README.md

---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- sentence-embedding
- mteb
model-index:
- name: bilingual-embedding-large
  results:
  - task:
      type: Clustering
    dataset:
      type: lyon-nlp/alloprof
      name: MTEB AlloProfClusteringP2P
      config: default
      split: test
      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
    metrics:
    - type: v_measure
      value: 65.3004467686438
    - type: v_measures
      value: [0.632560011824588, 0.6345771823814063, 0.6333686484625257, 0.6508206816667124, 0.6378451181543632]
  - task:
      type: Clustering
    dataset:
      type: lyon-nlp/alloprof
      name: MTEB AlloProfClusteringS2S
      config: default
      split: test
      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
    metrics:
    - type: v_measure
      value: 55.3684183324479
    - type: v_measures
      value: [0.5262468095085737, 0.586151012721014, 0.5192907959178751, 0.5610730679809162, 0.6360060059791816]
  - task:
      type: Reranking
    dataset:
      type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
      name: MTEB AlloprofReranking
      config: default
      split: test
      revision: 65393d0d7a08a10b4e348135e824f385d420b0fd
    metrics:
    - type: map
      value: 73.63055206572554
    - type: mrr
      value: 74.69705225210407
    - type: nAUC_map_diff1
      value: 56.61121737089957
    - type: nAUC_map_max
      value: 21.353273116363358
    - type: nAUC_mrr_diff1
      value: 55.98316099424804
    - type: nAUC_mrr_max
      value: 22.29736406333825
  - task:
      type: Retrieval
    dataset:
      type: lyon-nlp/alloprof
      name: MTEB AlloprofRetrieval
      config: default
      split: test
      revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
    metrics:
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      value: 30.009000000000004
    - type: map_at_10
      value: 41.563
    - type: map_at_100
      value: 42.498999999999995
    - type: map_at_1000
      value: 42.541000000000004
    - type: map_at_20
      value: 42.142
    - type: map_at_3
      value: 38.443
    - type: map_at_5
      value: 40.23
    - type: mrr_at_1
      value: 30.008635578583764
    - type: mrr_at_10
      value: 41.563313869013434
    - type: mrr_at_100
      value: 42.49919838395685
    - type: mrr_at_1000
      value: 42.54117981321103
    - type: mrr_at_20
      value: 42.14177102110932
    - type: mrr_at_3
      value: 38.44271732872777
    - type: mrr_at_5
      value: 40.23028209556721
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    - type: ndcg_at_1
      value: 30.009000000000004
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      value: 47.598
    - type: ndcg_at_100
      value: 52.293
    - type: ndcg_at_1000
      value: 53.525999999999996
    - type: ndcg_at_20
      value: 49.697
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      value: 41.159
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      value: 44.379000000000005
    - type: precision_at_1
      value: 30.009000000000004
    - type: precision_at_10
      value: 6.675000000000001
    - type: precision_at_100
      value: 0.8909999999999999
    - type: precision_at_1000
      value: 0.099
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      value: 3.752
    - type: precision_at_3
      value: 16.336000000000002
    - type: precision_at_5
      value: 11.364
    - type: recall_at_1
      value: 30.009000000000004
    - type: recall_at_10
      value: 66.753
    - type: recall_at_100
      value: 89.076
    - type: recall_at_1000
      value: 99.007
    - type: recall_at_20
      value: 75.043
    - type: recall_at_3
      value: 49.007
    - type: recall_at_5
      value: 56.821999999999996
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (fr)
      config: fr
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 43.422
    - type: f1
      value: 41.92216694262306
    - type: f1_weighted
      value: 41.92216694262306
  - task:
      type: Retrieval
    dataset:
      type: maastrichtlawtech/bsard
      name: MTEB BSARDRetrieval
      config: default
      split: test
      revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
    metrics:
    - type: map_at_1
      value: 10.811
    - type: map_at_10
      value: 15.839
    - type: map_at_100
      value: 17.031
    - type: map_at_1000
      value: 17.125
    - type: map_at_20
      value: 16.523
    - type: map_at_3
      value: 13.514000000000001
    - type: map_at_5
      value: 14.482000000000001
    - type: mrr_at_1
      value: 10.81081081081081
    - type: mrr_at_10
      value: 15.83923208923209
    - type: mrr_at_100
      value: 17.03089784389729
    - type: mrr_at_1000
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    - type: mrr_at_20
      value: 16.522687430195177
    - type: mrr_at_3
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    - type: mrr_at_5
      value: 14.481981981981978
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      value: 86.937
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      value: 42.342
    - type: recall_at_3
      value: 17.568
    - type: recall_at_5
      value: 22.072
  - task:
      type: Clustering
    dataset:
      type: lyon-nlp/clustering-hal-s2s
      name: MTEB HALClusteringS2S
      config: default
      split: test
      revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
    metrics:
    - type: v_measure
      value: 26.26502535631247
    - type: v_measures
      value: [0.30893096531878045, 0.27408569069152805, 0.2872676670832888, 0.26871778422889214, 0.2421329238735192]
  - task:
      type: Clustering
    dataset:
      type: reciTAL/mlsum
      name: MTEB MLSUMClusteringP2P
      config: fr
      split: test
      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
