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language:
  - multilingual
  - af
  - am
  - ar
  - as
  - az
  - be
  - bg
  - bn
  - br
  - bs
  - ca
  - cs
  - cy
  - da
  - de
  - el
  - en
  - eo
  - es
  - et
  - eu
  - fa
  - fi
  - fr
  - fy
  - ga
  - gd
  - gl
  - gu
  - ha
  - he
  - hi
  - hr
  - hu
  - hy
  - id
  - is
  - it
  - ja
  - jv
  - ka
  - kk
  - km
  - kn
  - ko
  - ku
  - ky
  - la
  - lo
  - lt
  - lv
  - mg
  - mk
  - ml
  - mn
  - mr
  - ms
  - my
  - ne
  - nl
  - 'no'
  - om
  - or
  - pa
  - pl
  - ps
  - pt
  - ro
  - ru
  - sa
  - sd
  - si
  - sk
  - sl
  - so
  - sq
  - sr
  - su
  - sv
  - sw
  - ta
  - te
  - th
  - tl
  - tr
  - ug
  - uk
  - ur
  - uz
  - vi
  - xh
  - yi
  - zh
license: mit
model-index:
  - name: intfloat/multilingual-e5-small
    results:
      - dataset:
          config: en
          name: MTEB AmazonCounterfactualClassification (en)
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
          split: test
          type: mteb/amazon_counterfactual
        metrics:
          - type: accuracy
            value: 73.79104477611939
          - type: ap
            value: 36.9996434842022
          - type: f1
            value: 67.95453679103099
        task:
          type: Classification
      - dataset:
          config: de
          name: MTEB AmazonCounterfactualClassification (de)
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
          split: test
          type: mteb/amazon_counterfactual
        metrics:
          - type: accuracy
            value: 71.64882226980728
          - type: ap
            value: 82.11942130026586
          - type: f1
            value: 69.87963421606715
        task:
          type: Classification
      - dataset:
          config: en-ext
          name: MTEB AmazonCounterfactualClassification (en-ext)
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
          split: test
          type: mteb/amazon_counterfactual
        metrics:
          - type: accuracy
            value: 75.8095952023988
          - type: ap
            value: 24.46869495579561
          - type: f1
            value: 63.00108480037597
        task:
          type: Classification
      - dataset:
          config: ja
          name: MTEB AmazonCounterfactualClassification (ja)
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
          split: test
          type: mteb/amazon_counterfactual
        metrics:
          - type: accuracy
            value: 64.186295503212
          - type: ap
            value: 15.496804690197042
          - type: f1
            value: 52.07153895475031
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB AmazonPolarityClassification
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
          split: test
          type: mteb/amazon_polarity
        metrics:
          - type: accuracy
            value: 88.699325
          - type: ap
            value: 85.27039559917269
          - type: f1
            value: 88.65556295032513
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB AmazonReviewsClassification (en)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: test
          type: mteb/amazon_reviews_multi
        metrics:
          - type: accuracy
            value: 44.69799999999999
          - type: f1
            value: 43.73187348654165
        task:
          type: Classification
      - dataset:
          config: de
          name: MTEB AmazonReviewsClassification (de)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: test
          type: mteb/amazon_reviews_multi
        metrics:
          - type: accuracy
            value: 40.245999999999995
          - type: f1
            value: 39.3863530637684
        task:
          type: Classification
      - dataset:
          config: es
          name: MTEB AmazonReviewsClassification (es)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: test
          type: mteb/amazon_reviews_multi
        metrics:
          - type: accuracy
            value: 40.394
          - type: f1
            value: 39.301223469483446
        task:
          type: Classification
      - dataset:
          config: fr
          name: MTEB AmazonReviewsClassification (fr)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: test
          type: mteb/amazon_reviews_multi
        metrics:
          - type: accuracy
            value: 38.864
          - type: f1
            value: 37.97974261868003
        task:
          type: Classification
      - dataset:
          config: ja
          name: MTEB AmazonReviewsClassification (ja)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: test
          type: mteb/amazon_reviews_multi
        metrics:
          - type: accuracy
            value: 37.682
          - type: f1
            value: 37.07399369768313
        task:
          type: Classification
      - dataset:
          config: zh
          name: MTEB AmazonReviewsClassification (zh)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: test
          type: mteb/amazon_reviews_multi
        metrics:
          - type: accuracy
            value: 37.504
          - type: f1
            value: 36.62317273874278
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB ArguAna
          revision: None
          split: test
          type: arguana
        metrics:
          - type: map_at_1
            value: 19.061
          - type: map_at_10
            value: 31.703
          - type: map_at_100
            value: 32.967
          - type: map_at_1000
            value: 33.001000000000005
          - type: map_at_3
            value: 27.466
          - type: map_at_5
            value: 29.564
          - type: mrr_at_1
            value: 19.559
          - type: mrr_at_10
            value: 31.874999999999996
          - type: mrr_at_100
            value: 33.146
          - type: mrr_at_1000
            value: 33.18
          - type: mrr_at_3
            value: 27.667
          - type: mrr_at_5
            value: 29.74
          - type: ndcg_at_1
            value: 19.061
          - type: ndcg_at_10
            value: 39.062999999999995
          - type: ndcg_at_100
            value: 45.184000000000005
          - type: ndcg_at_1000
            value: 46.115
          - type: ndcg_at_3
            value: 30.203000000000003
          - type: ndcg_at_5
            value: 33.953
          - type: precision_at_1
            value: 19.061
          - type: precision_at_10
            value: 6.279999999999999
          - type: precision_at_100
            value: 0.9129999999999999
          - type: precision_at_1000
            value: 0.099
          - type: precision_at_3
            value: 12.706999999999999
          - type: precision_at_5
            value: 9.431000000000001
          - type: recall_at_1
            value: 19.061
          - type: recall_at_10
            value: 62.802
          - type: recall_at_100
            value: 91.323
          - type: recall_at_1000
            value: 98.72
          - type: recall_at_3
            value: 38.122
          - type: recall_at_5
            value: 47.155
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ArxivClusteringP2P
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
          split: test
          type: mteb/arxiv-clustering-p2p
        metrics:
          - type: v_measure
            value: 39.22266660528253
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB ArxivClusteringS2S
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
          split: test
          type: mteb/arxiv-clustering-s2s
        metrics:
          - type: v_measure
            value: 30.79980849482483
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB AskUbuntuDupQuestions
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
          split: test
          type: mteb/askubuntudupquestions-reranking
        metrics:
          - type: map
            value: 57.8790068352054
          - type: mrr
            value: 71.78791276436706
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB BIOSSES
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
          split: test
          type: mteb/biosses-sts
        metrics:
          - type: cos_sim_pearson
            value: 82.36328364043163
          - type: cos_sim_spearman
            value: 82.26211536195868
          - type: euclidean_pearson
            value: 80.3183865039173
          - type: euclidean_spearman
            value: 79.88495276296132
          - type: manhattan_pearson
            value: 80.14484480692127
          - type: manhattan_spearman
            value: 80.39279565980743
        task:
          type: STS
      - dataset:
          config: de-en
          name: MTEB BUCC (de-en)
          revision: d51519689f32196a32af33b075a01d0e7c51e252
          split: test
          type: mteb/bucc-bitext-mining
        metrics:
          - type: accuracy
            value: 98.0375782881002
          - type: f1
            value: 97.86012526096033
          - type: precision
            value: 97.77139874739039
          - type: recall
            value: 98.0375782881002
        task:
          type: BitextMining
      - dataset:
          config: fr-en
          name: MTEB BUCC (fr-en)
          revision: d51519689f32196a32af33b075a01d0e7c51e252
          split: test
          type: mteb/bucc-bitext-mining
        metrics:
          - type: accuracy
            value: 93.35241030156286
          - type: f1
            value: 92.66050333846944
          - type: precision
            value: 92.3306919069631
          - type: recall
            value: 93.35241030156286
        task:
          type: BitextMining
      - dataset:
          config: ru-en
          name: MTEB BUCC (ru-en)
          revision: d51519689f32196a32af33b075a01d0e7c51e252
          split: test
          type: mteb/bucc-bitext-mining
        metrics:
          - type: accuracy
            value: 94.0699688257707
          - type: f1
            value: 93.50236693222492
          - type: precision
            value: 93.22791825424315
          - type: recall
            value: 94.0699688257707
        task:
          type: BitextMining
      - dataset:
          config: zh-en
          name: MTEB BUCC (zh-en)
          revision: d51519689f32196a32af33b075a01d0e7c51e252
          split: test
          type: mteb/bucc-bitext-mining
        metrics:
          - type: accuracy
            value: 89.25750394944708
          - type: f1
            value: 88.79234684921889
          - type: precision
            value: 88.57293312269616
          - type: recall
            value: 89.25750394944708
        task:
          type: BitextMining
      - dataset:
          config: default
          name: MTEB Banking77Classification
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
          split: test
          type: mteb/banking77
        metrics:
          - type: accuracy
            value: 79.41558441558442
          - type: f1
            value: 79.25886487487219
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB BiorxivClusteringP2P
          revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
          split: test
          type: mteb/biorxiv-clustering-p2p
        metrics:
          - type: v_measure
            value: 35.747820820329736
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB BiorxivClusteringS2S
          revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
          split: test
          type: mteb/biorxiv-clustering-s2s
        metrics:
          - type: v_measure
            value: 27.045143830596146
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB CQADupstackRetrieval
          revision: None
          split: test
          type: BeIR/cqadupstack
        metrics:
          - type: map_at_1
            value: 24.252999999999997
          - type: map_at_10
            value: 31.655916666666666
          - type: map_at_100
            value: 32.680749999999996
          - type: map_at_1000
            value: 32.79483333333334
          - type: map_at_3
            value: 29.43691666666666
          - type: map_at_5
            value: 30.717416666666665
          - type: mrr_at_1
            value: 28.602750000000004
          - type: mrr_at_10
            value: 35.56875
          - type: mrr_at_100
            value: 36.3595
          - type: mrr_at_1000
            value: 36.427749999999996
          - type: mrr_at_3
            value: 33.586166666666664
          - type: mrr_at_5
            value: 34.73641666666666
          - type: ndcg_at_1
            value: 28.602750000000004
          - type: ndcg_at_10
            value: 36.06933333333334
          - type: ndcg_at_100
            value: 40.70141666666667
          - type: ndcg_at_1000
            value: 43.24341666666667
          - type: ndcg_at_3
            value: 32.307916666666664
          - type: ndcg_at_5
            value: 34.129999999999995
          - type: precision_at_1
            value: 28.602750000000004
          - type: precision_at_10
            value: 6.097666666666667
          - type: precision_at_100
            value: 0.9809166666666668
          - type: precision_at_1000
            value: 0.13766666666666663
          - type: precision_at_3
            value: 14.628166666666667
          - type: precision_at_5
            value: 10.266916666666667
          - type: recall_at_1
            value: 24.252999999999997
          - type: recall_at_10
            value: 45.31916666666667
          - type: recall_at_100
            value: 66.03575000000001
          - type: recall_at_1000
            value: 83.94708333333334
          - type: recall_at_3
            value: 34.71941666666666
          - type: recall_at_5
            value: 39.46358333333333
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ClimateFEVER
          revision: None
          split: test
          type: climate-fever
        metrics:
          - type: map_at_1
            value: 9.024000000000001
          - type: map_at_10
            value: 15.644
          - type: map_at_100
            value: 17.154
          - type: map_at_1000
            value: 17.345
          - type: map_at_3
            value: 13.028
          - type: map_at_5
            value: 14.251
          - type: mrr_at_1
            value: 19.674
          - type: mrr_at_10
            value: 29.826999999999998
          - type: mrr_at_100
            value: 30.935000000000002
          - type: mrr_at_1000
            value: 30.987
          - type: mrr_at_3
            value: 26.645000000000003
          - type: mrr_at_5
            value: 28.29
          - type: ndcg_at_1
            value: 19.674
          - type: ndcg_at_10
            value: 22.545
          - type: ndcg_at_100
            value: 29.207
          - type: ndcg_at_1000
            value: 32.912
          - type: ndcg_at_3
            value: 17.952
          - type: ndcg_at_5
            value: 19.363
          - type: precision_at_1
            value: 19.674
          - type: precision_at_10
            value: 7.212000000000001
          - type: precision_at_100
            value: 1.435
          - type: precision_at_1000
            value: 0.212
          - type: precision_at_3
            value: 13.507
          - type: precision_at_5
            value: 10.397
          - type: recall_at_1
            value: 9.024000000000001
          - type: recall_at_10
            value: 28.077999999999996
          - type: recall_at_100
            value: 51.403
          - type: recall_at_1000
            value: 72.406
          - type: recall_at_3
            value: 16.768
          - type: recall_at_5
            value: 20.737
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB DBPedia
          revision: None
          split: test
          type: dbpedia-entity
        metrics:
          - type: map_at_1
            value: 8.012
          - type: map_at_10
            value: 17.138
          - type: map_at_100
            value: 24.146
          - type: map_at_1000
            value: 25.622
          - type: map_at_3
            value: 12.552
          - type: map_at_5
            value: 14.435
          - type: mrr_at_1
            value: 62.25000000000001
          - type: mrr_at_10
            value: 71.186
          - type: mrr_at_100
            value: 71.504
          - type: mrr_at_1000
            value: 71.514
          - type: mrr_at_3
            value: 69.333
          - type: mrr_at_5
            value: 70.408
          - type: ndcg_at_1
            value: 49.75
          - type: ndcg_at_10
            value: 37.76
          - type: ndcg_at_100
            value: 42.071
          - type: ndcg_at_1000
            value: 49.309
          - type: ndcg_at_3
            value: 41.644
          - type: ndcg_at_5
            value: 39.812999999999995
          - type: precision_at_1
            value: 62.25000000000001
          - type: precision_at_10
            value: 30.15
          - type: precision_at_100
            value: 9.753
          - type: precision_at_1000
            value: 1.9189999999999998
          - type: precision_at_3
            value: 45.667
          - type: precision_at_5
            value: 39.15
          - type: recall_at_1
            value: 8.012
          - type: recall_at_10
            value: 22.599
          - type: recall_at_100
            value: 48.068
          - type: recall_at_1000
            value: 71.328
          - type: recall_at_3
            value: 14.043
          - type: recall_at_5
            value: 17.124
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB EmotionClassification
          revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
          split: test
          type: mteb/emotion
        metrics:
          - type: accuracy
            value: 42.455
          - type: f1
            value: 37.59462649781862
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB FEVER
          revision: None
          split: test
          type: fever
        metrics:
          - type: map_at_1
            value: 58.092
          - type: map_at_10
            value: 69.586
          - type: map_at_100
            value: 69.968
          - type: map_at_1000
            value: 69.982
          - type: map_at_3
            value: 67.48100000000001
          - type: map_at_5
            value: 68.915
          - type: mrr_at_1
            value: 62.166
          - type: mrr_at_10
            value: 73.588
          - type: mrr_at_100
            value: 73.86399999999999
          - type: mrr_at_1000
            value: 73.868
          - type: mrr_at_3
            value: 71.6
          - type: mrr_at_5
            value: 72.99
          - type: ndcg_at_1
            value: 62.166
          - type: ndcg_at_10
            value: 75.27199999999999
          - type: ndcg_at_100
            value: 76.816
          - type: ndcg_at_1000
            value: 77.09700000000001
          - type: ndcg_at_3
            value: 71.36
          - type: ndcg_at_5
            value: 73.785
          - type: precision_at_1
            value: 62.166
          - type: precision_at_10
            value: 9.716
          - type: precision_at_100
            value: 1.065
          - type: precision_at_1000
            value: 0.11
          - type: precision_at_3
            value: 28.278
          - type: precision_at_5
            value: 18.343999999999998
          - type: recall_at_1
            value: 58.092
          - type: recall_at_10
            value: 88.73400000000001
          - type: recall_at_100
            value: 95.195
          - type: recall_at_1000
            value: 97.04599999999999
          - type: recall_at_3
            value: 78.45
          - type: recall_at_5
            value: 84.316
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB FiQA2018
          revision: None
          split: test
          type: fiqa
        metrics:
          - type: map_at_1
            value: 16.649
          - type: map_at_10
            value: 26.457000000000004
          - type: map_at_100
            value: 28.169
          - type: map_at_1000
            value: 28.352
          - type: map_at_3
            value: 23.305
          - type: map_at_5
            value: 25.169000000000004
          - type: mrr_at_1
            value: 32.407000000000004
          - type: mrr_at_10
            value: 40.922
          - type: mrr_at_100
            value: 41.931000000000004
          - type: mrr_at_1000
            value: 41.983
          - type: mrr_at_3
            value: 38.786
          - type: mrr_at_5
            value: 40.205999999999996
          - type: ndcg_at_1
            value: 32.407000000000004
          - type: ndcg_at_10
            value: 33.314
          - type: ndcg_at_100
            value: 40.312
          - type: ndcg_at_1000
            value: 43.685
          - type: ndcg_at_3
            value: 30.391000000000002
          - type: ndcg_at_5
            value: 31.525
          - type: precision_at_1
            value: 32.407000000000004
          - type: precision_at_10
            value: 8.966000000000001
          - type: precision_at_100
            value: 1.6019999999999999
          - type: precision_at_1000
            value: 0.22200000000000003
          - type: precision_at_3
            value: 20.165
          - type: precision_at_5
            value: 14.722
          - type: recall_at_1
            value: 16.649
          - type: recall_at_10
            value: 39.117000000000004
          - type: recall_at_100
            value: 65.726
          - type: recall_at_1000
            value: 85.784
          - type: recall_at_3
            value: 27.914
          - type: recall_at_5
            value: 33.289
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB HotpotQA
          revision: None
          split: test
          type: hotpotqa
        metrics:
          - type: map_at_1
            value: 36.253
          - type: map_at_10
            value: 56.16799999999999
          - type: map_at_100
            value: 57.06099999999999
          - type: map_at_1000
            value: 57.126
          - type: map_at_3
            value: 52.644999999999996
          - type: map_at_5
            value: 54.909
          - type: mrr_at_1
            value: 72.505
          - type: mrr_at_10
            value: 79.66
          - type: mrr_at_100
            value: 79.869
          - type: mrr_at_1000
            value: 79.88
          - type: mrr_at_3
            value: 78.411
          - type: mrr_at_5
            value: 79.19800000000001
          - type: ndcg_at_1
            value: 72.505
          - type: ndcg_at_10
            value: 65.094
          - type: ndcg_at_100
            value: 68.219
          - type: ndcg_at_1000
            value: 69.515
          - type: ndcg_at_3
            value: 59.99
          - type: ndcg_at_5
            value: 62.909000000000006
          - type: precision_at_1
            value: 72.505
          - type: precision_at_10
            value: 13.749
          - type: precision_at_100
            value: 1.619
          - type: precision_at_1000
            value: 0.179
          - type: precision_at_3
            value: 38.357
          - type: precision_at_5
            value: 25.313000000000002
          - type: recall_at_1
            value: 36.253
          - type: recall_at_10
            value: 68.744
          - type: recall_at_100
            value: 80.925
          - type: recall_at_1000
            value: 89.534
          - type: recall_at_3
            value: 57.535000000000004
          - type: recall_at_5
            value: 63.282000000000004
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ImdbClassification
          revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
          split: test
          type: mteb/imdb
        metrics:
          - type: accuracy
            value: 80.82239999999999
          - type: ap
            value: 75.65895781725314
          - type: f1
            value: 80.75880969095746
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB MSMARCO
          revision: None
          split: dev
          type: msmarco
        metrics:
          - type: map_at_1
            value: 21.624
          - type: map_at_10
            value: 34.075
          - type: map_at_100
            value: 35.229
          - type: map_at_1000
            value: 35.276999999999994
          - type: map_at_3
            value: 30.245
          - type: map_at_5
            value: 32.42
          - type: mrr_at_1
            value: 22.264
          - type: mrr_at_10
            value: 34.638000000000005
          - type: mrr_at_100
            value: 35.744
          - type: mrr_at_1000
            value: 35.787
          - type: mrr_at_3
            value: 30.891000000000002
          - type: mrr_at_5
            value: 33.042
          - type: ndcg_at_1
            value: 22.264
          - type: ndcg_at_10
            value: 40.991
          - type: ndcg_at_100
            value: 46.563
          - type: ndcg_at_1000
            value: 47.743
          - type: ndcg_at_3
            value: 33.198
          - type: ndcg_at_5
            value: 37.069
          - type: precision_at_1
            value: 22.264
          - type: precision_at_10
            value: 6.5089999999999995
          - type: precision_at_100
            value: 0.9299999999999999
          - type: precision_at_1000
            value: 0.10300000000000001
          - type: precision_at_3
            value: 14.216999999999999
          - type: precision_at_5
            value: 10.487
          - type: recall_at_1
            value: 21.624
          - type: recall_at_10
            value: 62.303
          - type: recall_at_100
            value: 88.124
          - type: recall_at_1000
            value: 97.08
          - type: recall_at_3
            value: 41.099999999999994
          - type: recall_at_5
            value: 50.381
        task:
          type: Retrieval
      - dataset:
          config: en
          name: MTEB MTOPDomainClassification (en)
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
          split: test
          type: mteb/mtop_domain
        metrics:
          - type: accuracy
            value: 91.06703146374831
          - type: f1
            value: 90.86867815863172
        task:
          type: Classification
      - dataset:
          config: de
          name: MTEB MTOPDomainClassification (de)
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
          split: test
          type: mteb/mtop_domain
        metrics:
          - type: accuracy
            value: 87.46970977740209
          - type: f1
            value: 86.36832872036588
        task:
          type: Classification
      - dataset:
          config: es
          name: MTEB MTOPDomainClassification (es)
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
          split: test
          type: mteb/mtop_domain
        metrics:
          - type: accuracy
            value: 89.26951300867245
          - type: f1
            value: 88.93561193959502
        task:
          type: Classification
      - dataset:
          config: fr
          name: MTEB MTOPDomainClassification (fr)
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
          split: test
          type: mteb/mtop_domain
        metrics:
          - type: accuracy
            value: 84.22799874725963
          - type: f1
            value: 84.30490069236556
        task:
          type: Classification
      - dataset:
          config: hi
          name: MTEB MTOPDomainClassification (hi)
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
          split: test
          type: mteb/mtop_domain
        metrics:
          - type: accuracy
            value: 86.02007888131948
          - type: f1
            value: 85.39376041027991
        task:
          type: Classification
      - dataset:
          config: th
          name: MTEB MTOPDomainClassification (th)
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
          split: test
          type: mteb/mtop_domain
        metrics:
          - type: accuracy
            value: 85.34900542495481
          - type: f1
            value: 85.39859673336713
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB MTOPIntentClassification (en)
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
          split: test
          type: mteb/mtop_intent
        metrics:
          - type: accuracy
            value: 71.078431372549
          - type: f1
            value: 53.45071102002276
        task:
          type: Classification
      - dataset:
          config: de
          name: MTEB MTOPIntentClassification (de)
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
          split: test
          type: mteb/mtop_intent
        metrics:
          - type: accuracy
            value: 65.85798816568047
          - type: f1
            value: 46.53112748993529
        task:
          type: Classification
      - dataset:
          config: es
          name: MTEB MTOPIntentClassification (es)
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
          split: test
          type: mteb/mtop_intent
        metrics:
          - type: accuracy
            value: 67.96864576384256
          - type: f1
            value: 45.966703022829506
        task:
          type: Classification
      - dataset:
          config: fr
          name: MTEB MTOPIntentClassification (fr)
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
          split: test
          type: mteb/mtop_intent
        metrics:
          - type: accuracy
            value: 61.31537738803633
          - type: f1
            value: 45.52601712835461
        task:
          type: Classification
      - dataset:
          config: hi
          name: MTEB MTOPIntentClassification (hi)
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
          split: test
          type: mteb/mtop_intent
        metrics:
          - type: accuracy
            value: 66.29616349946218
          - type: f1
            value: 47.24166485726613
        task:
          type: Classification
      - dataset:
          config: th
          name: MTEB MTOPIntentClassification (th)
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
          split: test
          type: mteb/mtop_intent
        metrics:
          - type: accuracy
            value: 67.51537070524412
          - type: f1
            value: 49.463476319014276
        task:
          type: Classification
      - dataset:
          config: af
          name: MTEB MassiveIntentClassification (af)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 57.06792199058508
          - type: f1
            value: 54.094921857502285
        task:
          type: Classification
      - dataset:
          config: am
          name: MTEB MassiveIntentClassification (am)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 51.960322797579025
          - type: f1
            value: 48.547371223370945
        task:
          type: Classification
      - dataset:
          config: ar
          name: MTEB MassiveIntentClassification (ar)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 54.425016812373904
          - type: f1
            value: 50.47069202054312
        task:
          type: Classification
      - dataset:
          config: az
          name: MTEB MassiveIntentClassification (az)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 59.798251513113655
          - type: f1
            value: 57.05013069086648
        task:
          type: Classification
      - dataset:
          config: bn
          name: MTEB MassiveIntentClassification (bn)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 59.37794216543376
          - type: f1
            value: 56.3607992649805
        task:
          type: Classification
      - dataset:
          config: cy
          name: MTEB MassiveIntentClassification (cy)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 46.56018829858777
          - type: f1
            value: 43.87319715715134
        task:
          type: Classification
      - dataset:
          config: da
          name: MTEB MassiveIntentClassification (da)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 62.9724277067922
          - type: f1
            value: 59.36480066245562
        task:
          type: Classification
      - dataset:
          config: de
          name: MTEB MassiveIntentClassification (de)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 62.72696704774715
          - type: f1
            value: 59.143595966615855
        task:
          type: Classification
      - dataset:
          config: el
          name: MTEB MassiveIntentClassification (el)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 61.5971755211836
          - type: f1
            value: 59.169445724946726
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB MassiveIntentClassification (en)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 70.29589778076665
          - type: f1
            value: 67.7577001808977
        task:
          type: Classification
      - dataset:
          config: es
          name: MTEB MassiveIntentClassification (es)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 66.31136516476126
          - type: f1
            value: 64.52032955983242
        task:
          type: Classification
      - dataset:
          config: fa
          name: MTEB MassiveIntentClassification (fa)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 65.54472091459314
          - type: f1
            value: 61.47903120066317
        task:
          type: Classification
      - dataset:
          config: fi
          name: MTEB MassiveIntentClassification (fi)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 61.45595158036314
          - type: f1
            value: 58.0891846024637
        task:
          type: Classification
      - dataset:
          config: fr
          name: MTEB MassiveIntentClassification (fr)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 65.47074646940149
          - type: f1
            value: 62.84830858877575
        task:
          type: Classification
      - dataset:
          config: he
          name: MTEB MassiveIntentClassification (he)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 58.046402151983855
          - type: f1
            value: 55.269074430533195
        task:
          type: Classification
      - dataset:
          config: hi
          name: MTEB MassiveIntentClassification (hi)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 64.06523201075991
          - type: f1
            value: 61.35339643021369
        task:
          type: Classification
      - dataset:
          config: hu
          name: MTEB MassiveIntentClassification (hu)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 60.954942837928726
          - type: f1
            value: 57.07035922704846
        task:
          type: Classification
      - dataset:
          config: hy
          name: MTEB MassiveIntentClassification (hy)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 57.404169468728995
          - type: f1
            value: 53.94259011839138
        task:
          type: Classification
      - dataset:
          config: id
          name: MTEB MassiveIntentClassification (id)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 64.16610625420309
          - type: f1
            value: 61.337103431499365
        task:
          type: Classification
      - dataset:
          config: is
          name: MTEB MassiveIntentClassification (is)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 52.262945527908535
          - type: f1
            value: 49.7610691598921
        task:
          type: Classification
      - dataset:
          config: it
          name: MTEB MassiveIntentClassification (it)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 65.54472091459314
          - type: f1
            value: 63.469099018440154
        task:
          type: Classification
      - dataset:
          config: ja
          name: MTEB MassiveIntentClassification (ja)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 68.22797579018157
          - type: f1
            value: 64.89098471083001
        task:
          type: Classification
      - dataset:
          config: jv
          name: MTEB MassiveIntentClassification (jv)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 50.847343644922674
          - type: f1
            value: 47.8536963168393
        task:
          type: Classification
      - dataset:
          config: ka
          name: MTEB MassiveIntentClassification (ka)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 48.45326160053799
          - type: f1
            value: 46.370078045805556
        task:
          type: Classification
      - dataset:
          config: km
          name: MTEB MassiveIntentClassification (km)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 42.83120376597175
          - type: f1
            value: 39.68948521599982
        task:
          type: Classification
      - dataset:
          config: kn
          name: MTEB MassiveIntentClassification (kn)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 57.5084061869536
          - type: f1
            value: 53.961876160401545
        task:
          type: Classification
      - dataset:
          config: ko
          name: MTEB MassiveIntentClassification (ko)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 63.7895090786819
          - type: f1
            value: 61.134223684676
        task:
          type: Classification
      - dataset:
          config: lv
          name: MTEB MassiveIntentClassification (lv)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 54.98991257565569
          - type: f1
            value: 52.579862862826296
        task:
          type: Classification
      - dataset:
          config: ml
          name: MTEB MassiveIntentClassification (ml)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 61.90316072629456
          - type: f1
            value: 58.203024538290336
        task:
          type: Classification
      - dataset:
          config: mn
          name: MTEB MassiveIntentClassification (mn)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 57.09818426361802
          - type: f1
            value: 54.22718458445455
        task:
          type: Classification
      - dataset:
          config: ms
          name: MTEB MassiveIntentClassification (ms)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 58.991257565568255
          - type: f1
            value: 55.84892781767421
        task:
          type: Classification
      - dataset:
          config: my
          name: MTEB MassiveIntentClassification (my)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 55.901143241425686
          - type: f1
            value: 52.25264332199797
        task:
          type: Classification
      - dataset:
          config: nb
          name: MTEB MassiveIntentClassification (nb)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 61.96368527236047
          - type: f1
            value: 58.927243876153454
        task:
          type: Classification
      - dataset:
          config: nl
          name: MTEB MassiveIntentClassification (nl)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 65.64223268325489
          - type: f1
            value: 62.340453718379706
        task:
          type: Classification
      - dataset:
          config: pl
          name: MTEB MassiveIntentClassification (pl)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 64.52589105581708
          - type: f1
            value: 61.661113187022174
        task:
          type: Classification
      - dataset:
          config: pt
          name: MTEB MassiveIntentClassification (pt)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 66.84599865501009
          - type: f1
            value: 64.59342572873005
        task:
          type: Classification
      - dataset:
          config: ro
          name: MTEB MassiveIntentClassification (ro)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 60.81035642232684
          - type: f1
            value: 57.5169089806797
        task:
          type: Classification
      - dataset:
          config: ru
          name: MTEB MassiveIntentClassification (ru)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 58.652238071815056
          - type: f1
            value: 53.22732406426353
          - type: f1_weighted
            value: 57.585586737209546
          - type: main_score
            value: 58.652238071815056
        task:
          type: Classification
      - dataset:
          config: sl
          name: MTEB MassiveIntentClassification (sl)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 56.51647612642906
          - type: f1
            value: 54.33154780100043
        task:
          type: Classification
      - dataset:
          config: sq
          name: MTEB MassiveIntentClassification (sq)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 57.985877605917956
          - type: f1
            value: 54.46187524463802
        task:
          type: Classification
      - dataset:
          config: sv
          name: MTEB MassiveIntentClassification (sv)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 65.03026227303296
          - type: f1
            value: 62.34377392877748
        task:
          type: Classification
      - dataset:
          config: sw
          name: MTEB MassiveIntentClassification (sw)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 53.567585743106925
          - type: f1
            value: 50.73770655983206
        task:
          type: Classification
      - dataset:
          config: ta
          name: MTEB MassiveIntentClassification (ta)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 57.2595830531271
          - type: f1
            value: 53.657327291708626
        task:
          type: Classification
      - dataset:
          config: te
          name: MTEB MassiveIntentClassification (te)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 57.82784129119032
          - type: f1
            value: 54.82518072665301
        task:
          type: Classification
      - dataset:
          config: th
          name: MTEB MassiveIntentClassification (th)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 64.06859448554137
          - type: f1
            value: 63.00185280500495
        task:
          type: Classification
      - dataset:
          config: tl
          name: MTEB MassiveIntentClassification (tl)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 58.91055817081371
          - type: f1
            value: 55.54116301224262
        task:
          type: Classification
      - dataset:
          config: tr
          name: MTEB MassiveIntentClassification (tr)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 63.54404841963686
          - type: f1
            value: 59.57650946030184
        task:
          type: Classification
      - dataset:
          config: ur
          name: MTEB MassiveIntentClassification (ur)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 59.27706792199059
          - type: f1
            value: 56.50010066083435
        task:
          type: Classification
      - dataset:
          config: vi
          name: MTEB MassiveIntentClassification (vi)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 64.0719569603228
          - type: f1
            value: 61.817075925647956
        task:
          type: Classification
      - dataset:
          config: zh-CN
          name: MTEB MassiveIntentClassification (zh-CN)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 68.23806321452591
          - type: f1
            value: 65.24917026029749
        task:
          type: Classification
      - dataset:
          config: zh-TW
          name: MTEB MassiveIntentClassification (zh-TW)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 62.53530598520511
          - type: f1
            value: 61.71131132295768
        task:
          type: Classification
      - dataset:
          config: af
          name: MTEB MassiveScenarioClassification (af)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 63.04303967720243
          - type: f1
            value: 60.3950085685985
        task:
          type: Classification
      - dataset:
          config: am
          name: MTEB MassiveScenarioClassification (am)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 56.83591123066578
          - type: f1
            value: 54.95059828830849
        task:
          type: Classification
      - dataset:
          config: ar
          name: MTEB MassiveScenarioClassification (ar)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 59.62340282447881
          - type: f1
            value: 59.525159996498225
        task:
          type: Classification
      - dataset:
          config: az
          name: MTEB MassiveScenarioClassification (az)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 60.85406859448555
          - type: f1
            value: 59.129299095681276
        task:
          type: Classification
      - dataset:
          config: bn
          name: MTEB MassiveScenarioClassification (bn)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 62.76731674512441
          - type: f1
            value: 61.159560612627715
        task:
          type: Classification
      - dataset:
          config: cy
          name: MTEB MassiveScenarioClassification (cy)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 50.181573638197705
          - type: f1
            value: 46.98422176289957
        task:
          type: Classification
      - dataset:
          config: da
          name: MTEB MassiveScenarioClassification (da)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 68.92737054472092
          - type: f1
            value: 67.69135611952979
        task:
          type: Classification
      - dataset:
          config: de
          name: MTEB MassiveScenarioClassification (de)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 69.18964357767318
          - type: f1
            value: 68.46106138186214
        task:
          type: Classification
      - dataset:
          config: el
          name: MTEB MassiveScenarioClassification (el)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 67.0712844653665
          - type: f1
            value: 66.75545422473901
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB MassiveScenarioClassification (en)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 74.4754539340955
          - type: f1
            value: 74.38427146553252
        task:
          type: Classification
      - dataset:
          config: es
          name: MTEB MassiveScenarioClassification (es)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 69.82515131136518
          - type: f1
            value: 69.63516462173847
        task:
          type: Classification
      - dataset:
          config: fa
          name: MTEB MassiveScenarioClassification (fa)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 68.70880968392737
          - type: f1
            value: 67.45420662567926
        task:
          type: Classification
      - dataset:
          config: fi
          name: MTEB MassiveScenarioClassification (fi)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 65.95494283792871
          - type: f1
            value: 65.06191009049222
        task:
          type: Classification
      - dataset:
          config: fr
          name: MTEB MassiveScenarioClassification (fr)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 68.75924680564896
          - type: f1
            value: 68.30833379585945
        task:
          type: Classification
      - dataset:
          config: he
          name: MTEB MassiveScenarioClassification (he)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 63.806321452589096
          - type: f1
            value: 63.273048243765054
        task:
          type: Classification
      - dataset:
          config: hi
          name: MTEB MassiveScenarioClassification (hi)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 67.68997982515133
          - type: f1
            value: 66.54703855381324
        task:
          type: Classification
      - dataset:
          config: hu
          name: MTEB MassiveScenarioClassification (hu)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 66.46940147948891
          - type: f1
            value: 65.91017343463396
        task:
          type: Classification
      - dataset:
          config: hy
          name: MTEB MassiveScenarioClassification (hy)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 59.49899125756556
          - type: f1
            value: 57.90333469917769
        task:
          type: Classification
      - dataset:
          config: id
          name: MTEB MassiveScenarioClassification (id)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 67.9219905850706
          - type: f1
            value: 67.23169403762938
        task:
          type: Classification
      - dataset:
          config: is
          name: MTEB MassiveScenarioClassification (is)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 56.486213853396094
          - type: f1
            value: 54.85282355583758
        task:
          type: Classification
      - dataset:
          config: it
          name: MTEB MassiveScenarioClassification (it)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 69.04169468728985
          - type: f1
            value: 68.83833333320462
        task:
          type: Classification
      - dataset:
          config: ja
          name: MTEB MassiveScenarioClassification (ja)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 73.88702084734365
          - type: f1
            value: 74.04474735232299
        task:
          type: Classification
      - dataset:
          config: jv
          name: MTEB MassiveScenarioClassification (jv)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 56.63416274377943
          - type: f1
            value: 55.11332211687954
        task:
          type: Classification
      - dataset:
          config: ka
          name: MTEB MassiveScenarioClassification (ka)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 52.23604572965702
          - type: f1
            value: 50.86529813991055
        task:
          type: Classification
      - dataset:
          config: km
          name: MTEB MassiveScenarioClassification (km)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 46.62407531943511
          - type: f1
            value: 43.63485467164535
        task:
          type: Classification
      - dataset:
          config: kn
          name: MTEB MassiveScenarioClassification (kn)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 59.15601882985878
          - type: f1
            value: 57.522837510959924
        task:
          type: Classification
      - dataset:
          config: ko
          name: MTEB MassiveScenarioClassification (ko)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 69.84532616005382
          - type: f1
            value: 69.60021127179697
        task:
          type: Classification
      - dataset:
          config: lv
          name: MTEB MassiveScenarioClassification (lv)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 56.65770006724949
          - type: f1
            value: 55.84219135523227
        task:
          type: Classification
      - dataset:
          config: ml
          name: MTEB MassiveScenarioClassification (ml)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 66.53665097511768
          - type: f1
            value: 65.09087787792639
        task:
          type: Classification
      - dataset:
          config: mn
          name: MTEB MassiveScenarioClassification (mn)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 59.31405514458642
          - type: f1
            value: 58.06135303831491
        task:
          type: Classification
      - dataset:
          config: ms
          name: MTEB MassiveScenarioClassification (ms)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 64.88231338264964
          - type: f1
            value: 62.751099407787926
        task:
          type: Classification
      - dataset:
          config: my
          name: MTEB MassiveScenarioClassification (my)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 58.86012104909213
          - type: f1
            value: 56.29118323058282
        task:
          type: Classification
      - dataset:
          config: nb
          name: MTEB MassiveScenarioClassification (nb)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 67.37390719569602
          - type: f1
            value: 66.27922244885102
        task:
          type: Classification
      - dataset:
          config: nl
          name: MTEB MassiveScenarioClassification (nl)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 70.8675184936113
          - type: f1
            value: 70.22146529932019
        task:
          type: Classification
      - dataset:
          config: pl
          name: MTEB MassiveScenarioClassification (pl)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 68.2212508406187
          - type: f1
            value: 67.77454802056282
        task:
          type: Classification
      - dataset:
          config: pt
          name: MTEB MassiveScenarioClassification (pt)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 68.18090114324143
          - type: f1
            value: 68.03737625431621
        task:
          type: Classification
      - dataset:
          config: ro
          name: MTEB MassiveScenarioClassification (ro)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 64.65030262273034
          - type: f1
            value: 63.792945486912856
        task:
          type: Classification
      - dataset:
          config: ru
          name: MTEB MassiveScenarioClassification (ru)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 63.772749631087066
          - type: f1
            value: 63.4539101720024
          - type: f1_weighted
            value: 62.778603897469566
          - type: main_score
            value: 63.772749631087066
        task:
          type: Classification
      - dataset:
          config: sl
          name: MTEB MassiveScenarioClassification (sl)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 60.17821116341627
          - type: f1
            value: 59.3935969827171
        task:
          type: Classification
      - dataset:
          config: sq
          name: MTEB MassiveScenarioClassification (sq)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 62.86146603900471
          - type: f1
            value: 60.133692735032376
        task:
          type: Classification
      - dataset:
          config: sv
          name: MTEB MassiveScenarioClassification (sv)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 70.89441829186282
          - type: f1
            value: 70.03064076194089
        task:
          type: Classification
      - dataset:
          config: sw
          name: MTEB MassiveScenarioClassification (sw)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 58.15063887020847
          - type: f1
            value: 56.23326278499678
        task:
          type: Classification
      - dataset:
          config: ta
          name: MTEB MassiveScenarioClassification (ta)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 59.43846671149966
          - type: f1
            value: 57.70440450281974
        task:
          type: Classification
      - dataset:
          config: te
          name: MTEB MassiveScenarioClassification (te)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 60.8507061197041
          - type: f1
            value: 59.22916396061171
        task:
          type: Classification
      - dataset:
          config: th
          name: MTEB MassiveScenarioClassification (th)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 70.65568258238063
          - type: f1
            value: 69.90736239440633
        task:
          type: Classification
      - dataset:
          config: tl
          name: MTEB MassiveScenarioClassification (tl)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 60.8843308675185
          - type: f1
            value: 59.30332663713599
        task:
          type: Classification
      - dataset:
          config: tr
          name: MTEB MassiveScenarioClassification (tr)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 68.05312710154674
          - type: f1
            value: 67.44024062594775
        task:
          type: Classification
      - dataset:
          config: ur
          name: MTEB MassiveScenarioClassification (ur)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 62.111634162743776
          - type: f1
            value: 60.89083013084519
        task:
          type: Classification
      - dataset:
          config: vi
          name: MTEB MassiveScenarioClassification (vi)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 67.44115669132482
          - type: f1
            value: 67.92227541674552
        task:
          type: Classification
      - dataset:
          config: zh-CN
          name: MTEB MassiveScenarioClassification (zh-CN)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 74.4687289845326
          - type: f1
            value: 74.16376793486025
        task:
          type: Classification
      - dataset:
          config: zh-TW
          name: MTEB MassiveScenarioClassification (zh-TW)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 68.31876260928043
          - type: f1
            value: 68.5246745215607
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB MedrxivClusteringP2P
          revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
          split: test
          type: mteb/medrxiv-clustering-p2p
        metrics:
          - type: v_measure
            value: 30.90431696479766
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB MedrxivClusteringS2S
          revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
          split: test
          type: mteb/medrxiv-clustering-s2s
        metrics:
          - type: v_measure
            value: 27.259158476693774
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB MindSmallReranking
          revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
          split: test
          type: mteb/mind_small
        metrics:
          - type: map
            value: 30.28445330838555
          - type: mrr
            value: 31.15758529581164
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB NFCorpus
          revision: None
          split: test
          type: nfcorpus
        metrics:
          - type: map_at_1
            value: 5.353
          - type: map_at_10
            value: 11.565
          - type: map_at_100
            value: 14.097000000000001
          - type: map_at_1000
            value: 15.354999999999999
          - type: map_at_3
            value: 8.749
          - type: map_at_5
            value: 9.974
          - type: mrr_at_1
            value: 42.105
          - type: mrr_at_10
            value: 50.589
          - type: mrr_at_100
            value: 51.187000000000005
          - type: mrr_at_1000
            value: 51.233
          - type: mrr_at_3
            value: 48.246
          - type: mrr_at_5
            value: 49.546
          - type: ndcg_at_1
            value: 40.402
          - type: ndcg_at_10
            value: 31.009999999999998
          - type: ndcg_at_100
            value: 28.026
          - type: ndcg_at_1000
            value: 36.905
          - type: ndcg_at_3
            value: 35.983
          - type: ndcg_at_5
            value: 33.764
          - type: precision_at_1
            value: 42.105
          - type: precision_at_10
            value: 22.786
          - type: precision_at_100
            value: 6.916
          - type: precision_at_1000
            value: 1.981
