pl
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 48.64639760720344
    - type: cos_sim_spearman
      value: 39.440531887330785
    - type: euclidean_pearson
      value: 37.75527464173489
    - type: euclidean_spearman
      value: 39.440531887330785
    - type: manhattan_pearson
      value: 32.324715276369474
    - type: manhattan_spearman
      value: 28.17180849095055
  - task:
      type: STS
    dataset:
      type: mteb/stsbenchmark-sts
      name: MTEB STSBenchmark
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 44.667456983937
    - type: cos_sim_spearman
      value: 46.04327333618551
    - type: euclidean_pearson
      value: 44.583522824155104
    - type: euclidean_spearman
      value: 44.77184813864239
    - type: manhattan_pearson
      value: 44.54496373721756
    - type: manhattan_spearman
      value: 44.830873857115996
  - task:
      type: Reranking
    dataset:
      type: mteb/scidocs-reranking
      name: MTEB SciDocsRR
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 49.756063724243
    - type: mrr
      value: 75.29077585450135
  - task:
      type: Retrieval
    dataset:
      type: scifact
      name: MTEB SciFact
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 14.194
    - type: map_at_10
      value: 18.756999999999998
    - type: map_at_100
      value: 19.743
    - type: map_at_1000
      value: 19.865
    - type: map_at_3
      value: 16.986
    - type: map_at_5
      value: 18.024
    - type: mrr_at_1
      value: 15.0
    - type: mrr_at_10
      value: 19.961000000000002
    - type: mrr_at_100
      value: 20.875
    - type: mrr_at_1000
      value: 20.982
    - type: mrr_at_3
      value: 18.056
    - type: mrr_at_5
      value: 19.406000000000002
    - type: ndcg_at_1
      value: 15.0
    - type: ndcg_at_10
      value: 21.775
    - type: ndcg_at_100
      value: 26.8
    - type: ndcg_at_1000
      value: 30.468
    - type: ndcg_at_3
      value: 18.199
    - type: ndcg_at_5
      value: 20.111
    - type: precision_at_1
      value: 15.0
    - type: precision_at_10
      value: 3.4000000000000004
    - type: precision_at_100
      value: 0.607
    - type: precision_at_1000
      value: 0.094
    - type: precision_at_3
      value: 7.444000000000001
    - type: precision_at_5
      value: 5.6000000000000005
    - type: recall_at_1
      value: 14.194
    - type: recall_at_10
      value: 30.0
    - type: recall_at_100
      value: 53.911
    - type: recall_at_1000
      value: 83.289
    - type: recall_at_3
      value: 20.556
    - type: recall_at_5
      value: 24.972
  - task:
      type: PairClassification
    dataset:
      type: mteb/sprintduplicatequestions-pairclassification
      name: MTEB SprintDuplicateQuestions
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.35544554455446
    - type: cos_sim_ap
      value: 62.596006705300724
    - type: cos_sim_f1
      value: 60.80283353010627
    - type: cos_sim_precision
      value: 74.20749279538906
    - type: cos_sim_recall
      value: 51.5
    - type: dot_accuracy
      value: 99.13564356435643
    - type: dot_ap
      value: 43.87589686325114
    - type: dot_f1
      value: 46.99663623258049
    - type: dot_precision
      value: 45.235892691951896
    - type: dot_recall
      value: 48.9
    - type: euclidean_accuracy
      value: 99.2
    - type: euclidean_ap
      value: 43.44660755386079
    - type: euclidean_f1
      value: 45.9016393442623
    - type: euclidean_precision
      value: 52.79583875162549
    - type: euclidean_recall
      value: 40.6
    - type: manhattan_accuracy
      value: 99.2
    - type: manhattan_ap
      value: 43.11790011749347
    - type: manhattan_f1
      value: 45.11023176936122
    - type: manhattan_precision
      value: 51.88556566970091
    - type: manhattan_recall
      value: 39.900000000000006
    - type: max_accuracy
      value: 99.35544554455446
    - type: max_ap
      value: 62.596006705300724
    - type: max_f1
      value: 60.80283353010627
  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering
      name: MTEB StackExchangeClustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 25.71674282500873
  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering-p2p
      name: MTEB StackExchangeClusteringP2P
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 25.465780711520985
  - task:
      type: Reranking
    dataset:
      type: mteb/stackoverflowdupquestions-reranking
      name: MTEB StackOverflowDupQuestions
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 35.35656209427094
    - type: mrr
      value: 35.10693860877685
  - task:
      type: Retrieval
    dataset:
      type: trec-covid
      name: MTEB TRECCOVID
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.074
    - type: map_at_10
      value: 0.47400000000000003
    - type: map_at_100
      value: 1.825
    - type: map_at_1000
      value: 4.056
    - type: map_at_3
      value: 0.199
    - type: map_at_5
      value: 0.301
    - type: mrr_at_1
      value: 34.0
    - type: mrr_at_10
      value: 46.06
    - type: mrr_at_100
      value: 47.506
    - type: mrr_at_1000
      value: 47.522999999999996
    - type: mrr_at_3
      value: 44.0
    - type: mrr_at_5
      value: 44.4
    - type: ndcg_at_1
      value: 32.0
    - type: ndcg_at_10
      value: 28.633999999999997
    - type: ndcg_at_100
      value: 18.547
    - type: ndcg_at_1000
      value: 16.142
    - type: ndcg_at_3
      value: 32.48
    - type: ndcg_at_5
      value: 31.163999999999998
    - type: precision_at_1
      value: 34.0
    - type: precision_at_10
      value: 30.4
    - type: precision_at_100
      value: 18.54
    - type: precision_at_1000
