README.md

---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- sentence-embedding
- mteb
- mteb
model-index:
- name: bilingual-embedding-base
  results:
  - task:
      type: Clustering
    dataset:
      type: lyon-nlp/alloprof
      name: MTEB AlloProfClusteringP2P
      config: default
      split: test
      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
    metrics:
    - type: v_measure
      value: 64.71347977413274
    - type: v_measures
      value: [0.6276406100583347, 0.6380984027637511, 0.6322659733117469, 0.652473343931587, 0.6411502615838207]
  - task:
      type: Clustering
    dataset:
      type: lyon-nlp/alloprof
      name: MTEB AlloProfClusteringS2S
      config: default
      split: test
      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
    metrics:
    - type: v_measure
      value: 45.56886694296516
    - type: v_measures
      value: [0.4359417286866465, 0.4218168523895086, 0.419693469863105, 0.4981808644314091, 0.4546120704986696]
  - task:
      type: Reranking
    dataset:
      type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
      name: MTEB AlloprofReranking
      config: default
      split: test
      revision: 65393d0d7a08a10b4e348135e824f385d420b0fd
    metrics:
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      value: 70.45979049191963
    - type: mrr
      value: 71.61250582558874
    - type: nAUC_map_diff1
      value: 53.81172404763562
    - type: nAUC_map_max
      value: 11.344605627946006
    - type: nAUC_mrr_diff1
      value: 53.73572411920392
    - type: nAUC_mrr_max
      value: 11.900785644756448
  - task:
      type: Retrieval
    dataset:
      type: lyon-nlp/alloprof
      name: MTEB AlloprofRetrieval
      config: default
      split: test
      revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
    metrics:
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      value: 24.956999999999997
    - type: map_at_10
      value: 35.519
    - type: map_at_100
      value: 36.602000000000004
    - type: map_at_1000
      value: 36.667
    - type: map_at_20
      value: 36.199
    - type: map_at_3
      value: 32.397999999999996
    - type: map_at_5
      value: 34.121
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      value: 24.956822107081177
    - type: mrr_at_10
      value: 35.5187515420676
    - type: mrr_at_100
      value: 36.60233410773095
    - type: mrr_at_1000
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    - type: mrr_at_20
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      value: 32.39781232009212
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      value: 24.956999999999997
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      value: 41.260999999999996
    - type: ndcg_at_100
      value: 46.628
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      value: 48.339999999999996
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      value: 43.71
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      value: 34.771
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      value: 37.862
    - type: precision_at_1
      value: 24.956999999999997
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      value: 0.098
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      value: 3.463
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      value: 13.874
    - type: precision_at_5
      value: 9.818999999999999
    - type: recall_at_1
      value: 24.956999999999997
    - type: recall_at_10
      value: 59.629
    - type: recall_at_100
      value: 84.974
    - type: recall_at_1000
      value: 98.402
    - type: recall_at_20
      value: 69.257
    - type: recall_at_3
      value: 41.623
    - type: recall_at_5
      value: 49.092999999999996
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (fr)
      config: fr
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 40.354
    - type: f1
      value: 38.96127209677864
    - type: f1_weighted
      value: 38.96127209677863
  - task:
      type: Retrieval
    dataset:
      type: maastrichtlawtech/bsard
      name: MTEB BSARDRetrieval
      config: default
      split: test
      revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
    metrics:
    - type: map_at_1
      value: 5.405
    - type: map_at_10
      value: 9.715
    - type: map_at_100
      value: 10.852
    - type: map_at_1000
      value: 10.986
    - type: map_at_20
      value: 10.324
    - type: map_at_3
      value: 8.183
    - type: map_at_5
      value: 9.129
    - type: mrr_at_1
      value: 5.405405405405405
    - type: mrr_at_10
      value: 9.715250965250963
    - type: mrr_at_100
      value: 10.852488713991455
    - type: mrr_at_1000
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    - type: mrr_at_20
      value: 10.324372731493472
    - type: mrr_at_3
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    - type: mrr_at_5
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      value: 5.405
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      value: 29.73
    - type: recall_at_3
      value: 11.712
    - type: recall_at_5
      value: 15.766
  - task:
      type: Clustering
    dataset:
      type: lyon-nlp/clustering-hal-s2s
      name: MTEB HALClusteringS2S
      config: default
      split: test
      revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
    metrics:
    - type: v_measure
      value: 25.366610995664384
    - type: v_measures
      value: [0.2853826102888054, 0.27554329622230517, 0.2659387504290534, 0.272421074779971, 0.23780511730712292]
  - task:
      type: Clustering
    dataset:
      type: reciTAL/mlsum
      name: MTEB MLSUMClusteringP2P
      config: fr
      split: test
      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
    metrics:
    - type: v_measure
      value: 44.22735442638259
    - type: v_measures
      value: [0.446401643334608, 0.45940182243882194, 0.4535014442023603, 0.4315840671354274, 0.40219963696623473]
  - task:
      type: Clustering
    dataset:
      type: reciTAL/mlsum
      name: MTEB MLSUMClusteringS2S
      config: fr
      split: test
      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
    metrics:
    - type: v_measure
      value: 44.57521454657146
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      value: [0.4501823523199927, 0.45800459800343646, 0.4535621236055794, 0.4353844290212204, 0.40539074932285035]
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (fr)
      config: fr
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 86.82743501409333
    - type: f1
      value: 87.07777165714171
    - type: f1_weighted
      value: 86.70555382175719
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (fr)
      config: fr
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 59.129345443156886
    - type: f1
      value: 40.374753319633946
    - type: f1_weighted
      value: 61.735222244513906
  - task:
      type: Classification
    dataset:
      type: mteb/masakhanews
      name: MTEB MasakhaNEWSClassification (fra)
      config: fra
      split: test
      revision: 18193f187b92da67168c655c9973a165ed9593dd
    metrics:
    - type: accuracy
      value: 77.44075829383887
    - type: f1
      value: 73.59099795975669
    - type: f1_weighted
      value: 77.53638597300765
  - task:
      type: Clustering
    dataset:
      type: masakhane/masakhanews
      name: MTEB MasakhaNEWSClusteringP2P (fra)
      config: fra
      split: test
      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
    metrics:
    - type: v_measure
      value: 61.57579457743856
    - type: v_measures
      value: [1.0, 0.19333147584460073, 0.1918473189330316, 0.9180419176341081, 0.7755690164601873]
  - task:
      type: Clustering
    dataset:
      type: masakhane/masakhanews
      name: MTEB MasakhaNEWSClusteringS2S (fra)
      config: fra
      split: test
      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
    metrics:
    - type: v_measure
      value: 46.09882977546196
    - type: v_measures
      value: [1.0, 0.0, 0.4881603944307428, 0.1341399033659235, 0.6826411909764316]
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (fr)
      config: fr
      split: test
      revision: 4672e20407010da34463acc759c162ca9734bca6
    metrics:
    - type: accuracy
      value: 64.98655010087424
    - type: f1
      value: 62.07892690857404
    - type: f1_weighted
      value: 64.04916798028313
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (fr)
      config: fr
      split: test
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
    metrics:
    - type: accuracy
      value: 71.72494956287828
    - type: f1
      value: 70.7613627592262
    - type: f1_weighted
      value: 71.59023734198762
  - task:
      type: Retrieval
    dataset:
      type: jinaai/mintakaqa
      name: MTEB MintakaRetrieval (fr)
      config: fr
      split: test
      revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
    metrics:
    - type: map_at_1
      value: 15.643
    - type: map_at_10
      value: 23.263
    - type: map_at_100
      value: 24.282
    - type: map_at_1000
      value: 24.386
    - type: map_at_20
      value: 23.810000000000002
    - type: map_at_3
      value: 20.857
    - type: map_at_5
      value: 22.127
    - type: mrr_at_1
      value: 15.642915642915641
    - type: mrr_at_10
      value: 23.26313326313326
    - type: mrr_at_100
      value: 24.28242740941141
    - type: mrr_at_1000
      value: 24.38579869502159
    - type: mrr_at_20
      value: 23.809503335080308
    - type: mrr_at_3
      value: 20.857220857220817
    - type: mrr_at_5
      value: 22.126672126672087
    - type: nauc_map_at_1000_diff1
      value: 22.791507278613405
    - type: nauc_map_at_1000_max
      value: 34.49738411163668
    - type: nauc_map_at_100_diff1
      value: 22.753348275392053
    - type: nauc_map_at_100_max
      value: 34.503329867035156
    - type: nauc_map_at_10_diff1
      value: 22.949396903228543
    - type: nauc_map_at_10_max
      value: 34.59635858547973
    - type: nauc_map_at_1_diff1
      value: 29.68671981414948
    - type: nauc_map_at_1_max
      value: 32.40476256531568
    - type: nauc_map_at_20_diff1
      value: 22.839943329322214
    - type: nauc_map_at_20_max
      value: 34.5512812666387
    - type: nauc_map_at_3_diff1
      value: 24.531567517208703
    - type: nauc_map_at_3_max
      value: 34.63194411307717
    - type: nauc_map_at_5_diff1
      value: 23.785928597532532
    - type: nauc_map_at_5_max
      value: 34.49711117096583
    - type: nauc_mrr_at_1000_diff1
      value: 22.791507278613405
    - type: nauc_mrr_at_1000_max
      value: 34.49738411163668
    - type: nauc_mrr_at_100_diff1
      value: 22.753348275392053
    - type: nauc_mrr_at_100_max
      value: 34.503329867035156
    - type: nauc_mrr_at_10_diff1
      value: 22.949396903228543
    - type: nauc_mrr_at_10_max
      value: 34.59635858547973
    - type: nauc_mrr_at_1_diff1
      value: 29.68671981414948
    - type: nauc_mrr_at_1_max
      value: 32.40476256531568
    - type: nauc_mrr_at_20_diff1
      value: 22.839943329322214
    - type: nauc_mrr_at_20_max
      value: 34.5512812666387