          - type: precision_at_3
            value: 33.333
          - type: precision_at_5
            value: 28.731
          - type: recall_at_1
            value: 5.353
          - type: recall_at_10
            value: 15.039
          - type: recall_at_100
            value: 27.348
          - type: recall_at_1000
            value: 59.453
          - type: recall_at_3
            value: 9.792
          - type: recall_at_5
            value: 11.882
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB NQ
          revision: None
          split: test
          type: nq
        metrics:
          - type: map_at_1
            value: 33.852
          - type: map_at_10
            value: 48.924
          - type: map_at_100
            value: 49.854
          - type: map_at_1000
            value: 49.886
          - type: map_at_3
            value: 44.9
          - type: map_at_5
            value: 47.387
          - type: mrr_at_1
            value: 38.035999999999994
          - type: mrr_at_10
            value: 51.644
          - type: mrr_at_100
            value: 52.339
          - type: mrr_at_1000
            value: 52.35999999999999
          - type: mrr_at_3
            value: 48.421
          - type: mrr_at_5
            value: 50.468999999999994
          - type: ndcg_at_1
            value: 38.007000000000005
          - type: ndcg_at_10
            value: 56.293000000000006
          - type: ndcg_at_100
            value: 60.167
          - type: ndcg_at_1000
            value: 60.916000000000004
          - type: ndcg_at_3
            value: 48.903999999999996
          - type: ndcg_at_5
            value: 52.978
          - type: precision_at_1
            value: 38.007000000000005
          - type: precision_at_10
            value: 9.041
          - type: precision_at_100
            value: 1.1199999999999999
          - type: precision_at_1000
            value: 0.11900000000000001
          - type: precision_at_3
            value: 22.084
          - type: precision_at_5
            value: 15.608
          - type: recall_at_1
            value: 33.852
          - type: recall_at_10
            value: 75.893
          - type: recall_at_100
            value: 92.589
          - type: recall_at_1000
            value: 98.153
          - type: recall_at_3
            value: 56.969
          - type: recall_at_5
            value: 66.283
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB QuoraRetrieval
          revision: None
          split: test
          type: quora
        metrics:
          - type: map_at_1
            value: 69.174
          - type: map_at_10
            value: 82.891
          - type: map_at_100
            value: 83.545
          - type: map_at_1000
            value: 83.56700000000001
          - type: map_at_3
            value: 79.944
          - type: map_at_5
            value: 81.812
          - type: mrr_at_1
            value: 79.67999999999999
          - type: mrr_at_10
            value: 86.279
          - type: mrr_at_100
            value: 86.39
          - type: mrr_at_1000
            value: 86.392
          - type: mrr_at_3
            value: 85.21
          - type: mrr_at_5
            value: 85.92999999999999
          - type: ndcg_at_1
            value: 79.69000000000001
          - type: ndcg_at_10
            value: 86.929
          - type: ndcg_at_100
            value: 88.266
          - type: ndcg_at_1000
            value: 88.428
          - type: ndcg_at_3
            value: 83.899
          - type: ndcg_at_5
            value: 85.56700000000001
          - type: precision_at_1
            value: 79.69000000000001
          - type: precision_at_10
            value: 13.161000000000001
          - type: precision_at_100
            value: 1.513
          - type: precision_at_1000
            value: 0.156
          - type: precision_at_3
            value: 36.603
          - type: precision_at_5
            value: 24.138
          - type: recall_at_1
            value: 69.174
          - type: recall_at_10
            value: 94.529
          - type: recall_at_100
            value: 99.15
          - type: recall_at_1000
            value: 99.925
          - type: recall_at_3
            value: 85.86200000000001
          - type: recall_at_5
            value: 90.501
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB RedditClustering
          revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
          split: test
          type: mteb/reddit-clustering
        metrics:
          - type: v_measure
            value: 39.13064340585255
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB RedditClusteringP2P
          revision: 282350215ef01743dc01b456c7f5241fa8937f16
          split: test
          type: mteb/reddit-clustering-p2p
        metrics:
          - type: v_measure
            value: 58.97884249325877
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB SCIDOCS
          revision: None
          split: test
          type: scidocs
        metrics:
          - type: map_at_1
            value: 3.4680000000000004
          - type: map_at_10
            value: 7.865
          - type: map_at_100
            value: 9.332
          - type: map_at_1000
            value: 9.587
          - type: map_at_3
            value: 5.800000000000001
          - type: map_at_5
            value: 6.8790000000000004
          - type: mrr_at_1
            value: 17
          - type: mrr_at_10
            value: 25.629
          - type: mrr_at_100
            value: 26.806
          - type: mrr_at_1000
            value: 26.889000000000003
          - type: mrr_at_3
            value: 22.8
          - type: mrr_at_5
            value: 24.26
          - type: ndcg_at_1
            value: 17
          - type: ndcg_at_10
            value: 13.895
          - type: ndcg_at_100
            value: 20.491999999999997
          - type: ndcg_at_1000
            value: 25.759999999999998
          - type: ndcg_at_3
            value: 13.347999999999999
          - type: ndcg_at_5
            value: 11.61
          - type: precision_at_1
            value: 17
          - type: precision_at_10
            value: 7.090000000000001
          - type: precision_at_100
            value: 1.669
          - type: precision_at_1000
            value: 0.294
          - type: precision_at_3
            value: 12.3
          - type: precision_at_5
            value: 10.02
          - type: recall_at_1
            value: 3.4680000000000004
          - type: recall_at_10
            value: 14.363000000000001
          - type: recall_at_100
            value: 33.875
          - type: recall_at_1000
            value: 59.711999999999996
          - type: recall_at_3
            value: 7.483
          - type: recall_at_5
            value: 10.173
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB SICK-R
          revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
          split: test
          type: mteb/sickr-sts
        metrics:
          - type: cos_sim_pearson
            value: 83.04084311714061
          - type: cos_sim_spearman
            value: 77.51342467443078
          - type: euclidean_pearson
            value: 80.0321166028479
          - type: euclidean_spearman
            value: 77.29249114733226
          - type: manhattan_pearson
            value: 80.03105964262431
          - type: manhattan_spearman
            value: 77.22373689514794
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS12
          revision: a0d554a64d88156834ff5ae9920b964011b16384
          split: test
          type: mteb/sts12-sts
        metrics:
          - type: cos_sim_pearson
            value: 84.1680158034387
          - type: cos_sim_spearman
            value: 76.55983344071117
          - type: euclidean_pearson
            value: 79.75266678300143
          - type: euclidean_spearman
            value: 75.34516823467025
          - type: manhattan_pearson
            value: 79.75959151517357
          - type: manhattan_spearman
            value: 75.42330344141912
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS13
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
          split: test
          type: mteb/sts13-sts
        metrics:
          - type: cos_sim_pearson
            value: 76.48898993209346
          - type: cos_sim_spearman
            value: 76.96954120323366
          - type: euclidean_pearson
            value: 76.94139109279668
          - type: euclidean_spearman
            value: 76.85860283201711
          - type: manhattan_pearson
            value: 76.6944095091912
          - type: manhattan_spearman
            value: 76.61096912972553
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS14
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
          split: test
          type: mteb/sts14-sts
        metrics:
          - type: cos_sim_pearson
            value: 77.85082366246944
          - type: cos_sim_spearman
            value: 75.52053350101731
          - type: euclidean_pearson
            value: 77.1165845070926
          - type: euclidean_spearman
            value: 75.31216065884388
          - type: manhattan_pearson
            value: 77.06193941833494
          - type: manhattan_spearman
            value: 75.31003701700112
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS15
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
          split: test
          type: mteb/sts15-sts
        metrics:
          - type: cos_sim_pearson
            value: 86.36305246526497
          - type: cos_sim_spearman
            value: 87.11704613927415
          - type: euclidean_pearson
            value: 86.04199125810939
          - type: euclidean_spearman
            value: 86.51117572414263
          - type: manhattan_pearson
            value: 86.0805106816633
          - type: manhattan_spearman
            value: 86.52798366512229
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS16
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
          split: test
          type: mteb/sts16-sts
        metrics:
          - type: cos_sim_pearson
            value: 82.18536255599724
          - type: cos_sim_spearman
            value: 83.63377151025418
          - type: euclidean_pearson
            value: 83.24657467993141
          - type: euclidean_spearman
            value: 84.02751481993825
          - type: manhattan_pearson
            value: 83.11941806582371
          - type: manhattan_spearman
            value: 83.84251281019304
        task:
          type: STS
      - dataset:
          config: ko-ko
          name: MTEB STS17 (ko-ko)
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 78.95816528475514
          - type: cos_sim_spearman
            value: 78.86607380120462
          - type: euclidean_pearson
            value: 78.51268699230545
          - type: euclidean_spearman
            value: 79.11649316502229
          - type: manhattan_pearson
            value: 78.32367302808157
          - type: manhattan_spearman
            value: 78.90277699624637
        task:
          type: STS
      - dataset:
          config: ar-ar
          name: MTEB STS17 (ar-ar)
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 72.89126914997624
          - type: cos_sim_spearman
            value: 73.0296921832678
          - type: euclidean_pearson
            value: 71.50385903677738
          - type: euclidean_spearman
            value: 73.13368899716289
          - type: manhattan_pearson
            value: 71.47421463379519
          - type: manhattan_spearman
            value: 73.03383242946575
        task:
          type: STS
      - dataset:
          config: en-ar
          name: MTEB STS17 (en-ar)
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 59.22923684492637
          - type: cos_sim_spearman
            value: 57.41013211368396
          - type: euclidean_pearson
            value: 61.21107388080905
          - type: euclidean_spearman
            value: 60.07620768697254
          - type: manhattan_pearson
            value: 59.60157142786555
          - type: manhattan_spearman
            value: 59.14069604103739
        task:
          type: STS
      - dataset:
          config: en-de
          name: MTEB STS17 (en-de)
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 76.24345978774299
          - type: cos_sim_spearman
            value: 77.24225743830719
          - type: euclidean_pearson
            value: 76.66226095469165
          - type: euclidean_spearman
            value: 77.60708820493146
          - type: manhattan_pearson
            value: 76.05303324760429
          - type: manhattan_spearman
            value: 76.96353149912348
        task:
          type: STS
      - dataset:
          config: en-en
          name: MTEB STS17 (en-en)
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 85.50879160160852
          - type: cos_sim_spearman
            value: 86.43594662965224
          - type: euclidean_pearson
            value: 86.06846012826577
          - type: euclidean_spearman
            value: 86.02041395794136
          - type: manhattan_pearson
            value: 86.10916255616904
          - type: manhattan_spearman
            value: 86.07346068198953
        task:
          type: STS
      - dataset:
          config: en-tr
          name: MTEB STS17 (en-tr)
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 58.39803698977196
          - type: cos_sim_spearman
            value: 55.96910950423142
          - type: euclidean_pearson
            value: 58.17941175613059
          - type: euclidean_spearman
            value: 55.03019330522745
          - type: manhattan_pearson
            value: 57.333358138183286
          - type: manhattan_spearman
            value: 54.04614023149965
        task:
          type: STS
      - dataset:
          config: es-en
          name: MTEB STS17 (es-en)
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 70.98304089637197
          - type: cos_sim_spearman
            value: 72.44071656215888
          - type: euclidean_pearson
            value: 72.19224359033983
          - type: euclidean_spearman
            value: 73.89871188913025
          - type: manhattan_pearson
            value: 71.21098311547406
          - type: manhattan_spearman
            value: 72.93405764824821
        task:
          type: STS
      - dataset:
          config: es-es
          name: MTEB STS17 (es-es)
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 85.99792397466308
          - type: cos_sim_spearman
            value: 84.83824377879495
          - type: euclidean_pearson
            value: 85.70043288694438
          - type: euclidean_spearman
            value: 84.70627558703686
          - type: manhattan_pearson
            value: 85.89570850150801
          - type: manhattan_spearman
            value: 84.95806105313007
        task:
          type: STS
      - dataset:
          config: fr-en
          name: MTEB STS17 (fr-en)
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 72.21850322994712
          - type: cos_sim_spearman
            value: 72.28669398117248
          - type: euclidean_pearson
            value: 73.40082510412948
          - type: euclidean_spearman
            value: 73.0326539281865
          - type: manhattan_pearson
            value: 71.8659633964841
          - type: manhattan_spearman
            value: 71.57817425823303
        task:
          type: STS
      - dataset:
          config: it-en
          name: MTEB STS17 (it-en)
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 75.80921368595645
          - type: cos_sim_spearman
            value: 77.33209091229315
          - type: euclidean_pearson
            value: 76.53159540154829
          - type: euclidean_spearman
            value: 78.17960842810093
          - type: manhattan_pearson
            value: 76.13530186637601
          - type: manhattan_spearman
            value: 78.00701437666875
        task:
          type: STS
      - dataset:
          config: nl-en
          name: MTEB STS17 (nl-en)
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 74.74980608267349
          - type: cos_sim_spearman
            value: 75.37597374318821
          - type: euclidean_pearson
            value: 74.90506081911661
          - type: euclidean_spearman
            value: 75.30151613124521
          - type: manhattan_pearson
            value: 74.62642745918002
          - type: manhattan_spearman
            value: 75.18619716592303
        task:
          type: STS
      - dataset:
          config: en
          name: MTEB STS22 (en)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 59.632662289205584
          - type: cos_sim_spearman
            value: 60.938543391610914
          - type: euclidean_pearson
            value: 62.113200529767056
          - type: euclidean_spearman
            value: 61.410312633261164
          - type: manhattan_pearson
            value: 61.75494698945686
          - type: manhattan_spearman
            value: 60.92726195322362
        task:
          type: STS
      - dataset:
          config: de
          name: MTEB STS22 (de)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 45.283470551557244
          - type: cos_sim_spearman
            value: 53.44833015864201
          - type: euclidean_pearson
            value: 41.17892011120893
          - type: euclidean_spearman
            value: 53.81441383126767
          - type: manhattan_pearson
            value: 41.17482200420659
          - type: manhattan_spearman
            value: 53.82180269276363
        task:
          type: STS
      - dataset:
          config: es
          name: MTEB STS22 (es)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 60.5069165306236
          - type: cos_sim_spearman
            value: 66.87803259033826
          - type: euclidean_pearson
            value: 63.5428979418236
          - type: euclidean_spearman
            value: 66.9293576586897
          - type: manhattan_pearson
            value: 63.59789526178922
          - type: manhattan_spearman
            value: 66.86555009875066
        task:
          type: STS
      - dataset:
          config: pl
          name: MTEB STS22 (pl)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 28.23026196280264
          - type: cos_sim_spearman
            value: 35.79397812652861
          - type: euclidean_pearson
            value: 17.828102102767353
          - type: euclidean_spearman
            value: 35.721501145568894
          - type: manhattan_pearson
            value: 17.77134274219677
          - type: manhattan_spearman
            value: 35.98107902846267
        task:
          type: STS
      - dataset:
          config: tr
          name: MTEB STS22 (tr)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 56.51946541393812
          - type: cos_sim_spearman
            value: 63.714686006214485
          - type: euclidean_pearson
            value: 58.32104651305898
          - type: euclidean_spearman
            value: 62.237110895702216
          - type: manhattan_pearson
            value: 58.579416468759185
          - type: manhattan_spearman
            value: 62.459738981727
        task:
          type: STS
      - dataset:
          config: ar
          name: MTEB STS22 (ar)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 48.76009839569795
          - type: cos_sim_spearman
            value: 56.65188431953149
          - type: euclidean_pearson
            value: 50.997682160915595
          - type: euclidean_spearman
            value: 55.99910008818135
          - type: manhattan_pearson
            value: 50.76220659606342
          - type: manhattan_spearman
            value: 55.517347595391456
        task:
          type: STS
      - dataset:
          config: ru
          name: MTEB STS22 (ru)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cosine_pearson
            value: 50.724322379215934
          - type: cosine_spearman
            value: 59.90449732164651
          - type: euclidean_pearson
            value: 50.227545226784024
          - type: euclidean_spearman
            value: 59.898906527601085
          - type: main_score
            value: 59.90449732164651
          - type: manhattan_pearson
            value: 50.21762139819405
          - type: manhattan_spearman
            value: 59.761039813759
          - type: pearson
            value: 50.724322379215934
          - type: spearman
            value: 59.90449732164651
        task:
          type: STS
      - dataset:
          config: zh
          name: MTEB STS22 (zh)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 54.717524559088005
          - type: cos_sim_spearman
            value: 66.83570886252286
          - type: euclidean_pearson
            value: 58.41338625505467
          - type: euclidean_spearman
            value: 66.68991427704938
          - type: manhattan_pearson
            value: 58.78638572916807
          - type: manhattan_spearman
            value: 66.58684161046335
        task:
          type: STS
      - dataset:
          config: fr
          name: MTEB STS22 (fr)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 73.2962042954962
          - type: cos_sim_spearman
            value: 76.58255504852025
          - type: euclidean_pearson
            value: 75.70983192778257
          - type: euclidean_spearman
            value: 77.4547684870542
          - type: manhattan_pearson
            value: 75.75565853870485
          - type: manhattan_spearman
            value: 76.90208974949428
        task:
          type: STS
      - dataset:
          config: de-en
          name: MTEB STS22 (de-en)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 54.47396266924846
          - type: cos_sim_spearman
            value: 56.492267162048606
          - type: euclidean_pearson
            value: 55.998505203070195
          - type: euclidean_spearman
            value: 56.46447012960222
          - type: manhattan_pearson
            value: 54.873172394430995
          - type: manhattan_spearman
            value: 56.58111534551218
        task:
          type: STS
      - dataset:
          config: es-en
          name: MTEB STS22 (es-en)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 69.87177267688686
          - type: cos_sim_spearman
            value: 74.57160943395763
          - type: euclidean_pearson
            value: 70.88330406826788
          - type: euclidean_spearman
            value: 74.29767636038422
          - type: manhattan_pearson
            value: 71.38245248369536
          - type: manhattan_spearman
            value: 74.53102232732175
        task:
          type: STS
      - dataset:
          config: it
          name: MTEB STS22 (it)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 72.80225656959544
          - type: cos_sim_spearman
            value: 76.52646173725735
          - type: euclidean_pearson
            value: 73.95710720200799
          - type: euclidean_spearman
            value: 76.54040031984111
          - type: manhattan_pearson
            value: 73.89679971946774
          - type: manhattan_spearman
            value: 76.60886958161574
        task:
          type: STS
      - dataset:
          config: pl-en
          name: MTEB STS22 (pl-en)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 70.70844249898789
          - type: cos_sim_spearman
            value: 72.68571783670241
          - type: euclidean_pearson
            value: 72.38800772441031
          - type: euclidean_spearman
            value: 72.86804422703312
          - type: manhattan_pearson
            value: 71.29840508203515
          - type: manhattan_spearman
            value: 71.86264441749513
        task:
          type: STS
      - dataset:
          config: zh-en
          name: MTEB STS22 (zh-en)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 58.647478923935694
          - type: cos_sim_spearman
            value: 63.74453623540931
          - type: euclidean_pearson
            value: 59.60138032437505
          - type: euclidean_spearman
            value: 63.947930832166065
          - type: manhattan_pearson
            value: 58.59735509491861
          - type: manhattan_spearman
            value: 62.082503844627404
        task:
          type: STS
      - dataset:
          config: es-it
          name: MTEB STS22 (es-it)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 65.8722516867162
          - type: cos_sim_spearman
            value: 71.81208592523012
          - type: euclidean_pearson
            value: 67.95315252165956
          - type: euclidean_spearman
            value: 73.00749822046009
          - type: manhattan_pearson
            value: 68.07884688638924
          - type: manhattan_spearman
            value: 72.34210325803069
        task:
          type: STS
      - dataset:
          config: de-fr
          name: MTEB STS22 (de-fr)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 54.5405814240949
          - type: cos_sim_spearman
            value: 60.56838649023775
          - type: euclidean_pearson
            value: 53.011731611314104
          - type: euclidean_spearman
            value: 58.533194841668426
          - type: manhattan_pearson
            value: 53.623067729338494
          - type: manhattan_spearman
            value: 58.018756154446926
        task:
          type: STS
      - dataset:
          config: de-pl
          name: MTEB STS22 (de-pl)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 13.611046866216112
          - type: cos_sim_spearman
            value: 28.238192909158492
          - type: euclidean_pearson
            value: 22.16189199885129
          - type: euclidean_spearman
            value: 35.012895679076564
          - type: manhattan_pearson
            value: 21.969771178698387
          - type: manhattan_spearman
            value: 32.456985088607475
        task:
          type: STS
      - dataset:
          config: fr-pl
          name: MTEB STS22 (fr-pl)
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 74.58077407011655
          - type: cos_sim_spearman
            value: 84.51542547285167
          - type: euclidean_pearson
            value: 74.64613843596234
          - type: euclidean_spearman
            value: 84.51542547285167
          - type: manhattan_pearson
            value: 75.15335973101396
          - type: manhattan_spearman
            value: 84.51542547285167
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STSBenchmark
          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
          split: test
          type: mteb/stsbenchmark-sts
        metrics:
          - type: cos_sim_pearson
            value: 82.0739825531578
          - type: cos_sim_spearman
            value: 84.01057479311115
          - type: euclidean_pearson
            value: 83.85453227433344
          - type: euclidean_spearman
            value: 84.01630226898655
          - type: manhattan_pearson
            value: 83.75323603028978
          - type: manhattan_spearman
            value: 83.89677983727685
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB SciDocsRR
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
          split: test
          type: mteb/scidocs-reranking
        metrics:
          - type: map
            value: 78.12945623123957
          - type: mrr
            value: 93.87738713719106
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB SciFact
          revision: None
          split: test
          type: scifact
        metrics:
          - type: map_at_1
            value: 52.983000000000004
          - type: map_at_10
            value: 62.946000000000005
          - type: map_at_100
            value: 63.514
          - type: map_at_1000
            value: 63.554
          - type: map_at_3
            value: 60.183
          - type: map_at_5
            value: 61.672000000000004
          - type: mrr_at_1
            value: 55.667
          - type: mrr_at_10
            value: 64.522
          - type: mrr_at_100
            value: 64.957
          - type: mrr_at_1000
            value: 64.995
          - type: mrr_at_3
            value: 62.388999999999996
          - type: mrr_at_5
            value: 63.639
          - type: ndcg_at_1
            value: 55.667
          - type: ndcg_at_10
            value: 67.704
          - type: ndcg_at_100
            value: 70.299
          - type: ndcg_at_1000
            value: 71.241
          - type: ndcg_at_3
            value: 62.866
          - type: ndcg_at_5
            value: 65.16999999999999
          - type: precision_at_1
            value: 55.667
          - type: precision_at_10
            value: 9.033
          - type: precision_at_100
            value: 1.053
          - type: precision_at_1000
            value: 0.11299999999999999
          - type: precision_at_3
            value: 24.444
          - type: precision_at_5
            value: 16.133
          - type: recall_at_1
            value: 52.983000000000004
          - type: recall_at_10
            value: 80.656
          - type: recall_at_100
            value: 92.5
          - type: recall_at_1000
            value: 99.667
          - type: recall_at_3
            value: 67.744
          - type: recall_at_5
            value: 73.433
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB SprintDuplicateQuestions
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
          split: test
          type: mteb/sprintduplicatequestions-pairclassification
        metrics:
          - type: cos_sim_accuracy
            value: 99.72772277227723
          - type: cos_sim_ap
            value: 92.17845897992215
          - type: cos_sim_f1
            value: 85.9746835443038
          - type: cos_sim_precision
            value: 87.07692307692308
          - type: cos_sim_recall
            value: 84.89999999999999
          - type: dot_accuracy
            value: 99.3039603960396
          - type: dot_ap
            value: 60.70244020124878
          - type: dot_f1
            value: 59.92742353551063
          - type: dot_precision
            value: 62.21743810548978
          - type: dot_recall
            value: 57.8
          - type: euclidean_accuracy
            value: 99.71683168316832
          - type: euclidean_ap
            value: 91.53997039964659
          - type: euclidean_f1
            value: 84.88372093023257
          - type: euclidean_precision
            value: 90.02242152466367
          - type: euclidean_recall
            value: 80.30000000000001
          - type: manhattan_accuracy
            value: 99.72376237623763
          - type: manhattan_ap
            value: 91.80756777790289
          - type: manhattan_f1
            value: 85.48468106479157
          - type: manhattan_precision
            value: 85.8728557013118
          - type: manhattan_recall
            value: 85.1
          - type: max_accuracy
            value: 99.72772277227723
          - type: max_ap
            value: 92.17845897992215
          - type: max_f1
            value: 85.9746835443038
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB StackExchangeClustering
          revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
          split: test
          type: mteb/stackexchange-clustering
        metrics:
          - type: v_measure
            value: 53.52464042600003
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB StackExchangeClusteringP2P
          revision: 815ca46b2622cec33ccafc3735d572c266efdb44
          split: test
          type: mteb/stackexchange-clustering-p2p
        metrics:
          - type: v_measure
            value: 32.071631948736
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB StackOverflowDupQuestions
          revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
          split: test
          type: mteb/stackoverflowdupquestions-reranking
        metrics:
          - type: map
            value: 49.19552407604654
          - type: mrr
            value: 49.95269130379425
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB SummEval
          revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
          split: test
          type: mteb/summeval
        metrics:
          - type: cos_sim_pearson
            value: 29.345293033095427
          - type: cos_sim_spearman
            value: 29.976931423258403
          - type: dot_pearson
            value: 27.047078008958408
          - type: dot_spearman
            value: 27.75894368380218
        task:
          type: Summarization
      - dataset:
          config: default
          name: MTEB TRECCOVID
          revision: None
          split: test
          type: trec-covid
        metrics:
          - type: map_at_1
            value: 0.22
          - type: map_at_10
            value: 1.706
          - type: map_at_100
            value: 9.634
          - type: map_at_1000
            value: 23.665
          - type: map_at_3
            value: 0.5950000000000001
          - type: map_at_5
            value: 0.95
          - type: mrr_at_1
            value: 86
          - type: mrr_at_10
            value: 91.8
          - type: mrr_at_100
            value: 91.8
          - type: mrr_at_1000
            value: 91.8
          - type: mrr_at_3
            value: 91
          - type: mrr_at_5
            value: 91.8
          - type: ndcg_at_1
            value: 80
          - type: ndcg_at_10
            value: 72.573
          - type: ndcg_at_100
            value: 53.954
          - type: ndcg_at_1000
            value: 47.760999999999996
          - type: ndcg_at_3
            value: 76.173
          - type: ndcg_at_5
            value: 75.264
          - type: precision_at_1
            value: 86
          - type: precision_at_10
            value: 76.4
          - type: precision_at_100
            value: 55.50000000000001
          - type: precision_at_1000
            value: 21.802
          - type: precision_at_3
            value: 81.333
          - type: precision_at_5
            value: 80.4
          - type: recall_at_1
            value: 0.22
          - type: recall_at_10
            value: 1.925
          - type: recall_at_100
            value: 12.762
          - type: recall_at_1000
            value: 44.946000000000005
          - type: recall_at_3
            value: 0.634
          - type: recall_at_5
            value: 1.051
        task:
          type: Retrieval
      - dataset:
          config: sqi-eng
          name: MTEB Tatoeba (sqi-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 91
          - type: f1
            value: 88.55666666666666
          - type: precision
            value: 87.46166666666667
          - type: recall
            value: 91
        task:
          type: BitextMining
      - dataset:
          config: fry-eng
          name: MTEB Tatoeba (fry-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 57.22543352601156
          - type: f1
            value: 51.03220478943021
          - type: precision
            value: 48.8150289017341
          - type: recall
            value: 57.22543352601156
        task:
          type: BitextMining
      - dataset:
          config: kur-eng
          name: MTEB Tatoeba (kur-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 46.58536585365854
          - type: f1
            value: 39.66870798578116
          - type: precision
            value: 37.416085946573745
          - type: recall
            value: 46.58536585365854
        task:
          type: BitextMining
      - dataset:
          config: tur-eng
          name: MTEB Tatoeba (tur-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 89.7
          - type: f1
            value: 86.77999999999999
          - type: precision
            value: 85.45333333333332
          - type: recall
            value: 89.7
        task:
          type: BitextMining
      - dataset:
          config: deu-eng
          name: MTEB Tatoeba (deu-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 97.39999999999999
          - type: f1
            value: 96.58333333333331
          - type: precision
            value: 96.2
          - type: recall
            value: 97.39999999999999
        task:
          type: BitextMining
      - dataset:
          config: nld-eng
          name: MTEB Tatoeba (nld-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 92.4
          - type: f1
            value: 90.3
          - type: precision
            value: 89.31666666666668
          - type: recall
            value: 92.4
        task:
          type: BitextMining
      - dataset:
          config: ron-eng
          name: MTEB Tatoeba (ron-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 86.9
          - type: f1
            value: 83.67190476190476
          - type: precision
            value: 82.23333333333332
          - type: recall
            value: 86.9
        task:
          type: BitextMining
      - dataset:
          config: ang-eng
          name: MTEB Tatoeba (ang-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 50
          - type: f1
            value: 42.23229092632078
          - type: precision
            value: 39.851634683724235
          - type: recall
            value: 50
        task:
          type: BitextMining
      - dataset:
          config: ido-eng
          name: MTEB Tatoeba (ido-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 76.3
          - type: f1
            value: 70.86190476190477
          - type: precision
            value: 68.68777777777777
          - type: recall
            value: 76.3
        task:
          type: BitextMining
      - dataset:
          config: jav-eng
          name: MTEB Tatoeba (jav-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 57.073170731707314
          - type: f1
            value: 50.658958927251604
          - type: precision
            value: 48.26480836236933
          - type: recall
            value: 57.073170731707314
        task:
          type: BitextMining
      - dataset:
          config: isl-eng
          name: MTEB Tatoeba (isl-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 68.2
          - type: f1
            value: 62.156507936507936
          - type: precision
            value: 59.84964285714286
          - type: recall
            value: 68.2
        task:
          type: BitextMining
      - dataset:
          config: slv-eng
          name: MTEB Tatoeba (slv-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 77.52126366950182
          - type: f1
            value: 72.8496210148701
          - type: precision
            value: 70.92171498003819
          - type: recall
            value: 77.52126366950182
        task:
          type: BitextMining
      - dataset:
          config: cym-eng
          name: MTEB Tatoeba (cym-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 70.78260869565217
          - type: f1
            value: 65.32422360248447
          - type: precision
            value: 63.063067367415194
          - type: recall
            value: 70.78260869565217
        task:
          type: BitextMining
      - dataset:
          config: kaz-eng
          name: MTEB Tatoeba (kaz-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 78.43478260869566
          - type: f1
            value: 73.02608695652172
          - type: precision
            value: 70.63768115942028
          - type: recall
            value: 78.43478260869566
        task:
          type: BitextMining
      - dataset:
          config: est-eng
          name: MTEB Tatoeba (est-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 60.9
          - type: f1
            value: 55.309753694581275
          - type: precision
            value: 53.130476190476195
          - type: recall
            value: 60.9
        task:
          type: BitextMining
      - dataset:
          config: heb-eng
          name: MTEB Tatoeba (heb-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 72.89999999999999
          - type: f1
            value: 67.92023809523809
          - type: precision
            value: 65.82595238095237
          - type: recall
            value: 72.89999999999999
        task:
          type: BitextMining
      - dataset:
          config: gla-eng
          name: MTEB Tatoeba (gla-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 46.80337756332931
          - type: f1
            value: 39.42174900558496
          - type: precision
            value: 36.97101116280851
          - type: recall
            value: 46.80337756332931
        task:
          type: BitextMining
      - dataset:
          config: mar-eng
          name: MTEB Tatoeba (mar-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 89.8
          - type: f1
            value: 86.79
          - type: precision
            value: 85.375
          - type: recall
            value: 89.8
        task:
          type: BitextMining
      - dataset:
          config: lat-eng
          name: MTEB Tatoeba (lat-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 47.199999999999996
          - type: f1
            value: 39.95484348984349
          - type: precision
            value: 37.561071428571424
          - type: recall
            value: 47.199999999999996
        task:
          type: BitextMining
      - dataset:
          config: bel-eng
          name: MTEB Tatoeba (bel-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 87.8
          - type: f1
            value: 84.68190476190475
          - type: precision
            value: 83.275
          - type: recall
            value: 87.8
        task:
          type: BitextMining
      - dataset:
          config: pms-eng
          name: MTEB Tatoeba (pms-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 48.76190476190476
          - type: f1
            value: 42.14965986394558
          - type: precision
            value: 39.96743626743626
          - type: recall
            value: 48.76190476190476
        task:
          type: BitextMining
      - dataset:
          config: gle-eng
          name: MTEB Tatoeba (gle-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 66.10000000000001
          - type: f1
            value: 59.58580086580086
          - type: precision
            value: 57.150238095238095
          - type: recall
            value: 66.10000000000001
        task:
          type: BitextMining
      - dataset:
          config: pes-eng
          name: MTEB Tatoeba (pes-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 87.3
          - type: f1
            value: 84
          - type: precision
            value: 82.48666666666666
          - type: recall
            value: 87.3
        task:
          type: BitextMining
      - dataset:
          config: nob-eng
          name: MTEB Tatoeba (nob-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 90.4
          - type: f1
            value: 87.79523809523809
          - type: precision
            value: 86.6
          - type: recall
            value: 90.4
        task:
          type: BitextMining
      - dataset:
          config: bul-eng
          name: MTEB Tatoeba (bul-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 87
          - type: f1
            value: 83.81
          - type: precision
            value: 82.36666666666666
          - type: recall
            value: 87
        task:
          type: BitextMining
      - dataset:
          config: cbk-eng
          name: MTEB Tatoeba (cbk-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 63.9
          - type: f1
            value: 57.76533189033189
          - type: precision
            value: 55.50595238095239
          - type: recall
            value: 63.9
        task:
          type: BitextMining
      - dataset:
          config: hun-eng
          name: MTEB Tatoeba (hun-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 76.1
          - type: f1
            value: 71.83690476190478
          - type: precision
            value: 70.04928571428573
          - type: recall
            value: 76.1
        task:
          type: BitextMining
      - dataset:
          config: uig-eng
          name: MTEB Tatoeba (uig-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 66.3
          - type: f1
            value: 59.32626984126984
          - type: precision
            value: 56.62535714285713
          - type: recall
            value: 66.3
        task:
          type: BitextMining
      - dataset:
          config: rus-eng
          name: MTEB Tatoeba (rus-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 92.10000000000001
          - type: f1
            value: 89.76666666666667
          - type: main_score
            value: 89.76666666666667
          - type: precision
            value: 88.64999999999999
          - type: recall
            value: 92.10000000000001
        task:
          type: BitextMining
      - dataset:
          config: spa-eng
          name: MTEB Tatoeba (spa-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 93.10000000000001
          - type: f1
            value: 91.10000000000001
          - type: precision
            value: 90.16666666666666
          - type: recall
            value: 93.10000000000001
        task:
          type: BitextMining
      - dataset:
          config: hye-eng
          name: MTEB Tatoeba (hye-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 85.71428571428571
          - type: f1
            value: 82.29142600436403
          - type: precision
            value: 80.8076626877166
          - type: recall
            value: 85.71428571428571
        task:
          type: BitextMining
      - dataset:
          config: tel-eng
          name: MTEB Tatoeba (tel-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 88.88888888888889
          - type: f1
            value: 85.7834757834758
          - type: precision
            value: 84.43732193732193
          - type: recall
            value: 88.88888888888889
        task:
          type: BitextMining
      - dataset:
          config: afr-eng
          name: MTEB Tatoeba (afr-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 88.5
          - type: f1
            value: 85.67190476190476
          - type: precision
            value: 84.43333333333332
          - type: recall
            value: 88.5
        task:
          type: BitextMining
      - dataset:
          config: mon-eng
          name: MTEB Tatoeba (mon-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 82.72727272727273
          - type: f1
            value: 78.21969696969695
          - type: precision
            value: 76.18181818181819
          - type: recall
            value: 82.72727272727273
        task:
          type: BitextMining
      - dataset:
          config: arz-eng
          name: MTEB Tatoeba (arz-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 61.0062893081761
          - type: f1
            value: 55.13976240391334
          - type: precision
            value: 52.92112499659669
          - type: recall
            value: 61.0062893081761
        task:
          type: BitextMining
      - dataset:
          config: hrv-eng
          name: MTEB Tatoeba (hrv-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 89.5
          - type: f1
            value: 86.86666666666666
          - type: precision
            value: 85.69166666666668
          - type: recall
            value: 89.5
        task:
          type: BitextMining
      - dataset:
          config: nov-eng
          name: MTEB Tatoeba (nov-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 73.54085603112841
          - type: f1
            value: 68.56031128404669
          - type: precision
            value: 66.53047989623866
          - type: recall
            value: 73.54085603112841
        task:
          type: BitextMining
      - dataset:
          config: gsw-eng
          name: MTEB Tatoeba (gsw-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 43.58974358974359
          - type: f1
            value: 36.45299145299145
          - type: precision
            value: 33.81155881155882
          - type: recall
            value: 43.58974358974359
        task:
          type: BitextMining
      - dataset:
          config: nds-eng
          name: MTEB Tatoeba (nds-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 59.599999999999994
          - type: f1
            value: 53.264689754689755
          - type: precision
            value: 50.869166666666665
          - type: recall
            value: 59.599999999999994
        task:
          type: BitextMining
      - dataset:
          config: ukr-eng
          name: MTEB Tatoeba (ukr-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 85.2
          - type: f1
            value: 81.61666666666665
          - type: precision
            value: 80.02833333333335
          - type: recall
            value: 85.2
        task:
          type: BitextMining
      - dataset:
          config: uzb-eng
          name: MTEB Tatoeba (uzb-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 63.78504672897196
          - type: f1
            value: 58.00029669188548
          - type: precision
            value: 55.815809968847354
          - type: recall
            value: 63.78504672897196
        task:
          type: BitextMining
      - dataset:
          config: lit-eng
          name: MTEB Tatoeba (lit-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 66.5
          - type: f1
            value: 61.518333333333345
          - type: precision
            value: 59.622363699102834
          - type: recall
            value: 66.5
        task:
          type: BitextMining
      - dataset:
          config: ina-eng
          name: MTEB Tatoeba (ina-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 88.6
          - type: f1
            value: 85.60222222222221
          - type: precision
            value: 84.27916666666665
          - type: recall
            value: 88.6
        task:
          type: BitextMining
      - dataset:
          config: lfn-eng
          name: MTEB Tatoeba (lfn-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 58.699999999999996
          - type: f1
            value: 52.732375957375965
          - type: precision
            value: 50.63214035964035
          - type: recall
            value: 58.699999999999996
        task:
          type: BitextMining
      - dataset:
          config: zsm-eng
          name: MTEB Tatoeba (zsm-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 92.10000000000001
          - type: f1
            value: 89.99666666666667
          - type: precision
            value: 89.03333333333333
          - type: recall
            value: 92.10000000000001
        task:
          type: BitextMining
      - dataset:
          config: ita-eng
          name: MTEB Tatoeba (ita-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 90.10000000000001
          - type: f1
            value: 87.55666666666667
          - type: precision
            value: 86.36166666666668
          - type: recall
            value: 90.10000000000001
        task:
          type: BitextMining
      - dataset:
          config: cmn-eng
          name: MTEB Tatoeba (cmn-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 91.4
          - type: f1
            value: 88.89000000000001
          - type: precision
            value: 87.71166666666666
          - type: recall
            value: 91.4
        task:
          type: BitextMining
      - dataset:
          config: lvs-eng
          name: MTEB Tatoeba (lvs-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 65.7
          - type: f1
            value: 60.67427750410509
          - type: precision
            value: 58.71785714285714
          - type: recall
            value: 65.7
        task:
          type: BitextMining
      - dataset:
          config: glg-eng
          name: MTEB Tatoeba (glg-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 85.39999999999999
          - type: f1
            value: 81.93190476190475
          - type: precision
            value: 80.37833333333333
          - type: recall
            value: 85.39999999999999
        task:
          type: BitextMining
      - dataset:
          config: ceb-eng
          name: MTEB Tatoeba (ceb-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 47.833333333333336
          - type: f1
            value: 42.006625781625786
          - type: precision
            value: 40.077380952380956
          - type: recall
            value: 47.833333333333336
        task:
          type: BitextMining
      - dataset:
          config: bre-eng
          name: MTEB Tatoeba (bre-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 10.4
          - type: f1
            value: 8.24465007215007
          - type: precision
            value: 7.664597069597071
          - type: recall
            value: 10.4
        task:
          type: BitextMining
      - dataset:
          config: ben-eng
          name: MTEB Tatoeba (ben-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 82.6
          - type: f1
            value: 77.76333333333334
          - type: precision
            value: 75.57833333333332
          - type: recall
            value: 82.6
        task:
          type: BitextMining
      - dataset:
          config: swg-eng
          name: MTEB Tatoeba (swg-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 52.67857142857143
          - type: f1
            value: 44.302721088435376
          - type: precision
            value: 41.49801587301587
          - type: recall
            value: 52.67857142857143
        task:
          type: BitextMining
      - dataset:
          config: arq-eng
          name: MTEB Tatoeba (arq-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 28.3205268935236
          - type: f1
            value: 22.426666605171157
          - type: precision
            value: 20.685900116470915
          - type: recall
            value: 28.3205268935236
        task:
          type: BitextMining
      - dataset:
          config: kab-eng
          name: MTEB Tatoeba (kab-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 22.7
          - type: f1
            value: 17.833970473970474
          - type: precision
            value: 16.407335164835164
          - type: recall
            value: 22.7
        task:
          type: BitextMining
      - dataset:
          config: fra-eng
          name: MTEB Tatoeba (fra-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 92.2
          - type: f1
            value: 89.92999999999999
          - type: precision
            value: 88.87
          - type: recall
            value: 92.2
        task:
          type: BitextMining
      - dataset:
          config: por-eng
          name: MTEB Tatoeba (por-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 91.4
          - type: f1
            value: 89.25
          - type: precision
            value: 88.21666666666667
          - type: recall
            value: 91.4
        task:
          type: BitextMining
      - dataset:
          config: tat-eng
          name: MTEB Tatoeba (tat-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 69.19999999999999
          - type: f1
            value: 63.38269841269841
          - type: precision
            value: 61.14773809523809
          - type: recall
            value: 69.19999999999999
        task:
          type: BitextMining
      - dataset:
          config: oci-eng
          name: MTEB Tatoeba (oci-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 48.8
          - type: f1
            value: 42.839915639915645
          - type: precision
            value: 40.770287114845935
          - type: recall
            value: 48.8
        task:
          type: BitextMining
      - dataset:
          config: pol-eng
          name: MTEB Tatoeba (pol-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 88.8
          - type: f1
            value: 85.90666666666668
          - type: precision
            value: 84.54166666666666
          - type: recall
            value: 88.8
        task:
          type: BitextMining
      - dataset:
          config: war-eng
          name: MTEB Tatoeba (war-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 46.6
          - type: f1
            value: 40.85892920804686
          - type: precision
            value: 38.838223114604695
          - type: recall
            value: 46.6
        task:
          type: BitextMining
      - dataset:
          config: aze-eng
          name: MTEB Tatoeba (aze-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 84
          - type: f1
            value: 80.14190476190475
          - type: precision
            value: 78.45333333333333
          - type: recall
            value: 84
        task:
          type: BitextMining
      - dataset:
          config: vie-eng
          name: MTEB Tatoeba (vie-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 90.5
          - type: f1
            value: 87.78333333333333
          - type: precision
            value: 86.5
          - type: recall
            value: 90.5
        task:
          type: BitextMining
      - dataset:
          config: nno-eng
          name: MTEB Tatoeba (nno-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 74.5
          - type: f1
            value: 69.48397546897547
          - type: precision
            value: 67.51869047619049
          - type: recall
            value: 74.5
        task:
          type: BitextMining
      - dataset:
          config: cha-eng
          name: MTEB Tatoeba (cha-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 32.846715328467155
          - type: f1
            value: 27.828177499710343
          - type: precision
            value: 26.63451511991658
          - type: recall
            value: 32.846715328467155
        task:
          type: BitextMining
      - dataset:
          config: mhr-eng
          name: MTEB Tatoeba (mhr-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 8
          - type: f1
            value: 6.07664116764988
          - type: precision
            value: 5.544177607179943
          - type: recall
            value: 8
        task:
          type: BitextMining
      - dataset:
          config: dan-eng
          name: MTEB Tatoeba (dan-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 87.6
          - type: f1
            value: 84.38555555555554
          - type: precision
            value: 82.91583333333334
          - type: recall
            value: 87.6
        task:
          type: BitextMining
      - dataset:
          config: ell-eng
          name: MTEB Tatoeba (ell-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 87.5
          - type: f1
            value: 84.08333333333331
          - type: precision
            value: 82.47333333333333
          - type: recall
            value: 87.5
        task:
          type: BitextMining
      - dataset:
          config: amh-eng
          name: MTEB Tatoeba (amh-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 80.95238095238095
          - type: f1
            value: 76.13095238095238
          - type: precision
            value: 74.05753968253967
          - type: recall
            value: 80.95238095238095
        task:
          type: BitextMining
      - dataset:
          config: pam-eng
          name: MTEB Tatoeba (pam-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 8.799999999999999
          - type: f1
            value: 6.971422975172975
          - type: precision
            value: 6.557814916172301
          - type: recall
            value: 8.799999999999999
        task:
          type: BitextMining
      - dataset:
          config: hsb-eng
          name: MTEB Tatoeba (hsb-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 44.099378881987576
          - type: f1
            value: 37.01649742022413
          - type: precision
            value: 34.69420618488942
          - type: recall
