woody72's picture
Duplicate from intfloat/multilingual-e5-base
c88fe71
metadata
tags:
  - mteb
  - Sentence Transformers
  - sentence-similarity
  - sentence-transformers
model-index:
  - name: multilingual-e5-base
    results:
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_counterfactual
          name: MTEB AmazonCounterfactualClassification (en)
          config: en
          split: test
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
        metrics:
          - type: accuracy
            value: 78.97014925373135
          - type: ap
            value: 43.69351129103008
          - type: f1
            value: 73.38075030070492
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_counterfactual
          name: MTEB AmazonCounterfactualClassification (de)
          config: de
          split: test
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
        metrics:
          - type: accuracy
            value: 71.7237687366167
          - type: ap
            value: 82.22089859962671
          - type: f1
            value: 69.95532758884401
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_counterfactual
          name: MTEB AmazonCounterfactualClassification (en-ext)
          config: en-ext
          split: test
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
        metrics:
          - type: accuracy
            value: 79.65517241379312
          - type: ap
            value: 28.507918657094738
          - type: f1
            value: 66.84516013726119
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_counterfactual
          name: MTEB AmazonCounterfactualClassification (ja)
          config: ja
          split: test
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
        metrics:
          - type: accuracy
            value: 73.32976445396146
          - type: ap
            value: 20.720481637566014
          - type: f1
            value: 59.78002763416003
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_polarity
          name: MTEB AmazonPolarityClassification
          config: default
          split: test
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
        metrics:
          - type: accuracy
            value: 90.63775
          - type: ap
            value: 87.22277903861716
          - type: f1
            value: 90.60378636386807
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (en)
          config: en
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 44.546
          - type: f1
            value: 44.05666638370923
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (de)
          config: de
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 41.828
          - type: f1
            value: 41.2710255644252
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (es)
          config: es
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 40.534
          - type: f1
            value: 39.820743174270326
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (fr)
          config: fr
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 39.684
          - type: f1
            value: 39.11052682815307
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (ja)
          config: ja
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 37.436
          - type: f1
            value: 37.07082931930871
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (zh)
          config: zh
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 37.226000000000006
          - type: f1
            value: 36.65372077739185
      - task:
          type: Retrieval
        dataset:
          type: arguana
          name: MTEB ArguAna
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 22.831000000000003
          - type: map_at_10
            value: 36.42
          - type: map_at_100
            value: 37.699
          - type: map_at_1000
            value: 37.724000000000004
          - type: map_at_3
            value: 32.207
          - type: map_at_5
            value: 34.312
          - type: mrr_at_1
            value: 23.257
          - type: mrr_at_10
            value: 36.574
          - type: mrr_at_100
            value: 37.854
          - type: mrr_at_1000
            value: 37.878
          - type: mrr_at_3
            value: 32.385000000000005
          - type: mrr_at_5
            value: 34.48
          - type: ndcg_at_1
            value: 22.831000000000003
          - type: ndcg_at_10
            value: 44.230000000000004
          - type: ndcg_at_100
            value: 49.974000000000004
          - type: ndcg_at_1000
            value: 50.522999999999996
          - type: ndcg_at_3
            value: 35.363
          - type: ndcg_at_5
            value: 39.164
          - type: precision_at_1
            value: 22.831000000000003
          - type: precision_at_10
            value: 6.935
          - type: precision_at_100
            value: 0.9520000000000001
          - type: precision_at_1000
            value: 0.099
          - type: precision_at_3
            value: 14.841
          - type: precision_at_5
            value: 10.754
          - type: recall_at_1
            value: 22.831000000000003
          - type: recall_at_10
            value: 69.346
          - type: recall_at_100
            value: 95.235
          - type: recall_at_1000
            value: 99.36
          - type: recall_at_3
            value: 44.523
          - type: recall_at_5
            value: 53.769999999999996
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-p2p
          name: MTEB ArxivClusteringP2P
          config: default
          split: test
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
        metrics:
          - type: v_measure
            value: 40.27789869854063
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-s2s
          name: MTEB ArxivClusteringS2S
          config: default
          split: test
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
        metrics:
          - type: v_measure
            value: 35.41979463347428
      - task:
          type: Reranking
        dataset:
          type: mteb/askubuntudupquestions-reranking
          name: MTEB AskUbuntuDupQuestions
          config: default
          split: test
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
        metrics:
          - type: map
            value: 58.22752045109304
          - type: mrr
            value: 71.51112430198303
      - task:
          type: STS
        dataset:
          type: mteb/biosses-sts
          name: MTEB BIOSSES
          config: default
          split: test
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
        metrics:
          - type: cos_sim_pearson
            value: 84.71147646622866
          - type: cos_sim_spearman
            value: 85.059167046486
          - type: euclidean_pearson
            value: 75.88421613600647
          - type: euclidean_spearman
            value: 75.12821787150585
          - type: manhattan_pearson
            value: 75.22005646957604
          - type: manhattan_spearman
            value: 74.42880434453272
      - task:
          type: BitextMining
        dataset:
          type: mteb/bucc-bitext-mining
          name: MTEB BUCC (de-en)
          config: de-en
          split: test
          revision: d51519689f32196a32af33b075a01d0e7c51e252
        metrics:
          - type: accuracy
            value: 99.23799582463465
          - type: f1
            value: 99.12665274878218
          - type: precision
            value: 99.07098121085595
          - type: recall
            value: 99.23799582463465
      - task:
          type: BitextMining
        dataset:
          type: mteb/bucc-bitext-mining
          name: MTEB BUCC (fr-en)
          config: fr-en
          split: test
          revision: d51519689f32196a32af33b075a01d0e7c51e252
        metrics:
          - type: accuracy
            value: 97.88685890380806
          - type: f1
            value: 97.59336708489249
          - type: precision
            value: 97.44662117543473
          - type: recall
            value: 97.88685890380806
      - task:
          type: BitextMining
        dataset:
          type: mteb/bucc-bitext-mining
          name: MTEB BUCC (ru-en)
          config: ru-en
          split: test
          revision: d51519689f32196a32af33b075a01d0e7c51e252
        metrics:
          - type: accuracy
            value: 97.47142362313821
          - type: f1
            value: 97.1989377670015
          - type: precision
            value: 97.06384944001847
          - type: recall
            value: 97.47142362313821
      - task:
          type: BitextMining
        dataset:
          type: mteb/bucc-bitext-mining
          name: MTEB BUCC (zh-en)
          config: zh-en
          split: test
          revision: d51519689f32196a32af33b075a01d0e7c51e252
        metrics:
          - type: accuracy
            value: 98.4728804634018
          - type: f1
            value: 98.2973494821836
          - type: precision
            value: 98.2095839915745
          - type: recall
            value: 98.4728804634018
      - task:
          type: Classification
        dataset:
          type: mteb/banking77
          name: MTEB Banking77Classification
          config: default
          split: test
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
        metrics:
          - type: accuracy
            value: 82.74025974025975
          - type: f1
            value: 82.67420447730439
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-p2p
          name: MTEB BiorxivClusteringP2P
          config: default
          split: test
          revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
        metrics:
          - type: v_measure
            value: 35.0380848063507
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-s2s
          name: MTEB BiorxivClusteringS2S
          config: default
          split: test
          revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
        metrics:
          - type: v_measure
            value: 29.45956405670166
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackAndroidRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 32.122
          - type: map_at_10
            value: 42.03
          - type: map_at_100
            value: 43.364000000000004
          - type: map_at_1000
            value: 43.474000000000004
          - type: map_at_3
            value: 38.804
          - type: map_at_5
            value: 40.585
          - type: mrr_at_1
            value: 39.914
          - type: mrr_at_10
            value: 48.227
          - type: mrr_at_100
            value: 49.018
          - type: mrr_at_1000
            value: 49.064
          - type: mrr_at_3
            value: 45.994
          - type: mrr_at_5
            value: 47.396
          - type: ndcg_at_1
            value: 39.914
          - type: ndcg_at_10
            value: 47.825
          - type: ndcg_at_100
            value: 52.852
          - type: ndcg_at_1000
            value: 54.891
          - type: ndcg_at_3
            value: 43.517
          - type: ndcg_at_5
            value: 45.493
          - type: precision_at_1
            value: 39.914
          - type: precision_at_10
            value: 8.956
          - type: precision_at_100
            value: 1.388
          - type: precision_at_1000
            value: 0.182
          - type: precision_at_3
            value: 20.791999999999998
          - type: precision_at_5
            value: 14.821000000000002
          - type: recall_at_1
            value: 32.122
          - type: recall_at_10
            value: 58.294999999999995
          - type: recall_at_100
            value: 79.726
          - type: recall_at_1000
            value: 93.099
          - type: recall_at_3
            value: 45.017
          - type: recall_at_5
            value: 51.002
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackEnglishRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 29.677999999999997
          - type: map_at_10
            value: 38.684000000000005
          - type: map_at_100
            value: 39.812999999999995
          - type: map_at_1000
            value: 39.945
          - type: map_at_3
            value: 35.831
          - type: map_at_5
            value: 37.446
          - type: mrr_at_1
            value: 37.771
          - type: mrr_at_10
            value: 44.936
          - type: mrr_at_100
            value: 45.583
          - type: mrr_at_1000
            value: 45.634
          - type: mrr_at_3
            value: 42.771
          - type: mrr_at_5
            value: 43.994
          - type: ndcg_at_1
            value: 37.771
          - type: ndcg_at_10
            value: 44.059
          - type: ndcg_at_100
            value: 48.192
          - type: ndcg_at_1000
            value: 50.375
          - type: ndcg_at_3
            value: 40.172000000000004
          - type: ndcg_at_5
            value: 41.899
          - type: precision_at_1
            value: 37.771
          - type: precision_at_10
            value: 8.286999999999999
          - type: precision_at_100
            value: 1.322
          - type: precision_at_1000
            value: 0.178
          - type: precision_at_3
            value: 19.406000000000002
          - type: precision_at_5
            value: 13.745
          - type: recall_at_1
            value: 29.677999999999997
          - type: recall_at_10
            value: 53.071
          - type: recall_at_100
            value: 70.812
          - type: recall_at_1000
            value: 84.841
          - type: recall_at_3
            value: 41.016000000000005
          - type: recall_at_5
            value: 46.22
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackGamingRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 42.675000000000004
          - type: map_at_10
            value: 53.93599999999999
          - type: map_at_100
            value: 54.806999999999995
          - type: map_at_1000
            value: 54.867
          - type: map_at_3
            value: 50.934000000000005
          - type: map_at_5
            value: 52.583
          - type: mrr_at_1
            value: 48.339
          - type: mrr_at_10
            value: 57.265
          - type: mrr_at_100
            value: 57.873
          - type: mrr_at_1000
            value: 57.906
          - type: mrr_at_3
            value: 55.193000000000005
          - type: mrr_at_5
            value: 56.303000000000004
          - type: ndcg_at_1
            value: 48.339
          - type: ndcg_at_10
            value: 59.19799999999999
          - type: ndcg_at_100
            value: 62.743
          - type: ndcg_at_1000
            value: 63.99399999999999
          - type: ndcg_at_3
            value: 54.367
          - type: ndcg_at_5
            value: 56.548
          - type: precision_at_1
            value: 48.339
          - type: precision_at_10
            value: 9.216000000000001
          - type: precision_at_100
            value: 1.1809999999999998
          - type: precision_at_1000
            value: 0.134
          - type: precision_at_3
            value: 23.72
          - type: precision_at_5
            value: 16.025
          - type: recall_at_1
            value: 42.675000000000004
          - type: recall_at_10
            value: 71.437
          - type: recall_at_100
            value: 86.803
          - type: recall_at_1000
            value: 95.581
          - type: recall_at_3
            value: 58.434
          - type: recall_at_5
            value: 63.754
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackGisRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 23.518
          - type: map_at_10
            value: 30.648999999999997
          - type: map_at_100
            value: 31.508999999999997
          - type: map_at_1000
            value: 31.604
          - type: map_at_3
            value: 28.247
          - type: map_at_5
            value: 29.65
          - type: mrr_at_1
            value: 25.650000000000002
          - type: mrr_at_10
            value: 32.771
          - type: mrr_at_100
            value: 33.554
          - type: mrr_at_1000
            value: 33.629999999999995
          - type: mrr_at_3
            value: 30.433
          - type: mrr_at_5
            value: 31.812
          - type: ndcg_at_1
            value: 25.650000000000002
          - type: ndcg_at_10
            value: 34.929
          - type: ndcg_at_100
            value: 39.382
          - type: ndcg_at_1000
            value: 41.913
          - type: ndcg_at_3
            value: 30.292
          - type: ndcg_at_5
            value: 32.629999999999995
          - type: precision_at_1
            value: 25.650000000000002
          - type: precision_at_10
            value: 5.311
          - type: precision_at_100
            value: 0.792
          - type: precision_at_1000
            value: 0.105
          - type: precision_at_3
            value: 12.58
          - type: precision_at_5
            value: 8.994
          - type: recall_at_1
            value: 23.518
          - type: recall_at_10
            value: 46.19
          - type: recall_at_100
            value: 67.123
          - type: recall_at_1000
            value: 86.442
          - type: recall_at_3
            value: 33.678000000000004
          - type: recall_at_5
            value: 39.244
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackMathematicaRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 15.891
          - type: map_at_10
            value: 22.464000000000002
          - type: map_at_100
            value: 23.483
          - type: map_at_1000
            value: 23.613
          - type: map_at_3
            value: 20.080000000000002
          - type: map_at_5
            value: 21.526
          - type: mrr_at_1
            value: 20.025000000000002
          - type: mrr_at_10
            value: 26.712999999999997
          - type: mrr_at_100
            value: 27.650000000000002
          - type: mrr_at_1000
            value: 27.737000000000002
          - type: mrr_at_3
            value: 24.274
          - type: mrr_at_5
            value: 25.711000000000002
          - type: ndcg_at_1
            value: 20.025000000000002
          - type: ndcg_at_10
            value: 27.028999999999996
          - type: ndcg_at_100
            value: 32.064
          - type: ndcg_at_1000
            value: 35.188
          - type: ndcg_at_3
            value: 22.512999999999998
          - type: ndcg_at_5
            value: 24.89
          - type: precision_at_1
            value: 20.025000000000002
          - type: precision_at_10
            value: 4.776
          - type: precision_at_100
            value: 0.8500000000000001
          - type: precision_at_1000
            value: 0.125
          - type: precision_at_3
            value: 10.531
          - type: precision_at_5
            value: 7.811
          - type: recall_at_1
            value: 15.891
          - type: recall_at_10
            value: 37.261
          - type: recall_at_100
            value: 59.12
          - type: recall_at_1000
            value: 81.356
          - type: recall_at_3
            value: 24.741
          - type: recall_at_5
            value: 30.753999999999998
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackPhysicsRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 27.544
          - type: map_at_10
            value: 36.283
          - type: map_at_100
            value: 37.467
          - type: map_at_1000
            value: 37.574000000000005
          - type: map_at_3
            value: 33.528999999999996
          - type: map_at_5
            value: 35.028999999999996
          - type: mrr_at_1
            value: 34.166999999999994
          - type: mrr_at_10
            value: 41.866
          - type: mrr_at_100
            value: 42.666
          - type: mrr_at_1000
            value: 42.716
          - type: mrr_at_3
            value: 39.541
          - type: mrr_at_5
            value: 40.768
          - type: ndcg_at_1
            value: 34.166999999999994
          - type: ndcg_at_10
            value: 41.577
          - type: ndcg_at_100
            value: 46.687
          - type: ndcg_at_1000
            value: 48.967
          - type: ndcg_at_3