    metrics:
    - type: v_measure
      value: 42.60059039120384
    - type: v_measures
      value: [0.4248169037837413, 0.44678284494908554, 0.4386784796938775, 0.41609051956546156, 0.37929269357080225]
  - task:
      type: Clustering
    dataset:
      type: reciTAL/mlsum
      name: MTEB MLSUMClusteringS2S
      config: fr
      split: test
      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
    metrics:
    - type: v_measure
      value: 42.92324222522204
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      value: [0.4320945601805418, 0.43467886343873713, 0.4345273113581795, 0.4277842446367462, 0.381555432691925]
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (fr)
      config: fr
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 88.33385530848732
    - type: f1
      value: 88.36975245849551
    - type: f1_weighted
      value: 88.310383667222
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (fr)
      config: fr
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 62.84685248982148
    - type: f1
      value: 44.420122133882366
    - type: f1_weighted
      value: 65.2728620649712
  - task:
      type: Classification
    dataset:
      type: mteb/masakhanews
      name: MTEB MasakhaNEWSClassification (fra)
      config: fra
      split: test
      revision: 18193f187b92da67168c655c9973a165ed9593dd
    metrics:
    - type: accuracy
      value: 80.56872037914692
    - type: f1
      value: 77.28557364601339
    - type: f1_weighted
      value: 80.51403795220486
  - task:
      type: Clustering
    dataset:
      type: masakhane/masakhanews
      name: MTEB MasakhaNEWSClusteringP2P (fra)
      config: fra
      split: test
      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
    metrics:
    - type: v_measure
      value: 71.29428035967938
    - type: v_measures
      value: [1.0, 0.2773866490640993, 0.7679216739314454, 0.8367645040119921, 0.6826411909764316]
  - task:
      type: Clustering
    dataset:
      type: masakhane/masakhanews
      name: MTEB MasakhaNEWSClusteringS2S (fra)
      config: fra
      split: test
      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
    metrics:
    - type: v_measure
      value: 55.090949643200084
    - type: v_measures
      value: [1.0, 0.0008196849334082873, 0.7532269197656756, 0.37056337344528145, 0.6299375040156386]
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (fr)
      config: fr
      split: test
      revision: 4672e20407010da34463acc759c162ca9734bca6
    metrics:
    - type: accuracy
      value: 66.80564895763281
    - type: f1
      value: 64.35238995318795
    - type: f1_weighted
      value: 65.7206181780162
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (fr)
      config: fr
      split: test
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
    metrics:
    - type: accuracy
      value: 72.98587760591795
    - type: f1
      value: 72.51250718054763
    - type: f1_weighted
      value: 72.81793917434213
  - task:
      type: Retrieval
    dataset:
      type: jinaai/mintakaqa
      name: MTEB MintakaRetrieval (fr)
      config: fr
      split: test
      revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
    metrics:
    - type: map_at_1
      value: 18.96
    - type: map_at_10
      value: 27.744999999999997
    - type: map_at_100
      value: 28.799000000000003
    - type: map_at_1000
      value: 28.884
    - type: map_at_20
      value: 28.375
    - type: map_at_3
      value: 25.108999999999998
    - type: map_at_5
      value: 26.508
    - type: mrr_at_1
      value: 18.95986895986896
    - type: mrr_at_10
      value: 27.744936494936507
    - type: mrr_at_100
      value: 28.79940115805784
    - type: mrr_at_1000
      value: 28.88414927603794
    - type: mrr_at_20
      value: 28.375232375900854
    - type: mrr_at_3
      value: 25.109200109200113
    - type: mrr_at_5
      value: 26.50764400764402
    - type: nauc_map_at_1000_diff1
      value: 18.685236785487458
    - type: nauc_map_at_1000_max
      value: 28.85413041872925
    - type: nauc_map_at_100_diff1
      value: 18.643854459374264
    - type: nauc_map_at_100_max
      value: 28.86568866859659
    - type: nauc_map_at_10_diff1
      value: 18.95179019467019
    - type: nauc_map_at_10_max
      value: 28.978754512041366
    - type: nauc_map_at_1_diff1
      value: 24.276017299858978
    - type: nauc_map_at_1_max
      value: 23.470875089293564
    - type: nauc_map_at_20_diff1
      value: 18.635084934956904
    - type: nauc_map_at_20_max
      value: 28.94762423672467
    - type: nauc_map_at_3_diff1
      value: 19.78833161521705
    - type: nauc_map_at_3_max
      value: 27.717678662759226
    - type: nauc_map_at_5_diff1
      value: 19.121183364075133
    - type: nauc_map_at_5_max
      value: 28.33281003699522
    - type: nauc_mrr_at_1000_diff1
      value: 18.685236785487458
    - type: nauc_mrr_at_1000_max
      value: 28.85413041872925
    - type: nauc_mrr_at_100_diff1
      value: 18.643854459374264
    - type: nauc_mrr_at_100_max
      value: 28.86568866859659
    - type: nauc_mrr_at_10_diff1
      value: 18.95179019467019
    - type: nauc_mrr_at_10_max
      value: 28.978754512041366
    - type: nauc_mrr_at_1_diff1
      value: 24.276017299858978
    - type: nauc_mrr_at_1_max
      value: 23.470875089293564
    - type: nauc_mrr_at_20_diff1
      value: 18.635084934956904
    - type: nauc_mrr_at_20_max
      value: 28.94762423672467
    - type: nauc_mrr_at_3_diff1
      value: 19.78833161521705
    - type: nauc_mrr_at_3_max
      value: 27.717678662759226
    - type: nauc_mrr_at_5_diff1
      value: 19.121183364075133
    - type: nauc_mrr_at_5_max
      value: 28.33281003699522
    - type: nauc_ndcg_at_1000_diff1
      value: 16.9385175619818
    - type: nauc_ndcg_at_1000_max