            value: 44.099378881987576
        task:
          type: BitextMining
      - dataset:
          config: srp-eng
          name: MTEB Tatoeba (srp-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 84.3
          - type: f1
            value: 80.32666666666667
          - type: precision
            value: 78.60666666666665
          - type: recall
            value: 84.3
        task:
          type: BitextMining
      - dataset:
          config: epo-eng
          name: MTEB Tatoeba (epo-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 92.5
          - type: f1
            value: 90.49666666666666
          - type: precision
            value: 89.56666666666668
          - type: recall
            value: 92.5
        task:
          type: BitextMining
      - dataset:
          config: kzj-eng
          name: MTEB Tatoeba (kzj-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 10
          - type: f1
            value: 8.268423529875141
          - type: precision
            value: 7.878118605532398
          - type: recall
            value: 10
        task:
          type: BitextMining
      - dataset:
          config: awa-eng
          name: MTEB Tatoeba (awa-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 79.22077922077922
          - type: f1
            value: 74.27128427128426
          - type: precision
            value: 72.28715728715729
          - type: recall
            value: 79.22077922077922
        task:
          type: BitextMining
      - dataset:
          config: fao-eng
          name: MTEB Tatoeba (fao-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 65.64885496183206
          - type: f1
            value: 58.87495456197747
          - type: precision
            value: 55.992366412213734
          - type: recall
            value: 65.64885496183206
        task:
          type: BitextMining
      - dataset:
          config: mal-eng
          name: MTEB Tatoeba (mal-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 96.06986899563319
          - type: f1
            value: 94.78408539543909
          - type: precision
            value: 94.15332362930616
          - type: recall
            value: 96.06986899563319
        task:
          type: BitextMining
      - dataset:
          config: ile-eng
          name: MTEB Tatoeba (ile-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 77.2
          - type: f1
            value: 71.72571428571428
          - type: precision
            value: 69.41000000000001
          - type: recall
            value: 77.2
        task:
          type: BitextMining
      - dataset:
          config: bos-eng
          name: MTEB Tatoeba (bos-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 86.4406779661017
          - type: f1
            value: 83.2391713747646
          - type: precision
            value: 81.74199623352166
          - type: recall
            value: 86.4406779661017
        task:
          type: BitextMining
      - dataset:
          config: cor-eng
          name: MTEB Tatoeba (cor-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 8.4
          - type: f1
            value: 6.017828743398003
          - type: precision
            value: 5.4829865484756795
          - type: recall
            value: 8.4
        task:
          type: BitextMining
      - dataset:
          config: cat-eng
          name: MTEB Tatoeba (cat-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 83.5
          - type: f1
            value: 79.74833333333333
          - type: precision
            value: 78.04837662337664
          - type: recall
            value: 83.5
        task:
          type: BitextMining
      - dataset:
          config: eus-eng
          name: MTEB Tatoeba (eus-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 60.4
          - type: f1
            value: 54.467301587301584
          - type: precision
            value: 52.23242424242424
          - type: recall
            value: 60.4
        task:
          type: BitextMining
      - dataset:
          config: yue-eng
          name: MTEB Tatoeba (yue-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 74.9
          - type: f1
            value: 69.68699134199134
          - type: precision
            value: 67.59873015873016
          - type: recall
            value: 74.9
        task:
          type: BitextMining
      - dataset:
          config: swe-eng
          name: MTEB Tatoeba (swe-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 88
          - type: f1
            value: 84.9652380952381
          - type: precision
            value: 83.66166666666666
          - type: recall
            value: 88
        task:
          type: BitextMining
      - dataset:
          config: dtp-eng
          name: MTEB Tatoeba (dtp-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 9.1
          - type: f1
            value: 7.681244588744588
          - type: precision
            value: 7.370043290043291
          - type: recall
            value: 9.1
        task:
          type: BitextMining
      - dataset:
          config: kat-eng
          name: MTEB Tatoeba (kat-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 80.9651474530831
          - type: f1
            value: 76.84220605132133
          - type: precision
            value: 75.19606398962966
          - type: recall
            value: 80.9651474530831
        task:
          type: BitextMining
      - dataset:
          config: jpn-eng
          name: MTEB Tatoeba (jpn-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 86.9
          - type: f1
            value: 83.705
          - type: precision
            value: 82.3120634920635
          - type: recall
            value: 86.9
        task:
          type: BitextMining
      - dataset:
          config: csb-eng
          name: MTEB Tatoeba (csb-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 29.64426877470356
          - type: f1
            value: 23.98763072676116
          - type: precision
            value: 22.506399397703746
          - type: recall
            value: 29.64426877470356
        task:
          type: BitextMining
      - dataset:
          config: xho-eng
          name: MTEB Tatoeba (xho-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 70.4225352112676
          - type: f1
            value: 62.84037558685445
          - type: precision
            value: 59.56572769953053
          - type: recall
            value: 70.4225352112676
        task:
          type: BitextMining
      - dataset:
          config: orv-eng
          name: MTEB Tatoeba (orv-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 19.64071856287425
          - type: f1
            value: 15.125271011207756
          - type: precision
            value: 13.865019261197494
          - type: recall
            value: 19.64071856287425
        task:
          type: BitextMining
      - dataset:
          config: ind-eng
          name: MTEB Tatoeba (ind-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 90.2
          - type: f1
            value: 87.80666666666666
          - type: precision
            value: 86.70833333333331
          - type: recall
            value: 90.2
        task:
          type: BitextMining
      - dataset:
          config: tuk-eng
          name: MTEB Tatoeba (tuk-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 23.15270935960591
          - type: f1
            value: 18.407224958949097
          - type: precision
            value: 16.982385430661292
          - type: recall
            value: 23.15270935960591
        task:
          type: BitextMining
      - dataset:
          config: max-eng
          name: MTEB Tatoeba (max-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 55.98591549295775
          - type: f1
            value: 49.94718309859154
          - type: precision
            value: 47.77864154624717
          - type: recall
            value: 55.98591549295775
        task:
          type: BitextMining
      - dataset:
          config: swh-eng
          name: MTEB Tatoeba (swh-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 73.07692307692307
          - type: f1
            value: 66.74358974358974
          - type: precision
            value: 64.06837606837607
          - type: recall
            value: 73.07692307692307
        task:
          type: BitextMining
      - dataset:
          config: hin-eng
          name: MTEB Tatoeba (hin-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 94.89999999999999
          - type: f1
            value: 93.25
          - type: precision
            value: 92.43333333333332
          - type: recall
            value: 94.89999999999999
        task:
          type: BitextMining
      - dataset:
          config: dsb-eng
          name: MTEB Tatoeba (dsb-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 37.78705636743215
          - type: f1
            value: 31.63899658680452
          - type: precision
            value: 29.72264397629742
          - type: recall
            value: 37.78705636743215
        task:
          type: BitextMining
      - dataset:
          config: ber-eng
          name: MTEB Tatoeba (ber-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 21.6
          - type: f1
            value: 16.91697302697303
          - type: precision
            value: 15.71225147075147
          - type: recall
            value: 21.6
        task:
          type: BitextMining
      - dataset:
          config: tam-eng
          name: MTEB Tatoeba (tam-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 85.01628664495115
          - type: f1
            value: 81.38514037536838
          - type: precision
            value: 79.83170466883823
          - type: recall
            value: 85.01628664495115
        task:
          type: BitextMining
      - dataset:
          config: slk-eng
          name: MTEB Tatoeba (slk-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 83.39999999999999
          - type: f1
            value: 79.96380952380952
          - type: precision
            value: 78.48333333333333
          - type: recall
            value: 83.39999999999999
        task:
          type: BitextMining
      - dataset:
          config: tgl-eng
          name: MTEB Tatoeba (tgl-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 83.2
          - type: f1
            value: 79.26190476190476
          - type: precision
            value: 77.58833333333334
          - type: recall
            value: 83.2
        task:
          type: BitextMining
      - dataset:
          config: ast-eng
          name: MTEB Tatoeba (ast-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 75.59055118110236
          - type: f1
            value: 71.66854143232096
          - type: precision
            value: 70.30183727034121
          - type: recall
            value: 75.59055118110236
        task:
          type: BitextMining
      - dataset:
          config: mkd-eng
          name: MTEB Tatoeba (mkd-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 65.5
          - type: f1
            value: 59.26095238095238
          - type: precision
            value: 56.81909090909092
          - type: recall
            value: 65.5
        task:
          type: BitextMining
      - dataset:
          config: khm-eng
          name: MTEB Tatoeba (khm-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 55.26315789473685
          - type: f1
            value: 47.986523325858506
          - type: precision
            value: 45.33950006595436
          - type: recall
            value: 55.26315789473685
        task:
          type: BitextMining
      - dataset:
          config: ces-eng
          name: MTEB Tatoeba (ces-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 82.89999999999999
          - type: f1
            value: 78.835
          - type: precision
            value: 77.04761904761905
          - type: recall
            value: 82.89999999999999
        task:
          type: BitextMining
      - dataset:
          config: tzl-eng
          name: MTEB Tatoeba (tzl-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 43.269230769230774
          - type: f1
            value: 36.20421245421245
          - type: precision
            value: 33.57371794871795
          - type: recall
            value: 43.269230769230774
        task:
          type: BitextMining
      - dataset:
          config: urd-eng
          name: MTEB Tatoeba (urd-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 88
          - type: f1
            value: 84.70666666666666
          - type: precision
            value: 83.23166666666665
          - type: recall
            value: 88
        task:
          type: BitextMining
      - dataset:
          config: ara-eng
          name: MTEB Tatoeba (ara-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 77.4
          - type: f1
            value: 72.54666666666667
          - type: precision
            value: 70.54318181818181
          - type: recall
            value: 77.4
        task:
          type: BitextMining
      - dataset:
          config: kor-eng
          name: MTEB Tatoeba (kor-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 78.60000000000001
          - type: f1
            value: 74.1588888888889
          - type: precision
            value: 72.30250000000001
          - type: recall
            value: 78.60000000000001
        task:
          type: BitextMining
      - dataset:
          config: yid-eng
          name: MTEB Tatoeba (yid-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 72.40566037735849
          - type: f1
            value: 66.82587328813744
          - type: precision
            value: 64.75039308176099
          - type: recall
            value: 72.40566037735849
        task:
          type: BitextMining
      - dataset:
          config: fin-eng
          name: MTEB Tatoeba (fin-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 73.8
          - type: f1
            value: 68.56357142857144
          - type: precision
            value: 66.3178822055138
          - type: recall
            value: 73.8
        task:
          type: BitextMining
      - dataset:
          config: tha-eng
          name: MTEB Tatoeba (tha-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 91.78832116788321
          - type: f1
            value: 89.3552311435523
          - type: precision
            value: 88.20559610705597
          - type: recall
            value: 91.78832116788321
        task:
          type: BitextMining
      - dataset:
          config: wuu-eng
          name: MTEB Tatoeba (wuu-eng)
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
          split: test
          type: mteb/tatoeba-bitext-mining
        metrics:
          - type: accuracy
            value: 74.3
          - type: f1
            value: 69.05085581085581
          - type: precision
            value: 66.955
          - type: recall
            value: 74.3
        task:
          type: BitextMining
      - dataset:
          config: default
          name: MTEB Touche2020
          revision: None
          split: test
          type: webis-touche2020
        metrics:
          - type: map_at_1
            value: 2.896
          - type: map_at_10
            value: 8.993
          - type: map_at_100
            value: 14.133999999999999
          - type: map_at_1000
            value: 15.668000000000001
          - type: map_at_3
            value: 5.862
          - type: map_at_5
            value: 7.17
          - type: mrr_at_1
            value: 34.694
          - type: mrr_at_10
            value: 42.931000000000004
          - type: mrr_at_100
            value: 44.81
          - type: mrr_at_1000
            value: 44.81
          - type: mrr_at_3
            value: 38.435
          - type: mrr_at_5
            value: 41.701
          - type: ndcg_at_1
            value: 31.633
          - type: ndcg_at_10
            value: 21.163
          - type: ndcg_at_100
            value: 33.306000000000004
          - type: ndcg_at_1000
            value: 45.275999999999996
          - type: ndcg_at_3
            value: 25.685999999999996
          - type: ndcg_at_5
            value: 23.732
          - type: precision_at_1
            value: 34.694
          - type: precision_at_10
            value: 17.755000000000003
          - type: precision_at_100
            value: 6.938999999999999
          - type: precision_at_1000
            value: 1.48
          - type: precision_at_3
            value: 25.85
          - type: precision_at_5
            value: 23.265
          - type: recall_at_1
            value: 2.896
          - type: recall_at_10
            value: 13.333999999999998
          - type: recall_at_100
            value: 43.517
          - type: recall_at_1000
            value: 79.836
          - type: recall_at_3
            value: 6.306000000000001
          - type: recall_at_5
            value: 8.825
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ToxicConversationsClassification
          revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
          split: test
          type: mteb/toxic_conversations_50k
        metrics:
          - type: accuracy
            value: 69.3874
          - type: ap
            value: 13.829909072469423
          - type: f1
            value: 53.54534203543492
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB TweetSentimentExtractionClassification
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
          split: test
          type: mteb/tweet_sentiment_extraction
        metrics:
          - type: accuracy
            value: 62.62026032823995
          - type: f1
            value: 62.85251350485221
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB TwentyNewsgroupsClustering
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
          split: test
          type: mteb/twentynewsgroups-clustering
        metrics:
          - type: v_measure
            value: 33.21527881409797
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB TwitterSemEval2015
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
          split: test
          type: mteb/twittersemeval2015-pairclassification
        metrics:
          - type: cos_sim_accuracy
            value: 84.97943613280086
          - type: cos_sim_ap
            value: 70.75454316885921
          - type: cos_sim_f1
            value: 65.38274012676743
          - type: cos_sim_precision
            value: 60.761214318078835
          - type: cos_sim_recall
            value: 70.76517150395777
          - type: dot_accuracy
            value: 79.0546581629612
          - type: dot_ap
            value: 47.3197121792147
          - type: dot_f1
            value: 49.20106524633821
          - type: dot_precision
            value: 42.45499808502489
          - type: dot_recall
            value: 58.49604221635884
          - type: euclidean_accuracy
            value: 85.08076533349228
          - type: euclidean_ap
            value: 70.95016106374474
          - type: euclidean_f1
            value: 65.43987900176455
          - type: euclidean_precision
            value: 62.64478764478765
          - type: euclidean_recall
            value: 68.49604221635884
          - type: manhattan_accuracy
            value: 84.93771234428085
          - type: manhattan_ap
            value: 70.63668388755362
          - type: manhattan_f1
            value: 65.23895401262398
          - type: manhattan_precision
            value: 56.946084218811485
          - type: manhattan_recall
            value: 76.35883905013192
          - type: max_accuracy
            value: 85.08076533349228
          - type: max_ap
            value: 70.95016106374474
          - type: max_f1
            value: 65.43987900176455
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB TwitterURLCorpus
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
          split: test
          type: mteb/twitterurlcorpus-pairclassification
        metrics:
          - type: cos_sim_accuracy
            value: 88.69096130709822
          - type: cos_sim_ap
            value: 84.82526278228542
          - type: cos_sim_f1
            value: 77.65485060585536
          - type: cos_sim_precision
            value: 75.94582658619167
          - type: cos_sim_recall
            value: 79.44256236526024
          - type: dot_accuracy
            value: 80.97954748321496
          - type: dot_ap
            value: 64.81642914145866
          - type: dot_f1
            value: 60.631996987229975
          - type: dot_precision
            value: 54.5897293631712
          - type: dot_recall
            value: 68.17831844779796
          - type: euclidean_accuracy
            value: 88.6987231730508
          - type: euclidean_ap
            value: 84.80003825477253
          - type: euclidean_f1
            value: 77.67194179854496
          - type: euclidean_precision
            value: 75.7128235122094
          - type: euclidean_recall
            value: 79.73514012935017
          - type: manhattan_accuracy
            value: 88.62692591298949
          - type: manhattan_ap
            value: 84.80451408255276
          - type: manhattan_f1
            value: 77.69888949572183
          - type: manhattan_precision
            value: 73.70311528631622
          - type: manhattan_recall
            value: 82.15275639051433
          - type: max_accuracy
            value: 88.6987231730508
          - type: max_ap
            value: 84.82526278228542
          - type: max_f1
            value: 77.69888949572183
        task:
          type: PairClassification
      - dataset:
          config: ru-en
          name: MTEB BUCC.v2 (ru-en)
          revision: 1739dc11ffe9b7bfccd7f3d585aeb4c544fc6677
          split: test
          type: mteb/bucc-bitext-mining
        metrics:
          - type: accuracy
            value: 95.72566678212678
          - type: f1
            value: 94.42443135896548
          - type: main_score
            value: 94.42443135896548
          - type: precision
            value: 93.80868260016165
          - type: recall
            value: 95.72566678212678
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-rus_Cyrl
          name: MTEB BelebeleRetrieval (rus_Cyrl-rus_Cyrl)
          revision: 75b399394a9803252cfec289d103de462763db7c
          split: test
          type: facebook/belebele
        metrics:
          - type: main_score
            value: 92.23599999999999
          - type: map_at_1
            value: 87.111
          - type: map_at_10
            value: 90.717
          - type: map_at_100
            value: 90.879
          - type: map_at_1000
            value: 90.881
          - type: map_at_20
            value: 90.849
          - type: map_at_3
            value: 90.074
          - type: map_at_5
            value: 90.535
          - type: mrr_at_1
            value: 87.1111111111111
          - type: mrr_at_10
            value: 90.7173721340388
          - type: mrr_at_100
            value: 90.87859682638407
          - type: mrr_at_1000
            value: 90.88093553612326
          - type: mrr_at_20
            value: 90.84863516113515
          - type: mrr_at_3
            value: 90.07407407407409
          - type: mrr_at_5
            value: 90.53518518518521
          - type: nauc_map_at_1000_diff1
            value: 92.37373187280554
          - type: nauc_map_at_1000_max
            value: 79.90465445423249
          - type: nauc_map_at_1000_std
            value: -0.6220290556185463
          - type: nauc_map_at_100_diff1
            value: 92.37386697345335
          - type: nauc_map_at_100_max
            value: 79.90991577223959
          - type: nauc_map_at_100_std
            value: -0.602247514642845
          - type: nauc_map_at_10_diff1
            value: 92.30907447072467
          - type: nauc_map_at_10_max
            value: 79.86831935337598
          - type: nauc_map_at_10_std
            value: -0.7455191860719699
          - type: nauc_map_at_1_diff1
            value: 93.29828518358822
          - type: nauc_map_at_1_max
            value: 78.69539619887887
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            value: -4.097150817605763
          - type: nauc_map_at_20_diff1
            value: 92.38414149703077
          - type: nauc_map_at_20_max
            value: 79.94789814504661
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            value: -0.3928031130400773
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            value: 92.21688899306734
          - type: nauc_map_at_3_max
            value: 80.34586671780885
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            value: 0.24088319695435909
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            value: -0.6296366922840796
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            value: 79.90991577223959
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          - type: nauc_mrr_at_5_max
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          - type: nauc_ndcg_at_1000_diff1
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          - type: nauc_ndcg_at_3_diff1
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          - type: nauc_ndcg_at_3_std
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          - type: nauc_ndcg_at_5_max
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          - type: nauc_precision_at_100_diff1
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            value: 100
          - type: nauc_precision_at_100_std
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          - type: nauc_precision_at_10_diff1
            value: 90.13938908896874
          - type: nauc_precision_at_10_max
            value: 82.21121782046167
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          - type: nauc_precision_at_20_diff1
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          - type: nauc_precision_at_20_max
            value: 91.04000574588684
          - type: nauc_precision_at_20_std
            value: 48.764634058749586
          - type: nauc_precision_at_3_diff1
            value: 90.52690041533852
          - type: nauc_precision_at_3_max
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            value: 12.036768730480507
          - type: nauc_precision_at_5_diff1
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          - type: nauc_precision_at_5_max
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          - type: nauc_precision_at_5_std
            value: 9.974323062558037
          - type: nauc_recall_at_1000_diff1
            value: .nan
          - type: nauc_recall_at_1000_max
            value: .nan
          - type: nauc_recall_at_1000_std
            value: .nan
          - type: nauc_recall_at_100_diff1
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            value: 61.856861655370686
          - type: nauc_recall_at_1_std
            value: 4.708911881992707
          - type: nauc_recall_at_20_diff1
            value: 73.47946930710269
          - type: nauc_recall_at_20_max
            value: 70.19520986689254
          - type: nauc_recall_at_20_std
            value: 45.93186111653943
          - type: nauc_recall_at_3_diff1
            value: 79.02026879450173
          - type: nauc_recall_at_3_max
            value: 58.750746246923924
          - type: nauc_recall_at_3_std
            value: 16.740684654251076
          - type: nauc_recall_at_5_diff1
            value: 76.4758566228162
          - type: nauc_recall_at_5_max
            value: 61.862709220131386
          - type: nauc_recall_at_5_std
            value: 20.18336254550361
          - type: ndcg_at_1
            value: 73.444
          - type: ndcg_at_10
            value: 82.748
          - type: ndcg_at_100
            value: 84.416
          - type: ndcg_at_1000
            value: 84.52300000000001
          - type: ndcg_at_20
            value: 83.646
          - type: ndcg_at_3
            value: 80.267
          - type: ndcg_at_5
            value: 81.922
          - type: precision_at_1
            value: 73.444
          - type: precision_at_10
            value: 9.167
          - type: precision_at_100
            value: 0.992
          - type: precision_at_1000
            value: 0.1
          - type: precision_at_20
            value: 4.761
          - type: precision_at_3
            value: 28.37
          - type: precision_at_5
            value: 17.822
          - type: recall_at_1
            value: 73.444
          - type: recall_at_10
            value: 91.667
          - type: recall_at_100
            value: 99.222
          - type: recall_at_1000
            value: 100
          - type: recall_at_20
            value: 95.222
          - type: recall_at_3
            value: 85.111
          - type: recall_at_5
            value: 89.11099999999999
        task:
          type: Retrieval
      - dataset:
          config: eng_Latn-rus_Cyrl
          name: MTEB BibleNLPBitextMining (eng_Latn-rus_Cyrl)
          revision: 264a18480c529d9e922483839b4b9758e690b762
          split: train
          type: davidstap/biblenlp-corpus-mmteb
        metrics:
          - type: accuracy
            value: 96.875
          - type: f1
            value: 95.83333333333333
          - type: main_score
            value: 95.83333333333333
          - type: precision
            value: 95.3125
          - type: recall
            value: 96.875
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-eng_Latn
          name: MTEB BibleNLPBitextMining (rus_Cyrl-eng_Latn)
          revision: 264a18480c529d9e922483839b4b9758e690b762
          split: train
          type: davidstap/biblenlp-corpus-mmteb
        metrics:
          - type: accuracy
            value: 88.671875
          - type: f1
            value: 85.3515625
          - type: main_score
            value: 85.3515625
          - type: precision
            value: 83.85416666666667
          - type: recall
            value: 88.671875
        task:
          type: BitextMining
      - dataset:
          config: default
          name: MTEB CEDRClassification (default)
          revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4
          split: test
          type: ai-forever/cedr-classification
        metrics:
          - type: accuracy
            value: 40.06907545164719
          - type: f1
            value: 26.285000550712407
          - type: lrap
            value: 64.4280021253997
          - type: main_score
            value: 40.06907545164719
        task:
          type: MultilabelClassification
      - dataset:
          config: default
          name: MTEB CyrillicTurkicLangClassification (default)
          revision: e42d330f33d65b7b72dfd408883daf1661f06f18
          split: test
          type: tatiana-merz/cyrillic_turkic_langs
        metrics:
          - type: accuracy
            value: 43.3447265625
          - type: f1
            value: 40.08400146827895
          - type: f1_weighted
            value: 40.08499428040896
          - type: main_score
            value: 43.3447265625
        task:
          type: Classification
      - dataset:
          config: ace_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (ace_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 6.225296442687747
          - type: f1
            value: 5.5190958860075
          - type: main_score
            value: 5.5190958860075
          - type: precision
            value: 5.3752643758000005
          - type: recall
            value: 6.225296442687747
        task:
          type: BitextMining
      - dataset:
          config: bam_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (bam_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 68.37944664031622
          - type: f1
            value: 64.54819836666252
          - type: main_score
            value: 64.54819836666252
          - type: precision
            value: 63.07479233454916
          - type: recall
            value: 68.37944664031622
        task:
          type: BitextMining
      - dataset:
          config: dzo_Tibt-rus_Cyrl
          name: MTEB FloresBitextMining (dzo_Tibt-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 0.09881422924901186
          - type: f1
            value: 0.00019509225912934226
          - type: main_score
            value: 0.00019509225912934226
          - type: precision
            value: 0.0000976425190207627
          - type: recall
            value: 0.09881422924901186
        task:
          type: BitextMining
      - dataset:
          config: hin_Deva-rus_Cyrl
          name: MTEB FloresBitextMining (hin_Deva-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.60474308300395
          - type: f1
            value: 99.47299077733861
          - type: main_score
            value: 99.47299077733861
          - type: precision
            value: 99.40711462450594
          - type: recall
            value: 99.60474308300395
        task:
          type: BitextMining
      - dataset:
          config: khm_Khmr-rus_Cyrl
          name: MTEB FloresBitextMining (khm_Khmr-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 88.83399209486166
          - type: f1
            value: 87.71151056318254
          - type: main_score
            value: 87.71151056318254
          - type: precision
            value: 87.32012500709193
          - type: recall
            value: 88.83399209486166
        task:
          type: BitextMining
      - dataset:
          config: mag_Deva-rus_Cyrl
          name: MTEB FloresBitextMining (mag_Deva-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.02371541501977
          - type: f1
            value: 97.7239789196311
          - type: main_score
            value: 97.7239789196311
          - type: precision
            value: 97.61904761904762
          - type: recall
            value: 98.02371541501977
        task:
          type: BitextMining
      - dataset:
          config: pap_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (pap_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 94.0711462450593
          - type: f1
            value: 93.68187806922984
          - type: main_score
            value: 93.68187806922984
          - type: precision
            value: 93.58925452707051
          - type: recall
            value: 94.0711462450593
        task:
          type: BitextMining
      - dataset:
          config: sot_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (sot_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 90.9090909090909
          - type: f1
            value: 89.23171936758892
          - type: main_score
            value: 89.23171936758892
          - type: precision
            value: 88.51790014083866
          - type: recall
            value: 90.9090909090909
        task:
          type: BitextMining
      - dataset:
          config: tur_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (tur_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.2094861660079
          - type: f1
            value: 98.9459815546772
          - type: main_score
            value: 98.9459815546772
          - type: precision
            value: 98.81422924901186
          - type: recall
            value: 99.2094861660079
        task:
          type: BitextMining
      - dataset:
          config: ace_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (ace_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 66.10671936758892
          - type: f1
            value: 63.81888256297873
          - type: main_score
            value: 63.81888256297873
          - type: precision
            value: 63.01614067933451
          - type: recall
            value: 66.10671936758892
        task:
          type: BitextMining
      - dataset:
          config: ban_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (ban_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 79.44664031620553
          - type: f1
            value: 77.6311962082713
          - type: main_score
            value: 77.6311962082713
          - type: precision
            value: 76.93977931929739
          - type: recall
            value: 79.44664031620553
        task:
          type: BitextMining
      - dataset:
          config: ell_Grek-rus_Cyrl
          name: MTEB FloresBitextMining (ell_Grek-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.40711462450594
          - type: f1
            value: 99.2094861660079
          - type: main_score
            value: 99.2094861660079
          - type: precision
            value: 99.1106719367589
          - type: recall
            value: 99.40711462450594
        task:
          type: BitextMining
      - dataset:
          config: hne_Deva-rus_Cyrl
          name: MTEB FloresBitextMining (hne_Deva-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 96.83794466403161
          - type: f1
            value: 96.25352907961603
          - type: main_score
            value: 96.25352907961603
          - type: precision
            value: 96.02155091285526
          - type: recall
            value: 96.83794466403161
        task:
          type: BitextMining
      - dataset:
          config: kik_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (kik_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 76.28458498023716
          - type: f1
            value: 73.5596919895859
          - type: main_score
            value: 73.5596919895859
          - type: precision
            value: 72.40900759055246
          - type: recall
            value: 76.28458498023716
        task:
          type: BitextMining
      - dataset:
          config: mai_Deva-rus_Cyrl
          name: MTEB FloresBitextMining (mai_Deva-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.72727272727273
          - type: f1
            value: 97.37812911725956
          - type: main_score
            value: 97.37812911725956
          - type: precision
            value: 97.26002258610953
          - type: recall
            value: 97.72727272727273
        task:
          type: BitextMining
      - dataset:
          config: pbt_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (pbt_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 94.0711462450593
          - type: f1
            value: 93.34700387331966
          - type: main_score
            value: 93.34700387331966
          - type: precision
            value: 93.06920556920556
          - type: recall
            value: 94.0711462450593
        task:
          type: BitextMining
      - dataset:
          config: spa_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (spa_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.2094861660079
          - type: f1
            value: 98.9459815546772
          - type: main_score
            value: 98.9459815546772
          - type: precision
            value: 98.81422924901186
          - type: recall
            value: 99.2094861660079
        task:
          type: BitextMining
      - dataset:
          config: twi_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (twi_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 80.73122529644269
          - type: f1
            value: 77.77434363246721
          - type: main_score
            value: 77.77434363246721
          - type: precision
            value: 76.54444287596462
          - type: recall
            value: 80.73122529644269
        task:
          type: BitextMining
      - dataset:
          config: acm_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (acm_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 94.56521739130434
          - type: f1
            value: 92.92490118577075
          - type: main_score
            value: 92.92490118577075
          - type: precision
            value: 92.16897233201581
          - type: recall
            value: 94.56521739130434
        task:
          type: BitextMining
      - dataset:
          config: bel_Cyrl-rus_Cyrl
          name: MTEB FloresBitextMining (bel_Cyrl-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.2094861660079
          - type: f1
            value: 98.98550724637681
          - type: main_score
            value: 98.98550724637681
          - type: precision
            value: 98.88833992094862
          - type: recall
            value: 99.2094861660079
        task:
          type: BitextMining
      - dataset:
          config: eng_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (eng_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.60474308300395
          - type: f1
            value: 99.4729907773386
          - type: main_score
            value: 99.4729907773386
          - type: precision
            value: 99.40711462450594
          - type: recall
            value: 99.60474308300395
        task:
          type: BitextMining
      - dataset:
          config: hrv_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (hrv_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.2094861660079
          - type: f1
            value: 99.05138339920948
          - type: main_score
            value: 99.05138339920948
          - type: precision
            value: 99.00691699604744
          - type: recall
            value: 99.2094861660079
        task:
          type: BitextMining
      - dataset:
          config: kin_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (kin_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 88.2411067193676
          - type: f1
            value: 86.5485246227658
          - type: main_score
            value: 86.5485246227658
          - type: precision
            value: 85.90652101521667
          - type: recall
            value: 88.2411067193676
        task:
          type: BitextMining
      - dataset:
          config: mal_Mlym-rus_Cyrl
          name: MTEB FloresBitextMining (mal_Mlym-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.51778656126481
          - type: f1
            value: 98.07971014492753
          - type: main_score
            value: 98.07971014492753
          - type: precision
            value: 97.88372859025033
          - type: recall
            value: 98.51778656126481
        task:
          type: BitextMining
      - dataset:
          config: pes_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (pes_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.51778656126481
          - type: f1
            value: 98.0566534914361
          - type: main_score
            value: 98.0566534914361
          - type: precision
            value: 97.82608695652173
          - type: recall
            value: 98.51778656126481
        task:
          type: BitextMining
      - dataset:
          config: srd_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (srd_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 82.6086956521739
          - type: f1
            value: 80.9173470979821
          - type: main_score
            value: 80.9173470979821
          - type: precision
            value: 80.24468672882627
          - type: recall
            value: 82.6086956521739
        task:
          type: BitextMining
      - dataset:
          config: tzm_Tfng-rus_Cyrl
          name: MTEB FloresBitextMining (tzm_Tfng-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 7.41106719367589
          - type: f1
            value: 6.363562740945329
          - type: main_score
            value: 6.363562740945329
          - type: precision
            value: 6.090373175353411
          - type: recall
            value: 7.41106719367589
        task:
          type: BitextMining
      - dataset:
          config: acq_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (acq_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.25691699604744
          - type: f1
            value: 93.81422924901187
          - type: main_score
            value: 93.81422924901187
          - type: precision
            value: 93.14064558629775
          - type: recall
            value: 95.25691699604744
        task:
          type: BitextMining
      - dataset:
          config: bem_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (bem_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 68.08300395256917
          - type: f1
            value: 65.01368772860867
          - type: main_score
            value: 65.01368772860867
          - type: precision
            value: 63.91052337510628
          - type: recall
            value: 68.08300395256917
        task:
          type: BitextMining
      - dataset:
          config: epo_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (epo_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.41897233201581
          - type: f1
            value: 98.17193675889328
          - type: main_score
            value: 98.17193675889328
          - type: precision
            value: 98.08210564139418
          - type: recall
            value: 98.41897233201581
        task:
          type: BitextMining
      - dataset:
          config: hun_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (hun_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.30830039525692
          - type: f1
            value: 99.1106719367589
          - type: main_score
            value: 99.1106719367589
          - type: precision
            value: 99.01185770750988
          - type: recall
            value: 99.30830039525692
        task:
          type: BitextMining
      - dataset:
          config: kir_Cyrl-rus_Cyrl
          name: MTEB FloresBitextMining (kir_Cyrl-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.5296442687747
          - type: f1
            value: 97.07549806364035
          - type: main_score
            value: 97.07549806364035
          - type: precision
            value: 96.90958498023716
          - type: recall
            value: 97.5296442687747
        task:
          type: BitextMining
      - dataset:
          config: mar_Deva-rus_Cyrl
          name: MTEB FloresBitextMining (mar_Deva-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.82608695652173
          - type: f1
            value: 97.44400527009222
          - type: main_score
            value: 97.44400527009222
          - type: precision
            value: 97.28966685488425
          - type: recall
            value: 97.82608695652173
        task:
          type: BitextMining
      - dataset:
          config: plt_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (plt_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 79.9407114624506
          - type: f1
            value: 78.3154177760691
          - type: main_score
            value: 78.3154177760691
          - type: precision
            value: 77.69877344877344
          - type: recall
            value: 79.9407114624506
        task:
          type: BitextMining
      - dataset:
          config: srp_Cyrl-rus_Cyrl
          name: MTEB FloresBitextMining (srp_Cyrl-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.70355731225297
          - type: f1
            value: 99.60474308300395
          - type: main_score
            value: 99.60474308300395
          - type: precision
            value: 99.55533596837944
          - type: recall
            value: 99.70355731225297
        task:
          type: BitextMining
      - dataset:
          config: uig_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (uig_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 83.20158102766798
          - type: f1
            value: 81.44381923034585
          - type: main_score
            value: 81.44381923034585
          - type: precision
            value: 80.78813411582477
          - type: recall
            value: 83.20158102766798
        task:
          type: BitextMining
      - dataset:
          config: aeb_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (aeb_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 91.20553359683794
          - type: f1
            value: 88.75352907961603
          - type: main_score
            value: 88.75352907961603
          - type: precision
            value: 87.64328063241106
          - type: recall
            value: 91.20553359683794
        task:
          type: BitextMining
      - dataset:
          config: ben_Beng-rus_Cyrl
          name: MTEB FloresBitextMining (ben_Beng-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.91304347826086
          - type: f1
            value: 98.60671936758894
          - type: main_score
            value: 98.60671936758894
          - type: precision
            value: 98.4766139657444
          - type: recall
            value: 98.91304347826086
        task:
          type: BitextMining
      - dataset:
          config: est_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (est_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 96.24505928853755
          - type: f1
            value: 95.27417027417027
          - type: main_score
            value: 95.27417027417027
          - type: precision
            value: 94.84107378129117
          - type: recall
            value: 96.24505928853755
        task:
          type: BitextMining
      - dataset:
          config: hye_Armn-rus_Cyrl
          name: MTEB FloresBitextMining (hye_Armn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.02371541501977
          - type: f1
            value: 97.67786561264822
          - type: main_score
            value: 97.67786561264822
          - type: precision
            value: 97.55839022637441
          - type: recall
            value: 98.02371541501977
        task:
          type: BitextMining
      - dataset:
          config: kmb_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (kmb_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 46.047430830039524
          - type: f1
            value: 42.94464804804471
          - type: main_score
            value: 42.94464804804471
          - type: precision
            value: 41.9851895607238
          - type: recall
            value: 46.047430830039524
        task:
          type: BitextMining
      - dataset:
          config: min_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (min_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 3.9525691699604746
          - type: f1
            value: 3.402665192725756
          - type: main_score
            value: 3.402665192725756
          - type: precision
            value: 3.303787557740127
          - type: recall
            value: 3.9525691699604746
        task:
          type: BitextMining
      - dataset:
          config: pol_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (pol_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.60474308300395
          - type: f1
            value: 99.4729907773386
          - type: main_score
            value: 99.4729907773386
          - type: precision
            value: 99.40711462450594
          - type: recall
            value: 99.60474308300395
        task:
          type: BitextMining
      - dataset:
          config: ssw_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (ssw_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 73.22134387351778
          - type: f1
            value: 70.43086049508975
          - type: main_score
            value: 70.43086049508975
          - type: precision
            value: 69.35312022355656
          - type: recall
            value: 73.22134387351778
        task:
          type: BitextMining
      - dataset:
          config: ukr_Cyrl-rus_Cyrl
          name: MTEB FloresBitextMining (ukr_Cyrl-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.90118577075098
          - type: f1
            value: 99.86824769433464
          - type: main_score
            value: 99.86824769433464
          - type: precision
            value: 99.85177865612648
          - type: recall
            value: 99.90118577075098
        task:
          type: BitextMining
      - dataset:
          config: afr_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (afr_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.2094861660079
          - type: f1
            value: 98.9459815546772
          - type: main_score
            value: 98.9459815546772
          - type: precision
            value: 98.81422924901186
          - type: recall
            value: 99.2094861660079
        task:
          type: BitextMining
      - dataset:
          config: bho_Deva-rus_Cyrl
          name: MTEB FloresBitextMining (bho_Deva-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 94.0711462450593
          - type: f1
            value: 93.12182382834557
          - type: main_score
            value: 93.12182382834557
          - type: precision
            value: 92.7523453232338
          - type: recall
            value: 94.0711462450593
        task:
          type: BitextMining
      - dataset:
          config: eus_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (eus_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 92.19367588932806
          - type: f1
            value: 91.23604975587072
          - type: main_score
            value: 91.23604975587072
          - type: precision
            value: 90.86697443588663
          - type: recall
            value: 92.19367588932806
        task:
          type: BitextMining
      - dataset:
          config: ibo_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (ibo_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 82.21343873517787
          - type: f1
            value: 80.17901604858126
          - type: main_score
            value: 80.17901604858126
          - type: precision
            value: 79.3792284780028
          - type: recall
            value: 82.21343873517787
        task:
          type: BitextMining
      - dataset:
          config: kmr_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (kmr_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 68.67588932806325
          - type: f1
            value: 66.72311714750278
          - type: main_score
            value: 66.72311714750278
          - type: precision
            value: 66.00178401554004
          - type: recall
            value: 68.67588932806325
        task:
          type: BitextMining
      - dataset:
          config: min_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (min_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 78.65612648221344
          - type: f1
            value: 76.26592719972166
          - type: main_score
            value: 76.26592719972166
          - type: precision
            value: 75.39980459997484
          - type: recall
            value: 78.65612648221344
        task:
          type: BitextMining
      - dataset:
          config: por_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (por_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 96.83794466403161
          - type: f1
            value: 95.9669678147939
          - type: main_score
            value: 95.9669678147939
          - type: precision
            value: 95.59453227931488
          - type: recall
            value: 96.83794466403161
        task:
          type: BitextMining
      - dataset:
          config: sun_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (sun_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 92.4901185770751
          - type: f1
            value: 91.66553983773662
          - type: main_score
            value: 91.66553983773662
          - type: precision
            value: 91.34530928009188
          - type: recall
            value: 92.4901185770751
        task:
          type: BitextMining
      - dataset:
          config: umb_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (umb_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 41.00790513833992
          - type: f1
            value: 38.21319326004483
          - type: main_score
            value: 38.21319326004483
          - type: precision
            value: 37.200655467675546
          - type: recall
            value: 41.00790513833992
        task:
          type: BitextMining
      - dataset:
          config: ajp_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (ajp_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.35573122529645
          - type: f1
            value: 93.97233201581028
          - type: main_score
            value: 93.97233201581028
          - type: precision
            value: 93.33333333333333
          - type: recall
            value: 95.35573122529645
        task:
          type: BitextMining
      - dataset:
          config: bjn_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (bjn_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 3.6561264822134385
          - type: f1
            value: 3.1071978056336484
          - type: main_score
            value: 3.1071978056336484
          - type: precision
            value: 3.0039741229718215
          - type: recall
            value: 3.6561264822134385
        task:
          type: BitextMining
      - dataset:
          config: ewe_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (ewe_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 62.845849802371546
          - type: f1
            value: 59.82201175670472
          - type: main_score
            value: 59.82201175670472
          - type: precision
            value: 58.72629236362003
          - type: recall
            value: 62.845849802371546
        task:
          type: BitextMining
      - dataset:
          config: ilo_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (ilo_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 83.10276679841897
          - type: f1
            value: 80.75065288987582
          - type: main_score
            value: 80.75065288987582
          - type: precision
            value: 79.80726451662179
          - type: recall
            value: 83.10276679841897
        task:
          type: BitextMining
      - dataset:
          config: knc_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (knc_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 10.079051383399209
          - type: f1
            value: 8.759282456080921
          - type: main_score
            value: 8.759282456080921
          - type: precision
            value: 8.474735138956142
          - type: recall
            value: 10.079051383399209
        task:
          type: BitextMining
      - dataset:
          config: mkd_Cyrl-rus_Cyrl
          name: MTEB FloresBitextMining (mkd_Cyrl-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.91304347826086
          - type: f1
            value: 98.55072463768116
          - type: main_score
            value: 98.55072463768116
          - type: precision
            value: 98.36956521739131
          - type: recall
            value: 98.91304347826086
        task:
          type: BitextMining
      - dataset:
          config: prs_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (prs_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.01185770750988
          - type: f1
            value: 98.68247694334651
          - type: main_score
            value: 98.68247694334651
          - type: precision
            value: 98.51778656126481
          - type: recall
            value: 99.01185770750988
        task:
          type: BitextMining
      - dataset:
          config: swe_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (swe_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.40711462450594
          - type: f1
            value: 99.22595520421606
          - type: main_score
            value: 99.22595520421606
          - type: precision
            value: 99.14361001317523
          - type: recall
            value: 99.40711462450594
        task:
          type: BitextMining
      - dataset:
          config: urd_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (urd_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.82608695652173
          - type: f1
            value: 97.25625823451911