            value: 37.177
          - type: ndcg_at_5
            value: 39.097
          - type: precision_at_1
            value: 34.166999999999994
          - type: precision_at_10
            value: 7.420999999999999
          - type: precision_at_100
            value: 1.165
          - type: precision_at_1000
            value: 0.154
          - type: precision_at_3
            value: 17.291999999999998
          - type: precision_at_5
            value: 12.166
          - type: recall_at_1
            value: 27.544
          - type: recall_at_10
            value: 51.99399999999999
          - type: recall_at_100
            value: 73.738
          - type: recall_at_1000
            value: 89.33
          - type: recall_at_3
            value: 39.179
          - type: recall_at_5
            value: 44.385999999999996
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackProgrammersRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 26.661
          - type: map_at_10
            value: 35.475
          - type: map_at_100
            value: 36.626999999999995
          - type: map_at_1000
            value: 36.741
          - type: map_at_3
            value: 32.818000000000005
          - type: map_at_5
            value: 34.397
          - type: mrr_at_1
            value: 32.647999999999996
          - type: mrr_at_10
            value: 40.784
          - type: mrr_at_100
            value: 41.602
          - type: mrr_at_1000
            value: 41.661
          - type: mrr_at_3
            value: 38.68
          - type: mrr_at_5
            value: 39.838
          - type: ndcg_at_1
            value: 32.647999999999996
          - type: ndcg_at_10
            value: 40.697
          - type: ndcg_at_100
            value: 45.799
          - type: ndcg_at_1000
            value: 48.235
          - type: ndcg_at_3
            value: 36.516
          - type: ndcg_at_5
            value: 38.515
          - type: precision_at_1
            value: 32.647999999999996
          - type: precision_at_10
            value: 7.202999999999999
          - type: precision_at_100
            value: 1.1360000000000001
          - type: precision_at_1000
            value: 0.151
          - type: precision_at_3
            value: 17.314
          - type: precision_at_5
            value: 12.145999999999999
          - type: recall_at_1
            value: 26.661
          - type: recall_at_10
            value: 50.995000000000005
          - type: recall_at_100
            value: 73.065
          - type: recall_at_1000
            value: 89.781
          - type: recall_at_3
            value: 39.073
          - type: recall_at_5
            value: 44.395
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 25.946583333333333
          - type: map_at_10
            value: 33.79725
          - type: map_at_100
            value: 34.86408333333333
          - type: map_at_1000
            value: 34.9795
          - type: map_at_3
            value: 31.259999999999998
          - type: map_at_5
            value: 32.71541666666666
          - type: mrr_at_1
            value: 30.863749999999996
          - type: mrr_at_10
            value: 37.99183333333333
          - type: mrr_at_100
            value: 38.790499999999994
          - type: mrr_at_1000
            value: 38.85575000000001
          - type: mrr_at_3
            value: 35.82083333333333
          - type: mrr_at_5
            value: 37.07533333333333
          - type: ndcg_at_1
            value: 30.863749999999996
          - type: ndcg_at_10
            value: 38.52141666666667
          - type: ndcg_at_100
            value: 43.17966666666667
          - type: ndcg_at_1000
            value: 45.64608333333333
          - type: ndcg_at_3
            value: 34.333000000000006
          - type: ndcg_at_5
            value: 36.34975
          - type: precision_at_1
            value: 30.863749999999996
          - type: precision_at_10
            value: 6.598999999999999
          - type: precision_at_100
            value: 1.0502500000000001
          - type: precision_at_1000
            value: 0.14400000000000002
          - type: precision_at_3
            value: 15.557583333333334
          - type: precision_at_5
            value: 11.020000000000001
          - type: recall_at_1
            value: 25.946583333333333
          - type: recall_at_10
            value: 48.36991666666666
          - type: recall_at_100
            value: 69.02408333333334
          - type: recall_at_1000
            value: 86.43858333333331
          - type: recall_at_3
            value: 36.4965
          - type: recall_at_5
            value: 41.76258333333334
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackStatsRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 22.431
          - type: map_at_10
            value: 28.889
          - type: map_at_100
            value: 29.642000000000003
          - type: map_at_1000
            value: 29.742
          - type: map_at_3
            value: 26.998
          - type: map_at_5
            value: 28.172000000000004
          - type: mrr_at_1
            value: 25.307000000000002
          - type: mrr_at_10
            value: 31.763
          - type: mrr_at_100
            value: 32.443
          - type: mrr_at_1000
            value: 32.531
          - type: mrr_at_3
            value: 29.959000000000003
          - type: mrr_at_5
            value: 31.063000000000002
          - type: ndcg_at_1
            value: 25.307000000000002
          - type: ndcg_at_10
            value: 32.586999999999996
          - type: ndcg_at_100
            value: 36.5
          - type: ndcg_at_1000
            value: 39.133
          - type: ndcg_at_3
            value: 29.25
          - type: ndcg_at_5
            value: 31.023
          - type: precision_at_1
            value: 25.307000000000002
          - type: precision_at_10
            value: 4.954
          - type: precision_at_100
            value: 0.747
          - type: precision_at_1000
            value: 0.104
          - type: precision_at_3
            value: 12.577
          - type: precision_at_5
            value: 8.741999999999999
          - type: recall_at_1
            value: 22.431
          - type: recall_at_10
            value: 41.134
          - type: recall_at_100
            value: 59.28600000000001
          - type: recall_at_1000
            value: 78.857
          - type: recall_at_3
            value: 31.926
          - type: recall_at_5
            value: 36.335
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackTexRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 17.586
          - type: map_at_10
            value: 23.304
          - type: map_at_100
            value: 24.159
          - type: map_at_1000
            value: 24.281
          - type: map_at_3
            value: 21.316
          - type: map_at_5
            value: 22.383
          - type: mrr_at_1
            value: 21.645
          - type: mrr_at_10
            value: 27.365000000000002
          - type: mrr_at_100
            value: 28.108
          - type: mrr_at_1000
            value: 28.192
          - type: mrr_at_3
            value: 25.482
          - type: mrr_at_5
            value: 26.479999999999997
          - type: ndcg_at_1
            value: 21.645
          - type: ndcg_at_10
            value: 27.306
          - type: ndcg_at_100
            value: 31.496000000000002
          - type: ndcg_at_1000
            value: 34.53
          - type: ndcg_at_3
            value: 23.73
          - type: ndcg_at_5
            value: 25.294
          - type: precision_at_1
            value: 21.645
          - type: precision_at_10
            value: 4.797
          - type: precision_at_100
            value: 0.8059999999999999
          - type: precision_at_1000
            value: 0.121
          - type: precision_at_3
            value: 10.850999999999999
          - type: precision_at_5
            value: 7.736
          - type: recall_at_1
            value: 17.586
          - type: recall_at_10
            value: 35.481
          - type: recall_at_100
            value: 54.534000000000006
          - type: recall_at_1000
            value: 76.456
          - type: recall_at_3
            value: 25.335
          - type: recall_at_5
            value: 29.473
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackUnixRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 25.095
          - type: map_at_10
            value: 32.374
          - type: map_at_100
            value: 33.537
          - type: map_at_1000
            value: 33.634
          - type: map_at_3
            value: 30.089
          - type: map_at_5
            value: 31.433
          - type: mrr_at_1
            value: 29.198
          - type: mrr_at_10
            value: 36.01
          - type: mrr_at_100
            value: 37.022
          - type: mrr_at_1000
            value: 37.083
          - type: mrr_at_3
            value: 33.94
          - type: mrr_at_5
            value: 35.148
          - type: ndcg_at_1
            value: 29.198
          - type: ndcg_at_10
            value: 36.729
          - type: ndcg_at_100
            value: 42.114000000000004
          - type: ndcg_at_1000
            value: 44.592
          - type: ndcg_at_3
            value: 32.644
          - type: ndcg_at_5
            value: 34.652
          - type: precision_at_1
            value: 29.198
          - type: precision_at_10
            value: 5.970000000000001
          - type: precision_at_100
            value: 0.967
          - type: precision_at_1000
            value: 0.129
          - type: precision_at_3
            value: 14.396999999999998
          - type: precision_at_5
            value: 10.093
          - type: recall_at_1
            value: 25.095
          - type: recall_at_10
            value: 46.392
          - type: recall_at_100
            value: 69.706
          - type: recall_at_1000
            value: 87.738
          - type: recall_at_3
            value: 35.303000000000004
          - type: recall_at_5
            value: 40.441
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackWebmastersRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 26.857999999999997
          - type: map_at_10
            value: 34.066
          - type: map_at_100
            value: 35.671
          - type: map_at_1000
            value: 35.881
          - type: map_at_3
            value: 31.304
          - type: map_at_5
            value: 32.885
          - type: mrr_at_1
            value: 32.411
          - type: mrr_at_10
            value: 38.987
          - type: mrr_at_100
            value: 39.894
          - type: mrr_at_1000
            value: 39.959
          - type: mrr_at_3
            value: 36.626999999999995
          - type: mrr_at_5
            value: 38.011
          - type: ndcg_at_1
            value: 32.411
          - type: ndcg_at_10
            value: 39.208
          - type: ndcg_at_100
            value: 44.626
          - type: ndcg_at_1000
            value: 47.43
          - type: ndcg_at_3
            value: 35.091
          - type: ndcg_at_5
            value: 37.119
          - type: precision_at_1
            value: 32.411
          - type: precision_at_10
            value: 7.51
          - type: precision_at_100
            value: 1.486
          - type: precision_at_1000
            value: 0.234
          - type: precision_at_3
            value: 16.14
          - type: precision_at_5
            value: 11.976
          - type: recall_at_1
            value: 26.857999999999997
          - type: recall_at_10
            value: 47.407
          - type: recall_at_100
            value: 72.236
          - type: recall_at_1000
            value: 90.77
          - type: recall_at_3
            value: 35.125
          - type: recall_at_5
            value: 40.522999999999996
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackWordpressRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 21.3
          - type: map_at_10
            value: 27.412999999999997
          - type: map_at_100
            value: 28.29
          - type: map_at_1000
            value: 28.398
          - type: map_at_3
            value: 25.169999999999998
          - type: map_at_5
            value: 26.496
          - type: mrr_at_1
            value: 23.29
          - type: mrr_at_10
            value: 29.215000000000003
          - type: mrr_at_100
            value: 30.073
          - type: mrr_at_1000
            value: 30.156
          - type: mrr_at_3
            value: 26.956000000000003
          - type: mrr_at_5
            value: 28.38
          - type: ndcg_at_1
            value: 23.29
          - type: ndcg_at_10
            value: 31.113000000000003
          - type: ndcg_at_100
            value: 35.701
          - type: ndcg_at_1000
            value: 38.505
          - type: ndcg_at_3
            value: 26.727
          - type: ndcg_at_5
            value: 29.037000000000003
          - type: precision_at_1
            value: 23.29
          - type: precision_at_10
            value: 4.787
          - type: precision_at_100
            value: 0.763
          - type: precision_at_1000
            value: 0.11100000000000002
          - type: precision_at_3
            value: 11.091
          - type: precision_at_5
            value: 7.985
          - type: recall_at_1
            value: 21.3
          - type: recall_at_10
            value: 40.782000000000004
          - type: recall_at_100
            value: 62.13999999999999
          - type: recall_at_1000
            value: 83.012
          - type: recall_at_3
            value: 29.131
          - type: recall_at_5
            value: 34.624
      - task:
          type: Retrieval
        dataset:
          type: climate-fever
          name: MTEB ClimateFEVER
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 9.631
          - type: map_at_10
            value: 16.634999999999998
          - type: map_at_100
            value: 18.23
          - type: map_at_1000
            value: 18.419
          - type: map_at_3
            value: 13.66
          - type: map_at_5
            value: 15.173
          - type: mrr_at_1
            value: 21.368000000000002
          - type: mrr_at_10
            value: 31.56
          - type: mrr_at_100
            value: 32.58
          - type: mrr_at_1000
            value: 32.633
          - type: mrr_at_3
            value: 28.241
          - type: mrr_at_5
            value: 30.225
          - type: ndcg_at_1
            value: 21.368000000000002
          - type: ndcg_at_10
            value: 23.855999999999998
          - type: ndcg_at_100
            value: 30.686999999999998
          - type: ndcg_at_1000
            value: 34.327000000000005
          - type: ndcg_at_3
            value: 18.781
          - type: ndcg_at_5
            value: 20.73
          - type: precision_at_1
            value: 21.368000000000002
          - type: precision_at_10
            value: 7.564
          - type: precision_at_100
            value: 1.496
          - type: precision_at_1000
            value: 0.217
          - type: precision_at_3
            value: 13.876
          - type: precision_at_5
            value: 11.062
          - type: recall_at_1
            value: 9.631
          - type: recall_at_10
            value: 29.517
          - type: recall_at_100
            value: 53.452
          - type: recall_at_1000
            value: 74.115
          - type: recall_at_3
            value: 17.605999999999998
          - type: recall_at_5
            value: 22.505
      - task:
          type: Retrieval
        dataset:
          type: dbpedia-entity
          name: MTEB DBPedia
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 8.885
          - type: map_at_10
            value: 18.798000000000002
          - type: map_at_100
            value: 26.316
          - type: map_at_1000
            value: 27.869
          - type: map_at_3
            value: 13.719000000000001
          - type: map_at_5
            value: 15.716
          - type: mrr_at_1
            value: 66
          - type: mrr_at_10
            value: 74.263
          - type: mrr_at_100
            value: 74.519
          - type: mrr_at_1000
            value: 74.531
          - type: mrr_at_3
            value: 72.458
          - type: mrr_at_5
            value: 73.321
          - type: ndcg_at_1
            value: 53.87499999999999
          - type: ndcg_at_10
            value: 40.355999999999995
          - type: ndcg_at_100
            value: 44.366
          - type: ndcg_at_1000
            value: 51.771
          - type: ndcg_at_3
            value: 45.195
          - type: ndcg_at_5
            value: 42.187000000000005
          - type: precision_at_1
            value: 66
          - type: precision_at_10
            value: 31.75
          - type: precision_at_100
            value: 10.11
          - type: precision_at_1000
            value: 1.9800000000000002
          - type: precision_at_3
            value: 48.167
          - type: precision_at_5
            value: 40.050000000000004
          - type: recall_at_1
            value: 8.885
          - type: recall_at_10
            value: 24.471999999999998
          - type: recall_at_100
            value: 49.669000000000004
          - type: recall_at_1000
            value: 73.383
          - type: recall_at_3
            value: 14.872
          - type: recall_at_5
            value: 18.262999999999998
      - task:
          type: Classification
        dataset:
          type: mteb/emotion
          name: MTEB EmotionClassification
          config: default
          split: test
          revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
        metrics:
          - type: accuracy
            value: 45.18
          - type: f1
            value: 40.26878691789978
      - task:
          type: Retrieval
        dataset:
          type: fever
          name: MTEB FEVER
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 62.751999999999995
          - type: map_at_10
            value: 74.131
          - type: map_at_100
            value: 74.407
          - type: map_at_1000
            value: 74.423
          - type: map_at_3
            value: 72.329
          - type: map_at_5
            value: 73.555
          - type: mrr_at_1
            value: 67.282
          - type: mrr_at_10
            value: 78.292
          - type: mrr_at_100
            value: 78.455
          - type: mrr_at_1000
            value: 78.458
          - type: mrr_at_3
            value: 76.755
          - type: mrr_at_5
            value: 77.839
          - type: ndcg_at_1
            value: 67.282
          - type: ndcg_at_10
            value: 79.443
          - type: ndcg_at_100
            value: 80.529
          - type: ndcg_at_1000
            value: 80.812
          - type: ndcg_at_3
            value: 76.281
          - type: ndcg_at_5
            value: 78.235
          - type: precision_at_1
            value: 67.282
          - type: precision_at_10
            value: 10.078
          - type: precision_at_100
            value: 1.082
          - type: precision_at_1000
            value: 0.11199999999999999
          - type: precision_at_3
            value: 30.178
          - type: precision_at_5
            value: 19.232
          - type: recall_at_1
            value: 62.751999999999995
          - type: recall_at_10
            value: 91.521
          - type: recall_at_100
            value: 95.997
          - type: recall_at_1000
            value: 97.775
          - type: recall_at_3
            value: 83.131
          - type: recall_at_5
            value: 87.93299999999999
      - task:
          type: Retrieval
        dataset:
          type: fiqa
          name: MTEB FiQA2018
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 18.861
          - type: map_at_10
            value: 30.252000000000002
          - type: map_at_100
            value: 32.082
          - type: map_at_1000
            value: 32.261
          - type: map_at_3
            value: 25.909
          - type: map_at_5
            value: 28.296
          - type: mrr_at_1
            value: 37.346000000000004
          - type: mrr_at_10