      value: 30.464626780924114
    - type: nauc_ndcg_at_100_diff1
      value: 15.784507139472703
    - type: nauc_ndcg_at_100_max
      value: 30.783304190943873
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      value: 17.074677821502657
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      value: 31.39661325771708
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      value: 24.276017299858978
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      value: 23.470875089293564
    - type: nauc_ndcg_at_20_diff1
      value: 15.905931373911173
    - type: nauc_ndcg_at_20_max
      value: 31.283157447315457
    - type: nauc_ndcg_at_3_diff1
      value: 18.520146441301954
    - type: nauc_ndcg_at_3_max
      value: 28.855566633100217
    - type: nauc_ndcg_at_5_diff1
      value: 17.414930054902594
    - type: nauc_ndcg_at_5_max
      value: 29.89288498763886
    - type: nauc_precision_at_1000_diff1
      value: 5.6404707169181485
    - type: nauc_precision_at_1000_max
      value: 51.53249587390901
    - type: nauc_precision_at_100_diff1
      value: 2.6401827420753463
    - type: nauc_precision_at_100_max
      value: 37.544518255619415
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      value: 12.07308037199035
    - type: nauc_precision_at_10_max
      value: 38.23001565740937
    - type: nauc_precision_at_1_diff1
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      value: 23.470875089293564
    - type: nauc_precision_at_20_diff1
      value: 7.157477225670103
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      value: 38.273237139593256
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    - type: nauc_precision_at_3_max
      value: 31.763923868965588
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      value: 13.005921624910583
    - type: nauc_precision_at_5_max
      value: 33.92162820494794
    - type: nauc_recall_at_1000_diff1
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    - type: nauc_recall_at_1000_max
      value: 51.53249587390878
    - type: nauc_recall_at_100_diff1
      value: 2.640182742075308
    - type: nauc_recall_at_100_max
      value: 37.544518255619444
    - type: nauc_recall_at_10_diff1
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      value: 38.230015657409375
    - type: nauc_recall_at_1_diff1
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    - type: nauc_recall_at_1_max
      value: 23.470875089293564
    - type: nauc_recall_at_20_diff1
      value: 7.157477225670139
    - type: nauc_recall_at_20_max
      value: 38.27323713959323
    - type: nauc_recall_at_3_diff1
      value: 15.259422549391505
    - type: nauc_recall_at_3_max
      value: 31.763923868965588
    - type: nauc_recall_at_5_diff1
      value: 13.005921624910567
    - type: nauc_recall_at_5_max
      value: 33.92162820494793
    - type: ndcg_at_1
      value: 18.96
    - type: ndcg_at_10
      value: 32.617000000000004
    - type: ndcg_at_100
      value: 37.974000000000004
    - type: ndcg_at_1000
      value: 40.65
    - type: ndcg_at_20
      value: 34.888000000000005
    - type: ndcg_at_3
      value: 27.106
    - type: ndcg_at_5
      value: 29.614
    - type: precision_at_1
      value: 18.96
    - type: precision_at_10
      value: 4.824
    - type: precision_at_100
      value: 0.738
    - type: precision_at_1000
      value: 0.096
    - type: precision_at_20
      value: 2.858
    - type: precision_at_3
      value: 10.961
    - type: precision_at_5
      value: 7.789
    - type: recall_at_1
      value: 18.96
    - type: recall_at_10
      value: 48.239
    - type: recall_at_100
      value: 73.833
    - type: recall_at_1000
      value: 95.82300000000001
    - type: recall_at_20
      value: 57.166
    - type: recall_at_3
      value: 32.883
    - type: recall_at_5
      value: 38.943
  - task:
      type: PairClassification
    dataset:
      type: GEM/opusparcus
      name: MTEB OpusparcusPC (fr)
      config: fr
      split: test
      revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
    metrics:
    - type: cos_sim_accuracy
      value: 84.80926430517711
    - type: cos_sim_ap
      value: 94.76661922683681
    - type: cos_sim_f1
      value: 89.31480594154289
    - type: cos_sim_precision
      value: 86.29629629629629
    - type: cos_sim_recall
      value: 92.55213505461768
    - type: dot_accuracy
      value: 84.80926430517711
    - type: dot_ap
      value: 94.76663088644299
    - type: dot_f1
      value: 89.31480594154289
    - type: dot_precision
      value: 86.29629629629629
    - type: dot_recall
      value: 92.55213505461768
    - type: euclidean_accuracy
      value: 84.80926430517711
    - type: euclidean_ap
      value: 94.76661922683681
    - type: euclidean_f1
      value: 89.31480594154289
    - type: euclidean_precision
      value: 86.29629629629629
    - type: euclidean_recall
      value: 92.55213505461768
    - type: manhattan_accuracy
      value: 84.94550408719346
    - type: manhattan_ap
      value: 94.78582392571815
    - type: manhattan_f1
      value: 89.33912204534491
    - type: manhattan_precision
      value: 86.86679174484052
    - type: manhattan_recall
      value: 91.95630585898709
    - type: max_accuracy
      value: 84.94550408719346
    - type: max_ap
      value: 94.78582392571815
    - type: max_f1
      value: 89.33912204534491
  - task:
      type: PairClassification
    dataset:
      type: google-research-datasets/paws-x
      name: MTEB PawsX (fr)
      config: fr
      split: test
      revision: 8a04d940a42cd40658986fdd8e3da561533a3646
    metrics:
    - type: cos_sim_accuracy
      value: 64.5
    - type: cos_sim_ap
      value: 64.51219412005997
    - type: cos_sim_f1
      value: 62.84885828198622
    - type: cos_sim_precision
      value: 46.713362068965516
    - type: cos_sim_recall