          - type: main_score
            value: 97.25625823451911
          - type: precision
            value: 97.03063241106719
          - type: recall
            value: 97.82608695652173
        task:
          type: BitextMining
      - dataset:
          config: aka_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (aka_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 81.22529644268775
          - type: f1
            value: 77.94307687941227
          - type: main_score
            value: 77.94307687941227
          - type: precision
            value: 76.58782793293665
          - type: recall
            value: 81.22529644268775
        task:
          type: BitextMining
      - dataset:
          config: bjn_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (bjn_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 85.27667984189723
          - type: f1
            value: 83.6869192829922
          - type: main_score
            value: 83.6869192829922
          - type: precision
            value: 83.08670670691656
          - type: recall
            value: 85.27667984189723
        task:
          type: BitextMining
      - dataset:
          config: fao_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (fao_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 80.9288537549407
          - type: f1
            value: 79.29806087454745
          - type: main_score
            value: 79.29806087454745
          - type: precision
            value: 78.71445871526987
          - type: recall
            value: 80.9288537549407
        task:
          type: BitextMining
      - dataset:
          config: ind_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (ind_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.12252964426878
          - type: f1
            value: 97.5296442687747
          - type: main_score
            value: 97.5296442687747
          - type: precision
            value: 97.23320158102767
          - type: recall
            value: 98.12252964426878
        task:
          type: BitextMining
      - dataset:
          config: knc_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (knc_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 33.49802371541502
          - type: f1
            value: 32.02378215033989
          - type: main_score
            value: 32.02378215033989
          - type: precision
            value: 31.511356103747406
          - type: recall
            value: 33.49802371541502
        task:
          type: BitextMining
      - dataset:
          config: mlt_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (mlt_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 91.40316205533597
          - type: f1
            value: 90.35317684386006
          - type: main_score
            value: 90.35317684386006
          - type: precision
            value: 89.94845939633488
          - type: recall
            value: 91.40316205533597
        task:
          type: BitextMining
      - dataset:
          config: quy_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (quy_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 40.612648221343875
          - type: f1
            value: 38.74337544712602
          - type: main_score
            value: 38.74337544712602
          - type: precision
            value: 38.133716022178575
          - type: recall
            value: 40.612648221343875
        task:
          type: BitextMining
      - dataset:
          config: swh_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (swh_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.13438735177866
          - type: f1
            value: 96.47435897435898
          - type: main_score
            value: 96.47435897435898
          - type: precision
            value: 96.18741765480895
          - type: recall
            value: 97.13438735177866
        task:
          type: BitextMining
      - dataset:
          config: uzn_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (uzn_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 96.83794466403161
          - type: f1
            value: 96.26355528529442
          - type: main_score
            value: 96.26355528529442
          - type: precision
            value: 96.0501756697409
          - type: recall
            value: 96.83794466403161
        task:
          type: BitextMining
      - dataset:
          config: als_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (als_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.91304347826086
          - type: f1
            value: 98.6907114624506
          - type: main_score
            value: 98.6907114624506
          - type: precision
            value: 98.6142480707698
          - type: recall
            value: 98.91304347826086
        task:
          type: BitextMining
      - dataset:
          config: bod_Tibt-rus_Cyrl
          name: MTEB FloresBitextMining (bod_Tibt-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 1.0869565217391304
          - type: f1
            value: 0.9224649610442628
          - type: main_score
            value: 0.9224649610442628
          - type: precision
            value: 0.8894275740459898
          - type: recall
            value: 1.0869565217391304
        task:
          type: BitextMining
      - dataset:
          config: fij_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (fij_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 63.24110671936759
          - type: f1
            value: 60.373189068189525
          - type: main_score
            value: 60.373189068189525
          - type: precision
            value: 59.32326368115546
          - type: recall
            value: 63.24110671936759
        task:
          type: BitextMining
      - dataset:
          config: isl_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (isl_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 89.03162055335969
          - type: f1
            value: 87.3102634715907
          - type: main_score
            value: 87.3102634715907
          - type: precision
            value: 86.65991814698712
          - type: recall
            value: 89.03162055335969
        task:
          type: BitextMining
      - dataset:
          config: kon_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (kon_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 73.91304347826086
          - type: f1
            value: 71.518235523573
          - type: main_score
            value: 71.518235523573
          - type: precision
            value: 70.58714102449801
          - type: recall
            value: 73.91304347826086
        task:
          type: BitextMining
      - dataset:
          config: mni_Beng-rus_Cyrl
          name: MTEB FloresBitextMining (mni_Beng-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 29.545454545454547
          - type: f1
            value: 27.59513619889114
          - type: main_score
            value: 27.59513619889114
          - type: precision
            value: 26.983849851025344
          - type: recall
            value: 29.545454545454547
        task:
          type: BitextMining
      - dataset:
          config: ron_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (ron_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.40711462450594
          - type: f1
            value: 99.2094861660079
          - type: main_score
            value: 99.2094861660079
          - type: precision
            value: 99.1106719367589
          - type: recall
            value: 99.40711462450594
        task:
          type: BitextMining
      - dataset:
          config: szl_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (szl_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 86.26482213438736
          - type: f1
            value: 85.18912031587512
          - type: main_score
            value: 85.18912031587512
          - type: precision
            value: 84.77199409959775
          - type: recall
            value: 86.26482213438736
        task:
          type: BitextMining
      - dataset:
          config: vec_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (vec_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 85.67193675889328
          - type: f1
            value: 84.62529734716581
          - type: main_score
            value: 84.62529734716581
          - type: precision
            value: 84.2611422440705
          - type: recall
            value: 85.67193675889328
        task:
          type: BitextMining
      - dataset:
          config: amh_Ethi-rus_Cyrl
          name: MTEB FloresBitextMining (amh_Ethi-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 94.76284584980237
          - type: f1
            value: 93.91735076517685
          - type: main_score
            value: 93.91735076517685
          - type: precision
            value: 93.57553798858147
          - type: recall
            value: 94.76284584980237
        task:
          type: BitextMining
      - dataset:
          config: bos_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (bos_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.2094861660079
          - type: f1
            value: 99.05655938264634
          - type: main_score
            value: 99.05655938264634
          - type: precision
            value: 99.01185770750988
          - type: recall
            value: 99.2094861660079
        task:
          type: BitextMining
      - dataset:
          config: fin_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (fin_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.02371541501977
          - type: f1
            value: 97.43741765480895
          - type: main_score
            value: 97.43741765480895
          - type: precision
            value: 97.1590909090909
          - type: recall
            value: 98.02371541501977
        task:
          type: BitextMining
      - dataset:
          config: ita_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (ita_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.70355731225297
          - type: f1
            value: 99.60474308300395
          - type: main_score
            value: 99.60474308300395
          - type: precision
            value: 99.55533596837944
          - type: recall
            value: 99.70355731225297
        task:
          type: BitextMining
      - dataset:
          config: kor_Hang-rus_Cyrl
          name: MTEB FloresBitextMining (kor_Hang-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.33201581027669
          - type: f1
            value: 96.49868247694334
          - type: main_score
            value: 96.49868247694334
          - type: precision
            value: 96.10507246376811
          - type: recall
            value: 97.33201581027669
        task:
          type: BitextMining
      - dataset:
          config: mos_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (mos_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 34.683794466403164
          - type: f1
            value: 32.766819308009076
          - type: main_score
            value: 32.766819308009076
          - type: precision
            value: 32.1637493670237
          - type: recall
            value: 34.683794466403164
        task:
          type: BitextMining
      - dataset:
          config: run_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (run_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 83.399209486166
          - type: f1
            value: 81.10578750604326
          - type: main_score
            value: 81.10578750604326
          - type: precision
            value: 80.16763162673529
          - type: recall
            value: 83.399209486166
        task:
          type: BitextMining
      - dataset:
          config: tam_Taml-rus_Cyrl
          name: MTEB FloresBitextMining (tam_Taml-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.41897233201581
          - type: f1
            value: 98.01548089591567
          - type: main_score
            value: 98.01548089591567
          - type: precision
            value: 97.84020327498588
          - type: recall
            value: 98.41897233201581
        task:
          type: BitextMining
      - dataset:
          config: vie_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (vie_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.1106719367589
          - type: f1
            value: 98.81422924901186
          - type: main_score
            value: 98.81422924901186
          - type: precision
            value: 98.66600790513834
          - type: recall
            value: 99.1106719367589
        task:
          type: BitextMining
      - dataset:
          config: apc_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (apc_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 93.87351778656127
          - type: f1
            value: 92.10803689064558
          - type: main_score
            value: 92.10803689064558
          - type: precision
            value: 91.30434782608695
          - type: recall
            value: 93.87351778656127
        task:
          type: BitextMining
      - dataset:
          config: bug_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (bug_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 57.608695652173914
          - type: f1
            value: 54.95878654927162
          - type: main_score
            value: 54.95878654927162
          - type: precision
            value: 54.067987427805654
          - type: recall
            value: 57.608695652173914
        task:
          type: BitextMining
      - dataset:
          config: fon_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (fon_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 61.95652173913043
          - type: f1
            value: 58.06537275812945
          - type: main_score
            value: 58.06537275812945
          - type: precision
            value: 56.554057596959204
          - type: recall
            value: 61.95652173913043
        task:
          type: BitextMining
      - dataset:
          config: jav_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (jav_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 93.47826086956522
          - type: f1
            value: 92.4784405318002
          - type: main_score
            value: 92.4784405318002
          - type: precision
            value: 92.09168143201127
          - type: recall
            value: 93.47826086956522
        task:
          type: BitextMining
      - dataset:
          config: lao_Laoo-rus_Cyrl
          name: MTEB FloresBitextMining (lao_Laoo-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 91.10671936758892
          - type: f1
            value: 89.76104922745239
          - type: main_score
            value: 89.76104922745239
          - type: precision
            value: 89.24754593232855
          - type: recall
            value: 91.10671936758892
        task:
          type: BitextMining
      - dataset:
          config: mri_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (mri_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 71.14624505928853
          - type: f1
            value: 68.26947125119062
          - type: main_score
            value: 68.26947125119062
          - type: precision
            value: 67.15942311051006
          - type: recall
            value: 71.14624505928853
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ace_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-ace_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 19.565217391304348
          - type: f1
            value: 16.321465000323805
          - type: main_score
            value: 16.321465000323805
          - type: precision
            value: 15.478527409347508
          - type: recall
            value: 19.565217391304348
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-bam_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-bam_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 73.41897233201581
          - type: f1
            value: 68.77366228182746
          - type: main_score
            value: 68.77366228182746
          - type: precision
            value: 66.96012924273795
          - type: recall
            value: 73.41897233201581
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-dzo_Tibt
          name: MTEB FloresBitextMining (rus_Cyrl-dzo_Tibt)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 0.592885375494071
          - type: f1
            value: 0.02458062426370458
          - type: main_score
            value: 0.02458062426370458
          - type: precision
            value: 0.012824114724683876
          - type: recall
            value: 0.592885375494071
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-hin_Deva
          name: MTEB FloresBitextMining (rus_Cyrl-hin_Deva)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.90118577075098
          - type: f1
            value: 99.86824769433464
          - type: main_score
            value: 99.86824769433464
          - type: precision
            value: 99.85177865612648
          - type: recall
            value: 99.90118577075098
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-khm_Khmr
          name: MTEB FloresBitextMining (rus_Cyrl-khm_Khmr)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.13438735177866
          - type: f1
            value: 96.24505928853755
          - type: main_score
            value: 96.24505928853755
          - type: precision
            value: 95.81686429512516
          - type: recall
            value: 97.13438735177866
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-mag_Deva
          name: MTEB FloresBitextMining (rus_Cyrl-mag_Deva)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.50592885375494
          - type: f1
            value: 99.35770750988142
          - type: main_score
            value: 99.35770750988142
          - type: precision
            value: 99.29183135704875
          - type: recall
            value: 99.50592885375494
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-pap_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-pap_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 96.93675889328063
          - type: f1
            value: 96.05072463768116
          - type: main_score
            value: 96.05072463768116
          - type: precision
            value: 95.66040843214758
          - type: recall
            value: 96.93675889328063
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-sot_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-sot_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 93.67588932806325
          - type: f1
            value: 91.7786561264822
          - type: main_score
            value: 91.7786561264822
          - type: precision
            value: 90.91238471673255
          - type: recall
            value: 93.67588932806325
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-tur_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-tur_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.01185770750988
          - type: f1
            value: 98.68247694334651
          - type: main_score
            value: 98.68247694334651
          - type: precision
            value: 98.51778656126481
          - type: recall
            value: 99.01185770750988
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ace_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-ace_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 74.1106719367589
          - type: f1
            value: 70.21737923911836
          - type: main_score
            value: 70.21737923911836
          - type: precision
            value: 68.7068791410511
          - type: recall
            value: 74.1106719367589
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ban_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-ban_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 81.7193675889328
          - type: f1
            value: 78.76470334510617
          - type: main_score
            value: 78.76470334510617
          - type: precision
            value: 77.76208475761422
          - type: recall
            value: 81.7193675889328
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ell_Grek
          name: MTEB FloresBitextMining (rus_Cyrl-ell_Grek)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.3201581027668
          - type: f1
            value: 97.76021080368908
          - type: main_score
            value: 97.76021080368908
          - type: precision
            value: 97.48023715415019
          - type: recall
            value: 98.3201581027668
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-hne_Deva
          name: MTEB FloresBitextMining (rus_Cyrl-hne_Deva)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.51778656126481
          - type: f1
            value: 98.0566534914361
          - type: main_score
            value: 98.0566534914361
          - type: precision
            value: 97.82608695652173
          - type: recall
            value: 98.51778656126481
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kik_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-kik_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 80.73122529644269
          - type: f1
            value: 76.42689244220864
          - type: main_score
            value: 76.42689244220864
          - type: precision
            value: 74.63877909530083
          - type: recall
            value: 80.73122529644269
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-mai_Deva
          name: MTEB FloresBitextMining (rus_Cyrl-mai_Deva)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.91304347826086
          - type: f1
            value: 98.56719367588933
          - type: main_score
            value: 98.56719367588933
          - type: precision
            value: 98.40250329380763
          - type: recall
            value: 98.91304347826086
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-pbt_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-pbt_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.5296442687747
          - type: f1
            value: 96.73913043478261
          - type: main_score
            value: 96.73913043478261
          - type: precision
            value: 96.36034255599473
          - type: recall
            value: 97.5296442687747
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-spa_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-spa_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.40711462450594
          - type: f1
            value: 99.20948616600789
          - type: main_score
            value: 99.20948616600789
          - type: precision
            value: 99.1106719367589
          - type: recall
            value: 99.40711462450594
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-twi_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-twi_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 82.01581027667984
          - type: f1
            value: 78.064787822953
          - type: main_score
            value: 78.064787822953
          - type: precision
            value: 76.43272186750448
          - type: recall
            value: 82.01581027667984
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-acm_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-acm_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.3201581027668
          - type: f1
            value: 97.76021080368908
          - type: main_score
            value: 97.76021080368908
          - type: precision
            value: 97.48023715415019
          - type: recall
            value: 98.3201581027668
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-bel_Cyrl
          name: MTEB FloresBitextMining (rus_Cyrl-bel_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.22134387351778
          - type: f1
            value: 97.67786561264822
          - type: main_score
            value: 97.67786561264822
          - type: precision
            value: 97.4308300395257
          - type: recall
            value: 98.22134387351778
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-eng_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-eng_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.70355731225297
          - type: f1
            value: 99.60474308300395
          - type: main_score
            value: 99.60474308300395
          - type: precision
            value: 99.55533596837944
          - type: recall
            value: 99.70355731225297
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-hrv_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-hrv_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.1106719367589
          - type: f1
            value: 98.83069828722002
          - type: main_score
            value: 98.83069828722002
          - type: precision
            value: 98.69894598155466
          - type: recall
            value: 99.1106719367589
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kin_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-kin_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 93.37944664031622
          - type: f1
            value: 91.53162055335969
          - type: main_score
            value: 91.53162055335969
          - type: precision
            value: 90.71475625823452
          - type: recall
            value: 93.37944664031622
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-mal_Mlym
          name: MTEB FloresBitextMining (rus_Cyrl-mal_Mlym)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.30830039525692
          - type: f1
            value: 99.07773386034255
          - type: main_score
            value: 99.07773386034255
          - type: precision
            value: 98.96245059288538
          - type: recall
            value: 99.30830039525692
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-pes_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-pes_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.71541501976284
          - type: f1
            value: 98.30368906455863
          - type: main_score
            value: 98.30368906455863
          - type: precision
            value: 98.10606060606061
          - type: recall
            value: 98.71541501976284
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-srd_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-srd_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 89.03162055335969
          - type: f1
            value: 86.11048371917937
          - type: main_score
            value: 86.11048371917937
          - type: precision
            value: 84.86001317523056
          - type: recall
            value: 89.03162055335969
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-tzm_Tfng
          name: MTEB FloresBitextMining (rus_Cyrl-tzm_Tfng)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 12.351778656126482
          - type: f1
            value: 10.112177999067715
          - type: main_score
            value: 10.112177999067715
          - type: precision
            value: 9.53495885438645
          - type: recall
            value: 12.351778656126482
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-acq_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-acq_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.91304347826086
          - type: f1
            value: 98.55072463768116
          - type: main_score
            value: 98.55072463768116
          - type: precision
            value: 98.36956521739131
          - type: recall
            value: 98.91304347826086
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-bem_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-bem_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 73.22134387351778
          - type: f1
            value: 68.30479412989295
          - type: main_score
            value: 68.30479412989295
          - type: precision
            value: 66.40073447632736
          - type: recall
            value: 73.22134387351778
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-epo_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-epo_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.1106719367589
          - type: f1
            value: 98.81422924901186
          - type: main_score
            value: 98.81422924901186
          - type: precision
            value: 98.66600790513834
          - type: recall
            value: 99.1106719367589
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-hun_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-hun_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 96.83794466403161
          - type: f1
            value: 95.88274044795784
          - type: main_score
            value: 95.88274044795784
          - type: precision
            value: 95.45454545454545
          - type: recall
            value: 96.83794466403161
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kir_Cyrl
          name: MTEB FloresBitextMining (rus_Cyrl-kir_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 96.34387351778656
          - type: f1
            value: 95.49280429715212
          - type: main_score
            value: 95.49280429715212
          - type: precision
            value: 95.14163372859026
          - type: recall
            value: 96.34387351778656
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-mar_Deva
          name: MTEB FloresBitextMining (rus_Cyrl-mar_Deva)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.71541501976284
          - type: f1
            value: 98.28722002635047
          - type: main_score
            value: 98.28722002635047
          - type: precision
            value: 98.07312252964427
          - type: recall
            value: 98.71541501976284
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-plt_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-plt_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 88.04347826086956
          - type: f1
            value: 85.14328063241106
          - type: main_score
            value: 85.14328063241106
          - type: precision
            value: 83.96339168078298
          - type: recall
            value: 88.04347826086956
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-srp_Cyrl
          name: MTEB FloresBitextMining (rus_Cyrl-srp_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.40711462450594
          - type: f1
            value: 99.2094861660079
          - type: main_score
            value: 99.2094861660079
          - type: precision
            value: 99.1106719367589
          - type: recall
            value: 99.40711462450594
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-uig_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-uig_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 92.19367588932806
          - type: f1
            value: 89.98541313758706
          - type: main_score
            value: 89.98541313758706
          - type: precision
            value: 89.01021080368906
          - type: recall
            value: 92.19367588932806
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-aeb_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-aeb_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.8498023715415
          - type: f1
            value: 94.63109354413703
          - type: main_score
            value: 94.63109354413703
          - type: precision
            value: 94.05467720685111
          - type: recall
            value: 95.8498023715415
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ben_Beng
          name: MTEB FloresBitextMining (rus_Cyrl-ben_Beng)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.40711462450594
          - type: f1
            value: 99.2094861660079
          - type: main_score
            value: 99.2094861660079
          - type: precision
            value: 99.1106719367589
          - type: recall
            value: 99.40711462450594
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-est_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-est_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.55335968379447
          - type: f1
            value: 94.2588932806324
          - type: main_score
            value: 94.2588932806324
          - type: precision
            value: 93.65118577075098
          - type: recall
            value: 95.55335968379447
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-hye_Armn
          name: MTEB FloresBitextMining (rus_Cyrl-hye_Armn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.71541501976284
          - type: f1
            value: 98.28722002635045
          - type: main_score
            value: 98.28722002635045
          - type: precision
            value: 98.07312252964427
          - type: recall
            value: 98.71541501976284
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kmb_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-kmb_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 54.24901185770751
          - type: f1
            value: 49.46146674116913
          - type: main_score
            value: 49.46146674116913
          - type: precision
            value: 47.81033799314432
          - type: recall
            value: 54.24901185770751
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-min_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-min_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 15.810276679841898
          - type: f1
            value: 13.271207641419332
          - type: main_score
            value: 13.271207641419332
          - type: precision
            value: 12.510673148766033
          - type: recall
            value: 15.810276679841898
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-pol_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-pol_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.71541501976284
          - type: f1
            value: 98.32674571805006
          - type: main_score
            value: 98.32674571805006
          - type: precision
            value: 98.14723320158103
          - type: recall
            value: 98.71541501976284
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ssw_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-ssw_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 80.8300395256917
          - type: f1
            value: 76.51717847370023
          - type: main_score
            value: 76.51717847370023
          - type: precision
            value: 74.74143610013175
          - type: recall
            value: 80.8300395256917
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ukr_Cyrl
          name: MTEB FloresBitextMining (rus_Cyrl-ukr_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.60474308300395
          - type: f1
            value: 99.4729907773386
          - type: main_score
            value: 99.4729907773386
          - type: precision
            value: 99.40711462450594
          - type: recall
            value: 99.60474308300395
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-afr_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-afr_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.1106719367589
          - type: f1
            value: 98.81422924901186
          - type: main_score
            value: 98.81422924901186
          - type: precision
            value: 98.66600790513834
          - type: recall
            value: 99.1106719367589
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-bho_Deva
          name: MTEB FloresBitextMining (rus_Cyrl-bho_Deva)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 96.6403162055336
          - type: f1
            value: 95.56982872200265
          - type: main_score
            value: 95.56982872200265
          - type: precision
            value: 95.0592885375494
          - type: recall
            value: 96.6403162055336
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-eus_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-eus_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.62845849802372
          - type: f1
            value: 96.9038208168643
          - type: main_score
            value: 96.9038208168643
          - type: precision
            value: 96.55797101449275
          - type: recall
            value: 97.62845849802372
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ibo_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-ibo_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 89.2292490118577
          - type: f1
            value: 86.35234330886506
          - type: main_score
            value: 86.35234330886506
          - type: precision
            value: 85.09881422924902
          - type: recall
            value: 89.2292490118577
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kmr_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-kmr_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 83.49802371541502
          - type: f1
            value: 79.23630717108978
          - type: main_score
            value: 79.23630717108978
          - type: precision
            value: 77.48188405797102
          - type: recall
            value: 83.49802371541502
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-min_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-min_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 79.34782608695652
          - type: f1
            value: 75.31689928429059
          - type: main_score
            value: 75.31689928429059
          - type: precision
            value: 73.91519410541149
          - type: recall
            value: 79.34782608695652
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-por_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-por_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 96.54150197628458
          - type: f1
            value: 95.53218520609825
          - type: main_score
            value: 95.53218520609825
          - type: precision
            value: 95.07575757575756
          - type: recall
            value: 96.54150197628458
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-sun_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-sun_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 93.2806324110672
          - type: f1
            value: 91.56973461321287
          - type: main_score
            value: 91.56973461321287
          - type: precision
            value: 90.84396334890405
          - type: recall
            value: 93.2806324110672
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-umb_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-umb_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 51.87747035573123
          - type: f1
            value: 46.36591778884269
          - type: main_score
            value: 46.36591778884269
          - type: precision
            value: 44.57730391234227
          - type: recall
            value: 51.87747035573123
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ajp_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-ajp_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.71541501976284
          - type: f1
            value: 98.30368906455863
          - type: main_score
            value: 98.30368906455863
          - type: precision
            value: 98.10606060606061
          - type: recall
            value: 98.71541501976284
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-bjn_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-bjn_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 14.82213438735178
          - type: f1
            value: 12.365434276616856
          - type: main_score
            value: 12.365434276616856
          - type: precision
            value: 11.802079517180589
          - type: recall
            value: 14.82213438735178
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ewe_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-ewe_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 71.44268774703558
          - type: f1
            value: 66.74603174603175
          - type: main_score
            value: 66.74603174603175
          - type: precision
            value: 64.99933339607253
          - type: recall
            value: 71.44268774703558
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ilo_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-ilo_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 85.86956521739131
          - type: f1
            value: 83.00139015960917
          - type: main_score
            value: 83.00139015960917
          - type: precision
            value: 81.91411396574439
          - type: recall
            value: 85.86956521739131
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-knc_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-knc_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 14.525691699604742
          - type: f1
            value: 12.618283715726806
          - type: main_score
            value: 12.618283715726806
          - type: precision
            value: 12.048458493742352
          - type: recall
            value: 14.525691699604742
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-mkd_Cyrl
          name: MTEB FloresBitextMining (rus_Cyrl-mkd_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.40711462450594
          - type: f1
            value: 99.22595520421606
          - type: main_score
            value: 99.22595520421606
          - type: precision
            value: 99.14361001317523
          - type: recall
            value: 99.40711462450594
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-prs_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-prs_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.30830039525692
          - type: f1
            value: 99.07773386034255
          - type: main_score
            value: 99.07773386034255
          - type: precision
            value: 98.96245059288538
          - type: recall
            value: 99.30830039525692
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-swe_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-swe_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.30830039525692
          - type: f1
            value: 99.07773386034256
          - type: main_score
            value: 99.07773386034256
          - type: precision
            value: 98.96245059288538
          - type: recall
            value: 99.30830039525692
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-urd_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-urd_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.61660079051383
          - type: f1
            value: 98.15546772068511
          - type: main_score
            value: 98.15546772068511
          - type: precision
            value: 97.92490118577075
          - type: recall
            value: 98.61660079051383
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-aka_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-aka_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 81.02766798418972
          - type: f1
            value: 76.73277809147375
          - type: main_score
            value: 76.73277809147375
          - type: precision
            value: 74.97404165882426
          - type: recall
            value: 81.02766798418972
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-bjn_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-bjn_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 86.7588932806324
          - type: f1
            value: 83.92064566965753
          - type: main_score
            value: 83.92064566965753
          - type: precision
            value: 82.83734079929732
          - type: recall
            value: 86.7588932806324
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-fao_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-fao_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 88.43873517786561
          - type: f1
            value: 85.48136645962732
          - type: main_score
            value: 85.48136645962732
          - type: precision
            value: 84.23418972332016
          - type: recall
            value: 88.43873517786561
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ind_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-ind_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.01185770750988
          - type: f1
            value: 98.68247694334651
          - type: main_score
            value: 98.68247694334651
          - type: precision
            value: 98.51778656126481
          - type: recall
            value: 99.01185770750988
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-knc_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-knc_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 45.8498023715415
          - type: f1
            value: 40.112030865489366
          - type: main_score
            value: 40.112030865489366
          - type: precision
            value: 38.28262440050776
          - type: recall
            value: 45.8498023715415
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-mlt_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-mlt_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 93.18181818181817
          - type: f1
            value: 91.30787690570298
          - type: main_score
            value: 91.30787690570298
          - type: precision
            value: 90.4983060417843
          - type: recall
            value: 93.18181818181817
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-quy_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-quy_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 62.450592885375485
          - type: f1
            value: 57.28742975628178
          - type: main_score
            value: 57.28742975628178
          - type: precision
            value: 55.56854987623269
          - type: recall
            value: 62.450592885375485
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-swh_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-swh_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.3201581027668
          - type: f1
            value: 97.77667984189723
          - type: main_score
            value: 97.77667984189723
          - type: precision
            value: 97.51317523056655
          - type: recall
            value: 98.3201581027668
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-uzn_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-uzn_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.12252964426878
          - type: f1
            value: 97.59081498211933
          - type: main_score
            value: 97.59081498211933
          - type: precision
            value: 97.34848484848484
          - type: recall
            value: 98.12252964426878
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-als_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-als_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.30830039525692
          - type: f1
            value: 99.09420289855073
          - type: main_score
            value: 99.09420289855073
          - type: precision
            value: 98.99538866930172
          - type: recall
            value: 99.30830039525692
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-bod_Tibt
          name: MTEB FloresBitextMining (rus_Cyrl-bod_Tibt)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 11.561264822134387
          - type: f1
            value: 8.121312045385636
          - type: main_score
            value: 8.121312045385636
          - type: precision
            value: 7.350577020893972
          - type: recall
            value: 11.561264822134387
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-fij_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-fij_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 72.23320158102767
          - type: f1
            value: 67.21000233846082
          - type: main_score
            value: 67.21000233846082
          - type: precision
            value: 65.3869439739005
          - type: recall
            value: 72.23320158102767
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-isl_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-isl_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 91.99604743083005
          - type: f1
            value: 89.75955204216073
          - type: main_score
            value: 89.75955204216073
          - type: precision
            value: 88.7598814229249
          - type: recall
            value: 91.99604743083005
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kon_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-kon_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 81.81818181818183
          - type: f1
            value: 77.77800098452272
          - type: main_score
            value: 77.77800098452272
          - type: precision
            value: 76.1521268586486
          - type: recall
            value: 81.81818181818183
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-mni_Beng
          name: MTEB FloresBitextMining (rus_Cyrl-mni_Beng)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 54.74308300395256
          - type: f1
            value: 48.97285299254615
          - type: main_score
            value: 48.97285299254615
          - type: precision
            value: 46.95125742968299
          - type: recall
            value: 54.74308300395256
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ron_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-ron_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.22134387351778
          - type: f1
            value: 97.64492753623189
          - type: main_score
            value: 97.64492753623189
          - type: precision
            value: 97.36495388669302
          - type: recall
            value: 98.22134387351778
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-szl_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-szl_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 92.09486166007905
          - type: f1
            value: 90.10375494071147
          - type: main_score
            value: 90.10375494071147
          - type: precision
            value: 89.29606625258798
          - type: recall
            value: 92.09486166007905
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-vec_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-vec_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 92.4901185770751
          - type: f1
            value: 90.51430453604365
          - type: main_score
            value: 90.51430453604365
          - type: precision
            value: 89.69367588932808
          - type: recall
            value: 92.4901185770751
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-amh_Ethi
          name: MTEB FloresBitextMining (rus_Cyrl-amh_Ethi)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.82608695652173
          - type: f1
            value: 97.11791831357048
          - type: main_score
            value: 97.11791831357048
          - type: precision
            value: 96.77206851119894
          - type: recall
            value: 97.82608695652173
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-bos_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-bos_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.91304347826086
          - type: f1
            value: 98.55072463768116
          - type: main_score
            value: 98.55072463768116
          - type: precision
            value: 98.36956521739131
          - type: recall
            value: 98.91304347826086
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-fin_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-fin_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.65217391304348
          - type: f1
            value: 94.4235836627141
          - type: main_score
            value: 94.4235836627141
          - type: precision
            value: 93.84881422924902
          - type: recall
            value: 95.65217391304348
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ita_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-ita_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.91304347826086
          - type: f1
            value: 98.55072463768117
          - type: main_score
            value: 98.55072463768117
          - type: precision
            value: 98.36956521739131
          - type: recall
            value: 98.91304347826086
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kor_Hang
          name: MTEB FloresBitextMining (rus_Cyrl-kor_Hang)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.55335968379447
          - type: f1
            value: 94.15349143610013
          - type: main_score
            value: 94.15349143610013
          - type: precision
            value: 93.49472990777339
          - type: recall
            value: 95.55335968379447
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-mos_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-mos_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 43.67588932806324
          - type: f1
            value: 38.84849721190082
          - type: main_score
            value: 38.84849721190082
          - type: precision
            value: 37.43294462099682
          - type: recall
            value: 43.67588932806324
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-run_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-run_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 90.21739130434783
          - type: f1
            value: 87.37483530961792
          - type: main_score
            value: 87.37483530961792
          - type: precision
            value: 86.07872200263506
          - type: recall
            value: 90.21739130434783
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-tam_Taml
          name: MTEB FloresBitextMining (rus_Cyrl-tam_Taml)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.40711462450594
          - type: f1
            value: 99.2094861660079
          - type: main_score
            value: 99.2094861660079
          - type: precision
            value: 99.1106719367589
          - type: recall
            value: 99.40711462450594
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-vie_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-vie_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.03557312252964
          - type: f1
            value: 96.13636363636364
          - type: main_score
            value: 96.13636363636364
          - type: precision
            value: 95.70981554677206
          - type: recall
            value: 97.03557312252964
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-apc_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-apc_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.12252964426878
          - type: f1
            value: 97.49670619235836
          - type: main_score
            value: 97.49670619235836
          - type: precision
            value: 97.18379446640316
          - type: recall
            value: 98.12252964426878
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-bug_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-bug_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 67.29249011857708
          - type: f1
            value: 62.09268717667927
          - type: main_score
            value: 62.09268717667927
          - type: precision
            value: 60.28554009748714
          - type: recall
            value: 67.29249011857708
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-fon_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-fon_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 63.43873517786561
          - type: f1
            value: 57.66660107569199
          - type: main_score
            value: 57.66660107569199
          - type: precision
            value: 55.66676396919363
          - type: recall
            value: 63.43873517786561
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-jav_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-jav_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 94.46640316205533
          - type: f1
            value: 92.89384528514964
          - type: main_score
            value: 92.89384528514964
          - type: precision
            value: 92.19367588932806
          - type: recall
            value: 94.46640316205533
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-lao_Laoo
          name: MTEB FloresBitextMining (rus_Cyrl-lao_Laoo)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.23320158102767
          - type: f1
            value: 96.40974967061922
          - type: main_score
            value: 96.40974967061922
          - type: precision
            value: 96.034255599473
          - type: recall
            value: 97.23320158102767
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-mri_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-mri_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 76.77865612648222
          - type: f1
            value: 73.11286539547409
          - type: main_score
            value: 73.11286539547409
          - type: precision
            value: 71.78177214337046
          - type: recall
            value: 76.77865612648222
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-taq_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-taq_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 41.99604743083004
          - type: f1
            value: 37.25127063318763
          - type: main_score
            value: 37.25127063318763
          - type: precision
            value: 35.718929186985726
          - type: recall
            value: 41.99604743083004
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-war_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-war_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.55335968379447
          - type: f1
            value: 94.1699604743083
          - type: main_score
            value: 94.1699604743083
          - type: precision
            value: 93.52766798418972
          - type: recall
            value: 95.55335968379447
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-arb_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-arb_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.60474308300395
          - type: f1
            value: 99.4729907773386
          - type: main_score
            value: 99.4729907773386
          - type: precision
            value: 99.40711462450594
          - type: recall
            value: 99.60474308300395
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-bul_Cyrl
          name: MTEB FloresBitextMining (rus_Cyrl-bul_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.70355731225297
          - type: f1
            value: 99.60474308300395
          - type: main_score
            value: 99.60474308300395
          - type: precision
            value: 99.55533596837944
          - type: recall
            value: 99.70355731225297
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-fra_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-fra_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.60474308300395
          - type: f1
            value: 99.47299077733861
          - type: main_score
            value: 99.47299077733861
          - type: precision
            value: 99.40711462450594
          - type: recall
            value: 99.60474308300395
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-jpn_Jpan
          name: MTEB FloresBitextMining (rus_Cyrl-jpn_Jpan)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 96.44268774703558
          - type: f1
            value: 95.30632411067194
          - type: main_score
            value: 95.30632411067194
          - type: precision
            value: 94.76284584980237
          - type: recall
            value: 96.44268774703558
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-lij_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-lij_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 90.21739130434783