    - type: nauc_mrr_at_3_diff1
      value: 24.531567517208703
    - type: nauc_mrr_at_3_max
      value: 34.63194411307717
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      value: 23.785928597532532
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      value: 34.49711117096583
    - type: nauc_ndcg_at_1000_diff1
      value: 20.36281863938966
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      value: 34.39195365895218
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      value: 19.226093736031558
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      value: 34.598730272263424
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      value: 20.175947236861635
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      value: 35.06289054957336
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      value: 29.68671981414948
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      value: 32.40476256531568
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      value: 19.76832604541867
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      value: 34.88676463098282
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      value: 23.165684344826936
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      value: 35.058555779606806
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      value: 22.008327776501197
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      value: 34.81586957799795
    - type: nauc_precision_at_1000_diff1
      value: -0.17116637411109903
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      value: 22.79067675552558
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      value: 6.442518254575118
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      value: 34.22531735083736
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      value: 13.139149147711764
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      value: 36.17629609592965
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      value: 29.68671981414948
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      value: 32.40476256531568
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      value: 11.54118462661454
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      value: 35.56967988819633
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      value: 36.06171743276854
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      value: 17.72477129059423
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      value: 34.22531735083735
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      value: 32.40476256531568
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      value: 35.56967988819635
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      value: 19.811964905552767
    - type: nauc_recall_at_3_max
      value: 36.06171743276855
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      value: 17.72477129059422
    - type: nauc_recall_at_5_max
      value: 35.49631000776651
    - type: ndcg_at_1
      value: 15.643
    - type: ndcg_at_10
      value: 27.596999999999998
    - type: ndcg_at_100
      value: 33.036
    - type: ndcg_at_1000
      value: 36.348
    - type: ndcg_at_20
      value: 29.586000000000002
    - type: ndcg_at_3
      value: 22.567
    - type: ndcg_at_5
      value: 24.858
    - type: precision_at_1
      value: 15.643
    - type: precision_at_10
      value: 4.152
    - type: precision_at_100
      value: 0.681
    - type: precision_at_1000
      value: 0.095
    - type: precision_at_20
      value: 2.469
    - type: precision_at_3
      value: 9.173
    - type: precision_at_5
      value: 6.618
    - type: recall_at_1
      value: 15.643
    - type: recall_at_10
      value: 41.522999999999996
    - type: recall_at_100
      value: 68.059
    - type: recall_at_1000
      value: 95.332
    - type: recall_at_20
      value: 49.386
    - type: recall_at_3
      value: 27.517999999999997
    - type: recall_at_5
      value: 33.088
  - task:
      type: PairClassification
    dataset:
      type: GEM/opusparcus
      name: MTEB OpusparcusPC (fr)
      config: fr
      split: test
      revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
    metrics:
    - type: cos_sim_accuracy
      value: 82.08446866485014
    - type: cos_sim_ap
      value: 93.63779278112501
    - type: cos_sim_f1
      value: 87.31307284129282
    - type: cos_sim_precision
      value: 84.8968105065666
    - type: cos_sim_recall
      value: 89.87090367428004
    - type: dot_accuracy
      value: 82.08446866485014
    - type: dot_ap
      value: 93.63779278112501
    - type: dot_f1
      value: 87.31307284129282
    - type: dot_precision
      value: 84.8968105065666
    - type: dot_recall
      value: 89.87090367428004
    - type: euclidean_accuracy
      value: 82.08446866485014
    - type: euclidean_ap
      value: 93.63779278112501
    - type: euclidean_f1
      value: 87.31307284129282
    - type: euclidean_precision
      value: 84.8968105065666
    - type: euclidean_recall
      value: 89.87090367428004
    - type: manhattan_accuracy
      value: 82.15258855585831
    - type: manhattan_ap
      value: 93.6291276133462
    - type: manhattan_f1
      value: 87.36740597878496
    - type: manhattan_precision
      value: 84.91096532333646
    - type: manhattan_recall
      value: 89.97020854021847
    - type: max_accuracy
      value: 82.15258855585831
    - type: max_ap
      value: 93.63779278112501
    - type: max_f1
      value: 87.36740597878496
  - 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: 60.85