      value: 96.01328903654485
    - type: dot_accuracy
      value: 64.5
    - type: dot_ap
      value: 64.50290830259848
    - type: dot_f1
      value: 62.84885828198622
    - type: dot_precision
      value: 46.713362068965516
    - type: dot_recall
      value: 96.01328903654485
    - type: euclidean_accuracy
      value: 64.5
    - type: euclidean_ap
      value: 64.51219412005995
    - type: euclidean_f1
      value: 62.84885828198622
    - type: euclidean_precision
      value: 46.713362068965516
    - type: euclidean_recall
      value: 96.01328903654485
    - type: manhattan_accuracy
      value: 64.55
    - type: manhattan_ap
      value: 64.54022554293084
    - type: manhattan_f1
      value: 62.836363636363636
    - type: manhattan_precision
      value: 46.778559826746076
    - type: manhattan_recall
      value: 95.68106312292359
    - type: max_accuracy
      value: 64.55
    - type: max_ap
      value: 64.54022554293084
    - type: max_f1
      value: 62.84885828198622
  - task:
      type: STS
    dataset:
      type: Lajavaness/SICK-fr
      name: MTEB SICKFr
      config: default
      split: test
      revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
    metrics:
    - type: cos_sim_pearson
      value: 85.15315949054092
    - type: cos_sim_spearman
      value: 79.19701933507372
    - type: euclidean_pearson
      value: 82.68441006897395
    - type: euclidean_spearman
      value: 79.1963186010215
    - type: manhattan_pearson
      value: 82.6725500567899
    - type: manhattan_spearman
      value: 79.13255295711785
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (fr)
      config: fr
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    metrics:
    - type: cos_sim_pearson
      value: 83.13328685349694
    - type: cos_sim_spearman
      value: 84.64291479319418
    - type: euclidean_pearson
      value: 83.28605886303359
    - type: euclidean_spearman
      value: 84.64291479319418
    - type: manhattan_pearson
      value: 83.01485484058145
    - type: manhattan_spearman
      value: 84.35826862976153
  - task:
      type: STS
    dataset:
      type: mteb/stsb_multi_mt
      name: MTEB STSBenchmarkMultilingualSTS (fr)
      config: fr
      split: test
      revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
    metrics:
    - type: cos_sim_pearson
      value: 86.15391910168253
    - type: cos_sim_spearman
      value: 87.0224186207858
    - type: euclidean_pearson
      value: 86.04463800957714
    - type: euclidean_spearman
      value: 87.02424394489165
    - type: manhattan_pearson
      value: 86.03126279628441
    - type: manhattan_spearman
      value: 86.99427177229043
  - task:
      type: Summarization
    dataset:
      type: lyon-nlp/summarization-summeval-fr-p2p
      name: MTEB SummEvalFr
      config: default
      split: test
      revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
    metrics:
    - type: cos_sim_pearson
      value: 31.415083738613355
    - type: cos_sim_spearman
      value: 30.301784303588285
    - type: dot_pearson
      value: 31.415089981266963
    - type: dot_spearman
      value: 30.286152348575108
  - task:
      type: Reranking
    dataset:
      type: lyon-nlp/mteb-fr-reranking-syntec-s2p
      name: MTEB SyntecReranking
      config: default
      split: test
      revision: daf0863838cd9e3ba50544cdce3ac2b338a1b0ad
    metrics:
    - type: map
      value: 85.95238095238095
    - type: mrr
      value: 85.95238095238095
    - type: nAUC_map_diff1
      value: 70.42176052252755
    - type: nAUC_map_max
      value: 19.806028833551718
    - type: nAUC_mrr_diff1
      value: 70.42176052252755
    - type: nAUC_mrr_max
      value: 19.806028833551718
  - task:
      type: Retrieval
    dataset:
      type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
      name: MTEB SyntecRetrieval
      config: default
      split: test
      revision: 19661ccdca4dfc2d15122d776b61685f48c68ca9
    metrics:
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      value: 69.0
    - type: map_at_10
      value: 79.668
    - type: map_at_100
      value: 79.791
    - type: map_at_1000
      value: 79.791
    - type: map_at_20
      value: 79.751
    - type: map_at_3
      value: 78.167
    - type: map_at_5
      value: 79.067
    - type: mrr_at_1
      value: 69.0
    - type: mrr_at_10
      value: 79.66785714285714
    - type: mrr_at_100
      value: 79.7911904761905
    - type: mrr_at_1000
      value: 79.7911904761905
    - type: mrr_at_20
      value: 79.75119047619049
    - type: mrr_at_3
      value: 78.16666666666666
    - type: mrr_at_5
      value: 79.06666666666666
    - type: nauc_map_at_1000_diff1
      value: 57.567834845260215
    - type: nauc_map_at_1000_max
      value: 19.884081021539316
    - type: nauc_map_at_100_diff1
      value: 57.567834845260215
    - type: nauc_map_at_100_max
      value: 19.884081021539316
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      value: 57.58744042822529
    - type: nauc_map_at_10_max
      value: 20.086792005769567
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      value: 58.094784556502134
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      value: 16.46471594616999
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      value: 57.51896058548769
    - type: nauc_map_at_20_max
      value: 19.71285790868927
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      value: 57.896383908331885
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      value: 57.45922462199208
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      value: 21.48138549193403
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  - task:
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      config: fr
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  - task:
      type: Classification
    dataset:
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      name: MTEB AmazonCounterfactualClassification (en)
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
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    - type: f1
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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_polarity
      name: MTEB AmazonPolarityClassification
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
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  - task:
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    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (en)
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
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  - task:
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      config: default
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      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
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  - task:
      type: Clustering
    dataset:
      type: mteb/arxiv-clustering-p2p
      name: MTEB ArxivClusteringP2P
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
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  - task:
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      type: mteb/arxiv-clustering-s2s
      name: MTEB ArxivClusteringS2S
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
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  - task:
      type: Reranking
    dataset:
      type: mteb/askubuntudupquestions-reranking
      name: MTEB AskUbuntuDupQuestions
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
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  - task:
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    dataset:
      type: mteb/biosses-sts
      name: MTEB BIOSSES
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
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    - type: euclidean_pearson
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    - type: manhattan_pearson
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  - task:
      type: Classification
    dataset:
      type: mteb/banking77
      name: MTEB Banking77Classification
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
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      value: 80.99025974025975
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  - task:
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    dataset:
      type: mteb/biorxiv-clustering-p2p
      name: MTEB BiorxivClusteringP2P
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
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      value: [0.36284135654233984, 0.3894746578427554, 0.3687193652607847, 0.369732449263521, 0.37046011245380284]
  - task:
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  - task:
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  - task:
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  - task:
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      config: default
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      revision: c5a29a104738b98a9e76336939199e264163d4a0
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  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (en)
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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  - task:
      type: Classification
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      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (en)
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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  - task:
      type: Classification
    dataset:
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      name: MTEB MassiveIntentClassification (en)
      config: en
      split: test
      revision: 4672e20407010da34463acc759c162ca9734bca6
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  - task:
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      config: en
      split: test
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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  - task:
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      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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  - task:
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      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
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  - task:
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      config: default
      split: test
      revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
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  - task:
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      name: MTEB NFCorpus
      config: default
      split: test
      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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  - task:
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    - type: v_measures
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  - task:
      type: Retrieval
    dataset:
      type: mteb/scidocs
      name: MTEB SCIDOCS
      config: default
      split: test
      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
    metrics:
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    - type: map_at_100
      value: 13.086
    - type: map_at_1000
      value: 13.406
    - type: map_at_20
      value: 12.006
    - type: map_at_3
      value: 7.505000000000001
    - type: map_at_5
      value: 9.139
    - type: mrr_at_1
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    - type: mrr_at_10
      value: 31.21436507936507
    - type: mrr_at_100