          - type: f1
            value: 87.4703557312253
          - type: main_score
            value: 87.4703557312253
          - type: precision
            value: 86.29611330698287
          - type: recall
            value: 90.21739130434783
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-mya_Mymr
          name: MTEB FloresBitextMining (rus_Cyrl-mya_Mymr)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.02371541501977
          - type: f1
            value: 97.364953886693
          - type: main_score
            value: 97.364953886693
          - type: precision
            value: 97.03557312252964
          - type: recall
            value: 98.02371541501977
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-sag_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-sag_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 54.841897233201585
          - type: f1
            value: 49.61882037503349
          - type: main_score
            value: 49.61882037503349
          - type: precision
            value: 47.831968755881796
          - type: recall
            value: 54.841897233201585
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-taq_Tfng
          name: MTEB FloresBitextMining (rus_Cyrl-taq_Tfng)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 15.316205533596838
          - type: f1
            value: 11.614836360389717
          - type: main_score
            value: 11.614836360389717
          - type: precision
            value: 10.741446193235223
          - type: recall
            value: 15.316205533596838
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-wol_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-wol_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 67.88537549407114
          - type: f1
            value: 62.2536417249856
          - type: main_score
            value: 62.2536417249856
          - type: precision
            value: 60.27629128666678
          - type: recall
            value: 67.88537549407114
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-arb_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-arb_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 27.766798418972332
          - type: f1
            value: 23.39674889624077
          - type: main_score
            value: 23.39674889624077
          - type: precision
            value: 22.28521155585345
          - type: recall
            value: 27.766798418972332
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-cat_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-cat_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.23320158102767
          - type: f1
            value: 96.42151326933936
          - type: main_score
            value: 96.42151326933936
          - type: precision
            value: 96.04743083003953
          - type: recall
            value: 97.23320158102767
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-fur_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-fur_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 88.63636363636364
          - type: f1
            value: 85.80792396009788
          - type: main_score
            value: 85.80792396009788
          - type: precision
            value: 84.61508901726293
          - type: recall
            value: 88.63636363636364
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kab_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-kab_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 48.12252964426877
          - type: f1
            value: 43.05387582971066
          - type: main_score
            value: 43.05387582971066
          - type: precision
            value: 41.44165117538212
          - type: recall
            value: 48.12252964426877
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-lim_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-lim_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 81.81818181818183
          - type: f1
            value: 77.81676163099087
          - type: main_score
            value: 77.81676163099087
          - type: precision
            value: 76.19565217391305
          - type: recall
            value: 81.81818181818183
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-nld_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-nld_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.33201581027669
          - type: f1
            value: 96.4756258234519
          - type: main_score
            value: 96.4756258234519
          - type: precision
            value: 96.06389986824769
          - type: recall
            value: 97.33201581027669
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-san_Deva
          name: MTEB FloresBitextMining (rus_Cyrl-san_Deva)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 93.47826086956522
          - type: f1
            value: 91.70289855072463
          - type: main_score
            value: 91.70289855072463
          - type: precision
            value: 90.9370882740448
          - type: recall
            value: 93.47826086956522
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-tat_Cyrl
          name: MTEB FloresBitextMining (rus_Cyrl-tat_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.72727272727273
          - type: f1
            value: 97.00263504611331
          - type: main_score
            value: 97.00263504611331
          - type: precision
            value: 96.65678524374177
          - type: recall
            value: 97.72727272727273
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-xho_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-xho_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 93.08300395256917
          - type: f1
            value: 91.12977602108036
          - type: main_score
            value: 91.12977602108036
          - type: precision
            value: 90.22562582345192
          - type: recall
            value: 93.08300395256917
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ars_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-ars_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.40711462450594
          - type: f1
            value: 99.2094861660079
          - type: main_score
            value: 99.2094861660079
          - type: precision
            value: 99.1106719367589
          - type: recall
            value: 99.40711462450594
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ceb_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-ceb_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.65217391304348
          - type: f1
            value: 94.3544137022398
          - type: main_score
            value: 94.3544137022398
          - type: precision
            value: 93.76646903820817
          - type: recall
            value: 95.65217391304348
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-fuv_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-fuv_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 51.18577075098815
          - type: f1
            value: 44.5990252610806
          - type: main_score
            value: 44.5990252610806
          - type: precision
            value: 42.34331599450177
          - type: recall
            value: 51.18577075098815
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kac_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-kac_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 46.93675889328063
          - type: f1
            value: 41.79004018701787
          - type: main_score
            value: 41.79004018701787
          - type: precision
            value: 40.243355662392624
          - type: recall
            value: 46.93675889328063
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-lin_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-lin_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 91.50197628458498
          - type: f1
            value: 89.1205533596838
          - type: main_score
            value: 89.1205533596838
          - type: precision
            value: 88.07147562582345
          - type: recall
            value: 91.50197628458498
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-nno_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-nno_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.81422924901186
          - type: f1
            value: 98.41897233201581
          - type: main_score
            value: 98.41897233201581
          - type: precision
            value: 98.22134387351778
          - type: recall
            value: 98.81422924901186
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-sat_Olck
          name: MTEB FloresBitextMining (rus_Cyrl-sat_Olck)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 2.371541501976284
          - type: f1
            value: 1.0726274943087382
          - type: main_score
            value: 1.0726274943087382
          - type: precision
            value: 0.875279634748803
          - type: recall
            value: 2.371541501976284
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-tel_Telu
          name: MTEB FloresBitextMining (rus_Cyrl-tel_Telu)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.01185770750988
          - type: f1
            value: 98.68247694334651
          - type: main_score
            value: 98.68247694334651
          - type: precision
            value: 98.51778656126481
          - type: recall
            value: 99.01185770750988
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ydd_Hebr
          name: MTEB FloresBitextMining (rus_Cyrl-ydd_Hebr)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 89.42687747035573
          - type: f1
            value: 86.47609636740073
          - type: main_score
            value: 86.47609636740073
          - type: precision
            value: 85.13669301712781
          - type: recall
            value: 89.42687747035573
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ary_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-ary_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 89.82213438735178
          - type: f1
            value: 87.04545454545456
          - type: main_score
            value: 87.04545454545456
          - type: precision
            value: 85.76910408432148
          - type: recall
            value: 89.82213438735178
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ces_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-ces_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.2094861660079
          - type: f1
            value: 98.9459815546772
          - type: main_score
            value: 98.9459815546772
          - type: precision
            value: 98.81422924901186
          - type: recall
            value: 99.2094861660079
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-gaz_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-gaz_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 64.9209486166008
          - type: f1
            value: 58.697458119394874
          - type: main_score
            value: 58.697458119394874
          - type: precision
            value: 56.43402189597842
          - type: recall
            value: 64.9209486166008
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kam_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-kam_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 59.18972332015811
          - type: f1
            value: 53.19031511966295
          - type: main_score
            value: 53.19031511966295
          - type: precision
            value: 51.08128357343655
          - type: recall
            value: 59.18972332015811
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-lit_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-lit_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 96.54150197628458
          - type: f1
            value: 95.5368906455863
          - type: main_score
            value: 95.5368906455863
          - type: precision
            value: 95.0592885375494
          - type: recall
            value: 96.54150197628458
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-nob_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-nob_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.12252964426878
          - type: f1
            value: 97.51317523056655
          - type: main_score
            value: 97.51317523056655
          - type: precision
            value: 97.2167325428195
          - type: recall
            value: 98.12252964426878
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-scn_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-scn_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 84.0909090909091
          - type: f1
            value: 80.37000439174352
          - type: main_score
            value: 80.37000439174352
          - type: precision
            value: 78.83994628559846
          - type: recall
            value: 84.0909090909091
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-tgk_Cyrl
          name: MTEB FloresBitextMining (rus_Cyrl-tgk_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 92.68774703557312
          - type: f1
            value: 90.86344814605684
          - type: main_score
            value: 90.86344814605684
          - type: precision
            value: 90.12516469038208
          - type: recall
            value: 92.68774703557312
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-yor_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-yor_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 72.13438735177866
          - type: f1
            value: 66.78759646150951
          - type: main_score
            value: 66.78759646150951
          - type: precision
            value: 64.85080192096002
          - type: recall
            value: 72.13438735177866
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-arz_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-arz_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.02371541501977
          - type: f1
            value: 97.364953886693
          - type: main_score
            value: 97.364953886693
          - type: precision
            value: 97.03557312252964
          - type: recall
            value: 98.02371541501977
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-cjk_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-cjk_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 51.976284584980235
          - type: f1
            value: 46.468762353149714
          - type: main_score
            value: 46.468762353149714
          - type: precision
            value: 44.64073366247278
          - type: recall
            value: 51.976284584980235
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-gla_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-gla_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 79.74308300395256
          - type: f1
            value: 75.55611165294958
          - type: main_score
            value: 75.55611165294958
          - type: precision
            value: 73.95033408620365
          - type: recall
            value: 79.74308300395256
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kan_Knda
          name: MTEB FloresBitextMining (rus_Cyrl-kan_Knda)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.2094861660079
          - type: f1
            value: 98.96245059288538
          - type: main_score
            value: 98.96245059288538
          - type: precision
            value: 98.84716732542819
          - type: recall
            value: 99.2094861660079
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-lmo_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-lmo_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 82.41106719367589
          - type: f1
            value: 78.56413514022209
          - type: main_score
            value: 78.56413514022209
          - type: precision
            value: 77.15313068573938
          - type: recall
            value: 82.41106719367589
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-npi_Deva
          name: MTEB FloresBitextMining (rus_Cyrl-npi_Deva)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.71541501976284
          - type: f1
            value: 98.3201581027668
          - type: main_score
            value: 98.3201581027668
          - type: precision
            value: 98.12252964426878
          - type: recall
            value: 98.71541501976284
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-shn_Mymr
          name: MTEB FloresBitextMining (rus_Cyrl-shn_Mymr)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 57.11462450592886
          - type: f1
            value: 51.51361369197337
          - type: main_score
            value: 51.51361369197337
          - type: precision
            value: 49.71860043649573
          - type: recall
            value: 57.11462450592886
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-tgl_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-tgl_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.82608695652173
          - type: f1
            value: 97.18379446640316
          - type: main_score
            value: 97.18379446640316
          - type: precision
            value: 96.88735177865613
          - type: recall
            value: 97.82608695652173
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-yue_Hant
          name: MTEB FloresBitextMining (rus_Cyrl-yue_Hant)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.30830039525692
          - type: f1
            value: 99.09420289855072
          - type: main_score
            value: 99.09420289855072
          - type: precision
            value: 98.9953886693017
          - type: recall
            value: 99.30830039525692
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-asm_Beng
          name: MTEB FloresBitextMining (rus_Cyrl-asm_Beng)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.55335968379447
          - type: f1
            value: 94.16007905138339
          - type: main_score
            value: 94.16007905138339
          - type: precision
            value: 93.50296442687747
          - type: recall
            value: 95.55335968379447
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ckb_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-ckb_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 92.88537549407114
          - type: f1
            value: 90.76745718050066
          - type: main_score
            value: 90.76745718050066
          - type: precision
            value: 89.80072463768116
          - type: recall
            value: 92.88537549407114
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-gle_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-gle_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 91.699604743083
          - type: f1
            value: 89.40899680030115
          - type: main_score
            value: 89.40899680030115
          - type: precision
            value: 88.40085638998683
          - type: recall
            value: 91.699604743083
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kas_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-kas_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 88.3399209486166
          - type: f1
            value: 85.14351590438548
          - type: main_score
            value: 85.14351590438548
          - type: precision
            value: 83.72364953886692
          - type: recall
            value: 88.3399209486166
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ltg_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-ltg_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 83.399209486166
          - type: f1
            value: 79.88408934061107
          - type: main_score
            value: 79.88408934061107
          - type: precision
            value: 78.53794509179885
          - type: recall
            value: 83.399209486166
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-nso_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-nso_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 91.20553359683794
          - type: f1
            value: 88.95406635525212
          - type: main_score
            value: 88.95406635525212
          - type: precision
            value: 88.01548089591567
          - type: recall
            value: 91.20553359683794
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-sin_Sinh
          name: MTEB FloresBitextMining (rus_Cyrl-sin_Sinh)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.91304347826086
          - type: f1
            value: 98.56719367588933
          - type: main_score
            value: 98.56719367588933
          - type: precision
            value: 98.40250329380763
          - type: recall
            value: 98.91304347826086
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-tha_Thai
          name: MTEB FloresBitextMining (rus_Cyrl-tha_Thai)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.94861660079052
          - type: f1
            value: 94.66403162055336
          - type: main_score
            value: 94.66403162055336
          - type: precision
            value: 94.03820816864295
          - type: recall
            value: 95.94861660079052
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-zho_Hans
          name: MTEB FloresBitextMining (rus_Cyrl-zho_Hans)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.4308300395257
          - type: f1
            value: 96.5909090909091
          - type: main_score
            value: 96.5909090909091
          - type: precision
            value: 96.17918313570487
          - type: recall
            value: 97.4308300395257
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ast_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-ast_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 94.46640316205533
          - type: f1
            value: 92.86890645586297
          - type: main_score
            value: 92.86890645586297
          - type: precision
            value: 92.14756258234519
          - type: recall
            value: 94.46640316205533
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-crh_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-crh_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 94.66403162055336
          - type: f1
            value: 93.2663592446201
          - type: main_score
            value: 93.2663592446201
          - type: precision
            value: 92.66716073781292
          - type: recall
            value: 94.66403162055336
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-glg_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-glg_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.81422924901186
          - type: f1
            value: 98.46837944664031
          - type: main_score
            value: 98.46837944664031
          - type: precision
            value: 98.3201581027668
          - type: recall
            value: 98.81422924901186
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kas_Deva
          name: MTEB FloresBitextMining (rus_Cyrl-kas_Deva)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 69.1699604743083
          - type: f1
            value: 63.05505292906477
          - type: main_score
            value: 63.05505292906477
          - type: precision
            value: 60.62594108789761
          - type: recall
            value: 69.1699604743083
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ltz_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-ltz_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 91.40316205533597
          - type: f1
            value: 89.26571616789009
          - type: main_score
            value: 89.26571616789009
          - type: precision
            value: 88.40179747788443
          - type: recall
            value: 91.40316205533597
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-nus_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-nus_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 38.93280632411067
          - type: f1
            value: 33.98513032905371
          - type: main_score
            value: 33.98513032905371
          - type: precision
            value: 32.56257884802308
          - type: recall
            value: 38.93280632411067
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-slk_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-slk_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.02371541501977
          - type: f1
            value: 97.42094861660078
          - type: main_score
            value: 97.42094861660078
          - type: precision
            value: 97.14262187088273
          - type: recall
            value: 98.02371541501977
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-tir_Ethi
          name: MTEB FloresBitextMining (rus_Cyrl-tir_Ethi)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 91.30434782608695
          - type: f1
            value: 88.78129117259552
          - type: main_score
            value: 88.78129117259552
          - type: precision
            value: 87.61528326745717
          - type: recall
            value: 91.30434782608695
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-zho_Hant
          name: MTEB FloresBitextMining (rus_Cyrl-zho_Hant)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.1106719367589
          - type: f1
            value: 98.81422924901186
          - type: main_score
            value: 98.81422924901186
          - type: precision
            value: 98.66600790513834
          - type: recall
            value: 99.1106719367589
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-awa_Deva
          name: MTEB FloresBitextMining (rus_Cyrl-awa_Deva)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.12252964426878
          - type: f1
            value: 97.70092226613966
          - type: main_score
            value: 97.70092226613966
          - type: precision
            value: 97.50494071146245
          - type: recall
            value: 98.12252964426878
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-cym_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-cym_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.94861660079052
          - type: f1
            value: 94.74308300395256
          - type: main_score
            value: 94.74308300395256
          - type: precision
            value: 94.20289855072464
          - type: recall
            value: 95.94861660079052
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-grn_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-grn_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 77.96442687747036
          - type: f1
            value: 73.64286789187975
          - type: main_score
            value: 73.64286789187975
          - type: precision
            value: 71.99324893260821
          - type: recall
            value: 77.96442687747036
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kat_Geor
          name: MTEB FloresBitextMining (rus_Cyrl-kat_Geor)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.91304347826086
          - type: f1
            value: 98.56719367588933
          - type: main_score
            value: 98.56719367588933
          - type: precision
            value: 98.40250329380764
          - type: recall
            value: 98.91304347826086
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-lua_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-lua_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 72.03557312252964
          - type: f1
            value: 67.23928163404449
          - type: main_score
            value: 67.23928163404449
          - type: precision
            value: 65.30797101449275
          - type: recall
            value: 72.03557312252964
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-nya_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-nya_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 92.29249011857708
          - type: f1
            value: 90.0494071146245
          - type: main_score
            value: 90.0494071146245
          - type: precision
            value: 89.04808959156786
          - type: recall
            value: 92.29249011857708
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-slv_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-slv_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.71541501976284
          - type: f1
            value: 98.30368906455863
          - type: main_score
            value: 98.30368906455863
          - type: precision
            value: 98.10606060606061
          - type: recall
            value: 98.71541501976284
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-tpi_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-tpi_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 80.53359683794467
          - type: f1
            value: 76.59481822525301
          - type: main_score
            value: 76.59481822525301
          - type: precision
            value: 75.12913223140497
          - type: recall
            value: 80.53359683794467
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-zsm_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-zsm_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.33201581027669
          - type: f1
            value: 96.58620365142104
          - type: main_score
            value: 96.58620365142104
          - type: precision
            value: 96.26152832674572
          - type: recall
            value: 97.33201581027669
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ayr_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-ayr_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 45.55335968379446
          - type: f1
            value: 40.13076578531388
          - type: main_score
            value: 40.13076578531388
          - type: precision
            value: 38.398064362362355
          - type: recall
            value: 45.55335968379446
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-dan_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-dan_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.01185770750988
          - type: f1
            value: 98.68247694334651
          - type: main_score
            value: 98.68247694334651
          - type: precision
            value: 98.51778656126481
          - type: recall
            value: 99.01185770750988
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-guj_Gujr
          name: MTEB FloresBitextMining (rus_Cyrl-guj_Gujr)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.01185770750988
          - type: f1
            value: 98.68247694334651
          - type: main_score
            value: 98.68247694334651
          - type: precision
            value: 98.51778656126481
          - type: recall
            value: 99.01185770750988
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kaz_Cyrl
          name: MTEB FloresBitextMining (rus_Cyrl-kaz_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.81422924901186
          - type: f1
            value: 98.43544137022398
          - type: main_score
            value: 98.43544137022398
          - type: precision
            value: 98.25428194993412
          - type: recall
            value: 98.81422924901186
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-lug_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-lug_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 82.21343873517787
          - type: f1
            value: 77.97485726833554
          - type: main_score
            value: 77.97485726833554
          - type: precision
            value: 76.22376717485415
          - type: recall
            value: 82.21343873517787
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-oci_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-oci_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 93.87351778656127
          - type: f1
            value: 92.25319969885187
          - type: main_score
            value: 92.25319969885187
          - type: precision
            value: 91.5638528138528
          - type: recall
            value: 93.87351778656127
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-smo_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-smo_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 84.88142292490119
          - type: f1
            value: 81.24364765669114
          - type: main_score
            value: 81.24364765669114
          - type: precision
            value: 79.69991416137661
          - type: recall
            value: 84.88142292490119
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-tsn_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-tsn_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 87.05533596837944
          - type: f1
            value: 83.90645586297761
          - type: main_score
            value: 83.90645586297761
          - type: precision
            value: 82.56752305665349
          - type: recall
            value: 87.05533596837944
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-zul_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-zul_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.15810276679841
          - type: f1
            value: 93.77140974967062
          - type: main_score
            value: 93.77140974967062
          - type: precision
            value: 93.16534914361002
          - type: recall
            value: 95.15810276679841
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-azb_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-azb_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 81.91699604743083
          - type: f1
            value: 77.18050065876152
          - type: main_score
            value: 77.18050065876152
          - type: precision
            value: 75.21519543258673
          - type: recall
            value: 81.91699604743083
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-deu_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-deu_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.50592885375494
          - type: f1
            value: 99.34123847167325
          - type: main_score
            value: 99.34123847167325
          - type: precision
            value: 99.2588932806324
          - type: recall
            value: 99.50592885375494
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-hat_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-hat_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 91.00790513833992
          - type: f1
            value: 88.69126043039086
          - type: main_score
            value: 88.69126043039086
          - type: precision
            value: 87.75774044795784
          - type: recall
            value: 91.00790513833992
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kbp_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-kbp_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 47.233201581027664
          - type: f1
            value: 43.01118618096943
          - type: main_score
            value: 43.01118618096943
          - type: precision
            value: 41.739069205043556
          - type: recall
            value: 47.233201581027664
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-luo_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-luo_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 60.47430830039525
          - type: f1
            value: 54.83210565429816
          - type: main_score
            value: 54.83210565429816
          - type: precision
            value: 52.81630744284779
          - type: recall
            value: 60.47430830039525
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ory_Orya
          name: MTEB FloresBitextMining (rus_Cyrl-ory_Orya)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.1106719367589
          - type: f1
            value: 98.83069828722003
          - type: main_score
            value: 98.83069828722003
          - type: precision
            value: 98.69894598155467
          - type: recall
            value: 99.1106719367589
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-sna_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-sna_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 89.72332015810277
          - type: f1
            value: 87.30013645774514
          - type: main_score
            value: 87.30013645774514
          - type: precision
            value: 86.25329380764163
          - type: recall
            value: 89.72332015810277
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-tso_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-tso_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 84.38735177865613
          - type: f1
            value: 80.70424744337788
          - type: main_score
            value: 80.70424744337788
          - type: precision
            value: 79.18560606060606
          - type: recall
            value: 84.38735177865613
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-azj_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-azj_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.33201581027669
          - type: f1
            value: 96.56455862977602
          - type: main_score
            value: 96.56455862977602
          - type: precision
            value: 96.23682476943345
          - type: recall
            value: 97.33201581027669
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-dik_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-dik_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 46.047430830039524
          - type: f1
            value: 40.05513069495283
          - type: main_score
            value: 40.05513069495283
          - type: precision
            value: 38.072590197096126
          - type: recall
            value: 46.047430830039524
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-hau_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-hau_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 87.94466403162056
          - type: f1
            value: 84.76943346508563
          - type: main_score
            value: 84.76943346508563
          - type: precision
            value: 83.34486166007905
          - type: recall
            value: 87.94466403162056
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kea_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-kea_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 89.42687747035573
          - type: f1
            value: 86.83803021747684
          - type: main_score
            value: 86.83803021747684
          - type: precision
            value: 85.78416149068323
          - type: recall
            value: 89.42687747035573
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-lus_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-lus_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 68.97233201581028
          - type: f1
            value: 64.05480726292745
          - type: main_score
            value: 64.05480726292745
          - type: precision
            value: 62.42670749487858
          - type: recall
            value: 68.97233201581028
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-pag_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-pag_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 78.75494071146245
          - type: f1
            value: 74.58573558401933
          - type: main_score
            value: 74.58573558401933
          - type: precision
            value: 73.05532028358115
          - type: recall
            value: 78.75494071146245
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-snd_Arab
          name: MTEB FloresBitextMining (rus_Cyrl-snd_Arab)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.8498023715415
          - type: f1
            value: 94.56521739130434
          - type: main_score
            value: 94.56521739130434
          - type: precision
            value: 93.97233201581028
          - type: recall
            value: 95.8498023715415
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-tuk_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-tuk_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 68.08300395256917
          - type: f1
            value: 62.93565240205557
          - type: main_score
            value: 62.93565240205557
          - type: precision
            value: 61.191590257043934
          - type: recall
            value: 68.08300395256917
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-bak_Cyrl
          name: MTEB FloresBitextMining (rus_Cyrl-bak_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 96.04743083003953
          - type: f1
            value: 94.86824769433464
          - type: main_score
            value: 94.86824769433464
          - type: precision
            value: 94.34288537549406
          - type: recall
            value: 96.04743083003953
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-dyu_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-dyu_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 37.45059288537549
          - type: f1
            value: 31.670482312800807
          - type: main_score
            value: 31.670482312800807
          - type: precision
            value: 29.99928568357422
          - type: recall
            value: 37.45059288537549
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-heb_Hebr
          name: MTEB FloresBitextMining (rus_Cyrl-heb_Hebr)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.23320158102767
          - type: f1
            value: 96.38998682476942
          - type: main_score
            value: 96.38998682476942
          - type: precision
            value: 95.99802371541502
          - type: recall
            value: 97.23320158102767
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-khk_Cyrl
          name: MTEB FloresBitextMining (rus_Cyrl-khk_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.41897233201581
          - type: f1
            value: 98.00724637681158
          - type: main_score
            value: 98.00724637681158
          - type: precision
            value: 97.82938076416336
          - type: recall
            value: 98.41897233201581
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-lvs_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-lvs_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.4308300395257
          - type: f1
            value: 96.61396574440053
          - type: main_score
            value: 96.61396574440053
          - type: precision
            value: 96.2203557312253
          - type: recall
            value: 97.4308300395257
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-pan_Guru
          name: MTEB FloresBitextMining (rus_Cyrl-pan_Guru)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.30830039525692
          - type: f1
            value: 99.07773386034256
          - type: main_score
            value: 99.07773386034256
          - type: precision
            value: 98.96245059288538
          - type: recall
            value: 99.30830039525692
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-som_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-som_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 87.74703557312253
          - type: f1
            value: 84.52898550724638
          - type: main_score
            value: 84.52898550724638
          - type: precision
            value: 83.09288537549409
          - type: recall
            value: 87.74703557312253
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-tum_Latn
          name: MTEB FloresBitextMining (rus_Cyrl-tum_Latn)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 87.15415019762845
          - type: f1
            value: 83.85069640504425
          - type: main_score
            value: 83.85069640504425
          - type: precision
            value: 82.43671183888576
          - type: recall
            value: 87.15415019762845
        task:
          type: BitextMining
      - dataset:
          config: taq_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (taq_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 28.55731225296443
          - type: f1
            value: 26.810726360049568
          - type: main_score
            value: 26.810726360049568
          - type: precision
            value: 26.260342858265577
          - type: recall
            value: 28.55731225296443
        task:
          type: BitextMining
      - dataset:
          config: war_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (war_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 94.86166007905138
          - type: f1
            value: 94.03147083483051
          - type: main_score
            value: 94.03147083483051
          - type: precision
            value: 93.70653606003322
          - type: recall
            value: 94.86166007905138
        task:
          type: BitextMining
      - dataset:
          config: arb_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (arb_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 96.34387351778656
          - type: f1
            value: 95.23056653491436
          - type: main_score
            value: 95.23056653491436
          - type: precision
            value: 94.70520421607378
          - type: recall
            value: 96.34387351778656
        task:
          type: BitextMining
      - dataset:
          config: bul_Cyrl-rus_Cyrl
          name: MTEB FloresBitextMining (bul_Cyrl-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.90118577075098
          - type: f1
            value: 99.86824769433464
          - type: main_score
            value: 99.86824769433464
          - type: precision
            value: 99.85177865612648
          - type: recall
            value: 99.90118577075098
        task:
          type: BitextMining
      - dataset:
          config: fra_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (fra_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.2094861660079
          - type: f1
            value: 98.9459815546772
          - type: main_score
            value: 98.9459815546772
          - type: precision
            value: 98.81422924901186
          - type: recall
            value: 99.2094861660079
        task:
          type: BitextMining
      - dataset:
          config: jpn_Jpan-rus_Cyrl
          name: MTEB FloresBitextMining (jpn_Jpan-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.3201581027668
          - type: f1
            value: 97.76021080368905
          - type: main_score
            value: 97.76021080368905
          - type: precision
            value: 97.48023715415019
          - type: recall
            value: 98.3201581027668
        task:
          type: BitextMining
      - dataset:
          config: lij_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (lij_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 83.49802371541502
          - type: f1
            value: 81.64800059239636
          - type: main_score
            value: 81.64800059239636
          - type: precision
            value: 80.9443055878478
          - type: recall
            value: 83.49802371541502
        task:
          type: BitextMining
      - dataset:
          config: mya_Mymr-rus_Cyrl
          name: MTEB FloresBitextMining (mya_Mymr-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 90.21739130434783
          - type: f1
            value: 88.76776366313682
          - type: main_score
            value: 88.76776366313682
          - type: precision
            value: 88.18370446119435
          - type: recall
            value: 90.21739130434783
        task:
          type: BitextMining
      - dataset:
          config: sag_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (sag_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 41.699604743083
          - type: f1
            value: 39.53066322643847
          - type: main_score
            value: 39.53066322643847
          - type: precision
            value: 38.822876239229274
          - type: recall
            value: 41.699604743083
        task:
          type: BitextMining
      - dataset:
          config: taq_Tfng-rus_Cyrl
          name: MTEB FloresBitextMining (taq_Tfng-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 10.67193675889328
          - type: f1
            value: 9.205744965817951
          - type: main_score
            value: 9.205744965817951
          - type: precision
            value: 8.85195219073817
          - type: recall
            value: 10.67193675889328
        task:
          type: BitextMining
      - dataset:
          config: wol_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (wol_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 63.537549407114625
          - type: f1
            value: 60.65190727391827
          - type: main_score
            value: 60.65190727391827
          - type: precision
            value: 59.61144833427442
          - type: recall
            value: 63.537549407114625
        task:
          type: BitextMining
      - dataset:
          config: arb_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (arb_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 13.142292490118576
          - type: f1
            value: 12.372910318176764
          - type: main_score
            value: 12.372910318176764
          - type: precision
            value: 12.197580895919188
          - type: recall
            value: 13.142292490118576
        task:
          type: BitextMining
      - dataset:
          config: cat_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (cat_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.01185770750988
          - type: f1
            value: 98.80599472990777
          - type: main_score
            value: 98.80599472990777
          - type: precision
            value: 98.72953133822698
          - type: recall
            value: 99.01185770750988
        task:
          type: BitextMining
      - dataset:
          config: fur_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (fur_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 81.02766798418972
          - type: f1
            value: 79.36184294084613
          - type: main_score
            value: 79.36184294084613
          - type: precision
            value: 78.69187826527705
          - type: recall
            value: 81.02766798418972
        task:
          type: BitextMining
      - dataset:
          config: kab_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (kab_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 34.387351778656125
          - type: f1
            value: 32.02306921576947
          - type: main_score
            value: 32.02306921576947
          - type: precision
            value: 31.246670347137467
          - type: recall
            value: 34.387351778656125
        task:
          type: BitextMining
      - dataset:
          config: lim_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (lim_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 78.26086956521739
          - type: f1
            value: 75.90239449214359
          - type: main_score
            value: 75.90239449214359
          - type: precision
            value: 75.02211430745493
          - type: recall
            value: 78.26086956521739
        task:
          type: BitextMining
      - dataset:
          config: nld_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (nld_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.2094861660079
          - type: f1
            value: 98.9459815546772
          - type: main_score
            value: 98.9459815546772
          - type: precision
            value: 98.81422924901186
          - type: recall
            value: 99.2094861660079
        task:
          type: BitextMining
      - dataset:
          config: san_Deva-rus_Cyrl
          name: MTEB FloresBitextMining (san_Deva-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 87.94466403162056
          - type: f1
            value: 86.68928897189767
          - type: main_score
            value: 86.68928897189767
          - type: precision
            value: 86.23822997079216
          - type: recall
            value: 87.94466403162056
        task:
          type: BitextMining
      - dataset:
          config: tat_Cyrl-rus_Cyrl
          name: MTEB FloresBitextMining (tat_Cyrl-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.03557312252964
          - type: f1
            value: 96.4167365353136
          - type: main_score
            value: 96.4167365353136
          - type: precision
            value: 96.16847826086958
          - type: recall
            value: 97.03557312252964
        task:
          type: BitextMining
      - dataset:
          config: xho_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (xho_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 86.95652173913044
          - type: f1
            value: 85.5506497283435
          - type: main_score
            value: 85.5506497283435
          - type: precision
            value: 84.95270479733395
          - type: recall
            value: 86.95652173913044
        task:
          type: BitextMining
      - dataset:
          config: ars_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (ars_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 96.6403162055336
          - type: f1
            value: 95.60935441370223
          - type: main_score
            value: 95.60935441370223
          - type: precision
            value: 95.13339920948617
          - type: recall
            value: 96.6403162055336
        task:
          type: BitextMining
      - dataset:
          config: ceb_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (ceb_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.7509881422925
          - type: f1
            value: 95.05209198303827
          - type: main_score
            value: 95.05209198303827
          - type: precision
            value: 94.77662283368805
          - type: recall
            value: 95.7509881422925
        task:
          type: BitextMining
      - dataset:
          config: fuv_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (fuv_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 45.25691699604743
          - type: f1
            value: 42.285666666742365
          - type: main_score
            value: 42.285666666742365
          - type: precision
            value: 41.21979853402283
          - type: recall
            value: 45.25691699604743
        task:
          type: BitextMining
      - dataset:
          config: kac_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (kac_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 34.683794466403164
          - type: f1
            value: 33.3235346229031
          - type: main_score
            value: 33.3235346229031
          - type: precision
            value: 32.94673924616852
          - type: recall
            value: 34.683794466403164
        task:
          type: BitextMining
      - dataset:
          config: lin_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (lin_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 86.85770750988142
          - type: f1
            value: 85.1867110799439
          - type: main_score
            value: 85.1867110799439
          - type: precision
            value: 84.53038212173273
          - type: recall
            value: 86.85770750988142
        task:
          type: BitextMining
      - dataset:
          config: nno_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (nno_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.4308300395257
          - type: f1
            value: 96.78383210991906
          - type: main_score
            value: 96.78383210991906
          - type: precision
            value: 96.51185770750989
          - type: recall
            value: 97.4308300395257
        task:
          type: BitextMining
      - dataset:
          config: sat_Olck-rus_Cyrl
          name: MTEB FloresBitextMining (sat_Olck-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 1.185770750988142
          - type: f1
            value: 1.0279253129117258
          - type: main_score
            value: 1.0279253129117258
          - type: precision
            value: 1.0129746819135175
          - type: recall
            value: 1.185770750988142
        task:
          type: BitextMining
      - dataset:
          config: tel_Telu-rus_Cyrl
          name: MTEB FloresBitextMining (tel_Telu-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.12252964426878
          - type: f1
            value: 97.61198945981555
          - type: main_score
            value: 97.61198945981555
          - type: precision
            value: 97.401185770751
          - type: recall
            value: 98.12252964426878
        task:
          type: BitextMining
      - dataset:
          config: ydd_Hebr-rus_Cyrl
          name: MTEB FloresBitextMining (ydd_Hebr-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 75.8893280632411
          - type: f1
            value: 74.00244008018511
          - type: main_score
            value: 74.00244008018511
          - type: precision
            value: 73.25683020960382
          - type: recall
            value: 75.8893280632411
        task:
          type: BitextMining
      - dataset:
          config: ary_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (ary_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 86.56126482213439
          - type: f1
            value: 83.72796285839765
          - type: main_score
            value: 83.72796285839765
          - type: precision
            value: 82.65014273166447
          - type: recall
            value: 86.56126482213439
        task:
          type: BitextMining
      - dataset:
          config: ces_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (ces_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.60474308300395
          - type: f1
            value: 99.4729907773386
          - type: main_score
            value: 99.4729907773386
          - type: precision
            value: 99.40711462450594
          - type: recall
            value: 99.60474308300395
        task:
          type: BitextMining
      - dataset:
          config: gaz_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (gaz_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 42.58893280632411
          - type: f1
            value: 40.75832866805978
          - type: main_score
            value: 40.75832866805978
          - type: precision
            value: 40.14285046917723
          - type: recall
            value: 42.58893280632411
        task:
          type: BitextMining
      - dataset:
          config: kam_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (kam_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 45.25691699604743
          - type: f1
            value: 42.6975518029456
          - type: main_score
            value: 42.6975518029456
          - type: precision
            value: 41.87472710984596
          - type: recall
            value: 45.25691699604743
        task:
          type: BitextMining
      - dataset:
          config: lit_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (lit_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.33201581027669
          - type: f1
            value: 96.62384716732542
          - type: main_score
            value: 96.62384716732542
          - type: precision
            value: 96.3175230566535
          - type: recall
            value: 97.33201581027669
        task:
          type: BitextMining
      - dataset:
          config: nob_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (nob_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.71541501976284
          - type: f1
            value: 98.30368906455863
          - type: main_score
            value: 98.30368906455863