            value: 45.802
          - type: mrr_at_100
            value: 46.611999999999995
          - type: mrr_at_1000
            value: 46.659
          - type: mrr_at_3
            value: 43.056
          - type: mrr_at_5
            value: 44.637
          - type: ndcg_at_1
            value: 37.346000000000004
          - type: ndcg_at_10
            value: 38.169
          - type: ndcg_at_100
            value: 44.864
          - type: ndcg_at_1000
            value: 47.974
          - type: ndcg_at_3
            value: 33.619
          - type: ndcg_at_5
            value: 35.317
          - type: precision_at_1
            value: 37.346000000000004
          - type: precision_at_10
            value: 10.693999999999999
          - type: precision_at_100
            value: 1.775
          - type: precision_at_1000
            value: 0.231
          - type: precision_at_3
            value: 22.325
          - type: precision_at_5
            value: 16.852
          - type: recall_at_1
            value: 18.861
          - type: recall_at_10
            value: 45.672000000000004
          - type: recall_at_100
            value: 70.60499999999999
          - type: recall_at_1000
            value: 89.216
          - type: recall_at_3
            value: 30.361
          - type: recall_at_5
            value: 36.998999999999995
      - task:
          type: Retrieval
        dataset:
          type: hotpotqa
          name: MTEB HotpotQA
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 37.852999999999994
          - type: map_at_10
            value: 59.961
          - type: map_at_100
            value: 60.78
          - type: map_at_1000
            value: 60.843
          - type: map_at_3
            value: 56.39999999999999
          - type: map_at_5
            value: 58.646
          - type: mrr_at_1
            value: 75.70599999999999
          - type: mrr_at_10
            value: 82.321
          - type: mrr_at_100
            value: 82.516
          - type: mrr_at_1000
            value: 82.525
          - type: mrr_at_3
            value: 81.317
          - type: mrr_at_5
            value: 81.922
          - type: ndcg_at_1
            value: 75.70599999999999
          - type: ndcg_at_10
            value: 68.557
          - type: ndcg_at_100
            value: 71.485
          - type: ndcg_at_1000
            value: 72.71600000000001
          - type: ndcg_at_3
            value: 63.524
          - type: ndcg_at_5
            value: 66.338
          - type: precision_at_1
            value: 75.70599999999999
          - type: precision_at_10
            value: 14.463000000000001
          - type: precision_at_100
            value: 1.677
          - type: precision_at_1000
            value: 0.184
          - type: precision_at_3
            value: 40.806
          - type: precision_at_5
            value: 26.709
          - type: recall_at_1
            value: 37.852999999999994
          - type: recall_at_10
            value: 72.316
          - type: recall_at_100
            value: 83.842
          - type: recall_at_1000
            value: 91.999
          - type: recall_at_3
            value: 61.209
          - type: recall_at_5
            value: 66.77199999999999
      - task:
          type: Classification
        dataset:
          type: mteb/imdb
          name: MTEB ImdbClassification
          config: default
          split: test
          revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
        metrics:
          - type: accuracy
            value: 85.46039999999999
          - type: ap
            value: 79.9812521351881
          - type: f1
            value: 85.31722909702084
      - task:
          type: Retrieval
        dataset:
          type: msmarco
          name: MTEB MSMARCO
          config: default
          split: dev
          revision: None
        metrics:
          - type: map_at_1
            value: 22.704
          - type: map_at_10
            value: 35.329
          - type: map_at_100
            value: 36.494
          - type: map_at_1000
            value: 36.541000000000004
          - type: map_at_3
            value: 31.476
          - type: map_at_5
            value: 33.731
          - type: mrr_at_1
            value: 23.294999999999998
          - type: mrr_at_10
            value: 35.859
          - type: mrr_at_100
            value: 36.968
          - type: mrr_at_1000
            value: 37.008
          - type: mrr_at_3
            value: 32.085
          - type: mrr_at_5
            value: 34.299
          - type: ndcg_at_1
            value: 23.324
          - type: ndcg_at_10
            value: 42.274
          - type: ndcg_at_100
            value: 47.839999999999996
          - type: ndcg_at_1000
            value: 48.971
          - type: ndcg_at_3
            value: 34.454
          - type: ndcg_at_5
            value: 38.464
          - type: precision_at_1
            value: 23.324
          - type: precision_at_10
            value: 6.648
          - type: precision_at_100
            value: 0.9440000000000001
          - type: precision_at_1000
            value: 0.104
          - type: precision_at_3
            value: 14.674999999999999
          - type: precision_at_5
            value: 10.850999999999999
          - type: recall_at_1
            value: 22.704
          - type: recall_at_10
            value: 63.660000000000004
          - type: recall_at_100
            value: 89.29899999999999
          - type: recall_at_1000
            value: 97.88900000000001
          - type: recall_at_3
            value: 42.441
          - type: recall_at_5
            value: 52.04
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_domain
          name: MTEB MTOPDomainClassification (en)
          config: en
          split: test
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
        metrics:
          - type: accuracy
            value: 93.1326949384405
          - type: f1
            value: 92.89743579612082
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_domain
          name: MTEB MTOPDomainClassification (de)
          config: de
          split: test
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
        metrics:
          - type: accuracy
            value: 89.62524654832347
          - type: f1
            value: 88.65106082263151
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_domain
          name: MTEB MTOPDomainClassification (es)
          config: es
          split: test
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
        metrics:
          - type: accuracy
            value: 90.59039359573046
          - type: f1
            value: 90.31532892105662
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_domain
          name: MTEB MTOPDomainClassification (fr)
          config: fr
          split: test
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
        metrics:
          - type: accuracy
            value: 86.21046038208581
          - type: f1
            value: 86.41459529813113
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_domain
          name: MTEB MTOPDomainClassification (hi)
          config: hi
          split: test
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
        metrics:
          - type: accuracy
            value: 87.3180351380423
          - type: f1
            value: 86.71383078226444
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_domain
          name: MTEB MTOPDomainClassification (th)
          config: th
          split: test
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
        metrics:
          - type: accuracy
            value: 86.24231464737792
          - type: f1
            value: 86.31845567592403
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_intent
          name: MTEB MTOPIntentClassification (en)
          config: en
          split: test
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
        metrics:
          - type: accuracy
            value: 75.27131782945736
          - type: f1
            value: 57.52079940417103
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_intent
          name: MTEB MTOPIntentClassification (de)
          config: de
          split: test
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
        metrics:
          - type: accuracy
            value: 71.2341504649197
          - type: f1
            value: 51.349951558039244
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_intent
          name: MTEB MTOPIntentClassification (es)
          config: es
          split: test
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
        metrics:
          - type: accuracy
            value: 71.27418278852569
          - type: f1
            value: 50.1714985749095
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_intent
          name: MTEB MTOPIntentClassification (fr)
          config: fr
          split: test
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
        metrics:
          - type: accuracy
            value: 67.68243031631694
          - type: f1
            value: 50.1066160836192
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_intent
          name: MTEB MTOPIntentClassification (hi)
          config: hi
          split: test
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
        metrics:
          - type: accuracy
            value: 69.2362854069559
          - type: f1
            value: 48.821279948766424
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_intent
          name: MTEB MTOPIntentClassification (th)
          config: th
          split: test
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
        metrics:
          - type: accuracy
            value: 71.71428571428571
          - type: f1
            value: 53.94611389496195
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (af)
          config: af
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 59.97646267652992
          - type: f1
            value: 57.26797883561521
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (am)
          config: am
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 53.65501008742435
          - type: f1
            value: 50.416258382177034
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (ar)
          config: ar
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 57.45796906523201
          - type: f1
            value: 53.306690547422185
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (az)
          config: az
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 62.59246805648957
          - type: f1
            value: 59.818381969051494
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (bn)
          config: bn
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 61.126429051782104
          - type: f1
            value: 58.25993593933026
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (cy)
          config: cy
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 50.057162071284466
          - type: f1
            value: 46.96095728790911
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (da)
          config: da
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 66.64425016812375
          - type: f1
            value: 62.858291698755764
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (de)
          config: de
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 66.08944182918628
          - type: f1
            value: 62.44639030604241
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (el)
          config: el
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 64.68056489576328
          - type: f1
            value: 61.775326758789504
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (en)
          config: en
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 72.11163416274377
          - type: f1
            value: 69.70789096927015
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (es)
          config: es
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 68.40282447881641
          - type: f1
            value: 66.38492065671895
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (fa)
          config: fa
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 67.24613315400134
          - type: f1
            value: 64.3348019501336
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (fi)
          config: fi
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 65.78345662407531
          - type: f1
            value: 62.21279452354622
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (fr)
          config: fr
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 67.9455279085407
          - type: f1
            value: 65.48193124964094
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (he)
          config: he
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 62.05110961667788
          - type: f1
            value: 58.097856564684534
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (hi)
          config: hi
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 64.95292535305985
          - type: f1
            value: 62.09182174767901
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (hu)
          config: hu
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 64.97310020174848
          - type: f1
            value: 61.14252567730396
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (hy)
          config: hy
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 60.08069939475453
          - type: f1
            value: 57.044041742492034
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (id)
          config: id
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 66.63752521856085
          - type: f1
            value: 63.889340907205316
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (is)
          config: is
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 56.385339609952936
          - type: f1
            value: 53.449033750088304
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (it)
          config: it
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 68.93073301950234
          - type: f1
            value: 65.9884357824104
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (ja)
          config: ja
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 68.94418291862812
          - type: f1
            value: 66.48740222583132
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
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          config: jv
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 54.26025554808339
          - type: f1
            value: 50.19562815100793
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (ka)
          config: ka
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 48.98789509078682
          - type: f1
            value: 46.65788438676836
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (km)
          config: km
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 44.68728984532616
          - type: f1
            value: 41.642419349541996
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (kn)
          config: kn
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 59.19300605245461
          - type: f1
            value: 55.8626492442437
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (ko)
          config: ko
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 66.33826496301278
          - type: f1
            value: 63.89499791648792
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
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          config: lv
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 60.33960995292536
          - type: f1
            value: 57.15242464180892
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (ml)
          config: ml
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 63.09347679892402
          - type: f1
            value: 59.64733214063841
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (mn)
          config: mn
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 58.75924680564896
          - type: f1
            value: 55.96585692366827
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (ms)
          config: ms
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 62.48486886348352
          - type: f1
            value: 59.45143559032946
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (my)
          config: my
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 58.56422326832549
          - type: f1
            value: 54.96368702901926
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
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          config: nb
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 66.18022864828512
          - type: f1
            value: 63.05369805040634
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
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          config: nl
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 67.30329522528581
          - type: f1
            value: 64.06084612020727
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (pl)
          config: pl
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 68.36919973100201
          - type: f1
            value: 65.12154124788887
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
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          config: pt
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 68.98117014122394
          - type: f1
            value: 66.41847559806962
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
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          config: ro
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 65.53799596503026
          - type: f1
            value: 62.17067330740817
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (ru)
          config: ru
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 69.01815736381977
          - type: f1
            value: 66.24988369607843
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (sl)
          config: sl
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 62.34700739744452
          - type: f1
            value: 59.957933424941636
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (sq)
          config: sq
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 61.23402824478815
          - type: f1
            value: 57.98836976018471
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (sv)
          config: sv
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
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          type: mteb/amazon_massive_scenario
          name: MTEB MassiveScenarioClassification (zh-CN)
          config: zh-CN
          split: test
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
        metrics:
          - type: accuracy
            value: 75.42030934767989
          - type: f1
            value: 75.2074842882598
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_scenario
          name: MTEB MassiveScenarioClassification (zh-TW)
          config: zh-TW
          split: test
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
        metrics:
          - type: accuracy
            value: 70.69266980497646
          - type: f1
            value: 70.94103167391192
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-p2p
          name: MTEB MedrxivClusteringP2P
          config: default
          split: test
          revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
        metrics:
          - type: v_measure
            value: 28.91697191169135
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-s2s
          name: MTEB MedrxivClusteringS2S
          config: default
          split: test
          revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
        metrics:
          - type: v_measure
            value: 28.434000079573313
      - task:
          type: Reranking
        dataset:
          type: mteb/mind_small
          name: MTEB MindSmallReranking
          config: default
          split: test
          revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
        metrics:
          - type: map
            value: 30.96683513343383
          - type: mrr
            value: 31.967364078714834