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    - type: mrr_at_1000
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    - type: mrr_at_20
      value: 31.961783515332247
    - type: mrr_at_3
      value: 27.833333333333343
    - type: mrr_at_5
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    - type: nauc_map_at_1000_diff1
      value: 14.661473310156135
    - type: nauc_map_at_1000_max
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      value: 14.703051233516987
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      value: 23.379808618858394
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      value: 17.087758058517867
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      value: 15.453826256469172
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      value: 15.130957558462777
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    - type: ndcg_at_100
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    - type: ndcg_at_1000
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      value: 17.041999999999998
    - type: ndcg_at_5
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    - type: precision_at_1
      value: 20.3
    - type: precision_at_10
      value: 10.09
    - type: precision_at_100
      value: 2.2089999999999996
    - type: precision_at_1000
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    - type: precision_at_20
      value: 6.68
    - type: precision_at_3
      value: 16.1
    - type: precision_at_5
      value: 13.56
    - type: recall_at_1
      value: 4.123
    - type: recall_at_10
      value: 20.487
    - type: recall_at_100
      value: 44.835
    - type: recall_at_1000
      value: 72.458
    - type: recall_at_20
      value: 27.102999999999998
    - type: recall_at_3
      value: 9.778
    - type: recall_at_5
      value: 13.763
  - task:
      type: STS
    dataset:
      type: mteb/sickr-sts
      name: MTEB SICK-R
      config: default
      split: test
      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
    metrics:
    - type: cos_sim_pearson
      value: 86.67710766878982
    - type: cos_sim_spearman
      value: 81.0146278511025
    - type: euclidean_pearson
      value: 84.6541976779553
    - type: euclidean_spearman
      value: 81.01462483847283
    - type: manhattan_pearson
      value: 84.63222929587954
    - type: manhattan_spearman
      value: 80.95879743785594
  - task:
      type: STS
    dataset:
      type: mteb/sts12-sts
      name: MTEB STS12
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 91.56759945236915
    - type: cos_sim_spearman
      value: 85.52036823639511
    - type: euclidean_pearson
      value: 89.13232574418899
    - type: euclidean_spearman
      value: 85.51983870200014
    - type: manhattan_pearson
      value: 89.13468354750995
    - type: manhattan_spearman
      value: 85.5125095149674
  - task:
      type: STS
    dataset:
      type: mteb/sts13-sts
      name: MTEB STS13
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
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      value: 88.77431350593656
    - type: cos_sim_spearman
      value: 89.36590409791387
    - type: euclidean_pearson
      value: 89.41057125926268
    - type: euclidean_spearman
      value: 89.36590409791387
    - type: manhattan_pearson
      value: 89.23527839147364
    - type: manhattan_spearman
      value: 89.1460164042126
  - task:
      type: STS
    dataset:
      type: mteb/sts14-sts
      name: MTEB STS14
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 91.28072839903398
    - type: cos_sim_spearman
      value: 91.60879188296313
    - type: euclidean_pearson
      value: 90.82019203957024
    - type: euclidean_spearman
      value: 91.60879056019314
    - type: manhattan_pearson
      value: 90.68711650077914
    - type: manhattan_spearman
      value: 91.51996736811303
  - task:
      type: STS
    dataset:
      type: mteb/sts15-sts
      name: MTEB STS15
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
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      value: 91.30086405535995
    - type: cos_sim_spearman
      value: 92.02450415044238
    - type: euclidean_pearson
      value: 91.62742541974103
    - type: euclidean_spearman
      value: 92.02448526713779
    - type: manhattan_pearson
      value: 91.58340156488379
    - type: manhattan_spearman
      value: 91.97028302271599
  - task:
      type: STS
    dataset:
      type: mteb/sts16-sts
      name: MTEB STS16
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
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      value: 84.26589373642062
    - type: cos_sim_spearman
      value: 86.29327410655272
    - type: euclidean_pearson
      value: 86.14121596120088
    - type: euclidean_spearman
      value: 86.2932736410034
    - type: manhattan_pearson
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    - type: manhattan_spearman
      value: 86.23990988150905
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (en-en)
      config: en-en
      split: test
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
    metrics:
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    - type: cos_sim_spearman
      value: 88.70915251373806
    - type: euclidean_pearson
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    - type: euclidean_spearman
      value: 88.70915251373806
    - type: manhattan_pearson
      value: 89.3066543956283
    - type: manhattan_spearman
      value: 88.72003093613347
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (en)
      config: en
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    metrics:
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      value: 69.16204861304973
    - type: cos_sim_spearman
      value: 68.57518139813385
    - type: euclidean_pearson