          - type: precision
            value: 98.10606060606061
          - type: recall
            value: 98.71541501976284
        task:
          type: BitextMining
      - dataset:
          config: scn_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (scn_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 70.45454545454545
          - type: f1
            value: 68.62561022640075
          - type: main_score
            value: 68.62561022640075
          - type: precision
            value: 67.95229103411222
          - type: recall
            value: 70.45454545454545
        task:
          type: BitextMining
      - dataset:
          config: tgk_Cyrl-rus_Cyrl
          name: MTEB FloresBitextMining (tgk_Cyrl-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 92.4901185770751
          - type: f1
            value: 91.58514492753623
          - type: main_score
            value: 91.58514492753623
          - type: precision
            value: 91.24759298672342
          - type: recall
            value: 92.4901185770751
        task:
          type: BitextMining
      - dataset:
          config: yor_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (yor_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 67.98418972332016
          - type: f1
            value: 64.72874247330768
          - type: main_score
            value: 64.72874247330768
          - type: precision
            value: 63.450823399938685
          - type: recall
            value: 67.98418972332016
        task:
          type: BitextMining
      - dataset:
          config: arz_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (arz_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 94.56521739130434
          - type: f1
            value: 93.07971014492755
          - type: main_score
            value: 93.07971014492755
          - type: precision
            value: 92.42753623188406
          - type: recall
            value: 94.56521739130434
        task:
          type: BitextMining
      - dataset:
          config: cjk_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (cjk_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 38.63636363636363
          - type: f1
            value: 36.25747140862938
          - type: main_score
            value: 36.25747140862938
          - type: precision
            value: 35.49101355074723
          - type: recall
            value: 38.63636363636363
        task:
          type: BitextMining
      - dataset:
          config: gla_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (gla_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 69.26877470355731
          - type: f1
            value: 66.11797423328613
          - type: main_score
            value: 66.11797423328613
          - type: precision
            value: 64.89369649409694
          - type: recall
            value: 69.26877470355731
        task:
          type: BitextMining
      - dataset:
          config: kan_Knda-rus_Cyrl
          name: MTEB FloresBitextMining (kan_Knda-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.02371541501977
          - type: f1
            value: 97.51505740636176
          - type: main_score
            value: 97.51505740636176
          - type: precision
            value: 97.30731225296442
          - type: recall
            value: 98.02371541501977
        task:
          type: BitextMining
      - dataset:
          config: lmo_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (lmo_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 73.3201581027668
          - type: f1
            value: 71.06371608677273
          - type: main_score
            value: 71.06371608677273
          - type: precision
            value: 70.26320288266223
          - type: recall
            value: 73.3201581027668
        task:
          type: BitextMining
      - dataset:
          config: npi_Deva-rus_Cyrl
          name: MTEB FloresBitextMining (npi_Deva-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.82608695652173
          - type: f1
            value: 97.36645107198466
          - type: main_score
            value: 97.36645107198466
          - type: precision
            value: 97.1772068511199
          - type: recall
            value: 97.82608695652173
        task:
          type: BitextMining
      - dataset:
          config: shn_Mymr-rus_Cyrl
          name: MTEB FloresBitextMining (shn_Mymr-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 39.426877470355734
          - type: f1
            value: 37.16728785513024
          - type: main_score
            value: 37.16728785513024
          - type: precision
            value: 36.56918548278505
          - type: recall
            value: 39.426877470355734
        task:
          type: BitextMining
      - dataset:
          config: tgl_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (tgl_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.92490118577075
          - type: f1
            value: 97.6378693769998
          - type: main_score
            value: 97.6378693769998
          - type: precision
            value: 97.55371440154047
          - type: recall
            value: 97.92490118577075
        task:
          type: BitextMining
      - dataset:
          config: yue_Hant-rus_Cyrl
          name: MTEB FloresBitextMining (yue_Hant-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.92490118577075
          - type: f1
            value: 97.3833051006964
          - type: main_score
            value: 97.3833051006964
          - type: precision
            value: 97.1590909090909
          - type: recall
            value: 97.92490118577075
        task:
          type: BitextMining
      - dataset:
          config: asm_Beng-rus_Cyrl
          name: MTEB FloresBitextMining (asm_Beng-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 92.78656126482213
          - type: f1
            value: 91.76917395296842
          - type: main_score
            value: 91.76917395296842
          - type: precision
            value: 91.38292866553736
          - type: recall
            value: 92.78656126482213
        task:
          type: BitextMining
      - dataset:
          config: ckb_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (ckb_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 80.8300395256917
          - type: f1
            value: 79.17664345468799
          - type: main_score
            value: 79.17664345468799
          - type: precision
            value: 78.5622171683459
          - type: recall
            value: 80.8300395256917
        task:
          type: BitextMining
      - dataset:
          config: gle_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (gle_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 85.86956521739131
          - type: f1
            value: 84.45408265372492
          - type: main_score
            value: 84.45408265372492
          - type: precision
            value: 83.8774340026703
          - type: recall
            value: 85.86956521739131
        task:
          type: BitextMining
      - dataset:
          config: kas_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (kas_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 76.28458498023716
          - type: f1
            value: 74.11216313578267
          - type: main_score
            value: 74.11216313578267
          - type: precision
            value: 73.2491277759584
          - type: recall
            value: 76.28458498023716
        task:
          type: BitextMining
      - dataset:
          config: ltg_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (ltg_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 71.14624505928853
          - type: f1
            value: 68.69245357723618
          - type: main_score
            value: 68.69245357723618
          - type: precision
            value: 67.8135329666459
          - type: recall
            value: 71.14624505928853
        task:
          type: BitextMining
      - dataset:
          config: nso_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (nso_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 87.64822134387352
          - type: f1
            value: 85.98419219986725
          - type: main_score
            value: 85.98419219986725
          - type: precision
            value: 85.32513873917036
          - type: recall
            value: 87.64822134387352
        task:
          type: BitextMining
      - dataset:
          config: sin_Sinh-rus_Cyrl
          name: MTEB FloresBitextMining (sin_Sinh-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.62845849802372
          - type: f1
            value: 97.10144927536231
          - type: main_score
            value: 97.10144927536231
          - type: precision
            value: 96.87986585219788
          - type: recall
            value: 97.62845849802372
        task:
          type: BitextMining
      - dataset:
          config: tha_Thai-rus_Cyrl
          name: MTEB FloresBitextMining (tha_Thai-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.71541501976284
          - type: f1
            value: 98.28722002635045
          - type: main_score
            value: 98.28722002635045
          - type: precision
            value: 98.07312252964427
          - type: recall
            value: 98.71541501976284
        task:
          type: BitextMining
      - dataset:
          config: zho_Hans-rus_Cyrl
          name: MTEB FloresBitextMining (zho_Hans-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.01185770750988
          - type: f1
            value: 98.68247694334651
          - type: main_score
            value: 98.68247694334651
          - type: precision
            value: 98.51778656126481
          - type: recall
            value: 99.01185770750988
        task:
          type: BitextMining
      - dataset:
          config: ast_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (ast_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.65217391304348
          - type: f1
            value: 94.90649683857505
          - type: main_score
            value: 94.90649683857505
          - type: precision
            value: 94.61352657004831
          - type: recall
            value: 95.65217391304348
        task:
          type: BitextMining
      - dataset:
          config: crh_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (crh_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 93.08300395256917
          - type: f1
            value: 92.20988998886428
          - type: main_score
            value: 92.20988998886428
          - type: precision
            value: 91.85631013694254
          - type: recall
            value: 93.08300395256917
        task:
          type: BitextMining
      - dataset:
          config: glg_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (glg_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.55335968379447
          - type: f1
            value: 95.18006148440931
          - type: main_score
            value: 95.18006148440931
          - type: precision
            value: 95.06540560888386
          - type: recall
            value: 95.55335968379447
        task:
          type: BitextMining
      - dataset:
          config: kas_Deva-rus_Cyrl
          name: MTEB FloresBitextMining (kas_Deva-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 55.03952569169961
          - type: f1
            value: 52.19871938895554
          - type: main_score
            value: 52.19871938895554
          - type: precision
            value: 51.17660971469557
          - type: recall
            value: 55.03952569169961
        task:
          type: BitextMining
      - dataset:
          config: ltz_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (ltz_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 87.64822134387352
          - type: f1
            value: 86.64179841897234
          - type: main_score
            value: 86.64179841897234
          - type: precision
            value: 86.30023235431587
          - type: recall
            value: 87.64822134387352
        task:
          type: BitextMining
      - dataset:
          config: nus_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (nus_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 27.4703557312253
          - type: f1
            value: 25.703014277858088
          - type: main_score
            value: 25.703014277858088
          - type: precision
            value: 25.194105476917315
          - type: recall
            value: 27.4703557312253
        task:
          type: BitextMining
      - dataset:
          config: slk_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (slk_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.30830039525692
          - type: f1
            value: 99.1106719367589
          - type: main_score
            value: 99.1106719367589
          - type: precision
            value: 99.02832674571805
          - type: recall
            value: 99.30830039525692
        task:
          type: BitextMining
      - dataset:
          config: tir_Ethi-rus_Cyrl
          name: MTEB FloresBitextMining (tir_Ethi-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 80.73122529644269
          - type: f1
            value: 78.66903754775608
          - type: main_score
            value: 78.66903754775608
          - type: precision
            value: 77.86431694163612
          - type: recall
            value: 80.73122529644269
        task:
          type: BitextMining
      - dataset:
          config: zho_Hant-rus_Cyrl
          name: MTEB FloresBitextMining (zho_Hant-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.22134387351778
          - type: f1
            value: 97.66798418972333
          - type: main_score
            value: 97.66798418972333
          - type: precision
            value: 97.40612648221344
          - type: recall
            value: 98.22134387351778
        task:
          type: BitextMining
      - dataset:
          config: awa_Deva-rus_Cyrl
          name: MTEB FloresBitextMining (awa_Deva-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.5296442687747
          - type: f1
            value: 96.94224857268335
          - type: main_score
            value: 96.94224857268335
          - type: precision
            value: 96.68560606060606
          - type: recall
            value: 97.5296442687747
        task:
          type: BitextMining
      - dataset:
          config: cym_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (cym_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 92.68774703557312
          - type: f1
            value: 91.69854302097961
          - type: main_score
            value: 91.69854302097961
          - type: precision
            value: 91.31236846157795
          - type: recall
            value: 92.68774703557312
        task:
          type: BitextMining
      - dataset:
          config: grn_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (grn_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 64.13043478260869
          - type: f1
            value: 61.850586118740004
          - type: main_score
            value: 61.850586118740004
          - type: precision
            value: 61.0049495186209
          - type: recall
            value: 64.13043478260869
        task:
          type: BitextMining
      - dataset:
          config: kat_Geor-rus_Cyrl
          name: MTEB FloresBitextMining (kat_Geor-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.02371541501977
          - type: f1
            value: 97.59881422924902
          - type: main_score
            value: 97.59881422924902
          - type: precision
            value: 97.42534036012296
          - type: recall
            value: 98.02371541501977
        task:
          type: BitextMining
      - dataset:
          config: lua_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (lua_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 63.63636363636363
          - type: f1
            value: 60.9709122526128
          - type: main_score
            value: 60.9709122526128
          - type: precision
            value: 60.03915902282226
          - type: recall
            value: 63.63636363636363
        task:
          type: BitextMining
      - dataset:
          config: nya_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (nya_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 89.2292490118577
          - type: f1
            value: 87.59723824473149
          - type: main_score
            value: 87.59723824473149
          - type: precision
            value: 86.90172707867349
          - type: recall
            value: 89.2292490118577
        task:
          type: BitextMining
      - dataset:
          config: slv_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (slv_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.01185770750988
          - type: f1
            value: 98.74835309617917
          - type: main_score
            value: 98.74835309617917
          - type: precision
            value: 98.63636363636364
          - type: recall
            value: 99.01185770750988
        task:
          type: BitextMining
      - dataset:
          config: tpi_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (tpi_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 77.37154150197628
          - type: f1
            value: 75.44251611276084
          - type: main_score
            value: 75.44251611276084
          - type: precision
            value: 74.78103665109595
          - type: recall
            value: 77.37154150197628
        task:
          type: BitextMining
      - dataset:
          config: zsm_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (zsm_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.2094861660079
          - type: f1
            value: 98.96245059288538
          - type: main_score
            value: 98.96245059288538
          - type: precision
            value: 98.8471673254282
          - type: recall
            value: 99.2094861660079
        task:
          type: BitextMining
      - dataset:
          config: ayr_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (ayr_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 27.766798418972332
          - type: f1
            value: 26.439103195281312
          - type: main_score
            value: 26.439103195281312
          - type: precision
            value: 26.052655604573964
          - type: recall
            value: 27.766798418972332
        task:
          type: BitextMining
      - dataset:
          config: dan_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (dan_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.30830039525692
          - type: f1
            value: 99.07773386034255
          - type: main_score
            value: 99.07773386034255
          - type: precision
            value: 98.96245059288538
          - type: recall
            value: 99.30830039525692
        task:
          type: BitextMining
      - dataset:
          config: guj_Gujr-rus_Cyrl
          name: MTEB FloresBitextMining (guj_Gujr-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.82608695652173
          - type: f1
            value: 97.26449275362317
          - type: main_score
            value: 97.26449275362317
          - type: precision
            value: 97.02498588368154
          - type: recall
            value: 97.82608695652173
        task:
          type: BitextMining
      - dataset:
          config: kaz_Cyrl-rus_Cyrl
          name: MTEB FloresBitextMining (kaz_Cyrl-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.5296442687747
          - type: f1
            value: 97.03557312252964
          - type: main_score
            value: 97.03557312252964
          - type: precision
            value: 96.85022158342316
          - type: recall
            value: 97.5296442687747
        task:
          type: BitextMining
      - dataset:
          config: lug_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (lug_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 68.57707509881423
          - type: f1
            value: 65.93361605820395
          - type: main_score
            value: 65.93361605820395
          - type: precision
            value: 64.90348248593789
          - type: recall
            value: 68.57707509881423
        task:
          type: BitextMining
      - dataset:
          config: oci_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (oci_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 86.26482213438736
          - type: f1
            value: 85.33176417155623
          - type: main_score
            value: 85.33176417155623
          - type: precision
            value: 85.00208833384637
          - type: recall
            value: 86.26482213438736
        task:
          type: BitextMining
      - dataset:
          config: smo_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (smo_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 77.96442687747036
          - type: f1
            value: 75.70960450188885
          - type: main_score
            value: 75.70960450188885
          - type: precision
            value: 74.8312632736777
          - type: recall
            value: 77.96442687747036
        task:
          type: BitextMining
      - dataset:
          config: tsn_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (tsn_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 84.38735177865613
          - type: f1
            value: 82.13656376349225
          - type: main_score
            value: 82.13656376349225
          - type: precision
            value: 81.16794543904518
          - type: recall
            value: 84.38735177865613
        task:
          type: BitextMining
      - dataset:
          config: zul_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (zul_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 90.21739130434783
          - type: f1
            value: 88.77570602050753
          - type: main_score
            value: 88.77570602050753
          - type: precision
            value: 88.15978104021582
          - type: recall
            value: 90.21739130434783
        task:
          type: BitextMining
      - dataset:
          config: azb_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (azb_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 65.71146245059289
          - type: f1
            value: 64.18825390221271
          - type: main_score
            value: 64.18825390221271
          - type: precision
            value: 63.66811154793568
          - type: recall
            value: 65.71146245059289
        task:
          type: BitextMining
      - dataset:
          config: deu_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (deu_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 99.70355731225297
          - type: f1
            value: 99.60474308300395
          - type: main_score
            value: 99.60474308300395
          - type: precision
            value: 99.55533596837944
          - type: recall
            value: 99.70355731225297
        task:
          type: BitextMining
      - dataset:
          config: hat_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (hat_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 86.7588932806324
          - type: f1
            value: 85.86738623695146
          - type: main_score
            value: 85.86738623695146
          - type: precision
            value: 85.55235467420822
          - type: recall
            value: 86.7588932806324
        task:
          type: BitextMining
      - dataset:
          config: kbp_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (kbp_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 34.88142292490119
          - type: f1
            value: 32.16511669463015
          - type: main_score
            value: 32.16511669463015
          - type: precision
            value: 31.432098549546318
          - type: recall
            value: 34.88142292490119
        task:
          type: BitextMining
      - dataset:
          config: luo_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (luo_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 52.27272727272727
          - type: f1
            value: 49.60489626836975
          - type: main_score
            value: 49.60489626836975
          - type: precision
            value: 48.69639631803339
          - type: recall
            value: 52.27272727272727
        task:
          type: BitextMining
      - dataset:
          config: ory_Orya-rus_Cyrl
          name: MTEB FloresBitextMining (ory_Orya-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.82608695652173
          - type: f1
            value: 97.27437417654808
          - type: main_score
            value: 97.27437417654808
          - type: precision
            value: 97.04968944099377
          - type: recall
            value: 97.82608695652173
        task:
          type: BitextMining
      - dataset:
          config: sna_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (sna_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 85.37549407114624
          - type: f1
            value: 83.09911316305177
          - type: main_score
            value: 83.09911316305177
          - type: precision
            value: 82.1284950958864
          - type: recall
            value: 85.37549407114624
        task:
          type: BitextMining
      - dataset:
          config: tso_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (tso_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 82.90513833992095
          - type: f1
            value: 80.28290385503824
          - type: main_score
            value: 80.28290385503824
          - type: precision
            value: 79.23672543237761
          - type: recall
            value: 82.90513833992095
        task:
          type: BitextMining
      - dataset:
          config: azj_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (azj_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.02371541501977
          - type: f1
            value: 97.49200075287031
          - type: main_score
            value: 97.49200075287031
          - type: precision
            value: 97.266139657444
          - type: recall
            value: 98.02371541501977
        task:
          type: BitextMining
      - dataset:
          config: dik_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (dik_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 38.43873517786561
          - type: f1
            value: 35.78152442955223
          - type: main_score
            value: 35.78152442955223
          - type: precision
            value: 34.82424325078237
          - type: recall
            value: 38.43873517786561
        task:
          type: BitextMining
      - dataset:
          config: hau_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (hau_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 81.42292490118577
          - type: f1
            value: 79.24612283124593
          - type: main_score
            value: 79.24612283124593
          - type: precision
            value: 78.34736070751448
          - type: recall
            value: 81.42292490118577
        task:
          type: BitextMining
      - dataset:
          config: kea_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (kea_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 81.62055335968378
          - type: f1
            value: 80.47015182884748
          - type: main_score
            value: 80.47015182884748
          - type: precision
            value: 80.02671028885862
          - type: recall
            value: 81.62055335968378
        task:
          type: BitextMining
      - dataset:
          config: lus_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (lus_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 62.74703557312253
          - type: f1
            value: 60.53900079111122
          - type: main_score
            value: 60.53900079111122
          - type: precision
            value: 59.80024202850289
          - type: recall
            value: 62.74703557312253
        task:
          type: BitextMining
      - dataset:
          config: pag_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (pag_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 74.01185770750988
          - type: f1
            value: 72.57280648279529
          - type: main_score
            value: 72.57280648279529
          - type: precision
            value: 71.99952968456789
          - type: recall
            value: 74.01185770750988
        task:
          type: BitextMining
      - dataset:
          config: snd_Arab-rus_Cyrl
          name: MTEB FloresBitextMining (snd_Arab-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 91.30434782608695
          - type: f1
            value: 90.24653499445358
          - type: main_score
            value: 90.24653499445358
          - type: precision
            value: 89.83134068200232
          - type: recall
            value: 91.30434782608695
        task:
          type: BitextMining
      - dataset:
          config: tuk_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (tuk_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 47.62845849802372
          - type: f1
            value: 45.812928836644254
          - type: main_score
            value: 45.812928836644254
          - type: precision
            value: 45.23713833170355
          - type: recall
            value: 47.62845849802372
        task:
          type: BitextMining
      - dataset:
          config: bak_Cyrl-rus_Cyrl
          name: MTEB FloresBitextMining (bak_Cyrl-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.8498023715415
          - type: f1
            value: 95.18904459615922
          - type: main_score
            value: 95.18904459615922
          - type: precision
            value: 94.92812441182006
          - type: recall
            value: 95.8498023715415
        task:
          type: BitextMining
      - dataset:
          config: dyu_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (dyu_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 29.64426877470356
          - type: f1
            value: 27.287335193938166
          - type: main_score
            value: 27.287335193938166
          - type: precision
            value: 26.583996026587492
          - type: recall
            value: 29.64426877470356
        task:
          type: BitextMining
      - dataset:
          config: heb_Hebr-rus_Cyrl
          name: MTEB FloresBitextMining (heb_Hebr-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 98.91304347826086
          - type: f1
            value: 98.55072463768116
          - type: main_score
            value: 98.55072463768116
          - type: precision
            value: 98.36956521739131
          - type: recall
            value: 98.91304347826086
        task:
          type: BitextMining
      - dataset:
          config: khk_Cyrl-rus_Cyrl
          name: MTEB FloresBitextMining (khk_Cyrl-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 95.15810276679841
          - type: f1
            value: 94.44009547764487
          - type: main_score
            value: 94.44009547764487
          - type: precision
            value: 94.16579797014579
          - type: recall
            value: 95.15810276679841
        task:
          type: BitextMining
      - dataset:
          config: lvs_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (lvs_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.92490118577075
          - type: f1
            value: 97.51467241585817
          - type: main_score
            value: 97.51467241585817
          - type: precision
            value: 97.36166007905138
          - type: recall
            value: 97.92490118577075
        task:
          type: BitextMining
      - dataset:
          config: pan_Guru-rus_Cyrl
          name: MTEB FloresBitextMining (pan_Guru-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 97.92490118577075
          - type: f1
            value: 97.42918313570486
          - type: main_score
            value: 97.42918313570486
          - type: precision
            value: 97.22261434217955
          - type: recall
            value: 97.92490118577075
        task:
          type: BitextMining
      - dataset:
          config: som_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (som_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 75.69169960474308
          - type: f1
            value: 73.7211667065916
          - type: main_score
            value: 73.7211667065916
          - type: precision
            value: 72.95842401892384
          - type: recall
            value: 75.69169960474308
        task:
          type: BitextMining
      - dataset:
          config: tum_Latn-rus_Cyrl
          name: MTEB FloresBitextMining (tum_Latn-rus_Cyrl)
          revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
          split: devtest
          type: mteb/flores
        metrics:
          - type: accuracy
            value: 85.67193675889328
          - type: f1
            value: 82.9296066252588
          - type: main_score
            value: 82.9296066252588
          - type: precision
            value: 81.77330225447936
          - type: recall
            value: 85.67193675889328
        task:
          type: BitextMining
      - dataset:
          config: default
          name: MTEB GeoreviewClassification (default)
          revision: 3765c0d1de6b7d264bc459433c45e5a75513839c
          split: test
          type: ai-forever/georeview-classification
        metrics:
          - type: accuracy
            value: 44.6630859375
          - type: f1
            value: 42.607425073610536
          - type: f1_weighted
            value: 42.60639474586065
          - type: main_score
            value: 44.6630859375
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB GeoreviewClusteringP2P (default)
          revision: 97a313c8fc85b47f13f33e7e9a95c1ad888c7fec
          split: test
          type: ai-forever/georeview-clustering-p2p
        metrics:
          - type: main_score
            value: 58.15951247070825
          - type: v_measure
            value: 58.15951247070825
          - type: v_measure_std
            value: 0.6739615788288809
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB HeadlineClassification (default)
          revision: 2fe05ee6b5832cda29f2ef7aaad7b7fe6a3609eb
          split: test
          type: ai-forever/headline-classification
        metrics:
          - type: accuracy
            value: 73.935546875
          - type: f1
            value: 73.8654872186846
          - type: f1_weighted
            value: 73.86733122685095
          - type: main_score
            value: 73.935546875
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB InappropriatenessClassification (default)
          revision: 601651fdc45ef243751676e62dd7a19f491c0285
          split: test
          type: ai-forever/inappropriateness-classification
        metrics:
          - type: accuracy
            value: 59.16015624999999
          - type: ap
            value: 55.52276605836938
          - type: ap_weighted
            value: 55.52276605836938
          - type: f1
            value: 58.614248199637956
          - type: f1_weighted
            value: 58.614248199637956
          - type: main_score
            value: 59.16015624999999
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB KinopoiskClassification (default)
          revision: 5911f26666ac11af46cb9c6849d0dc80a378af24
          split: test
          type: ai-forever/kinopoisk-sentiment-classification
        metrics:
          - type: accuracy
            value: 49.959999999999994
          - type: f1
            value: 48.4900332316098
          - type: f1_weighted
            value: 48.4900332316098
          - type: main_score
            value: 49.959999999999994
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB LanguageClassification (default)
          revision: aa56583bf2bc52b0565770607d6fc3faebecf9e2
          split: test
          type: papluca/language-identification
        metrics:
          - type: accuracy
            value: 71.005859375
          - type: f1
            value: 69.63481100303348
          - type: f1_weighted
            value: 69.64640413409529
          - type: main_score
            value: 71.005859375
        task:
          type: Classification
      - dataset:
          config: ru
          name: MTEB MLSUMClusteringP2P (ru)
          revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
          split: test
          type: reciTAL/mlsum
        metrics:
          - type: main_score
            value: 42.11280087032343
          - type: v_measure
            value: 42.11280087032343
          - type: v_measure_std
            value: 6.7619971723605135
          - type: main_score
            value: 43.00112546945811
          - type: v_measure
            value: 43.00112546945811
          - type: v_measure_std
            value: 1.4740560414835675
          - type: main_score
            value: 39.81446080575161
          - type: v_measure
            value: 39.81446080575161
          - type: v_measure_std
            value: 7.125661320308298
          - type: main_score
            value: 39.29659668980239
          - type: v_measure
            value: 39.29659668980239
          - type: v_measure_std
            value: 2.6570502923023094
        task:
          type: Clustering
      - dataset:
          config: ru
          name: MTEB MultiLongDocRetrieval (ru)
          revision: d67138e705d963e346253a80e59676ddb418810a
          split: dev
          type: Shitao/MLDR
        metrics:
          - type: main_score
            value: 38.671
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            value: 30
          - type: map_at_10
            value: 36.123
          - type: map_at_100
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          - type: map_at_1000
            value: 36.806
          - type: map_at_20
            value: 36.464
          - type: map_at_3
            value: 35.25
          - type: map_at_5
            value: 35.8
          - type: mrr_at_1
            value: 30
          - type: mrr_at_10
            value: 36.122817460317464
          - type: mrr_at_100
            value: 36.75467016625293
          - type: mrr_at_1000
            value: 36.80612724920882
          - type: mrr_at_20
            value: 36.46359681984682
          - type: mrr_at_3
            value: 35.25
          - type: mrr_at_5
            value: 35.800000000000004
          - type: nauc_map_at_1000_diff1
            value: 55.61987610843598
          - type: nauc_map_at_1000_max
            value: 52.506795017152186
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            value: 2.95487192066911
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            value: 2.930120252521189
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            value: 52.739573233234424
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            value: 2.4073432421641545
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            value: 50.55668152952304
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            value: 52.53663737242853
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            value: 2.8489192879814
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            value: 53.10608389782041
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            value: 1.4909731657889491
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            value: 50.55668152952304
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            value: 1.6572084853398048
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            value: 52.53663737242853
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          - type: nauc_mrr_at_3_max
            value: 53.10608389782041
          - type: nauc_mrr_at_3_std
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            value: 52.398078357541564
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          - type: nauc_ndcg_at_1000_max
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            value: 7.133789405525702
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          - type: nauc_ndcg_at_100_std
            value: 6.878823275077807
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          - type: nauc_ndcg_at_10_max
            value: 53.52837255793743
          - type: nauc_ndcg_at_10_std
            value: 3.756832592964262
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            value: 50.55668152952304
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            value: 53.89907760306972
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            value: 1.6661401245309218
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            value: 52.66800998045517
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            value: 65.1132590931922
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            value: 40.60788709618145
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          - type: nauc_precision_at_100_max
            value: 53.02960148167071
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            value: 28.206028867032863
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            value: 56.562744749606765
          - type: nauc_precision_at_10_max
            value: 56.00594967783547
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            value: 54.03255118937036
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            value: 15.161611674272718
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            value: 56.139896875869354
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            value: 2.2306901035769893
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          - type: nauc_precision_at_5_max
            value: 53.28665761862506
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            value: 4.358720050112237
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            value: 57.259240202383964
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            value: 65.11325909319218
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            value: 40.60788709618142
          - type: nauc_recall_at_100_diff1
            value: 46.49620002554603
          - type: nauc_recall_at_100_max
            value: 53.02960148167071
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            value: 28.206028867032835
          - type: nauc_recall_at_10_diff1
            value: 56.562744749606765
          - type: nauc_recall_at_10_max
            value: 56.00594967783549
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            value: 8.368379831645147
          - type: nauc_recall_at_1_diff1
            value: 52.57059856776112
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            value: 50.55668152952304
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            value: 1.6572084853398048
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            value: 53.259157546141154
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            value: 54.03255118937038
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            value: 15.16161167427274
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            value: 60.72678574894387
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            value: 56.13989687586933
          - type: nauc_recall_at_3_std
            value: 2.2306901035770066
          - type: nauc_recall_at_5_diff1
            value: 57.12011275251864
          - type: nauc_recall_at_5_max
            value: 53.28665761862502
          - type: nauc_recall_at_5_std
            value: 4.3587200501122245
          - type: ndcg_at_1
            value: 30
          - type: ndcg_at_10
            value: 38.671
          - type: ndcg_at_100
            value: 42.173
          - type: ndcg_at_1000
            value: 44.016
          - type: ndcg_at_20
            value: 39.845000000000006
          - type: ndcg_at_3
            value: 36.863
          - type: ndcg_at_5
            value: 37.874
          - type: precision_at_1
            value: 30
          - type: precision_at_10
            value: 4.65
          - type: precision_at_100
            value: 0.64
          - type: precision_at_1000
            value: 0.08
          - type: precision_at_20
            value: 2.55
          - type: precision_at_3
            value: 13.833
          - type: precision_at_5
            value: 8.799999999999999
          - type: recall_at_1
            value: 30
          - type: recall_at_10
            value: 46.5
          - type: recall_at_100
            value: 64
          - type: recall_at_1000
            value: 79.5
          - type: recall_at_20
            value: 51
          - type: recall_at_3
            value: 41.5
          - type: recall_at_5
            value: 44
        task:
          type: Retrieval
      - dataset:
          config: rus
          name: MTEB MultilingualSentimentClassification (rus)
          revision: 2b9b4d10fc589af67794141fe8cbd3739de1eb33
          split: test
          type: mteb/multilingual-sentiment-classification
        metrics:
          - type: accuracy
            value: 79.52710495963092
          - type: ap
            value: 84.5713457178972
          - type: ap_weighted
            value: 84.5713457178972
          - type: f1
            value: 77.88661181524105
          - type: f1_weighted
            value: 79.87563079922718
          - type: main_score
            value: 79.52710495963092
        task:
          type: Classification
      - dataset:
          config: arb_Arab-rus_Cyrl
          name: MTEB NTREXBitextMining (arb_Arab-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 86.47971957936905
          - type: f1
            value: 82.79864240805654
          - type: main_score
            value: 82.79864240805654
          - type: precision
            value: 81.21485800128767
          - type: recall
            value: 86.47971957936905
        task:
          type: BitextMining
      - dataset:
          config: bel_Cyrl-rus_Cyrl
          name: MTEB NTREXBitextMining (bel_Cyrl-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 94.84226339509264
          - type: f1
            value: 93.56399067465667
          - type: main_score
            value: 93.56399067465667
          - type: precision
            value: 93.01619095309631
          - type: recall
            value: 94.84226339509264
        task:
          type: BitextMining
      - dataset:
          config: ben_Beng-rus_Cyrl
          name: MTEB NTREXBitextMining (ben_Beng-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 92.18828242363544
          - type: f1
            value: 90.42393889620612
          - type: main_score
            value: 90.42393889620612
          - type: precision
            value: 89.67904925153297
          - type: recall
            value: 92.18828242363544
        task:
          type: BitextMining
      - dataset:
          config: bos_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (bos_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 94.69203805708563
          - type: f1
            value: 93.37172425304624
          - type: main_score
            value: 93.37172425304624
          - type: precision
            value: 92.79204521067315
          - type: recall
            value: 94.69203805708563
        task:
          type: BitextMining
      - dataset:
          config: bul_Cyrl-rus_Cyrl
          name: MTEB NTREXBitextMining (bul_Cyrl-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 96.99549323985978
          - type: f1
            value: 96.13086296110833
          - type: main_score
            value: 96.13086296110833
          - type: precision
            value: 95.72441996327827
          - type: recall
            value: 96.99549323985978
        task:
          type: BitextMining
      - dataset:
          config: ces_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (ces_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 95.94391587381071
          - type: f1
            value: 94.90680465142157
          - type: main_score
            value: 94.90680465142157
          - type: precision
            value: 94.44541812719079
          - type: recall
            value: 95.94391587381071
        task:
          type: BitextMining
      - dataset:
          config: deu_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (deu_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 96.09414121181773
          - type: f1
            value: 94.94408279085295
          - type: main_score
            value: 94.94408279085295
          - type: precision
            value: 94.41245201135037
          - type: recall
            value: 96.09414121181773
        task:
          type: BitextMining
      - dataset:
          config: ell_Grek-rus_Cyrl
          name: MTEB NTREXBitextMining (ell_Grek-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 96.19429143715573
          - type: f1
            value: 95.12101485561676
          - type: main_score
            value: 95.12101485561676
          - type: precision
            value: 94.60440660991488
          - type: recall
            value: 96.19429143715573
        task:
          type: BitextMining
      - dataset:
          config: eng_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (eng_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 96.49474211316975
          - type: f1
            value: 95.46581777428045
          - type: main_score
            value: 95.46581777428045
          - type: precision
            value: 94.98414288098814
          - type: recall
            value: 96.49474211316975
        task:
          type: BitextMining
      - dataset:
          config: fas_Arab-rus_Cyrl
          name: MTEB NTREXBitextMining (fas_Arab-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 94.44166249374061
          - type: f1
            value: 92.92383018972905
          - type: main_score
            value: 92.92383018972905
          - type: precision
            value: 92.21957936905358
          - type: recall
            value: 94.44166249374061
        task:
          type: BitextMining
      - dataset:
          config: fin_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (fin_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 92.18828242363544
          - type: f1
            value: 90.2980661468393
          - type: main_score
            value: 90.2980661468393
          - type: precision
            value: 89.42580537472877
          - type: recall
            value: 92.18828242363544
        task:
          type: BitextMining
      - dataset:
          config: fra_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (fra_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 95.84376564847271
          - type: f1
            value: 94.81054915706895
          - type: main_score
            value: 94.81054915706895
          - type: precision
            value: 94.31369276136427
          - type: recall
            value: 95.84376564847271
        task:
          type: BitextMining
      - dataset:
          config: heb_Hebr-rus_Cyrl
          name: MTEB NTREXBitextMining (heb_Hebr-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 94.89233850776164
          - type: f1
            value: 93.42513770655985
          - type: main_score
            value: 93.42513770655985
          - type: precision
            value: 92.73493573693875
          - type: recall
            value: 94.89233850776164
        task:
          type: BitextMining
      - dataset:
          config: hin_Deva-rus_Cyrl
          name: MTEB NTREXBitextMining (hin_Deva-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 93.23985978968453
          - type: f1
            value: 91.52816526376867
          - type: main_score
            value: 91.52816526376867
          - type: precision
            value: 90.76745946425466
          - type: recall
            value: 93.23985978968453
        task:
          type: BitextMining
      - dataset:
          config: hrv_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (hrv_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 93.99098647971958
          - type: f1
            value: 92.36354531797697
          - type: main_score
            value: 92.36354531797697
          - type: precision
            value: 91.63228970439788
          - type: recall
            value: 93.99098647971958
        task:
          type: BitextMining
      - dataset:
          config: hun_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (hun_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 93.64046069103655
          - type: f1
            value: 92.05224503421799
          - type: main_score
            value: 92.05224503421799
          - type: precision
            value: 91.33998616973079
          - type: recall
            value: 93.64046069103655
        task:
          type: BitextMining
      - dataset:
          config: ind_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (ind_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 91.68753129694541
          - type: f1
            value: 89.26222667334335
          - type: main_score
            value: 89.26222667334335
          - type: precision
            value: 88.14638624603572
          - type: recall
            value: 91.68753129694541
        task:
          type: BitextMining
      - dataset:
          config: jpn_Jpan-rus_Cyrl
          name: MTEB NTREXBitextMining (jpn_Jpan-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 91.28693039559339
          - type: f1
            value: 89.21161763348957
          - type: main_score
            value: 89.21161763348957
          - type: precision
            value: 88.31188340952988
          - type: recall
            value: 91.28693039559339
        task:
          type: BitextMining
      - dataset:
          config: kor_Hang-rus_Cyrl
          name: MTEB NTREXBitextMining (kor_Hang-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 89.53430145217827
          - type: f1
            value: 86.88322165788365
          - type: main_score
            value: 86.88322165788365
          - type: precision
            value: 85.73950211030831
          - type: recall
            value: 89.53430145217827
        task:
          type: BitextMining
      - dataset:
          config: lit_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (lit_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 90.28542814221332
          - type: f1
            value: 88.10249103814452
          - type: main_score
            value: 88.10249103814452
          - type: precision
            value: 87.17689323973752
          - type: recall
            value: 90.28542814221332
        task:
          type: BitextMining
      - dataset:
          config: mkd_Cyrl-rus_Cyrl
          name: MTEB NTREXBitextMining (mkd_Cyrl-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 95.04256384576865
          - type: f1
            value: 93.65643703650713
          - type: main_score
            value: 93.65643703650713
          - type: precision
            value: 93.02036387915207
          - type: recall
            value: 95.04256384576865
        task:
          type: BitextMining
      - dataset:
          config: nld_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (nld_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 95.39308963445168
          - type: f1
            value: 94.16207644800535
          - type: main_score
            value: 94.16207644800535
          - type: precision
            value: 93.582516632091
          - type: recall
            value: 95.39308963445168
        task:
          type: BitextMining
      - dataset:
          config: pol_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (pol_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 95.7436154231347
          - type: f1
            value: 94.5067601402103
          - type: main_score
            value: 94.5067601402103
          - type: precision
            value: 93.91587381071608
          - type: recall
            value: 95.7436154231347
        task:
          type: BitextMining
      - dataset:
          config: por_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (por_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 65.89884827240861
          - type: f1
            value: 64.61805459419219
          - type: main_score
            value: 64.61805459419219
          - type: precision
            value: 64.07119451106485
          - type: recall
            value: 65.89884827240861
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-arb_Arab
          name: MTEB NTREXBitextMining (rus_Cyrl-arb_Arab)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 94.2413620430646
          - type: f1
            value: 92.67663399861698
          - type: main_score
            value: 92.67663399861698
          - type: precision
            value: 91.94625271240193
          - type: recall
            value: 94.2413620430646
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-bel_Cyrl
          name: MTEB NTREXBitextMining (rus_Cyrl-bel_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 94.89233850776164
          - type: f1
            value: 93.40343849106993
          - type: main_score
            value: 93.40343849106993
          - type: precision
            value: 92.74077783341679
          - type: recall
            value: 94.89233850776164
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ben_Beng
          name: MTEB NTREXBitextMining (rus_Cyrl-ben_Beng)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 94.2914371557336
          - type: f1
            value: 92.62226673343348
          - type: main_score
            value: 92.62226673343348
          - type: precision
            value: 91.84610248706393
          - type: recall
            value: 94.2914371557336
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-bos_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-bos_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 95.69354031046569
          - type: f1
            value: 94.50418051319403
          - type: main_score
            value: 94.50418051319403
          - type: precision
            value: 93.95843765648473
          - type: recall
            value: 95.69354031046569
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-bul_Cyrl
          name: MTEB NTREXBitextMining (rus_Cyrl-bul_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 95.89384076114172
          - type: f1
            value: 94.66199298948423
          - type: main_score
            value: 94.66199298948423
          - type: precision
            value: 94.08028709731263
          - type: recall
            value: 95.89384076114172
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ces_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-ces_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 93.94091136705057
          - type: f1
            value: 92.3746731207923
          - type: main_score
            value: 92.3746731207923
          - type: precision
            value: 91.66207644800535
          - type: recall
            value: 93.94091136705057
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-deu_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-deu_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 95.94391587381071
          - type: f1
            value: 94.76214321482223
          - type: main_score
            value: 94.76214321482223
          - type: precision