      - task:
          type: Retrieval
        dataset:
          type: nfcorpus
          name: MTEB NFCorpus
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 5.5280000000000005
          - type: map_at_10
            value: 11.793
          - type: map_at_100
            value: 14.496999999999998
          - type: map_at_1000
            value: 15.783
          - type: map_at_3
            value: 8.838
          - type: map_at_5
            value: 10.07
          - type: mrr_at_1
            value: 43.653
          - type: mrr_at_10
            value: 51.531000000000006
          - type: mrr_at_100
            value: 52.205
          - type: mrr_at_1000
            value: 52.242999999999995
          - type: mrr_at_3
            value: 49.431999999999995
          - type: mrr_at_5
            value: 50.470000000000006
          - type: ndcg_at_1
            value: 42.415000000000006
          - type: ndcg_at_10
            value: 32.464999999999996
          - type: ndcg_at_100
            value: 28.927999999999997
          - type: ndcg_at_1000
            value: 37.629000000000005
          - type: ndcg_at_3
            value: 37.845
          - type: ndcg_at_5
            value: 35.147
          - type: precision_at_1
            value: 43.653
          - type: precision_at_10
            value: 23.932000000000002
          - type: precision_at_100
            value: 7.17
          - type: precision_at_1000
            value: 1.967
          - type: precision_at_3
            value: 35.397
          - type: precision_at_5
            value: 29.907
          - type: recall_at_1
            value: 5.5280000000000005
          - type: recall_at_10
            value: 15.568000000000001
          - type: recall_at_100
            value: 28.54
          - type: recall_at_1000
            value: 59.864
          - type: recall_at_3
            value: 9.822000000000001
          - type: recall_at_5
            value: 11.726
      - task:
          type: Retrieval
        dataset:
          type: nq
          name: MTEB NQ
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 37.041000000000004
          - type: map_at_10
            value: 52.664
          - type: map_at_100
            value: 53.477
          - type: map_at_1000
            value: 53.505
          - type: map_at_3
            value: 48.510999999999996
          - type: map_at_5
            value: 51.036
          - type: mrr_at_1
            value: 41.338
          - type: mrr_at_10
            value: 55.071000000000005
          - type: mrr_at_100
            value: 55.672
          - type: mrr_at_1000
            value: 55.689
          - type: mrr_at_3
            value: 51.82
          - type: mrr_at_5
            value: 53.852
          - type: ndcg_at_1
            value: 41.338
          - type: ndcg_at_10
            value: 60.01800000000001
          - type: ndcg_at_100
            value: 63.409000000000006
          - type: ndcg_at_1000
            value: 64.017
          - type: ndcg_at_3
            value: 52.44799999999999
          - type: ndcg_at_5
            value: 56.571000000000005
          - type: precision_at_1
            value: 41.338
          - type: precision_at_10
            value: 9.531
          - type: precision_at_100
            value: 1.145
          - type: precision_at_1000
            value: 0.12
          - type: precision_at_3
            value: 23.416
          - type: precision_at_5
            value: 16.46
          - type: recall_at_1
            value: 37.041000000000004
          - type: recall_at_10
            value: 79.76299999999999
          - type: recall_at_100
            value: 94.39
          - type: recall_at_1000
            value: 98.851
          - type: recall_at_3
            value: 60.465
          - type: recall_at_5
            value: 69.906
      - task:
          type: Retrieval
        dataset:
          type: quora
          name: MTEB QuoraRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 69.952
          - type: map_at_10
            value: 83.758
          - type: map_at_100
            value: 84.406
          - type: map_at_1000
            value: 84.425
          - type: map_at_3
            value: 80.839
          - type: map_at_5
            value: 82.646
          - type: mrr_at_1
            value: 80.62
          - type: mrr_at_10
            value: 86.947
          - type: mrr_at_100
            value: 87.063
          - type: mrr_at_1000
            value: 87.064
          - type: mrr_at_3
            value: 85.96000000000001
          - type: mrr_at_5
            value: 86.619
          - type: ndcg_at_1
            value: 80.63
          - type: ndcg_at_10
            value: 87.64800000000001
          - type: ndcg_at_100
            value: 88.929
          - type: ndcg_at_1000
            value: 89.054
          - type: ndcg_at_3
            value: 84.765
          - type: ndcg_at_5
            value: 86.291
          - type: precision_at_1
            value: 80.63
          - type: precision_at_10
            value: 13.314
          - type: precision_at_100
            value: 1.525
          - type: precision_at_1000
            value: 0.157
          - type: precision_at_3
            value: 37.1
          - type: precision_at_5
            value: 24.372
          - type: recall_at_1
            value: 69.952
          - type: recall_at_10
            value: 94.955
          - type: recall_at_100
            value: 99.38
          - type: recall_at_1000
            value: 99.96000000000001
          - type: recall_at_3
            value: 86.60600000000001
          - type: recall_at_5
            value: 90.997
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering
          name: MTEB RedditClustering
          config: default
          split: test
          revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
        metrics:
          - type: v_measure
            value: 42.41329517878427
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering-p2p
          name: MTEB RedditClusteringP2P
          config: default
          split: test
          revision: 282350215ef01743dc01b456c7f5241fa8937f16
        metrics:
          - type: v_measure
            value: 55.171278362748666
      - task:
          type: Retrieval
        dataset:
          type: scidocs
          name: MTEB SCIDOCS
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 4.213
          - type: map_at_10
            value: 9.895
          - type: map_at_100
            value: 11.776
          - type: map_at_1000
            value: 12.084
          - type: map_at_3
            value: 7.2669999999999995
          - type: map_at_5
            value: 8.620999999999999
          - type: mrr_at_1
            value: 20.8
          - type: mrr_at_10
            value: 31.112000000000002
          - type: mrr_at_100
            value: 32.274
          - type: mrr_at_1000
            value: 32.35
          - type: mrr_at_3
            value: 28.133000000000003
          - type: mrr_at_5
            value: 29.892999999999997
          - type: ndcg_at_1
            value: 20.8
          - type: ndcg_at_10
            value: 17.163999999999998
          - type: ndcg_at_100
            value: 24.738
          - type: ndcg_at_1000
            value: 30.316
          - type: ndcg_at_3
            value: 16.665
          - type: ndcg_at_5
            value: 14.478
          - type: precision_at_1
            value: 20.8
          - type: precision_at_10
            value: 8.74
          - type: precision_at_100
            value: 1.963
          - type: precision_at_1000
            value: 0.33
          - type: precision_at_3
            value: 15.467
          - type: precision_at_5
            value: 12.6
          - type: recall_at_1
            value: 4.213
          - type: recall_at_10
            value: 17.698
          - type: recall_at_100
            value: 39.838
          - type: recall_at_1000
            value: 66.893
          - type: recall_at_3
            value: 9.418
          - type: recall_at_5
            value: 12.773000000000001
      - task:
          type: STS
        dataset:
          type: mteb/sickr-sts
          name: MTEB SICK-R
          config: default
          split: test
          revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
        metrics:
          - type: cos_sim_pearson
            value: 82.90453315738294
          - type: cos_sim_spearman
            value: 78.51197850080254
          - type: euclidean_pearson
            value: 80.09647123597748
          - type: euclidean_spearman
            value: 78.63548011514061
          - type: manhattan_pearson
            value: 80.10645285675231
          - type: manhattan_spearman
            value: 78.57861806068901
      - task:
          type: STS
        dataset:
          type: mteb/sts12-sts
          name: MTEB STS12
          config: default
          split: test
          revision: a0d554a64d88156834ff5ae9920b964011b16384
        metrics:
          - type: cos_sim_pearson
            value: 84.2616156846401
          - type: cos_sim_spearman
            value: 76.69713867850156
          - type: euclidean_pearson
            value: 77.97948563800394
          - type: euclidean_spearman
            value: 74.2371211567807
          - type: manhattan_pearson
            value: 77.69697879669705
          - type: manhattan_spearman
            value: 73.86529778022278
      - task:
          type: STS
        dataset:
          type: mteb/sts13-sts
          name: MTEB STS13
          config: default
          split: test
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
        metrics:
          - type: cos_sim_pearson
            value: 77.0293269315045
          - type: cos_sim_spearman
            value: 78.02555120584198
          - type: euclidean_pearson
            value: 78.25398100379078
          - type: euclidean_spearman
            value: 78.66963870599464
          - type: manhattan_pearson
            value: 78.14314682167348
          - type: manhattan_spearman
            value: 78.57692322969135
      - task:
          type: STS
        dataset:
          type: mteb/sts14-sts
          name: MTEB STS14
          config: default
          split: test
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
        metrics:
          - type: cos_sim_pearson
            value: 79.16989925136942
          - type: cos_sim_spearman
            value: 76.5996225327091
          - type: euclidean_pearson
            value: 77.8319003279786
          - type: euclidean_spearman
            value: 76.42824009468998
          - type: manhattan_pearson
            value: 77.69118862737736
          - type: manhattan_spearman
            value: 76.25568104762812
      - task:
          type: STS
        dataset:
          type: mteb/sts15-sts
          name: MTEB STS15
          config: default
          split: test
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
        metrics:
          - type: cos_sim_pearson
            value: 87.42012286935325
          - type: cos_sim_spearman
            value: 88.15654297884122
          - type: euclidean_pearson
            value: 87.34082819427852
          - type: euclidean_spearman
            value: 88.06333589547084
          - type: manhattan_pearson
            value: 87.25115596784842
          - type: manhattan_spearman
            value: 87.9559927695203
      - task:
          type: STS
        dataset:
          type: mteb/sts16-sts
          name: MTEB STS16
          config: default
          split: test
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
        metrics:
          - type: cos_sim_pearson
            value: 82.88222044996712
          - type: cos_sim_spearman
            value: 84.28476589061077
          - type: euclidean_pearson
            value: 83.17399758058309
          - type: euclidean_spearman
            value: 83.85497357244542
          - type: manhattan_pearson
            value: 83.0308397703786
          - type: manhattan_spearman
            value: 83.71554539935046
      - task:
          type: STS
        dataset:
          type: mteb/sts17-crosslingual-sts
          name: MTEB STS17 (ko-ko)
          config: ko-ko
          split: test
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
        metrics:
          - type: cos_sim_pearson
            value: 80.20682986257339
          - type: cos_sim_spearman
            value: 79.94567120362092
          - type: euclidean_pearson
            value: 79.43122480368902
          - type: euclidean_spearman
            value: 79.94802077264987
          - type: manhattan_pearson
            value: 79.32653021527081
          - type: manhattan_spearman
            value: 79.80961146709178
      - task:
          type: STS
        dataset:
          type: mteb/sts17-crosslingual-sts
          name: MTEB STS17 (ar-ar)
          config: ar-ar
          split: test
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
        metrics:
          - type: cos_sim_pearson
            value: 74.46578144394383
          - type: cos_sim_spearman
            value: 74.52496637472179
          - type: euclidean_pearson
            value: 72.2903807076809
          - type: euclidean_spearman
            value: 73.55549359771645
          - type: manhattan_pearson
            value: 72.09324837709393
          - type: manhattan_spearman
            value: 73.36743103606581
      - task:
          type: STS
        dataset:
          type: mteb/sts17-crosslingual-sts
          name: MTEB STS17 (en-ar)
          config: en-ar
          split: test
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
        metrics:
          - type: cos_sim_pearson
            value: 71.37272335116
          - type: cos_sim_spearman
            value: 71.26702117766037
          - type: euclidean_pearson
            value: 67.114829954434
          - type: euclidean_spearman
            value: 66.37938893947761
          - type: manhattan_pearson
            value: 66.79688574095246
          - type: manhattan_spearman
            value: 66.17292828079667
      - task:
          type: STS
        dataset:
          type: mteb/sts17-crosslingual-sts
          name: MTEB STS17 (en-de)
          config: en-de
          split: test
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
        metrics:
          - type: cos_sim_pearson
            value: 80.61016770129092
          - type: cos_sim_spearman
            value: 82.08515426632214
          - type: euclidean_pearson
            value: 80.557340361131
          - type: euclidean_spearman
            value: 80.37585812266175
          - type: manhattan_pearson
            value: 80.6782873404285
          - type: manhattan_spearman
            value: 80.6678073032024
      - task:
          type: STS
        dataset:
          type: mteb/sts17-crosslingual-sts
          name: MTEB STS17 (en-en)
          config: en-en
          split: test
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
        metrics:
          - type: cos_sim_pearson
            value: 87.00150745350108
          - type: cos_sim_spearman
            value: 87.83441972211425
          - type: euclidean_pearson
            value: 87.94826702308792
          - type: euclidean_spearman
            value: 87.46143974860725
          - type: manhattan_pearson
            value: 87.97560344306105
          - type: manhattan_spearman
            value: 87.5267102829796
      - task:
          type: STS
        dataset:
          type: mteb/sts17-crosslingual-sts
          name: MTEB STS17 (en-tr)
          config: en-tr
          split: test
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
        metrics:
          - type: cos_sim_pearson
            value: 64.76325252267235
          - type: cos_sim_spearman
            value: 63.32615095463905
          - type: euclidean_pearson
            value: 64.07920669155716
          - type: euclidean_spearman
            value: 61.21409893072176
          - type: manhattan_pearson
            value: 64.26308625680016
          - type: manhattan_spearman
            value: 61.2438185254079
      - task:
          type: STS
        dataset:
          type: mteb/sts17-crosslingual-sts
          name: MTEB STS17 (es-en)
          config: es-en
          split: test
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
        metrics:
          - type: cos_sim_pearson
            value: 75.82644463022595
          - type: cos_sim_spearman
            value: 76.50381269945073
          - type: euclidean_pearson
            value: 75.1328548315934
          - type: euclidean_spearman
            value: 75.63761139408453
          - type: manhattan_pearson
            value: 75.18610101241407
          - type: manhattan_spearman
            value: 75.30669266354164
      - task:
          type: STS
        dataset:
          type: mteb/sts17-crosslingual-sts
          name: MTEB STS17 (es-es)
          config: es-es
          split: test
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
        metrics:
          - type: cos_sim_pearson
            value: 87.49994164686832
          - type: cos_sim_spearman
            value: 86.73743986245549
          - type: euclidean_pearson
            value: 86.8272894387145
          - type: euclidean_spearman
            value: 85.97608491000507
          - type: manhattan_pearson
            value: 86.74960140396779
          - type: manhattan_spearman
            value: 85.79285984190273
      - task:
          type: STS
        dataset:
          type: mteb/sts17-crosslingual-sts
          name: MTEB STS17 (fr-en)
          config: fr-en
          split: test
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
        metrics:
          - type: cos_sim_pearson
            value: 79.58172210788469
          - type: cos_sim_spearman
            value: 80.17516468334607
          - type: euclidean_pearson
            value: 77.56537843470504
          - type: euclidean_spearman
            value: 77.57264627395521
          - type: manhattan_pearson
            value: 78.09703521695943
          - type: manhattan_spearman
            value: 78.15942760916954
      - task:
          type: STS
        dataset:
          type: mteb/sts17-crosslingual-sts
          name: MTEB STS17 (it-en)
          config: it-en
          split: test
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
        metrics:
          - type: cos_sim_pearson
            value: 79.7589932931751
          - type: cos_sim_spearman
            value: 80.15210089028162
          - type: euclidean_pearson
            value: 77.54135223516057
          - type: euclidean_spearman
            value: 77.52697996368764
          - type: manhattan_pearson
            value: 77.65734439572518
          - type: manhattan_spearman
            value: 77.77702992016121
      - task:
          type: STS
        dataset:
          type: mteb/sts17-crosslingual-sts
          name: MTEB STS17 (nl-en)
          config: nl-en
          split: test
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
        metrics:
          - type: cos_sim_pearson
            value: 79.16682365511267
          - type: cos_sim_spearman
            value: 79.25311267628506
          - type: euclidean_pearson
            value: 77.54882036762244
          - type: euclidean_spearman
            value: 77.33212935194827
          - type: manhattan_pearson
            value: 77.98405516064015
          - type: manhattan_spearman
            value: 77.85075717865719
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (en)
          config: en
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 59.10473294775917
          - type: cos_sim_spearman
            value: 61.82780474476838
          - type: euclidean_pearson
            value: 45.885111672377256
          - type: euclidean_spearman
            value: 56.88306351932454
          - type: manhattan_pearson
            value: 46.101218127323186
          - type: manhattan_spearman
            value: 56.80953694186333
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (de)
          config: de
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 45.781923079584146
          - type: cos_sim_spearman
            value: 55.95098449691107