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    - type: euclidean_spearman
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    - type: manhattan_pearson
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    - type: manhattan_spearman
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  - task:
      type: STS
    dataset:
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      name: MTEB STSBenchmark
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
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  - task:
      type: Reranking
    dataset:
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      name: MTEB SciDocsRR
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
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    - type: nAUC_mrr_diff1
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  - task:
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      type: mteb/scifact
      name: MTEB SciFact
      config: default
      split: test
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
    metrics:
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    - type: map_at_1000
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      value: 5.017
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      value: 24.778
    - type: precision_at_5
      value: 17.0
    - type: recall_at_1
      value: 52.261
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      value: 82.756
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      value: 94.667
    - type: recall_at_1000
      value: 99.667
    - type: recall_at_20
      value: 88.1
    - type: recall_at_3
      value: 68.072
    - type: recall_at_5
      value: 75.594
  - task:
      type: PairClassification
    dataset:
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      name: MTEB SprintDuplicateQuestions
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
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      value: 99.7089108910891
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      value: 92.56973112464647
    - type: cos_sim_f1
      value: 85.71428571428572
    - type: cos_sim_precision
      value: 83.36483931947069
    - type: cos_sim_recall
      value: 88.2
    - type: dot_accuracy
      value: 99.7089108910891
    - type: dot_ap
      value: 92.56973112464647
    - type: dot_f1
      value: 85.71428571428572
    - type: dot_precision
      value: 83.36483931947069
    - type: dot_recall
      value: 88.2
    - type: euclidean_accuracy
      value: 99.7089108910891
    - type: euclidean_ap
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    - type: euclidean_f1
      value: 85.71428571428572
    - type: euclidean_precision
      value: 83.36483931947069
    - type: euclidean_recall
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    - type: manhattan_accuracy
      value: 99.71089108910891
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    - type: manhattan_precision
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    - type: manhattan_recall
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      value: 99.71089108910891
    - type: max_ap
      value: 92.61210920251231
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      value: 85.71428571428572
  - task:
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    dataset:
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      name: MTEB StackExchangeClustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
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  - task:
      type: Clustering
    dataset:
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      name: MTEB StackExchangeClusteringP2P
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
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  - task:
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    dataset:
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      name: MTEB StackOverflowDupQuestions
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
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  - task:
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      name: MTEB SummEval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
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  - task:
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      name: MTEB TRECCOVID
      config: default
      split: test
      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
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  - task:
      type: Retrieval
    dataset:
      type: mteb/touche2020
      name: MTEB Touche2020
      config: default
      split: test
      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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    - type: mrr_at_1
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  - task:
      type: Classification
    dataset:
      type: mteb/toxic_conversations_50k
      name: MTEB ToxicConversationsClassification
      config: default
      split: test
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
    metrics:
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    - type: ap_weighted
      value: 12.336183192628836
    - type: f1
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    - type: f1_weighted
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  - task:
      type: Classification
    dataset:
      type: mteb/tweet_sentiment_extraction
      name: MTEB TweetSentimentExtractionClassification
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
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      value: 62.80418788907753
    - type: f1
      value: 63.050557758931134
    - type: f1_weighted
      value: 62.13337985337418
  - task:
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    dataset:
      type: mteb/twentynewsgroups-clustering
      name: MTEB TwentyNewsgroupsClustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
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      value: [0.49421217801171224, 0.4740440424893081, 0.4886726035776056, 0.5198976504195676, 0.4827070012054274]
  - task:
      type: PairClassification
    dataset:
      type: mteb/twittersemeval2015-pairclassification
      name: MTEB TwitterSemEval2015
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 86.97025689932646
    - type: cos_sim_ap
      value: 77.06565012359437
    - type: cos_sim_f1
      value: 70.32217308907138
    - type: cos_sim_precision
      value: 67.46666666666667
    - type: cos_sim_recall
      value: 73.43007915567283
    - type: dot_accuracy
      value: 86.97025689932646
    - type: dot_ap
      value: 77.0656524331512
    - type: dot_f1
      value: 70.32217308907138
    - type: dot_precision
      value: 67.46666666666667
    - type: dot_recall
      value: 73.43007915567283
    - type: euclidean_accuracy
      value: 86.97025689932646
    - type: euclidean_ap
      value: 77.06564828845742
    - type: euclidean_f1
      value: 70.32217308907138
    - type: euclidean_precision
      value: 67.46666666666667
    - type: euclidean_recall
      value: 73.43007915567283
    - type: manhattan_accuracy
      value: 86.90469094593789
    - type: manhattan_ap
      value: 76.94347285253252
    - type: manhattan_f1
      value: 70.18523217457499
    - type: manhattan_precision
      value: 67.59530791788856
    - type: manhattan_recall
      value: 72.98153034300792
    - type: max_accuracy
      value: 86.97025689932646
    - type: max_ap
      value: 77.0656524331512
    - type: max_f1
      value: 70.32217308907138
  - task:
      type: PairClassification
    dataset:
      type: mteb/twitterurlcorpus-pairclassification
      name: MTEB TwitterURLCorpus
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 89.64567081926495
    - type: cos_sim_ap
      value: 87.19162831580245
    - type: cos_sim_f1
      value: 79.67696578577352
    - type: cos_sim_precision
      value: 74.92033358193775
    - type: cos_sim_recall
      value: 85.07853403141361
    - type: dot_accuracy
      value: 89.64567081926495
    - type: dot_ap
      value: 87.19162304433766
    - type: dot_f1
      value: 79.67696578577352
    - type: dot_precision
      value: 74.92033358193775
    - type: dot_recall
      value: 85.07853403141361
    - type: euclidean_accuracy
      value: 89.64567081926495
    - type: euclidean_ap
      value: 87.19162847931055
    - type: euclidean_f1
      value: 79.67696578577352
    - type: euclidean_precision
      value: 74.92033358193775
    - type: euclidean_recall
      value: 85.07853403141361
    - type: manhattan_accuracy
      value: 89.67283735009897
    - type: manhattan_ap
      value: 87.19033616510255
    - type: manhattan_f1
      value: 79.67444226437031
    - type: manhattan_precision
      value: 75.43690656391908
    - type: manhattan_recall
      value: 84.41638435478903
    - type: max_accuracy
      value: 89.67283735009897
    - type: max_ap
      value: 87.19162847931055
    - type: max_f1
      value: 79.67696578577352
license: apache-2.0
language:
    - fr
    - en
---

# [bilingual-embedding-large](https://huggingface.co/Lajavaness/bilingual-embedding-large)

Bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-large), a pre-trained language model based on the [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-large) architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language.


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BilingualModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Training and Fine-tuning process
#### Stage 1: NLI Training
- Dataset: [(SNLI+XNLI) for english+french]
- Method: Training using Multi-Negative Ranking Loss. This stage focused on improving the model's ability to discern and rank nuanced differences in sentence semantics.
### Stage 3: Continued Fine-tuning for Semantic Textual Similarity on STS Benchmark
- Dataset: [STSB-fr and en]
- Method: Fine-tuning specifically for the semantic textual similarity benchmark using Siamese BERT-Networks configured with the 'sentence-transformers' library. 
### Stage 4: Advanced Augmentation Fine-tuning
- Dataset: STSB with generate [silver sample from gold sample](https://www.sbert.net/examples/training/data_augmentation/README.html)
- Method: Employed an advanced strategy using [Augmented SBERT](https://arxiv.org/abs/2010.08240) with Pair Sampling Strategies, integrating both Cross-Encoder and Bi-Encoder models. This stage further refined the embeddings by enriching the training data dynamically, enhancing the model's robustness and accuracy.


## Usage:

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer

sentences = ["Paris est une capitale de la France", "Paris is a capital of France"]

model = SentenceTransformer('Lajavaness/bilingual-embedding-large', trust_remote_code=True)
print(embeddings)

```





## Evaluation

TODO

## Citation

    @article{conneau2019unsupervised,
      title={Unsupervised cross-lingual representation learning at scale},
      author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin},
      journal={arXiv preprint arXiv:1911.02116},
      year={2019}
    }

	@article{reimers2019sentence,
	   title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
	   author={Nils Reimers, Iryna Gurevych},
	   journal={https://arxiv.org/abs/1908.10084},
	   year={2019}
	}

    @article{thakur2020augmented,
      title={Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks},
      author={Thakur, Nandan and Reimers, Nils and Daxenberger, Johannes and Gurevych, Iryna},
      journal={arXiv e-prints},
      pages={arXiv--2010},
      year={2020}