            value: 94.20380570856285
          - type: recall
            value: 95.94391587381071
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ell_Grek
          name: MTEB NTREXBitextMining (rus_Cyrl-ell_Grek)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 95.44316474712068
          - type: f1
            value: 94.14788849941579
          - type: main_score
            value: 94.14788849941579
          - type: precision
            value: 93.54197963612084
          - type: recall
            value: 95.44316474712068
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-eng_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-eng_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 98.14722083124687
          - type: f1
            value: 97.57135703555333
          - type: main_score
            value: 97.57135703555333
          - type: precision
            value: 97.2959439158738
          - type: recall
            value: 98.14722083124687
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-fas_Arab
          name: MTEB NTREXBitextMining (rus_Cyrl-fas_Arab)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 94.64196294441662
          - type: f1
            value: 93.24653647137372
          - type: main_score
            value: 93.24653647137372
          - type: precision
            value: 92.60724419963279
          - type: recall
            value: 94.64196294441662
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-fin_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-fin_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 87.98197295943916
          - type: f1
            value: 85.23368385912201
          - type: main_score
            value: 85.23368385912201
          - type: precision
            value: 84.08159858835873
          - type: recall
            value: 87.98197295943916
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-fra_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-fra_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 96.24436654982473
          - type: f1
            value: 95.07093974294774
          - type: main_score
            value: 95.07093974294774
          - type: precision
            value: 94.49591053246536
          - type: recall
            value: 96.24436654982473
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-heb_Hebr
          name: MTEB NTREXBitextMining (rus_Cyrl-heb_Hebr)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 91.08662994491738
          - type: f1
            value: 88.5161074945752
          - type: main_score
            value: 88.5161074945752
          - type: precision
            value: 87.36187614755467
          - type: recall
            value: 91.08662994491738
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-hin_Deva
          name: MTEB NTREXBitextMining (rus_Cyrl-hin_Deva)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 95.04256384576865
          - type: f1
            value: 93.66382907694876
          - type: main_score
            value: 93.66382907694876
          - type: precision
            value: 93.05291270238692
          - type: recall
            value: 95.04256384576865
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-hrv_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-hrv_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 95.14271407110667
          - type: f1
            value: 93.7481221832749
          - type: main_score
            value: 93.7481221832749
          - type: precision
            value: 93.10930681736892
          - type: recall
            value: 95.14271407110667
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-hun_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-hun_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 90.18527791687532
          - type: f1
            value: 87.61415933423946
          - type: main_score
            value: 87.61415933423946
          - type: precision
            value: 86.5166400394242
          - type: recall
            value: 90.18527791687532
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ind_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-ind_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 93.69053580370556
          - type: f1
            value: 91.83608746453012
          - type: main_score
            value: 91.83608746453012
          - type: precision
            value: 90.97145718577868
          - type: recall
            value: 93.69053580370556
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-jpn_Jpan
          name: MTEB NTREXBitextMining (rus_Cyrl-jpn_Jpan)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 89.48422633950926
          - type: f1
            value: 86.91271033534429
          - type: main_score
            value: 86.91271033534429
          - type: precision
            value: 85.82671626487351
          - type: recall
            value: 89.48422633950926
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-kor_Hang
          name: MTEB NTREXBitextMining (rus_Cyrl-kor_Hang)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 88.4827240861292
          - type: f1
            value: 85.35080398375342
          - type: main_score
            value: 85.35080398375342
          - type: precision
            value: 83.9588549490903
          - type: recall
            value: 88.4827240861292
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-lit_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-lit_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 90.33550325488233
          - type: f1
            value: 87.68831819157307
          - type: main_score
            value: 87.68831819157307
          - type: precision
            value: 86.51524906407231
          - type: recall
            value: 90.33550325488233
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-mkd_Cyrl
          name: MTEB NTREXBitextMining (rus_Cyrl-mkd_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 95.94391587381071
          - type: f1
            value: 94.90402270071775
          - type: main_score
            value: 94.90402270071775
          - type: precision
            value: 94.43915873810715
          - type: recall
            value: 95.94391587381071
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-nld_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-nld_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 92.98948422633951
          - type: f1
            value: 91.04323151393756
          - type: main_score
            value: 91.04323151393756
          - type: precision
            value: 90.14688699716241
          - type: recall
            value: 92.98948422633951
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-pol_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-pol_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 94.34151226840261
          - type: f1
            value: 92.8726422967785
          - type: main_score
            value: 92.8726422967785
          - type: precision
            value: 92.19829744616925
          - type: recall
            value: 94.34151226840261
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-por_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-por_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 86.17926890335504
          - type: f1
            value: 82.7304882287356
          - type: main_score
            value: 82.7304882287356
          - type: precision
            value: 81.28162481817964
          - type: recall
            value: 86.17926890335504
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-slk_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-slk_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 92.7391086629945
          - type: f1
            value: 90.75112669003506
          - type: main_score
            value: 90.75112669003506
          - type: precision
            value: 89.8564513436822
          - type: recall
            value: 92.7391086629945
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-slv_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-slv_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 92.8893340010015
          - type: f1
            value: 91.05992321816058
          - type: main_score
            value: 91.05992321816058
          - type: precision
            value: 90.22589439715128
          - type: recall
            value: 92.8893340010015
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-spa_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-spa_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 96.49474211316975
          - type: f1
            value: 95.4715406442998
          - type: main_score
            value: 95.4715406442998
          - type: precision
            value: 94.9799699549324
          - type: recall
            value: 96.49474211316975
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-srp_Cyrl
          name: MTEB NTREXBitextMining (rus_Cyrl-srp_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 81.07160741111667
          - type: f1
            value: 76.55687285507015
          - type: main_score
            value: 76.55687285507015
          - type: precision
            value: 74.71886401030116
          - type: recall
            value: 81.07160741111667
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-srp_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-srp_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 95.14271407110667
          - type: f1
            value: 93.73302377809138
          - type: main_score
            value: 93.73302377809138
          - type: precision
            value: 93.06960440660991
          - type: recall
            value: 95.14271407110667
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-swa_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-swa_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 94.79218828242364
          - type: f1
            value: 93.25988983475212
          - type: main_score
            value: 93.25988983475212
          - type: precision
            value: 92.53463528626273
          - type: recall
            value: 94.79218828242364
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-swe_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-swe_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 95.04256384576865
          - type: f1
            value: 93.58704723752295
          - type: main_score
            value: 93.58704723752295
          - type: precision
            value: 92.91437155733601
          - type: recall
            value: 95.04256384576865
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-tam_Taml
          name: MTEB NTREXBitextMining (rus_Cyrl-tam_Taml)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 93.28993490235354
          - type: f1
            value: 91.63912535469872
          - type: main_score
            value: 91.63912535469872
          - type: precision
            value: 90.87738750983617
          - type: recall
            value: 93.28993490235354
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-tur_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-tur_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 93.74061091637456
          - type: f1
            value: 91.96628275746953
          - type: main_score
            value: 91.96628275746953
          - type: precision
            value: 91.15923885828742
          - type: recall
            value: 93.74061091637456
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-ukr_Cyrl
          name: MTEB NTREXBitextMining (rus_Cyrl-ukr_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 95.99399098647972
          - type: f1
            value: 94.89567684860624
          - type: main_score
            value: 94.89567684860624
          - type: precision
            value: 94.37072275079286
          - type: recall
            value: 95.99399098647972
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-vie_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-vie_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 91.4371557336004
          - type: f1
            value: 88.98681355366382
          - type: main_score
            value: 88.98681355366382
          - type: precision
            value: 87.89183775663496
          - type: recall
            value: 91.4371557336004
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-zho_Hant
          name: MTEB NTREXBitextMining (rus_Cyrl-zho_Hant)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 92.7891837756635
          - type: f1
            value: 90.79047142141783
          - type: main_score
            value: 90.79047142141783
          - type: precision
            value: 89.86980470706058
          - type: recall
            value: 92.7891837756635
        task:
          type: BitextMining
      - dataset:
          config: rus_Cyrl-zul_Latn
          name: MTEB NTREXBitextMining (rus_Cyrl-zul_Latn)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 87.43114672008012
          - type: f1
            value: 84.04618833011422
          - type: main_score
            value: 84.04618833011422
          - type: precision
            value: 82.52259341393041
          - type: recall
            value: 87.43114672008012
        task:
          type: BitextMining
      - dataset:
          config: slk_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (slk_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 95.34301452178268
          - type: f1
            value: 94.20392493502158
          - type: main_score
            value: 94.20392493502158
          - type: precision
            value: 93.67384409948257
          - type: recall
            value: 95.34301452178268
        task:
          type: BitextMining
      - dataset:
          config: slv_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (slv_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 92.23835753630446
          - type: f1
            value: 90.5061759305625
          - type: main_score
            value: 90.5061759305625
          - type: precision
            value: 89.74231188051918
          - type: recall
            value: 92.23835753630446
        task:
          type: BitextMining
      - dataset:
          config: spa_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (spa_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 96.54481722583876
          - type: f1
            value: 95.54665331330328
          - type: main_score
            value: 95.54665331330328
          - type: precision
            value: 95.06342847604739
          - type: recall
            value: 96.54481722583876
        task:
          type: BitextMining
      - dataset:
          config: srp_Cyrl-rus_Cyrl
          name: MTEB NTREXBitextMining (srp_Cyrl-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 83.62543815723585
          - type: f1
            value: 80.77095672699816
          - type: main_score
            value: 80.77095672699816
          - type: precision
            value: 79.74674313056886
          - type: recall
            value: 83.62543815723585
        task:
          type: BitextMining
      - dataset:
          config: srp_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (srp_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 94.44166249374061
          - type: f1
            value: 93.00733206591994
          - type: main_score
            value: 93.00733206591994
          - type: precision
            value: 92.37203026762366
          - type: recall
            value: 94.44166249374061
        task:
          type: BitextMining
      - dataset:
          config: swa_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (swa_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 90.23535302954431
          - type: f1
            value: 87.89596482636041
          - type: main_score
            value: 87.89596482636041
          - type: precision
            value: 86.87060227370694
          - type: recall
            value: 90.23535302954431
        task:
          type: BitextMining
      - dataset:
          config: swe_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (swe_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 95.44316474712068
          - type: f1
            value: 94.1896177599733
          - type: main_score
            value: 94.1896177599733
          - type: precision
            value: 93.61542313470206
          - type: recall
            value: 95.44316474712068
        task:
          type: BitextMining
      - dataset:
          config: tam_Taml-rus_Cyrl
          name: MTEB NTREXBitextMining (tam_Taml-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 89.68452679018529
          - type: f1
            value: 87.37341160650037
          - type: main_score
            value: 87.37341160650037
          - type: precision
            value: 86.38389402285247
          - type: recall
            value: 89.68452679018529
        task:
          type: BitextMining
      - dataset:
          config: tur_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (tur_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 93.89083625438157
          - type: f1
            value: 92.33892505424804
          - type: main_score
            value: 92.33892505424804
          - type: precision
            value: 91.63125640842216
          - type: recall
            value: 93.89083625438157
        task:
          type: BitextMining
      - dataset:
          config: ukr_Cyrl-rus_Cyrl
          name: MTEB NTREXBitextMining (ukr_Cyrl-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 96.14421632448673
          - type: f1
            value: 95.11028447433054
          - type: main_score
            value: 95.11028447433054
          - type: precision
            value: 94.62944416624937
          - type: recall
            value: 96.14421632448673
        task:
          type: BitextMining
      - dataset:
          config: vie_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (vie_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 93.79068602904357
          - type: f1
            value: 92.14989150392256
          - type: main_score
            value: 92.14989150392256
          - type: precision
            value: 91.39292271740945
          - type: recall
            value: 93.79068602904357
        task:
          type: BitextMining
      - dataset:
          config: zho_Hant-rus_Cyrl
          name: MTEB NTREXBitextMining (zho_Hant-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 89.13370055082625
          - type: f1
            value: 86.51514618639217
          - type: main_score
            value: 86.51514618639217
          - type: precision
            value: 85.383920035898
          - type: recall
            value: 89.13370055082625
        task:
          type: BitextMining
      - dataset:
          config: zul_Latn-rus_Cyrl
          name: MTEB NTREXBitextMining (zul_Latn-rus_Cyrl)
          revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
          split: test
          type: mteb/NTREX
        metrics:
          - type: accuracy
            value: 81.17175763645467
          - type: f1
            value: 77.72331766047338
          - type: main_score
            value: 77.72331766047338
          - type: precision
            value: 76.24629555848075
          - type: recall
            value: 81.17175763645467
        task:
          type: BitextMining
      - dataset:
          config: ru
          name: MTEB OpusparcusPC (ru)
          revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
          split: test.full
          type: GEM/opusparcus
        metrics:
          - type: cosine_accuracy
            value: 73.09136420525657
          - type: cosine_accuracy_threshold
            value: 87.70400881767273
          - type: cosine_ap
            value: 86.51938550599533
          - type: cosine_f1
            value: 80.84358523725834
          - type: cosine_f1_threshold
            value: 86.90648078918457
          - type: cosine_precision
            value: 73.24840764331209
          - type: cosine_recall
            value: 90.19607843137256
          - type: dot_accuracy
            value: 73.09136420525657
          - type: dot_accuracy_threshold
            value: 87.7040147781372
          - type: dot_ap
            value: 86.51934769946833
          - type: dot_f1
            value: 80.84358523725834
          - type: dot_f1_threshold
            value: 86.90648078918457
          - type: dot_precision
            value: 73.24840764331209
          - type: dot_recall
            value: 90.19607843137256
          - type: euclidean_accuracy
            value: 73.09136420525657
          - type: euclidean_accuracy_threshold
            value: 49.590304493904114
          - type: euclidean_ap
            value: 86.51934769946833
          - type: euclidean_f1
            value: 80.84358523725834
          - type: euclidean_f1_threshold
            value: 51.173269748687744
          - type: euclidean_precision
            value: 73.24840764331209
          - type: euclidean_recall
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          - type: main_score
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          - type: nauc_recall_at_5_diff1
            value: 59.450505195434125
          - type: nauc_recall_at_5_max
            value: 32.07638712418387
          - type: nauc_recall_at_5_std
            value: -10.024459103498472
          - type: ndcg_at_1
            value: 55.97
          - type: ndcg_at_10
            value: 70.00999999999999
          - type: ndcg_at_100
            value: 72.20100000000001
          - type: ndcg_at_1000
            value: 72.65599999999999
          - type: ndcg_at_20
            value: 71.068
          - type: ndcg_at_3
            value: 66.228
          - type: ndcg_at_5
            value: 68.191
          - type: precision_at_1
            value: 55.97
          - type: precision_at_10
            value: 8.373999999999999
          - type: precision_at_100
            value: 0.9390000000000001
          - type: precision_at_1000
            value: 0.097
          - type: precision_at_20
            value: 4.3950000000000005
          - type: precision_at_3
            value: 24.46
          - type: precision_at_5
            value: 15.626000000000001
          - type: recall_at_1
            value: 55.97
          - type: recall_at_10
            value: 83.74000000000001
          - type: recall_at_100
            value: 93.87
          - type: recall_at_1000
            value: 97.49
          - type: recall_at_20
            value: 87.89
          - type: recall_at_3
            value: 73.38
          - type: recall_at_5
            value: 78.13
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB RuBQReranking (default)
          revision: 2e96b8f098fa4b0950fc58eacadeb31c0d0c7fa2
          split: test
          type: ai-forever/rubq-reranking
        metrics:
          - type: main_score
            value: 71.44929565043827
          - type: map
            value: 71.44929565043827
          - type: mrr
            value: 77.78391820945014
          - type: nAUC_map_diff1
            value: 38.140840668080244
          - type: nAUC_map_max
            value: 27.54328688105381
          - type: nAUC_map_std
            value: 16.81572082284672
          - type: nAUC_mrr_diff1
            value: 44.51350415961509
          - type: nAUC_mrr_max
            value: 36.491182016669754
          - type: nAUC_mrr_std
            value: 22.47139593052269
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB RuBQRetrieval (default)
          revision: e19b6ffa60b3bc248e0b41f4cc37c26a55c2a67b
          split: test
          type: ai-forever/rubq-retrieval
        metrics:
          - type: main_score
            value: 68.529
          - type: map_at_1
            value: 42.529
          - type: map_at_10
            value: 60.864
          - type: map_at_100
            value: 61.868
          - type: map_at_1000
            value: 61.907000000000004
          - type: map_at_20
            value: 61.596
          - type: map_at_3
            value: 55.701
          - type: map_at_5
            value: 58.78
          - type: mrr_at_1
            value: 60.57919621749409
          - type: mrr_at_10
            value: 70.55614188149649
          - type: mrr_at_100
            value: 70.88383816664494
          - type: mrr_at_1000
            value: 70.89719252668833
          - type: mrr_at_20
            value: 70.79839750105347
          - type: mrr_at_3
            value: 68.4594168636722
          - type: mrr_at_5
            value: 69.67100078802214
          - type: nauc_map_at_1000_diff1
            value: 40.67438785660885
          - type: nauc_map_at_1000_max
            value: 32.79981738507424
          - type: nauc_map_at_1000_std
            value: -6.873402600044831
          - type: nauc_map_at_100_diff1
            value: 40.65643664443284
          - type: nauc_map_at_100_max
            value: 32.81594799919249
          - type: nauc_map_at_100_std
            value: -6.8473246794498195
          - type: nauc_map_at_10_diff1
            value: 40.39048268484908
          - type: nauc_map_at_10_max
            value: 32.403242161479525
          - type: nauc_map_at_10_std
            value: -7.344413799841244
          - type: nauc_map_at_1_diff1
            value: 44.36306892906905
          - type: nauc_map_at_1_max
            value: 25.61348630699028
          - type: nauc_map_at_1_std
            value: -8.713074613333902
          - type: nauc_map_at_20_diff1
            value: 40.530326570124615
          - type: nauc_map_at_20_max
            value: 32.74028319323205
          - type: nauc_map_at_20_std
            value: -7.008180779820569
          - type: nauc_map_at_3_diff1
            value: 40.764924859364044
          - type: nauc_map_at_3_max
            value: 29.809671682025336
          - type: nauc_map_at_3_std
            value: -9.205620202725564
          - type: nauc_map_at_5_diff1
            value: 40.88599496021476
          - type: nauc_map_at_5_max
            value: 32.1701894666848
          - type: nauc_map_at_5_std
            value: -7.801251849010623
          - type: nauc_mrr_at_1000_diff1
            value: 48.64181373540728
          - type: nauc_mrr_at_1000_max
            value: 40.136947990653546
          - type: nauc_mrr_at_1000_std
            value: -7.250260497468805
          - type: nauc_mrr_at_100_diff1
            value: 48.63349902496212
          - type: nauc_mrr_at_100_max
            value: 40.14510559704008
          - type: nauc_mrr_at_100_std
            value: -7.228702374801103
          - type: nauc_mrr_at_10_diff1
            value: 48.58580560194813
          - type: nauc_mrr_at_10_max
            value: 40.15075599433366
          - type: nauc_mrr_at_10_std
            value: -7.267928771548688
          - type: nauc_mrr_at_1_diff1
            value: 51.47535097164919
          - type: nauc_mrr_at_1_max
            value: 38.23579750430856
          - type: nauc_mrr_at_1_std
            value: -9.187785187137633
          - type: nauc_mrr_at_20_diff1
            value: 48.58688378336222
          - type: nauc_mrr_at_20_max
            value: 40.13408744088299
          - type: nauc_mrr_at_20_std
            value: -7.283132775160146
          - type: nauc_mrr_at_3_diff1
            value: 48.66833005454742
          - type: nauc_mrr_at_3_max
            value: 40.07987333638038
          - type: nauc_mrr_at_3_std
            value: -7.738819947521418
          - type: nauc_mrr_at_5_diff1
            value: 48.76536305941537
          - type: nauc_mrr_at_5_max
            value: 40.381929739522185
          - type: nauc_mrr_at_5_std
            value: -7.592858318378928
          - type: nauc_ndcg_at_1000_diff1
            value: 41.67304442004693
          - type: nauc_ndcg_at_1000_max
            value: 35.84126926253235
          - type: nauc_ndcg_at_1000_std
            value: -4.78971011604655
          - type: nauc_ndcg_at_100_diff1
            value: 41.16918850185783
          - type: nauc_ndcg_at_100_max
            value: 36.082461962326505
          - type: nauc_ndcg_at_100_std
            value: -4.092442251697269
          - type: nauc_ndcg_at_10_diff1
            value: 40.300065598615205
          - type: nauc_ndcg_at_10_max
            value: 34.87866296788365
          - type: nauc_ndcg_at_10_std
            value: -5.866529277842453
          - type: nauc_ndcg_at_1_diff1
            value: 51.74612915209495
          - type: nauc_ndcg_at_1_max
            value: 37.71907067970078
          - type: nauc_ndcg_at_1_std
            value: -9.064124266098696
          - type: nauc_ndcg_at_20_diff1
            value: 40.493949850214584
          - type: nauc_ndcg_at_20_max
            value: 35.69331503650286
          - type: nauc_ndcg_at_20_std
            value: -4.995310342975443
          - type: nauc_ndcg_at_3_diff1
            value: 41.269443212112364
          - type: nauc_ndcg_at_3_max
            value: 32.572844460953334
          - type: nauc_ndcg_at_3_std
            value: -9.063015396458791
          - type: nauc_ndcg_at_5_diff1
            value: 41.37039652522888
          - type: nauc_ndcg_at_5_max
            value: 34.67416011393571
          - type: nauc_ndcg_at_5_std
            value: -7.106845569862319
          - type: nauc_precision_at_1000_diff1
            value: -9.571769961090155
          - type: nauc_precision_at_1000_max
            value: 5.574782583417188
          - type: nauc_precision_at_1000_std
            value: 7.28333847923847
          - type: nauc_precision_at_100_diff1
            value: -7.7405012003383735
          - type: nauc_precision_at_100_max
            value: 9.67745355070353
          - type: nauc_precision_at_100_std
            value: 9.327890294080992
          - type: nauc_precision_at_10_diff1
            value: -1.006879647532931
          - type: nauc_precision_at_10_max
            value: 15.899825481231064
          - type: nauc_precision_at_10_std
            value: 4.2284084852153105
          - type: nauc_precision_at_1_diff1
            value: 51.74612915209495
          - type: nauc_precision_at_1_max
            value: 37.71907067970078
          - type: nauc_precision_at_1_std
            value: -9.064124266098696
          - type: nauc_precision_at_20_diff1
            value: -4.982301544401409
          - type: nauc_precision_at_20_max
            value: 13.241674471380568
          - type: nauc_precision_at_20_std
            value: 7.052280133821539
          - type: nauc_precision_at_3_diff1
            value: 15.442614376387374
          - type: nauc_precision_at_3_max
            value: 25.12695418083
          - type: nauc_precision_at_3_std
            value: -3.1150066697920638
          - type: nauc_precision_at_5_diff1
            value: 8.381026072692444
          - type: nauc_precision_at_5_max
            value: 22.839056540604822
          - type: nauc_precision_at_5_std
            value: 1.5126905486524331
          - type: nauc_recall_at_1000_diff1
            value: -0.8869709920433502
          - type: nauc_recall_at_1000_max
            value: 45.092324433377264
          - type: nauc_recall_at_1000_std
            value: 62.21264093315108
          - type: nauc_recall_at_100_diff1
            value: 16.036715011075714
          - type: nauc_recall_at_100_max
            value: 39.79963411771158
          - type: nauc_recall_at_100_std
            value: 28.41850069503361
          - type: nauc_recall_at_10_diff1
            value: 25.189622794479998
          - type: nauc_recall_at_10_max
            value: 30.82355277039427
          - type: nauc_recall_at_10_std
            value: 0.0964544736531047
          - type: nauc_recall_at_1_diff1
            value: 44.36306892906905
          - type: nauc_recall_at_1_max
            value: 25.61348630699028
          - type: nauc_recall_at_1_std
            value: -8.713074613333902
          - type: nauc_recall_at_20_diff1
            value: 20.43424504746087
          - type: nauc_recall_at_20_max
            value: 33.96010554649377
          - type: nauc_recall_at_20_std
            value: 6.900984030301936
          - type: nauc_recall_at_3_diff1
            value: 33.86531858793492
          - type: nauc_recall_at_3_max
            value: 27.725692256711188
          - type: nauc_recall_at_3_std
            value: -8.533124289305709
          - type: nauc_recall_at_5_diff1
            value: 32.006964557701686
          - type: nauc_recall_at_5_max
            value: 31.493370659289806
          - type: nauc_recall_at_5_std
            value: -4.8639793547793255
          - type: ndcg_at_1
            value: 60.461
          - type: ndcg_at_10
            value: 68.529
          - type: ndcg_at_100
            value: 71.664
          - type: ndcg_at_1000
            value: 72.396
          - type: ndcg_at_20
            value: 70.344
          - type: ndcg_at_3
            value: 61.550000000000004
          - type: ndcg_at_5
            value: 64.948
          - type: precision_at_1
            value: 60.461
          - type: precision_at_10
            value: 13.28
          - type: precision_at_100
            value: 1.555
          - type: precision_at_1000
            value: 0.164
          - type: precision_at_20
            value: 7.216
          - type: precision_at_3
            value: 33.077
          - type: precision_at_5
            value: 23.014000000000003
          - type: recall_at_1
            value: 42.529
          - type: recall_at_10
            value: 81.169
          - type: recall_at_100
            value: 93.154
          - type: recall_at_1000
            value: 98.18299999999999
          - type: recall_at_20
            value: 87.132
          - type: recall_at_3
            value: 63.905
          - type: recall_at_5
            value: 71.967
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB RuReviewsClassification (default)
          revision: f6d2c31f4dc6b88f468552750bfec05b4b41b05a
          split: test
          type: ai-forever/ru-reviews-classification
        metrics:
          - type: accuracy
            value: 61.17675781250001
          - type: f1
            value: 60.354535346041374
          - type: f1_weighted
            value: 60.35437313166116
          - type: main_score
            value: 61.17675781250001
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB RuSTSBenchmarkSTS (default)
          revision: 7cf24f325c6da6195df55bef3d86b5e0616f3018
          split: test
          type: ai-forever/ru-stsbenchmark-sts
        metrics:
          - type: cosine_pearson
            value: 78.1301041727274
          - type: cosine_spearman
            value: 78.08238025421747
          - type: euclidean_pearson
            value: 77.35224254583635
          - type: euclidean_spearman
            value: 78.08235336582496
          - type: main_score
            value: 78.08238025421747
          - type: manhattan_pearson
            value: 77.24138550052075
          - type: manhattan_spearman
            value: 77.98199107904142
          - type: pearson
            value: 78.1301041727274
          - type: spearman
            value: 78.08238025421747
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB RuSciBenchGRNTIClassification (default)
          revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
          split: test
          type: ai-forever/ru-scibench-grnti-classification
        metrics:
          - type: accuracy
            value: 54.990234375
          - type: f1
            value: 53.537019057131374
          - type: f1_weighted
            value: 53.552745354520766
          - type: main_score
            value: 54.990234375
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB RuSciBenchGRNTIClusteringP2P (default)
          revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
          split: test
          type: ai-forever/ru-scibench-grnti-classification
        metrics:
          - type: main_score
            value: 50.775228895355106
          - type: v_measure
            value: 50.775228895355106
          - type: v_measure_std
            value: 0.9533571150165796
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB RuSciBenchOECDClassification (default)
          revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
          split: test
          type: ai-forever/ru-scibench-oecd-classification
        metrics:
          - type: accuracy
            value: 41.71875
          - type: f1
            value: 39.289100975858304
          - type: f1_weighted
            value: 39.29257829217775
          - type: main_score
            value: 41.71875
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB RuSciBenchOECDClusteringP2P (default)
          revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
          split: test
          type: ai-forever/ru-scibench-oecd-classification
        metrics:
          - type: main_score
            value: 45.10904808834516
          - type: v_measure
            value: 45.10904808834516
          - type: v_measure_std
            value: 1.0572643410157534
        task:
          type: Clustering
      - dataset:
          config: rus_Cyrl
          name: MTEB SIB200Classification (rus_Cyrl)
          revision: a74d7350ea12af010cfb1c21e34f1f81fd2e615b
          split: test
          type: mteb/sib200
        metrics:
          - type: accuracy
            value: 66.36363636363637
          - type: f1
            value: 64.6940336621617
          - type: f1_weighted
            value: 66.43317771876966
          - type: main_score
            value: 66.36363636363637
        task:
          type: Classification
      - dataset:
          config: rus_Cyrl
          name: MTEB SIB200ClusteringS2S (rus_Cyrl)
          revision: a74d7350ea12af010cfb1c21e34f1f81fd2e615b
          split: test
          type: mteb/sib200
        metrics:
          - type: main_score
            value: 33.99178497314711
          - type: v_measure
            value: 33.99178497314711
          - type: v_measure_std
            value: 4.036337464043786
        task:
          type: Clustering
      - dataset:
          config: ru
          name: MTEB STS22.v2 (ru)
          revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cosine_pearson
            value: 50.724322379215934
          - type: cosine_spearman
            value: 59.90449732164651
          - type: euclidean_pearson
            value: 50.227545226784024
          - type: euclidean_spearman
            value: 59.898906527601085
          - type: main_score
            value: 59.90449732164651
          - type: manhattan_pearson
            value: 50.21762139819405
          - type: manhattan_spearman
            value: 59.761039813759
          - type: pearson
            value: 50.724322379215934
          - type: spearman
            value: 59.90449732164651
        task:
          type: STS
      - dataset:
          config: ru
          name: MTEB STSBenchmarkMultilingualSTS (ru)
          revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
          split: dev
          type: mteb/stsb_multi_mt
        metrics:
          - type: cosine_pearson
            value: 78.43928769569945
          - type: cosine_spearman
            value: 78.23961768018884
          - type: euclidean_pearson
            value: 77.4718694027985
          - type: euclidean_spearman
            value: 78.23887044760475
          - type: main_score
            value: 78.23961768018884
          - type: manhattan_pearson
            value: 77.34517128089547
          - type: manhattan_spearman
            value: 78.1146477340426
          - type: pearson
            value: 78.43928769569945
          - type: spearman
            value: 78.23961768018884
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB SensitiveTopicsClassification (default)
          revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2
          split: test
          type: ai-forever/sensitive-topics-classification
        metrics:
          - type: accuracy
            value: 22.8125
          - type: f1
            value: 17.31969589593409
          - type: lrap
            value: 33.82412380642287
          - type: main_score
            value: 22.8125
        task:
          type: MultilabelClassification
      - dataset:
          config: default
          name: MTEB TERRa (default)
          revision: 7b58f24536063837d644aab9a023c62199b2a612
          split: dev
          type: ai-forever/terra-pairclassification
        metrics:
          - type: cosine_accuracy
            value: 57.32899022801303
          - type: cosine_accuracy_threshold
            value: 85.32201051712036
          - type: cosine_ap
            value: 55.14264553720072
          - type: cosine_f1
            value: 66.83544303797468
          - type: cosine_f1_threshold
            value: 85.32201051712036
          - type: cosine_precision
            value: 54.54545454545454
          - type: cosine_recall
            value: 86.27450980392157
          - type: dot_accuracy
            value: 57.32899022801303
          - type: dot_accuracy_threshold
            value: 85.32201051712036
          - type: dot_ap
            value: 55.14264553720072
          - type: dot_f1
            value: 66.83544303797468
          - type: dot_f1_threshold
            value: 85.32201051712036
          - type: dot_precision
            value: 54.54545454545454
          - type: dot_recall
            value: 86.27450980392157
          - type: euclidean_accuracy
            value: 57.32899022801303
          - type: euclidean_accuracy_threshold
            value: 54.18117046356201
          - type: euclidean_ap
            value: 55.14264553720072
          - type: euclidean_f1
            value: 66.83544303797468
          - type: euclidean_f1_threshold
            value: 54.18117046356201
          - type: euclidean_precision
            value: 54.54545454545454
          - type: euclidean_recall
            value: 86.27450980392157
          - type: main_score
            value: 55.14264553720072
          - type: manhattan_accuracy
            value: 57.32899022801303
          - type: manhattan_accuracy_threshold
            value: 828.8480758666992
          - type: manhattan_ap
            value: 55.077974053622555
          - type: manhattan_f1
            value: 66.82352941176471
          - type: manhattan_f1_threshold
            value: 885.6784820556641
          - type: manhattan_precision
            value: 52.20588235294118
          - type: manhattan_recall
            value: 92.81045751633987
          - type: max_ap
            value: 55.14264553720072
          - type: max_f1
            value: 66.83544303797468
          - type: max_precision
            value: 54.54545454545454
          - type: max_recall
            value: 92.81045751633987
          - type: similarity_accuracy
            value: 57.32899022801303
          - type: similarity_accuracy_threshold
            value: 85.32201051712036
          - type: similarity_ap
            value: 55.14264553720072
          - type: similarity_f1
            value: 66.83544303797468
          - type: similarity_f1_threshold
            value: 85.32201051712036
          - type: similarity_precision
            value: 54.54545454545454
          - type: similarity_recall
            value: 86.27450980392157
        task:
          type: PairClassification
      - dataset:
          config: ru
          name: MTEB XNLI (ru)
          revision: 09698e0180d87dc247ca447d3a1248b931ac0cdb
          split: test
          type: mteb/xnli
        metrics:
          - type: cosine_accuracy
            value: 67.6923076923077
          - type: cosine_accuracy_threshold
            value: 87.6681923866272
          - type: cosine_ap
            value: 73.18693800863593
          - type: cosine_f1
            value: 70.40641099026904
          - type: cosine_f1_threshold
            value: 85.09706258773804
          - type: cosine_precision
            value: 57.74647887323944
          - type: cosine_recall
            value: 90.17595307917888
          - type: dot_accuracy
            value: 67.6923076923077
          - type: dot_accuracy_threshold
            value: 87.66818642616272
          - type: dot_ap
            value: 73.18693800863593
          - type: dot_f1
            value: 70.40641099026904
          - type: dot_f1_threshold
            value: 85.09706258773804
          - type: dot_precision
            value: 57.74647887323944
          - type: dot_recall
            value: 90.17595307917888
          - type: euclidean_accuracy
            value: 67.6923076923077
          - type: euclidean_accuracy_threshold
            value: 49.662476778030396
          - type: euclidean_ap
            value: 73.18693800863593
          - type: euclidean_f1
            value: 70.40641099026904
          - type: euclidean_f1_threshold
            value: 54.59475517272949
          - type: euclidean_precision
            value: 57.74647887323944
          - type: euclidean_recall
            value: 90.17595307917888
          - type: main_score
            value: 73.18693800863593
          - type: manhattan_accuracy
            value: 67.54578754578755
          - type: manhattan_accuracy_threshold
            value: 777.1001815795898
          - type: manhattan_ap
            value: 72.98861474758783
          - type: manhattan_f1
            value: 70.6842435655995
          - type: manhattan_f1_threshold
            value: 810.3782653808594
          - type: manhattan_precision
            value: 61.80021953896817
          - type: manhattan_recall
            value: 82.55131964809385
          - type: max_ap
            value: 73.18693800863593
          - type: max_f1
            value: 70.6842435655995
          - type: max_precision
            value: 61.80021953896817
          - type: max_recall
            value: 90.17595307917888
          - type: similarity_accuracy
            value: 67.6923076923077
          - type: similarity_accuracy_threshold
            value: 87.6681923866272
          - type: similarity_ap
            value: 73.18693800863593
          - type: similarity_f1
            value: 70.40641099026904
          - type: similarity_f1_threshold
            value: 85.09706258773804
          - type: similarity_precision
            value: 57.74647887323944
          - type: similarity_recall
            value: 90.17595307917888
        task:
          type: PairClassification
      - dataset:
          config: russian
          name: MTEB XNLIV2 (russian)
          revision: 5b7d477a8c62cdd18e2fed7e015497c20b4371ad
          split: test
          type: mteb/xnli2.0-multi-pair
        metrics:
          - type: cosine_accuracy
            value: 68.35164835164835
          - type: cosine_accuracy_threshold
            value: 88.48621845245361
          - type: cosine_ap
            value: 73.10205506215699
          - type: cosine_f1
            value: 71.28712871287128
          - type: cosine_f1_threshold
            value: 87.00399398803711
          - type: cosine_precision
            value: 61.67023554603854
          - type: cosine_recall
            value: 84.4574780058651
          - type: dot_accuracy
            value: 68.35164835164835
          - type: dot_accuracy_threshold
            value: 88.48622441291809
          - type: dot_ap
            value: 73.10191110714706
          - type: dot_f1
            value: 71.28712871287128
          - type: dot_f1_threshold
            value: 87.00399398803711
          - type: dot_precision
            value: 61.67023554603854
          - type: dot_recall
            value: 84.4574780058651
          - type: euclidean_accuracy
            value: 68.35164835164835
          - type: euclidean_accuracy_threshold
            value: 47.98704385757446
          - type: euclidean_ap
            value: 73.10205506215699
          - type: euclidean_f1
            value: 71.28712871287128
          - type: euclidean_f1_threshold
            value: 50.982362031936646
          - type: euclidean_precision
            value: 61.67023554603854
          - type: euclidean_recall
            value: 84.4574780058651
          - type: main_score
            value: 73.10205506215699
          - type: manhattan_accuracy
            value: 67.91208791208791
          - type: manhattan_accuracy_threshold
            value: 746.1360931396484
          - type: manhattan_ap
            value: 72.8954736175069
          - type: manhattan_f1
            value: 71.1297071129707
          - type: manhattan_f1_threshold
            value: 808.0789566040039
          - type: manhattan_precision
            value: 60.04036326942482
          - type: manhattan_recall
            value: 87.2434017595308
          - type: max_ap
            value: 73.10205506215699
          - type: max_f1
            value: 71.28712871287128
          - type: max_precision
            value: 61.67023554603854
          - type: max_recall
            value: 87.2434017595308
          - type: similarity_accuracy
            value: 68.35164835164835
          - type: similarity_accuracy_threshold
            value: 88.48621845245361
          - type: similarity_ap
            value: 73.10205506215699
          - type: similarity_f1
            value: 71.28712871287128
          - type: similarity_f1_threshold
            value: 87.00399398803711
          - type: similarity_precision
            value: 61.67023554603854
          - type: similarity_recall
            value: 84.4574780058651
        task:
          type: PairClassification
      - dataset:
          config: ru
          name: MTEB XQuADRetrieval (ru)
          revision: 51adfef1c1287aab1d2d91b5bead9bcfb9c68583
          split: validation
          type: google/xquad
        metrics:
          - type: main_score
            value: 95.705
          - type: map_at_1
            value: 90.802
          - type: map_at_10
            value: 94.427
          - type: map_at_100
            value: 94.451
          - type: map_at_1000
            value: 94.451
          - type: map_at_20
            value: 94.446
          - type: map_at_3
            value: 94.121
          - type: map_at_5
            value: 94.34
          - type: mrr_at_1
            value: 90.80168776371308
          - type: mrr_at_10
            value: 94.42659567343111
          - type: mrr_at_100
            value: 94.45099347521871
          - type: mrr_at_1000
            value: 94.45099347521871
          - type: mrr_at_20
            value: 94.44574530017569
          - type: mrr_at_3
            value: 94.12095639943743
          - type: mrr_at_5
            value: 94.34036568213786
          - type: nauc_map_at_1000_diff1
            value: 87.40573202946949
          - type: nauc_map_at_1000_max
            value: 65.56220344468791
          - type: nauc_map_at_1000_std
            value: 8.865583291735863
          - type: nauc_map_at_100_diff1
            value: 87.40573202946949
          - type: nauc_map_at_100_max
            value: 65.56220344468791
          - type: nauc_map_at_100_std
            value: 8.865583291735863
          - type: nauc_map_at_10_diff1
            value: 87.43657080570291
          - type: nauc_map_at_10_max
            value: 65.71295628534446
          - type: nauc_map_at_10_std
            value: 9.055399339099655
          - type: nauc_map_at_1_diff1
            value: 88.08395824560428
          - type: nauc_map_at_1_max
            value: 62.92813192908893
          - type: nauc_map_at_1_std
            value: 6.738987385482432
          - type: nauc_map_at_20_diff1
            value: 87.40979818966589
          - type: nauc_map_at_20_max
            value: 65.59474346926105
          - type: nauc_map_at_20_std
            value: 8.944420599300914
          - type: nauc_map_at_3_diff1
            value: 86.97771892161035
          - type: nauc_map_at_3_max
            value: 66.14330030122467
          - type: nauc_map_at_3_std
            value: 8.62516327793521
          - type: nauc_map_at_5_diff1
            value: 87.30273362211798
          - type: nauc_map_at_5_max
            value: 66.1522476584607
          - type: nauc_map_at_5_std
            value: 9.780940862679724
          - type: nauc_mrr_at_1000_diff1
            value: 87.40573202946949
          - type: nauc_mrr_at_1000_max
            value: 65.56220344468791
          - type: nauc_mrr_at_1000_std
            value: 8.865583291735863
          - type: nauc_mrr_at_100_diff1
            value: 87.40573202946949
          - type: nauc_mrr_at_100_max
            value: 65.56220344468791
          - type: nauc_mrr_at_100_std
            value: 8.865583291735863
          - type: nauc_mrr_at_10_diff1
            value: 87.43657080570291
          - type: nauc_mrr_at_10_max
            value: 65.71295628534446
          - type: nauc_mrr_at_10_std
            value: 9.055399339099655
          - type: nauc_mrr_at_1_diff1
            value: 88.08395824560428
          - type: nauc_mrr_at_1_max
            value: 62.92813192908893
          - type: nauc_mrr_at_1_std
            value: 6.738987385482432
          - type: nauc_mrr_at_20_diff1
            value: 87.40979818966589
          - type: nauc_mrr_at_20_max
            value: 65.59474346926105
          - type: nauc_mrr_at_20_std
            value: 8.944420599300914
          - type: nauc_mrr_at_3_diff1
            value: 86.97771892161035
          - type: nauc_mrr_at_3_max
            value: 66.14330030122467
          - type: nauc_mrr_at_3_std
            value: 8.62516327793521
          - type: nauc_mrr_at_5_diff1
            value: 87.30273362211798
          - type: nauc_mrr_at_5_max
            value: 66.1522476584607
          - type: nauc_mrr_at_5_std
            value: 9.780940862679724
          - type: nauc_ndcg_at_1000_diff1
            value: 87.37823158814116
          - type: nauc_ndcg_at_1000_max
            value: 66.00874244792789
          - type: nauc_ndcg_at_1000_std
            value: 9.479929342875067
          - type: nauc_ndcg_at_100_diff1
            value: 87.37823158814116
          - type: nauc_ndcg_at_100_max
            value: 66.00874244792789
          - type: nauc_ndcg_at_100_std
            value: 9.479929342875067
          - type: nauc_ndcg_at_10_diff1
            value: 87.54508467181488
          - type: nauc_ndcg_at_10_max
            value: 66.88756470312894
          - type: nauc_ndcg_at_10_std
            value: 10.812624405397022
          - type: nauc_ndcg_at_1_diff1
            value: 88.08395824560428
          - type: nauc_ndcg_at_1_max
            value: 62.92813192908893
          - type: nauc_ndcg_at_1_std
            value: 6.738987385482432
          - type: nauc_ndcg_at_20_diff1
            value: 87.42097894104597
          - type: nauc_ndcg_at_20_max
            value: 66.37031898778943
          - type: nauc_ndcg_at_20_std
            value: 10.34862538094813
          - type: nauc_ndcg_at_3_diff1
            value: 86.50039907157999
          - type: nauc_ndcg_at_3_max
            value: 67.97798288917929
          - type: nauc_ndcg_at_3_std
            value: 10.162410286746852
          - type: nauc_ndcg_at_5_diff1
            value: 87.13322094568531
          - type: nauc_ndcg_at_5_max
            value: 68.08576118683821
          - type: nauc_ndcg_at_5_std
            value: 12.639637379592855
          - type: nauc_precision_at_1000_diff1
            value: 100
          - type: nauc_precision_at_1000_max
            value: 100
          - type: nauc_precision_at_1000_std
            value: 100
          - type: nauc_precision_at_100_diff1
            value: 100
          - type: nauc_precision_at_100_max
            value: 100
          - type: nauc_precision_at_100_std
            value: 100
          - type: nauc_precision_at_10_diff1
            value: 93.46711505595813
          - type: nauc_precision_at_10_max
            value: 100
          - type: nauc_precision_at_10_std
            value: 65.42573557179935
          - type: nauc_precision_at_1_diff1
            value: 88.08395824560428
          - type: nauc_precision_at_1_max
            value: 62.92813192908893
          - type: nauc_precision_at_1_std
            value: 6.738987385482432
          - type: nauc_precision_at_20_diff1
            value: 91.28948674127133
          - type: nauc_precision_at_20_max
            value: 100
          - type: nauc_precision_at_20_std
            value: 90.74278258632364
          - type: nauc_precision_at_3_diff1
            value: 82.64606115071832
          - type: nauc_precision_at_3_max
            value: 83.26201582412921
          - type: nauc_precision_at_3_std
            value: 23.334013491433762
          - type: nauc_precision_at_5_diff1
            value: 85.0867539350284
          - type: nauc_precision_at_5_max
            value: 96.57011448655484
          - type: nauc_precision_at_5_std
            value: 56.46869543426768
          - type: nauc_recall_at_1000_diff1
            value: .nan
          - type: nauc_recall_at_1000_max
            value: .nan
          - type: nauc_recall_at_1000_std
            value: .nan
          - type: nauc_recall_at_100_diff1
            value: .nan
          - type: nauc_recall_at_100_max
            value: .nan
          - type: nauc_recall_at_100_std
            value: .nan
          - type: nauc_recall_at_10_diff1
            value: 93.46711505595623
          - type: nauc_recall_at_10_max
            value: 100
          - type: nauc_recall_at_10_std
            value: 65.42573557180279
          - type: nauc_recall_at_1_diff1
            value: 88.08395824560428
          - type: nauc_recall_at_1_max
            value: 62.92813192908893
          - type: nauc_recall_at_1_std
            value: 6.738987385482432
          - type: nauc_recall_at_20_diff1
            value: 91.28948674127474
          - type: nauc_recall_at_20_max
            value: 100
          - type: nauc_recall_at_20_std
            value: 90.74278258632704
          - type: nauc_recall_at_3_diff1
            value: 82.64606115071967
          - type: nauc_recall_at_3_max
            value: 83.26201582413023
          - type: nauc_recall_at_3_std
            value: 23.334013491434007
          - type: nauc_recall_at_5_diff1
            value: 85.08675393502854
          - type: nauc_recall_at_5_max
            value: 96.57011448655487
          - type: nauc_recall_at_5_std
            value: 56.46869543426658
          - type: ndcg_at_1
            value: 90.802
          - type: ndcg_at_10
            value: 95.705
          - type: ndcg_at_100
            value: 95.816
          - type: ndcg_at_1000
            value: 95.816
          - type: ndcg_at_20
            value: 95.771
          - type: ndcg_at_3
            value: 95.11699999999999
          - type: ndcg_at_5
            value: 95.506
          - type: precision_at_1
            value: 90.802
          - type: precision_at_10
            value: 9.949
          - type: precision_at_100
            value: 1
          - type: precision_at_1000
            value: 0.1
          - type: precision_at_20
            value: 4.987
          - type: precision_at_3
            value: 32.658
          - type: precision_at_5
            value: 19.781000000000002
          - type: recall_at_1
            value: 90.802
          - type: recall_at_10
            value: 99.494
          - type: recall_at_100
            value: 100
          - type: recall_at_1000
            value: 100
          - type: recall_at_20
            value: 99.747
          - type: recall_at_3
            value: 97.975
          - type: recall_at_5
            value: 98.90299999999999
        task:
          type: Retrieval
tags:
  - mteb
  - Sentence Transformers
  - sentence-similarity
  - onnx
  - teradata