          - type: euclidean_pearson
            value: 25.4571031323205
          - type: euclidean_spearman
            value: 49.859978118078935
          - type: manhattan_pearson
            value: 25.624938455041384
          - type: manhattan_spearman
            value: 49.99546185049401
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (es)
          config: es
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 60.00618133997907
          - type: cos_sim_spearman
            value: 66.57896677718321
          - type: euclidean_pearson
            value: 42.60118466388821
          - type: euclidean_spearman
            value: 62.8210759715209
          - type: manhattan_pearson
            value: 42.63446860604094
          - type: manhattan_spearman
            value: 62.73803068925271
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (pl)
          config: pl
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 28.460759121626943
          - type: cos_sim_spearman
            value: 34.13459007469131
          - type: euclidean_pearson
            value: 6.0917739325525195
          - type: euclidean_spearman
            value: 27.9947262664867
          - type: manhattan_pearson
            value: 6.16877864169911
          - type: manhattan_spearman
            value: 28.00664163971514
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (tr)
          config: tr
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 57.42546621771696
          - type: cos_sim_spearman
            value: 63.699663168970474
          - type: euclidean_pearson
            value: 38.12085278789738
          - type: euclidean_spearman
            value: 58.12329140741536
          - type: manhattan_pearson
            value: 37.97364549443335
          - type: manhattan_spearman
            value: 57.81545502318733
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (ar)
          config: ar
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 46.82241380954213
          - type: cos_sim_spearman
            value: 57.86569456006391
          - type: euclidean_pearson
            value: 31.80480070178813
          - type: euclidean_spearman
            value: 52.484000620130104
          - type: manhattan_pearson
            value: 31.952708554646097
          - type: manhattan_spearman
            value: 52.8560972356195
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (ru)
          config: ru
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 52.00447170498087
          - type: cos_sim_spearman
            value: 60.664116225735164
          - type: euclidean_pearson
            value: 33.87382555421702
          - type: euclidean_spearman
            value: 55.74649067458667
          - type: manhattan_pearson
            value: 33.99117246759437
          - type: manhattan_spearman
            value: 55.98749034923899
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (zh)
          config: zh
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 58.06497233105448
          - type: cos_sim_spearman
            value: 65.62968801135676
          - type: euclidean_pearson
            value: 47.482076613243905
          - type: euclidean_spearman
            value: 62.65137791498299
          - type: manhattan_pearson
            value: 47.57052626104093
          - type: manhattan_spearman
            value: 62.436916516613294
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (fr)
          config: fr
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 70.49397298562575
          - type: cos_sim_spearman
            value: 74.79604041187868
          - type: euclidean_pearson
            value: 49.661891561317795
          - type: euclidean_spearman
            value: 70.31535537621006
          - type: manhattan_pearson
            value: 49.553715741850006
          - type: manhattan_spearman
            value: 70.24779344636806
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (de-en)
          config: de-en
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 55.640574515348696
          - type: cos_sim_spearman
            value: 54.927959317689
          - type: euclidean_pearson
            value: 29.00139666967476
          - type: euclidean_spearman
            value: 41.86386566971605
          - type: manhattan_pearson
            value: 29.47411067730344
          - type: manhattan_spearman
            value: 42.337438424952786
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (es-en)
          config: es-en
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 68.14095292259312
          - type: cos_sim_spearman
            value: 73.99017581234789
          - type: euclidean_pearson
            value: 46.46304297872084
          - type: euclidean_spearman
            value: 60.91834114800041
          - type: manhattan_pearson
            value: 47.07072666338692
          - type: manhattan_spearman
            value: 61.70415727977926
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (it)
          config: it
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 73.27184653359575
          - type: cos_sim_spearman
            value: 77.76070252418626
          - type: euclidean_pearson
            value: 62.30586577544778
          - type: euclidean_spearman
            value: 75.14246629110978
          - type: manhattan_pearson
            value: 62.328196884927046
          - type: manhattan_spearman
            value: 75.1282792981433
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (pl-en)
          config: pl-en
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 71.59448528829957
          - type: cos_sim_spearman
            value: 70.37277734222123
          - type: euclidean_pearson
            value: 57.63145565721123
          - type: euclidean_spearman
            value: 66.10113048304427
          - type: manhattan_pearson
            value: 57.18897811586808
          - type: manhattan_spearman
            value: 66.5595511215901
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (zh-en)
          config: zh-en
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 66.37520607720838
          - type: cos_sim_spearman
            value: 69.92282148997948
          - type: euclidean_pearson
            value: 40.55768770125291
          - type: euclidean_spearman
            value: 55.189128944669605
          - type: manhattan_pearson
            value: 41.03566433468883
          - type: manhattan_spearman
            value: 55.61251893174558
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (es-it)
          config: es-it
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 57.791929533771835
          - type: cos_sim_spearman
            value: 66.45819707662093
          - type: euclidean_pearson
            value: 39.03686018511092
          - type: euclidean_spearman
            value: 56.01282695640428
          - type: manhattan_pearson
            value: 38.91586623619632
          - type: manhattan_spearman
            value: 56.69394943612747
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (de-fr)
          config: de-fr
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 47.82224468473866
          - type: cos_sim_spearman
            value: 59.467307194781164
          - type: euclidean_pearson
            value: 27.428459190256145
          - type: euclidean_spearman
            value: 60.83463107397519
          - type: manhattan_pearson
            value: 27.487391578496638
          - type: manhattan_spearman
            value: 61.281380460246496
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (de-pl)
          config: de-pl
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 16.306666792752644
          - type: cos_sim_spearman
            value: 39.35486427252405
          - type: euclidean_pearson
            value: -2.7887154897955435
          - type: euclidean_spearman
            value: 27.1296051831719
          - type: manhattan_pearson
            value: -3.202291270581297
          - type: manhattan_spearman
            value: 26.32895849218158
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (fr-pl)
          config: fr-pl
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 59.67006803805076
          - type: cos_sim_spearman
            value: 73.24670207647144
          - type: euclidean_pearson
            value: 46.91884681500483
          - type: euclidean_spearman
            value: 16.903085094570333
          - type: manhattan_pearson
            value: 46.88391675325812
          - type: manhattan_spearman
            value: 28.17180849095055
      - task:
          type: STS
        dataset:
          type: mteb/stsbenchmark-sts
          name: MTEB STSBenchmark
          config: default
          split: test
          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
        metrics:
          - type: cos_sim_pearson
            value: 83.79555591223837
          - type: cos_sim_spearman
            value: 85.63658602085185
          - type: euclidean_pearson
            value: 85.22080894037671
          - type: euclidean_spearman
            value: 85.54113580167038
          - type: manhattan_pearson
            value: 85.1639505960118
          - type: manhattan_spearman
            value: 85.43502665436196
      - task:
          type: Reranking
        dataset:
          type: mteb/scidocs-reranking
          name: MTEB SciDocsRR
          config: default
          split: test
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
        metrics:
          - type: map
            value: 80.73900991689766
          - type: mrr
            value: 94.81624131133934
      - task:
          type: Retrieval
        dataset:
          type: scifact
          name: MTEB SciFact
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 55.678000000000004
          - type: map_at_10
            value: 65.135
          - type: map_at_100
            value: 65.824
          - type: map_at_1000
            value: 65.852
          - type: map_at_3
            value: 62.736000000000004
          - type: map_at_5
            value: 64.411
          - type: mrr_at_1
            value: 58.333
          - type: mrr_at_10
            value: 66.5
          - type: mrr_at_100
            value: 67.053
          - type: mrr_at_1000
            value: 67.08
          - type: mrr_at_3
            value: 64.944
          - type: mrr_at_5
            value: 65.89399999999999
          - type: ndcg_at_1
            value: 58.333
          - type: ndcg_at_10
            value: 69.34700000000001
          - type: ndcg_at_100
            value: 72.32
          - type: ndcg_at_1000
            value: 73.014
          - type: ndcg_at_3
            value: 65.578
          - type: ndcg_at_5
            value: 67.738
          - type: precision_at_1
            value: 58.333
          - type: precision_at_10
            value: 9.033
          - type: precision_at_100
            value: 1.0670000000000002
          - type: precision_at_1000
            value: 0.11199999999999999
          - type: precision_at_3
            value: 25.444
          - type: precision_at_5
            value: 16.933
          - type: recall_at_1
            value: 55.678000000000004
          - type: recall_at_10
            value: 80.72200000000001
          - type: recall_at_100
            value: 93.93299999999999
          - type: recall_at_1000
            value: 99.333
          - type: recall_at_3
            value: 70.783
          - type: recall_at_5
            value: 75.978
      - task:
          type: PairClassification
        dataset:
          type: mteb/sprintduplicatequestions-pairclassification
          name: MTEB SprintDuplicateQuestions
          config: default
          split: test
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
        metrics:
          - type: cos_sim_accuracy
            value: 99.74653465346535
          - type: cos_sim_ap
            value: 93.01476369929063
          - type: cos_sim_f1
            value: 86.93009118541033
          - type: cos_sim_precision
            value: 88.09034907597535
          - type: cos_sim_recall
            value: 85.8
          - type: dot_accuracy
            value: 99.22970297029703
          - type: dot_ap
            value: 51.58725659485144
          - type: dot_f1
            value: 53.51351351351352
          - type: dot_precision
            value: 58.235294117647065
          - type: dot_recall
            value: 49.5
          - type: euclidean_accuracy
            value: 99.74356435643564
          - type: euclidean_ap
            value: 92.40332894384368
          - type: euclidean_f1
            value: 86.97838109602817
          - type: euclidean_precision
            value: 87.46208291203236
          - type: euclidean_recall
            value: 86.5
          - type: manhattan_accuracy
            value: 99.73069306930694
          - type: manhattan_ap
            value: 92.01320815721121
          - type: manhattan_f1
            value: 86.4135864135864
          - type: manhattan_precision
            value: 86.32734530938124
          - type: manhattan_recall
            value: 86.5
          - type: max_accuracy
            value: 99.74653465346535
          - type: max_ap
            value: 93.01476369929063
          - type: max_f1
            value: 86.97838109602817
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering
          name: MTEB StackExchangeClustering
          config: default
          split: test
          revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
        metrics:
          - type: v_measure
            value: 55.2660514302523
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering-p2p
          name: MTEB StackExchangeClusteringP2P
          config: default
          split: test
          revision: 815ca46b2622cec33ccafc3735d572c266efdb44
        metrics:
          - type: v_measure
            value: 30.4637783572547
      - task:
          type: Reranking
        dataset:
          type: mteb/stackoverflowdupquestions-reranking
          name: MTEB StackOverflowDupQuestions
          config: default
          split: test
          revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
        metrics:
          - type: map
            value: 49.41377758357637
          - type: mrr
            value: 50.138451213818854
      - task:
          type: Summarization
        dataset:
          type: mteb/summeval
          name: MTEB SummEval
          config: default
          split: test
          revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
        metrics:
          - type: cos_sim_pearson
            value: 28.887846011166594
          - type: cos_sim_spearman
            value: 30.10823258355903
          - type: dot_pearson
            value: 12.888049550236385
          - type: dot_spearman
            value: 12.827495903098123
      - task:
          type: Retrieval
        dataset:
          type: trec-covid
          name: MTEB TRECCOVID
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 0.21
          - type: map_at_10
            value: 1.667
          - type: map_at_100
            value: 9.15
          - type: map_at_1000
            value: 22.927
          - type: map_at_3
            value: 0.573
          - type: map_at_5
            value: 0.915
          - type: mrr_at_1
            value: 80
          - type: mrr_at_10
            value: 87.167
          - type: mrr_at_100
            value: 87.167
          - type: mrr_at_1000
            value: 87.167
          - type: mrr_at_3
            value: 85.667
          - type: mrr_at_5
            value: 87.167
          - type: ndcg_at_1
            value: 76
          - type: ndcg_at_10
            value: 69.757
          - type: ndcg_at_100
            value: 52.402
          - type: ndcg_at_1000
            value: 47.737
          - type: ndcg_at_3
            value: 71.866
          - type: ndcg_at_5
            value: 72.225
          - type: precision_at_1
            value: 80
          - type: precision_at_10
            value: 75
          - type: precision_at_100
            value: 53.959999999999994
          - type: precision_at_1000
            value: 21.568
          - type: precision_at_3
            value: 76.667
          - type: precision_at_5
            value: 78
          - type: recall_at_1
            value: 0.21
          - type: recall_at_10
            value: 1.9189999999999998
          - type: recall_at_100
            value: 12.589
          - type: recall_at_1000
            value: 45.312000000000005
          - type: recall_at_3
            value: 0.61
          - type: recall_at_5
            value: 1.019
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (sqi-eng)
          config: sqi-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 92.10000000000001
          - type: f1
            value: 90.06
          - type: precision
            value: 89.17333333333333
          - type: recall
            value: 92.10000000000001
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (fry-eng)
          config: fry-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 56.06936416184971
          - type: f1
            value: 50.87508028259473
          - type: precision
            value: 48.97398843930635
          - type: recall
            value: 56.06936416184971
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (kur-eng)
          config: kur-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 57.3170731707317
          - type: f1
            value: 52.96080139372822
          - type: precision
            value: 51.67861124382864
          - type: recall
            value: 57.3170731707317
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (tur-eng)
          config: tur-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 94.3
          - type: f1
            value: 92.67333333333333
          - type: precision
            value: 91.90833333333333
          - type: recall
            value: 94.3
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (deu-eng)
          config: deu-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 97.7
          - type: f1
            value: 97.07333333333332
          - type: precision
            value: 96.79500000000002
          - type: recall
            value: 97.7
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (nld-eng)
          config: nld-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 94.69999999999999
          - type: f1
            value: 93.2
          - type: precision
            value: 92.48333333333333
          - type: recall
            value: 94.69999999999999
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ron-eng)
          config: ron-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 92.9
          - type: f1
            value: 91.26666666666667
          - type: precision
            value: 90.59444444444445
          - type: recall
            value: 92.9
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ang-eng)
          config: ang-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 34.32835820895522
          - type: f1
            value: 29.074180380150533
          - type: precision
            value: 28.068207322920596
          - type: recall
            value: 34.32835820895522
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ido-eng)
          config: ido-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 78.5
          - type: f1
            value: 74.3945115995116
          - type: precision
            value: 72.82967843459222
          - type: recall
            value: 78.5
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (jav-eng)
          config: jav-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 66.34146341463415
          - type: f1
            value: 61.2469400518181
          - type: precision
            value: 59.63977756660683
          - type: recall
            value: 66.34146341463415
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (isl-eng)
          config: isl-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 80.9
          - type: f1
            value: 76.90349206349207
          - type: precision
            value: 75.32921568627451
          - type: recall
            value: 80.9