      value: 7.942
    - type: precision_at_3
      value: 35.333
    - type: precision_at_5
      value: 34.0
    - type: recall_at_1
      value: 0.074
    - type: recall_at_10
      value: 0.641
    - type: recall_at_100
      value: 3.675
    - type: recall_at_1000
      value: 15.706000000000001
    - type: recall_at_3
      value: 0.231
    - type: recall_at_5
      value: 0.367
  - task:
      type: Retrieval
    dataset:
      type: webis-touche2020
      name: MTEB Touche2020
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.6799999999999999
    - type: map_at_10
      value: 2.1420000000000003
    - type: map_at_100
      value: 2.888
    - type: map_at_1000
      value: 3.3779999999999997
    - type: map_at_3
      value: 1.486
    - type: map_at_5
      value: 1.7579999999999998
    - type: mrr_at_1
      value: 12.245000000000001
    - type: mrr_at_10
      value: 22.12
    - type: mrr_at_100
      value: 23.407
    - type: mrr_at_1000
      value: 23.483999999999998
    - type: mrr_at_3
      value: 19.048000000000002
    - type: mrr_at_5
      value: 20.986
    - type: ndcg_at_1
      value: 10.204
    - type: ndcg_at_10
      value: 7.374
    - type: ndcg_at_100
      value: 10.524000000000001
    - type: ndcg_at_1000
      value: 18.4
    - type: ndcg_at_3
      value: 9.913
    - type: ndcg_at_5
      value: 8.938
    - type: precision_at_1
      value: 12.245000000000001
    - type: precision_at_10
      value: 7.142999999999999
    - type: precision_at_100
      value: 2.4490000000000003
    - type: precision_at_1000
      value: 0.731
    - type: precision_at_3
      value: 11.565
    - type: precision_at_5
      value: 9.796000000000001
    - type: recall_at_1
      value: 0.6799999999999999
    - type: recall_at_10
      value: 4.038
    - type: recall_at_100
      value: 14.151
    - type: recall_at_1000
      value: 40.111999999999995
    - type: recall_at_3
      value: 1.921
    - type: recall_at_5
      value: 2.604
  - task:
      type: Classification
    dataset:
      type: mteb/toxic_conversations_50k
      name: MTEB ToxicConversationsClassification
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 54.625600000000006
    - type: ap
      value: 9.425323874806459
    - type: f1
      value: 42.38724794017267
  - task:
      type: Classification
    dataset:
      type: mteb/tweet_sentiment_extraction
      name: MTEB TweetSentimentExtractionClassification
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 42.8494623655914
    - type: f1
      value: 42.66062148844617
  - task:
      type: Clustering
    dataset:
      type: mteb/twentynewsgroups-clustering
      name: MTEB TwentyNewsgroupsClustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 12.464890895237952
  - task:
      type: PairClassification
    dataset:
      type: mteb/twittersemeval2015-pairclassification
      name: MTEB TwitterSemEval2015
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 79.97854205161829
    - type: cos_sim_ap
      value: 47.45175747605773
    - type: cos_sim_f1
      value: 46.55775962660444
    - type: cos_sim_precision
      value: 41.73640167364017
    - type: cos_sim_recall
      value: 52.638522427440634
    - type: dot_accuracy
      value: 77.76718126005842
    - type: dot_ap
      value: 35.97737653101504
    - type: dot_f1
      value: 41.1975475754439
    - type: dot_precision
      value: 29.50165355228646
    - type: dot_recall
      value: 68.25857519788919
    - type: euclidean_accuracy
      value: 79.34076414138403
    - type: euclidean_ap
      value: 45.309577778755134
    - type: euclidean_f1
      value: 45.09938313913639
    - type: euclidean_precision
      value: 39.76631748589847
    - type: euclidean_recall
      value: 52.0844327176781
    - type: manhattan_accuracy
      value: 79.31692197651546
    - type: manhattan_ap
      value: 45.2433373222626
    - type: manhattan_f1
      value: 45.04624986069319
    - type: manhattan_precision
      value: 38.99286127725256
    - type: manhattan_recall
      value: 53.324538258575195
    - type: max_accuracy
      value: 79.97854205161829
    - type: max_ap
      value: 47.45175747605773
    - type: max_f1
      value: 46.55775962660444
  - task:
      type: PairClassification
    dataset:
      type: mteb/twitterurlcorpus-pairclassification
      name: MTEB TwitterURLCorpus
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 81.76737687740133
    - type: cos_sim_ap
      value: 64.59241956109807
    - type: cos_sim_f1
      value: 57.83203629255339
    - type: cos_sim_precision
      value: 55.50442477876106
    - type: cos_sim_recall
      value: 60.363412380659064
    - type: dot_accuracy
      value: 78.96922420149805
    - type: dot_ap
      value: 56.11775087282065
    - type: dot_f1
      value: 52.92134831460675
    - type: dot_precision
      value: 51.524212368728115
    - type: dot_recall
      value: 54.39636587619341
    - type: euclidean_accuracy
      value: 80.8611790274382
    - type: euclidean_ap
      value: 61.28070098354092
    - type: euclidean_f1
      value: 54.58334971882497
    - type: euclidean_precision
      value: 55.783297162607504
    - type: euclidean_recall
      value: 53.43393902063443
    - type: manhattan_accuracy
      value: 80.72534637326814
    - type: manhattan_ap
      value: 61.18048430787254
    - type: manhattan_f1
      value: 54.50978912822061
    - type: manhattan_precision
      value: 53.435396790178245
    - type: manhattan_recall
      value: 55.6282722513089
    - type: max_accuracy
      value: 81.76737687740133
    - type: max_ap
      value: 64.59241956109807
    - type: max_f1
      value: 57.83203629255339