See Disclaimer below


A Teradata Vantage compatible Embeddings Model

intfloat/multilingual-e5-small

Overview of this Model

An Embedding Model which maps text (sentence/ paragraphs) into a vector. The intfloat/multilingual-e5-small model well known for its effectiveness in capturing semantic meanings in text data. It's a state-of-the-art model trained on a large corpus, capable of generating high-quality text embeddings.

  • 117.65M params (Sizes in ONNX format - "fp32": 448.58MB, "int8": 112.8MB, "uint8": 112.8MB)
  • 512 maximum input tokens
  • 384 dimensions of output vector
  • Licence: mit. The released models can be used for commercial purposes free of charge.
  • Reference to Original Model: https://huggingface.co/intfloat/multilingual-e5-small

Quickstart: Deploying this Model in Teradata Vantage

We have pre-converted the model into the ONNX format compatible with BYOM 6.0, eliminating the need for manual conversion.

Note: Ensure you have access to a Teradata Database with BYOM 6.0 installed.

To get started, clone the pre-converted model directly from the Teradata HuggingFace repository.


import teradataml as tdml
import getpass
from huggingface_hub import hf_hub_download

model_name = "multilingual-e5-small"
number_dimensions_output = 384
model_file_name = "model.onnx"

# Step 1: Download Model from Teradata HuggingFace Page

hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"onnx/{model_file_name}", local_dir="./")
hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"tokenizer.json", local_dir="./")

# Step 2: Create Connection to Vantage

tdml.create_context(host = input('enter your hostname'), 
                    username=input('enter your username'), 
                    password = getpass.getpass("enter your password"))

# Step 3: Load Models into Vantage
# a) Embedding model
tdml.save_byom(model_id = model_name, # must be unique in the models table
               model_file = f"onnx/{model_file_name}",
               table_name = 'embeddings_models' )
# b) Tokenizer
tdml.save_byom(model_id = model_name, # must be unique in the models table
              model_file = 'tokenizer.json',
              table_name = 'embeddings_tokenizers') 

# Step 4: Test ONNXEmbeddings Function
# Note that ONNXEmbeddings expects the 'payload' column to be 'txt'. 
# If it has got a different name, just rename it in a subquery/CTE.
input_table = "emails.emails"
embeddings_query = f"""
SELECT 
        *
from mldb.ONNXEmbeddings(
        on {input_table} as InputTable
        on (select * from embeddings_models where model_id = '{model_name}') as ModelTable DIMENSION
        on (select model as tokenizer from embeddings_tokenizers where model_id = '{model_name}') as TokenizerTable DIMENSION
        using
            Accumulate('id', 'txt') 
            ModelOutputTensor('sentence_embedding')
            EnableMemoryCheck('false')
            OutputFormat('FLOAT32({number_dimensions_output})')
            OverwriteCachedModel('true')
    ) a 
"""
DF_embeddings = tdml.DataFrame.from_query(embeddings_query)
DF_embeddings

What Can I Do with the Embeddings?

Teradata Vantage includes pre-built in-database functions to process embeddings further. Explore the following examples:

Deep Dive into Model Conversion to ONNX

The steps below outline how we converted the open-source Hugging Face model into an ONNX file compatible with the in-database ONNXEmbeddings function.

You do not need to perform these steps—they are provided solely for documentation and transparency. However, they may be helpful if you wish to convert another model to the required format.

Part 1. Importing and Converting Model using optimum

We start by importing the pre-trained intfloat/multilingual-e5-small model from Hugging Face.

To enhance performance and ensure compatibility with various execution environments, we'll use the Optimum utility to convert the model into the ONNX (Open Neural Network Exchange) format.

After conversion to ONNX, we are fixing the opset in the ONNX file for compatibility with ONNX runtime used in Teradata Vantage

We are generating ONNX files for multiple different precisions: fp32, int8, uint8

You can find the detailed conversion steps in the file convert.py

Part 2. Running the model in Python with onnxruntime & compare results

Once the fixes are applied, we proceed to test the correctness of the ONNX model by calculating cosine similarity between two texts using native SentenceTransformers and ONNX runtime, comparing the results.

If the results are identical, it confirms that the ONNX model gives the same result as the native models, validating its correctness and suitability for further use in the database.

import onnxruntime as rt

from sentence_transformers.util import cos_sim
from sentence_transformers import SentenceTransformer

import transformers


sentences_1 = 'How is the weather today?'
sentences_2 = 'What is the current weather like today?'

# Calculate ONNX result
tokenizer = transformers.AutoTokenizer.from_pretrained("intfloat/multilingual-e5-small")
predef_sess = rt.InferenceSession("onnx/model.onnx")

enc1 = tokenizer(sentences_1)
embeddings_1_onnx = predef_sess.run(None,     {"input_ids": [enc1.input_ids], 
     "attention_mask": [enc1.attention_mask]})

enc2 = tokenizer(sentences_2)
embeddings_2_onnx = predef_sess.run(None,     {"input_ids": [enc2.input_ids], 
     "attention_mask": [enc2.attention_mask]})


# Calculate embeddings with SentenceTransformer
model = SentenceTransformer(model_id, trust_remote_code=True)
embeddings_1_sentence_transformer = model.encode(sentences_1, normalize_embeddings=True, trust_remote_code=True)
embeddings_2_sentence_transformer = model.encode(sentences_2, normalize_embeddings=True, trust_remote_code=True)

# Compare results
print("Cosine similiarity for embeddings calculated with ONNX:" + str(cos_sim(embeddings_1_onnx[1][0], embeddings_2_onnx[1][0])))
print("Cosine similiarity for embeddings calculated with SentenceTransformer:" + str(cos_sim(embeddings_1_sentence_transformer, embeddings_2_sentence_transformer)))

You can find the detailed ONNX vs. SentenceTransformer result comparison steps in the file test_local.py


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