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (slv-eng)
          config: slv-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 84.93317132442284
          - type: f1
            value: 81.92519105034295
          - type: precision
            value: 80.71283920615635
          - type: recall
            value: 84.93317132442284
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (cym-eng)
          config: cym-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 71.1304347826087
          - type: f1
            value: 65.22394755003451
          - type: precision
            value: 62.912422360248435
          - type: recall
            value: 71.1304347826087
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (kaz-eng)
          config: kaz-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 79.82608695652173
          - type: f1
            value: 75.55693581780538
          - type: precision
            value: 73.79420289855072
          - type: recall
            value: 79.82608695652173
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (est-eng)
          config: est-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 74
          - type: f1
            value: 70.51022222222223
          - type: precision
            value: 69.29673599347512
          - type: recall
            value: 74
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (heb-eng)
          config: heb-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 78.7
          - type: f1
            value: 74.14238095238095
          - type: precision
            value: 72.27214285714285
          - type: recall
            value: 78.7
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (gla-eng)
          config: gla-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 48.97466827503016
          - type: f1
            value: 43.080330405420874
          - type: precision
            value: 41.36505499593557
          - type: recall
            value: 48.97466827503016
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (mar-eng)
          config: mar-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 89.60000000000001
          - type: f1
            value: 86.62333333333333
          - type: precision
            value: 85.225
          - type: recall
            value: 89.60000000000001
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (lat-eng)
          config: lat-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 45.2
          - type: f1
            value: 39.5761253006253
          - type: precision
            value: 37.991358436312
          - type: recall
            value: 45.2
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (bel-eng)
          config: bel-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 89.5
          - type: f1
            value: 86.70333333333333
          - type: precision
            value: 85.53166666666667
          - type: recall
            value: 89.5
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (pms-eng)
          config: pms-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 50.095238095238095
          - type: f1
            value: 44.60650460650461
          - type: precision
            value: 42.774116796477045
          - type: recall
            value: 50.095238095238095
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (gle-eng)
          config: gle-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 63.4
          - type: f1
            value: 58.35967261904762
          - type: precision
            value: 56.54857142857143
          - type: recall
            value: 63.4
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (pes-eng)
          config: pes-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 89.2
          - type: f1
            value: 87.075
          - type: precision
            value: 86.12095238095239
          - type: recall
            value: 89.2
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (nob-eng)
          config: nob-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 96.8
          - type: f1
            value: 95.90333333333334
          - type: precision
            value: 95.50833333333333
          - type: recall
            value: 96.8
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (bul-eng)
          config: bul-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 90.9
          - type: f1
            value: 88.6288888888889
          - type: precision
            value: 87.61607142857142
          - type: recall
            value: 90.9
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (cbk-eng)
          config: cbk-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 65.2
          - type: f1
            value: 60.54377630539395
          - type: precision
            value: 58.89434482711381
          - type: recall
            value: 65.2
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (hun-eng)
          config: hun-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 87
          - type: f1
            value: 84.32412698412699
          - type: precision
            value: 83.25527777777778
          - type: recall
            value: 87
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (uig-eng)
          config: uig-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 68.7
          - type: f1
            value: 63.07883541295306
          - type: precision
            value: 61.06117424242426
          - type: recall
            value: 68.7
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (rus-eng)
          config: rus-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 93.7
          - type: f1
            value: 91.78333333333335
          - type: precision
            value: 90.86666666666667
          - type: recall
            value: 93.7
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (spa-eng)
          config: spa-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 97.7
          - type: f1
            value: 96.96666666666667
          - type: precision
            value: 96.61666666666667
          - type: recall
            value: 97.7
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (hye-eng)
          config: hye-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 88.27493261455525
          - type: f1
            value: 85.90745732255168
          - type: precision
            value: 84.91389637616052
          - type: recall
            value: 88.27493261455525
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (tel-eng)
          config: tel-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 90.5982905982906
          - type: f1
            value: 88.4900284900285
          - type: precision
            value: 87.57122507122507
          - type: recall
            value: 90.5982905982906
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (afr-eng)
          config: afr-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 89.5
          - type: f1
            value: 86.90769841269842
          - type: precision
            value: 85.80178571428571
          - type: recall
            value: 89.5
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (mon-eng)
          config: mon-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 82.5
          - type: f1
            value: 78.36796536796538
          - type: precision
            value: 76.82196969696969
          - type: recall
            value: 82.5
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (arz-eng)
          config: arz-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 71.48846960167715
          - type: f1
            value: 66.78771089148448
          - type: precision
            value: 64.98302885095339
          - type: recall
            value: 71.48846960167715
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (hrv-eng)
          config: hrv-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 94.1
          - type: f1
            value: 92.50333333333333
          - type: precision
            value: 91.77499999999999
          - type: recall
            value: 94.1
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (nov-eng)
          config: nov-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 71.20622568093385
          - type: f1
            value: 66.83278891450098
          - type: precision
            value: 65.35065777283677
          - type: recall
            value: 71.20622568093385
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (gsw-eng)
          config: gsw-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 48.717948717948715
          - type: f1
            value: 43.53146853146853
          - type: precision
            value: 42.04721204721204
          - type: recall
            value: 48.717948717948715
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (nds-eng)
          config: nds-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 58.5
          - type: f1
            value: 53.8564991863928
          - type: precision
            value: 52.40329436122275
          - type: recall
            value: 58.5
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ukr-eng)
          config: ukr-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 90.8
          - type: f1
            value: 88.29
          - type: precision
            value: 87.09166666666667
          - type: recall
            value: 90.8
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (uzb-eng)
          config: uzb-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 67.28971962616822
          - type: f1
            value: 62.63425307817832
          - type: precision
            value: 60.98065939771546
          - type: recall
            value: 67.28971962616822
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (lit-eng)
          config: lit-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 78.7
          - type: f1
            value: 75.5264472455649
          - type: precision
            value: 74.38205086580086
          - type: recall
            value: 78.7
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ina-eng)
          config: ina-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 88.7
          - type: f1
            value: 86.10809523809525
          - type: precision
            value: 85.07602564102565
          - type: recall
            value: 88.7
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (lfn-eng)
          config: lfn-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 56.99999999999999
          - type: f1
            value: 52.85487521402737
          - type: precision
            value: 51.53985162713104
          - type: recall
            value: 56.99999999999999
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (zsm-eng)
          config: zsm-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 94
          - type: f1
            value: 92.45333333333333
          - type: precision
            value: 91.79166666666667
          - type: recall
            value: 94
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ita-eng)
          config: ita-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 92.30000000000001
          - type: f1
            value: 90.61333333333333
          - type: precision
            value: 89.83333333333331
          - type: recall
            value: 92.30000000000001
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (cmn-eng)
          config: cmn-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 94.69999999999999
          - type: f1
            value: 93.34555555555555
          - type: precision
            value: 92.75416666666668
          - type: recall
            value: 94.69999999999999
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (lvs-eng)
          config: lvs-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 80.2
          - type: f1
            value: 76.6563035113035
          - type: precision
            value: 75.3014652014652
          - type: recall
            value: 80.2
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (glg-eng)
          config: glg-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 84.7
          - type: f1
            value: 82.78689263765207
          - type: precision
            value: 82.06705086580087
          - type: recall
            value: 84.7
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ceb-eng)
          config: ceb-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 50.33333333333333
          - type: f1
            value: 45.461523661523664
          - type: precision
            value: 43.93545574795575
          - type: recall
            value: 50.33333333333333
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (bre-eng)
          config: bre-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 6.6000000000000005
          - type: f1
            value: 5.442121400446441
          - type: precision
            value: 5.146630385487529
          - type: recall
            value: 6.6000000000000005
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ben-eng)
          config: ben-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 85
          - type: f1
            value: 81.04666666666667
          - type: precision
            value: 79.25
          - type: recall
            value: 85
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (swg-eng)
          config: swg-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 47.32142857142857
          - type: f1
            value: 42.333333333333336
          - type: precision
            value: 40.69196428571429
          - type: recall
            value: 47.32142857142857
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (arq-eng)
          config: arq-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 30.735455543358945
          - type: f1
            value: 26.73616790022338
          - type: precision
            value: 25.397823220451283
          - type: recall
            value: 30.735455543358945
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (kab-eng)
          config: kab-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 25.1
          - type: f1
            value: 21.975989896371022
          - type: precision
            value: 21.059885632257203
          - type: recall
            value: 25.1
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          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (fra-eng)
          config: fra-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 94.3
          - type: f1
            value: 92.75666666666666
          - type: precision
            value: 92.06166666666665
          - type: recall
            value: 94.3
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (por-eng)
          config: por-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 94.1
          - type: f1
            value: 92.74
          - type: precision
            value: 92.09166666666667
          - type: recall
            value: 94.1
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (tat-eng)
          config: tat-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 71.3
          - type: f1
            value: 66.922442002442
          - type: precision
            value: 65.38249567099568
          - type: recall
            value: 71.3
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (oci-eng)
          config: oci-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 40.300000000000004
          - type: f1
            value: 35.78682789299971
          - type: precision
            value: 34.66425128716588
          - type: recall
            value: 40.300000000000004
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (pol-eng)
          config: pol-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 96
          - type: f1
            value: 94.82333333333334
          - type: precision
            value: 94.27833333333334
          - type: recall
            value: 96
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (war-eng)
          config: war-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 51.1
          - type: f1
            value: 47.179074753133584
          - type: precision
            value: 46.06461044702424
          - type: recall
            value: 51.1
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (aze-eng)
          config: aze-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 87.7
          - type: f1
            value: 84.71
          - type: precision
            value: 83.46166666666667
          - type: recall
            value: 87.7
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (vie-eng)
          config: vie-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 95.8
          - type: f1
            value: 94.68333333333334
          - type: precision
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          - type: recall
            value: 95.8
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (nno-eng)
          config: nno-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 85.39999999999999
          - type: f1
            value: 82.5577380952381
          - type: precision
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          - type: recall
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      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
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          config: cha-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 21.16788321167883
          - type: f1
            value: 16.948865627297987
          - type: precision
            value: 15.971932568647897
          - type: recall
            value: 21.16788321167883
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
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          config: mhr-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 6.9
          - type: f1
            value: 5.515526831658907
          - type: precision
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          - type: recall
            value: 6.9
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (dan-eng)
          config: dan-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 93.2
          - type: f1
            value: 91.39666666666668
          - type: precision
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          - type: recall
            value: 93.2
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ell-eng)
          config: ell-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 92.2
          - type: f1
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          - type: precision
            value: 88.92833333333333
          - type: recall
            value: 92.2
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (amh-eng)
          config: amh-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 79.76190476190477
          - type: f1
            value: 74.93386243386244
          - type: precision
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          - type: recall
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      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
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          config: pam-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 8.799999999999999
          - type: f1
            value: 6.921439712248537
          - type: precision
            value: 6.489885109680683
          - type: recall
            value: 8.799999999999999
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (hsb-eng)
          config: hsb-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 45.75569358178054
          - type: f1
            value: 40.34699501312631
          - type: precision
            value: 38.57886764719063
          - type: recall
            value: 45.75569358178054
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
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          config: srp-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 91.4
          - type: f1
            value: 89.08333333333333
          - type: precision
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          - type: recall
            value: 91.4
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (epo-eng)
          config: epo-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 93.60000000000001
          - type: f1
            value: 92.06690476190477
          - type: precision
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          - type: recall
            value: 93.60000000000001
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
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          config: kzj-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 7.5
          - type: f1
            value: 6.200363129378736
          - type: precision
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          - type: recall
            value: 7.5
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (awa-eng)
          config: awa-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 73.59307359307358
          - type: f1
            value: 68.38933553219267
          - type: precision
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          - type: recall
            value: 73.59307359307358
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (fao-eng)
          config: fao-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 69.8473282442748
          - type: f1
            value: 64.72373682297346
          - type: precision
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          - type: recall
            value: 69.8473282442748
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (mal-eng)
          config: mal-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 97.5254730713246
          - type: f1
            value: 96.72489082969432
          - type: precision
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          - type: recall
            value: 97.5254730713246
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ile-eng)
          config: ile-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 75.6
          - type: f1
            value: 72.42746031746033
          - type: precision
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          - type: recall
            value: 75.6
      - task:
          type: BitextMining
        dataset:
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          name: MTEB Tatoeba (bos-eng)
          config: bos-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 91.24293785310734
          - type: f1
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          - type: precision
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          - type: recall
            value: 91.24293785310734
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (cor-eng)
          config: cor-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 6.2
          - type: f1
            value: 4.383083659794954
          - type: precision
            value: 4.027861324289673
          - type: recall
            value: 6.2
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (cat-eng)
          config: cat-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 86.8
          - type: f1
            value: 84.09428571428572
          - type: precision
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          - type: recall
            value: 86.8
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (eus-eng)
          config: eus-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 60.699999999999996
          - type: f1
            value: 56.1584972394755
          - type: precision
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          - type: recall
            value: 60.699999999999996
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (yue-eng)
          config: yue-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 84.2
          - type: f1
            value: 80.66190476190475
          - type: precision
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          - type: recall
            value: 84.2
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (swe-eng)
          config: swe-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 93.2
          - type: f1
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          - type: precision
            value: 90.45
          - type: recall
            value: 93.2
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (dtp-eng)
          config: dtp-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 6.3
          - type: f1
            value: 5.126828976748276
          - type: precision
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          - type: recall
            value: 6.3
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (kat-eng)
          config: kat-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 81.76943699731903
          - type: f1
            value: 77.82873739308057
          - type: precision
            value: 76.27622452019234
          - type: recall
            value: 81.76943699731903
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (jpn-eng)
          config: jpn-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 92.30000000000001
          - type: f1
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          - type: precision
            value: 89.40333333333334
          - type: recall
            value: 92.30000000000001
      - task:
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        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (csb-eng)
          config: csb-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 29.249011857707508
          - type: f1
            value: 24.561866096392947
          - type: precision
            value: 23.356583740215456
          - type: recall
            value: 29.249011857707508
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (xho-eng)
          config: xho-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 77.46478873239437
          - type: f1
            value: 73.23943661971832
          - type: precision
            value: 71.66666666666667
          - type: recall
            value: 77.46478873239437
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (orv-eng)
          config: orv-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 20.35928143712575
          - type: f1
            value: 15.997867865075824
          - type: precision
            value: 14.882104658301346
          - type: recall
            value: 20.35928143712575
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ind-eng)
          config: ind-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 92.2
          - type: f1
            value: 90.25999999999999
          - type: precision
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          - type: recall
            value: 92.2
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (tuk-eng)
          config: tuk-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 23.15270935960591
          - type: f1
            value: 19.65673625772148
          - type: precision
            value: 18.793705293464992
          - type: recall
            value: 23.15270935960591
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (max-eng)
          config: max-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 59.154929577464785
          - type: f1
            value: 52.3868463305083
          - type: precision
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          - type: recall
            value: 59.154929577464785
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (swh-eng)
          config: swh-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 70.51282051282051
          - type: f1
            value: 66.8089133089133
          - type: precision
            value: 65.37645687645687
          - type: recall
            value: 70.51282051282051
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (hin-eng)
          config: hin-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 94.6
          - type: f1
            value: 93
          - type: precision
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          - type: recall
            value: 94.6
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (dsb-eng)
          config: dsb-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 38.62212943632568
          - type: f1
            value: 34.3278276962583
          - type: precision
            value: 33.07646935732408
          - type: recall
            value: 38.62212943632568
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ber-eng)
          config: ber-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 28.1
          - type: f1
            value: 23.579609223054604
          - type: precision
            value: 22.39622774921555
          - type: recall
            value: 28.1
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (tam-eng)
          config: tam-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 88.27361563517914
          - type: f1
            value: 85.12486427795874
          - type: precision
            value: 83.71335504885994
          - type: recall
            value: 88.27361563517914
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (slk-eng)
          config: slk-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 88.6
          - type: f1
            value: 86.39928571428571
          - type: precision
            value: 85.4947557997558
          - type: recall
            value: 88.6
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (tgl-eng)
          config: tgl-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 86.5
          - type: f1
            value: 83.77952380952381
          - type: precision
            value: 82.67602564102565
          - type: recall
            value: 86.5
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ast-eng)
          config: ast-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 79.52755905511812
          - type: f1
            value: 75.3055868016498
          - type: precision
            value: 73.81889763779527
          - type: recall
            value: 79.52755905511812
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (mkd-eng)
          config: mkd-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 77.9
          - type: f1
            value: 73.76261904761905
          - type: precision
            value: 72.11670995670995
          - type: recall
            value: 77.9
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (khm-eng)
          config: khm-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 53.8781163434903
          - type: f1
            value: 47.25804051288816
          - type: precision
            value: 45.0603482390186
          - type: recall
            value: 53.8781163434903
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ces-eng)
          config: ces-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 91.10000000000001
          - type: f1
            value: 88.88
          - type: precision
            value: 87.96333333333334
          - type: recall
            value: 91.10000000000001
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (tzl-eng)
          config: tzl-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 38.46153846153847
          - type: f1
            value: 34.43978243978244
          - type: precision
            value: 33.429487179487175
          - type: recall
            value: 38.46153846153847
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (urd-eng)
          config: urd-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 88.9
          - type: f1
            value: 86.19888888888887
          - type: precision
            value: 85.07440476190476
          - type: recall
            value: 88.9
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ara-eng)
          config: ara-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 85.9
          - type: f1
            value: 82.58857142857143
          - type: precision
            value: 81.15666666666667
          - type: recall
            value: 85.9
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (kor-eng)
          config: kor-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 86.8
          - type: f1
            value: 83.36999999999999
          - type: precision
            value: 81.86833333333333
          - type: recall
            value: 86.8
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (yid-eng)
          config: yid-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 68.51415094339622
          - type: f1
            value: 63.195000099481234
          - type: precision
            value: 61.394033442972116
          - type: recall
            value: 68.51415094339622
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (fin-eng)
          config: fin-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 88.5
          - type: f1
            value: 86.14603174603175
          - type: precision
            value: 85.1162037037037
          - type: recall
            value: 88.5
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (tha-eng)
          config: tha-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 95.62043795620438
          - type: f1
            value: 94.40389294403892
          - type: precision
            value: 93.7956204379562
          - type: recall
            value: 95.62043795620438
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (wuu-eng)
          config: wuu-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 81.8
          - type: f1
            value: 78.6532178932179
          - type: precision
            value: 77.46348795840176
          - type: recall
            value: 81.8
      - task:
          type: Retrieval
        dataset:
          type: webis-touche2020
          name: MTEB Touche2020
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 2.603
          - type: map_at_10
            value: 8.5
          - type: map_at_100
            value: 12.985
          - type: map_at_1000
            value: 14.466999999999999
          - type: map_at_3
            value: 4.859999999999999
          - type: map_at_5
            value: 5.817
          - type: mrr_at_1
            value: 28.571
          - type: mrr_at_10
            value: 42.331
          - type: mrr_at_100
            value: 43.592999999999996
          - type: mrr_at_1000
            value: 43.592999999999996
          - type: mrr_at_3
            value: 38.435
          - type: mrr_at_5
            value: 39.966
          - type: ndcg_at_1
            value: 26.531
          - type: ndcg_at_10
            value: 21.353
          - type: ndcg_at_100
            value: 31.087999999999997
          - type: ndcg_at_1000
            value: 43.163000000000004
          - type: ndcg_at_3
            value: 22.999
          - type: ndcg_at_5
            value: 21.451
          - type: precision_at_1
            value: 28.571
          - type: precision_at_10
            value: 19.387999999999998
          - type: precision_at_100
            value: 6.265
          - type: precision_at_1000
            value: 1.4160000000000001
          - type: precision_at_3
            value: 24.490000000000002
          - type: precision_at_5
            value: 21.224
          - type: recall_at_1
            value: 2.603
          - type: recall_at_10
            value: 14.474
          - type: recall_at_100
            value: 40.287
          - type: recall_at_1000
            value: 76.606
          - type: recall_at_3
            value: 5.978
          - type: recall_at_5
            value: 7.819
      - task:
          type: Classification
        dataset:
          type: mteb/toxic_conversations_50k
          name: MTEB ToxicConversationsClassification
          config: default
          split: test
          revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
        metrics:
          - type: accuracy
            value: 69.7848
          - type: ap
            value: 13.661023167088224
          - type: f1
            value: 53.61686134460943
      - task:
          type: Classification
        dataset:
          type: mteb/tweet_sentiment_extraction
          name: MTEB TweetSentimentExtractionClassification
          config: default
          split: test
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
        metrics:
          - type: accuracy
            value: 61.28183361629882
          - type: f1
            value: 61.55481034919965
      - task:
          type: Clustering
        dataset:
          type: mteb/twentynewsgroups-clustering
          name: MTEB TwentyNewsgroupsClustering
          config: default
          split: test
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
        metrics:
          - type: v_measure
            value: 35.972128420092396
      - task:
          type: PairClassification
        dataset:
          type: mteb/twittersemeval2015-pairclassification
          name: MTEB TwitterSemEval2015
          config: default
          split: test
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
        metrics:
          - type: cos_sim_accuracy
            value: 85.59933241938367
          - type: cos_sim_ap
            value: 72.20760361208136
          - type: cos_sim_f1
            value: 66.4447731755424
          - type: cos_sim_precision
            value: 62.35539102267469
          - type: cos_sim_recall
            value: 71.10817941952506
          - type: dot_accuracy
            value: 78.98313166835548
          - type: dot_ap
            value: 44.492521645493795
          - type: dot_f1
            value: 45.814889336016094
          - type: dot_precision
            value: 37.02439024390244
          - type: dot_recall
            value: 60.07915567282321
          - type: euclidean_accuracy
            value: 85.3907134767837
          - type: euclidean_ap
            value: 71.53847289080343
          - type: euclidean_f1
            value: 65.95952206778834
          - type: euclidean_precision
            value: 61.31006346328196
          - type: euclidean_recall
            value: 71.37203166226914
          - type: manhattan_accuracy
            value: 85.40859510043511
          - type: manhattan_ap
            value: 71.49664104395515
          - type: manhattan_f1
            value: 65.98569969356485
          - type: manhattan_precision
            value: 63.928748144482924
          - type: manhattan_recall
            value: 68.17941952506597
          - type: max_accuracy
            value: 85.59933241938367
          - type: max_ap
            value: 72.20760361208136
          - type: max_f1
            value: 66.4447731755424
      - task:
          type: PairClassification
        dataset:
          type: mteb/twitterurlcorpus-pairclassification
          name: MTEB TwitterURLCorpus
          config: default
          split: test
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
        metrics:
          - type: cos_sim_accuracy
            value: 88.83261536073273
          - type: cos_sim_ap
            value: 85.48178133644264
          - type: cos_sim_f1
            value: 77.87816307403935
          - type: cos_sim_precision
            value: 75.88953021114926
          - type: cos_sim_recall
            value: 79.97382198952879
          - type: dot_accuracy
            value: 79.76287499514883
          - type: dot_ap
            value: 59.17438838475084
          - type: dot_f1
            value: 56.34566667855996
          - type: dot_precision
            value: 52.50349092359864
          - type: dot_recall
            value: 60.794579611949494
          - type: euclidean_accuracy
            value: 88.76857996662397
          - type: euclidean_ap
            value: 85.22764834359887
          - type: euclidean_f1
            value: 77.65379751543554
          - type: euclidean_precision
            value: 75.11152683839401
          - type: euclidean_recall
            value: 80.37419156144134
          - type: manhattan_accuracy
            value: 88.6987231730508
          - type: manhattan_ap
            value: 85.18907981724007
          - type: manhattan_f1
            value: 77.51967028849757
          - type: manhattan_precision
            value: 75.49992701795358
          - type: manhattan_recall
            value: 79.65044656606098
          - type: max_accuracy
            value: 88.83261536073273
          - type: max_ap
            value: 85.48178133644264
          - type: max_f1
            value: 77.87816307403935
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

Multilingual-E5-base

Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022

This model has 12 layers and the embedding size is 768.

Usage

Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.

import torch.nn.functional as F

from torch import Tensor
from transformers import AutoTokenizer, AutoModel


def average_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]


# Each input text should start with "query: " or "passage: ", even for non-English texts.
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
               'query: 南瓜的家常做法',
               "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
               "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"]

tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-base')
model = AutoModel.from_pretrained('intfloat/multilingual-e5-base')

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')

outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())

Supported Languages

This model is initialized from xlm-roberta-base and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation.

Training Details

Initialization: xlm-roberta-base

First stage: contrastive pre-training with weak supervision

Dataset Weak supervision # of text pairs
Filtered mC4 (title, page content) 1B
CC News (title, news content) 400M
NLLB translation pairs 2.4B
Wikipedia (hierarchical section title, passage) 150M
Filtered Reddit (comment, response) 800M
S2ORC (title, abstract) and citation pairs 100M
Stackexchange (question, answer) 50M
xP3 (input prompt, response) 80M
Miscellaneous unsupervised SBERT data - 10M

Second stage: supervised fine-tuning

Dataset Language # of text pairs
MS MARCO English 500k
NQ English 70k
Trivia QA English 60k
NLI from SimCSE English <300k
ELI5 English 500k
DuReader Retrieval Chinese 86k
KILT Fever English 70k
KILT HotpotQA English 70k
SQuAD English 87k
Quora English 150k
Mr. TyDi 11 languages 50k
MIRACL 16 languages 40k

For all labeled datasets, we only use its training set for fine-tuning.

For other training details, please refer to our paper at https://arxiv.org/pdf/2212.03533.pdf.

Benchmark Results on Mr. TyDi

Model Avg MRR@10 ar bn en fi id ja ko ru sw te th
BM25 33.3 36.7 41.3 15.1 28.8 38.2 21.7 28.1 32.9 39.6 42.4 41.7
mDPR 16.7 26.0 25.8 16.2 11.3 14.6 18.1 21.9 18.5 7.3 10.6 13.5
BM25 + mDPR 41.7 49.1 53.5 28.4 36.5 45.5 35.5 36.2 42.7 40.5 42.0 49.2
multilingual-e5-small 64.4 71.5 66.3 54.5 57.7 63.2 55.4 54.3 60.8 65.4 89.1 70.1
multilingual-e5-base 65.9 72.3 65.0 58.5 60.8 64.9 56.6 55.8 62.7 69.0 86.6 72.7
multilingual-e5-large 70.5 77.5 73.2 60.8 66.8 68.5 62.5 61.6 65.8 72.7 90.2 76.2

MTEB Benchmark Evaluation

Check out unilm/e5 to reproduce evaluation results on the BEIR and MTEB benchmark.

Support for Sentence Transformers

Below is an example for usage with sentence_transformers.

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/multilingual-e5-base')
input_texts = [
    'query: how much protein should a female eat',
    'query: 南瓜的家常做法',
    "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i     s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini     ng for a marathon. Check out the chart below to see how much protein you should be eating each day.",
    "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮     ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,     放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油     锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀      6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
]
embeddings = model.encode(input_texts, normalize_embeddings=True)

Package requirements

pip install sentence_transformers~=2.2.2

Contributors: michaelfeil

FAQ

1. Do I need to add the prefix "query: " and "passage: " to input texts?

Yes, this is how the model is trained, otherwise you will see a performance degradation.

Here are some rules of thumb:

  • Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.

  • Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval.

  • Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.

2. Why are my reproduced results slightly different from reported in the model card?

Different versions of transformers and pytorch could cause negligible but non-zero performance differences.

3. Why does the cosine similarity scores distribute around 0.7 to 1.0?

This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.

For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue.

Citation

If you find our paper or models helpful, please consider cite as follows:

@article{wang2022text,
  title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
  author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
  journal={arXiv preprint arXiv:2212.03533},
  year={2022}
}

Limitations

Long texts will be truncated to at most 512 tokens.