    - type: cos_sim_ap
      value: 60.37878110577144
    - type: cos_sim_f1
      value: 62.67978803936413
    - type: cos_sim_precision
      value: 47.61357101782634
    - type: cos_sim_recall
      value: 91.69435215946844
    - type: dot_accuracy
      value: 60.85
    - type: dot_ap
      value: 60.282876235548
    - type: dot_f1
      value: 62.67978803936413
    - type: dot_precision
      value: 47.61357101782634
    - type: dot_recall
      value: 91.69435215946844
    - type: euclidean_accuracy
      value: 60.85
    - type: euclidean_ap
      value: 60.37878110577144
    - type: euclidean_f1
      value: 62.67978803936413
    - type: euclidean_precision
      value: 47.61357101782634
    - type: euclidean_recall
      value: 91.69435215946844
    - type: manhattan_accuracy
      value: 61.0
    - type: manhattan_ap
      value: 60.32592616688752
    - type: manhattan_f1
      value: 62.70871985157699
    - type: manhattan_precision
      value: 47.154017857142854
    - type: manhattan_recall
      value: 93.57696566998892
    - type: max_accuracy
      value: 61.0
    - type: max_ap
      value: 60.37878110577144
    - type: max_f1
      value: 62.70871985157699
  - task:
      type: STS
    dataset:
      type: Lajavaness/SICK-fr
      name: MTEB SICKFr
      config: default
      split: test
      revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
    metrics:
    - type: cos_sim_pearson
      value: 82.41331853949572
    - type: cos_sim_spearman
      value: 75.4815169821101
    - type: euclidean_pearson
      value: 79.81380205322172
    - type: euclidean_spearman
      value: 75.48076002322844
    - type: manhattan_pearson
      value: 79.86035354304643
    - type: manhattan_spearman
      value: 75.51770448845875
  - 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: 78.63723398201921
    - type: cos_sim_spearman
      value: 81.1413239539243
    - type: euclidean_pearson
      value: 79.65250044199216
    - type: euclidean_spearman
      value: 81.1413239539243
    - type: manhattan_pearson
      value: 79.4690995487022
    - type: manhattan_spearman
      value: 80.61411938663267
  - 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: 83.34057641357742
    - type: cos_sim_spearman
      value: 83.74771008807434
    - type: euclidean_pearson
      value: 83.22245971062527
    - type: euclidean_spearman
      value: 83.74894925253341
    - type: manhattan_pearson
      value: 83.06789257151542
    - type: manhattan_spearman
      value: 83.52796149940158
  - 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: 29.949370780736455
    - type: cos_sim_spearman
      value: 30.16472987232583
    - type: dot_pearson
      value: 29.94937008799093
    - type: dot_spearman
      value: 30.16472987232583
  - task:
      type: Reranking
    dataset:
      type: lyon-nlp/mteb-fr-reranking-syntec-s2p
      name: MTEB SyntecReranking
      config: default
      split: test
      revision: daf0863838cd9e3ba50544cdce3ac2b338a1b0ad
    metrics:
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      value: 83.82619047619046
    - type: mrr
      value: 83.82619047619046
    - type: nAUC_map_diff1
      value: 56.291594426865686
    - type: nAUC_map_max
      value: 9.006252496368798
    - type: nAUC_mrr_diff1
      value: 56.291594426865686
    - type: nAUC_mrr_max
      value: 9.006252496368798
  - 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: 62.0
    - type: map_at_10
      value: 74.369
    - type: map_at_100
      value: 74.626
    - type: map_at_1000
      value: 74.626
    - type: map_at_20
      value: 74.626
    - type: map_at_3
      value: 72.333
    - type: map_at_5
      value: 73.533
    - type: mrr_at_1
      value: 62.0
    - type: mrr_at_10
      value: 74.36944444444444
    - type: mrr_at_100
      value: 74.62553418803418
    - type: mrr_at_1000
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    - type: mrr_at_20
      value: 74.62553418803418
    - type: mrr_at_3
      value: 72.33333333333333
    - type: mrr_at_5
      value: 73.53333333333333
    - type: nauc_map_at_1000_diff1
      value: 59.84829658893367
    - type: nauc_map_at_1000_max
      value: 25.92680320484747
    - type: nauc_map_at_100_diff1
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      value: 60.14734510056334
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      value: 26.4859845903765
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    - type: nauc_precision_at_10_max
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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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  - task:
      type: Classification
    dataset:
      type: mteb/amazon_polarity
      name: MTEB AmazonPolarityClassification
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
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    - type: f1
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  - task:
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      type: mteb/amazon_reviews_multi
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      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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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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    dataset:
      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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      value: 87.84186171115664
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    - type: euclidean_pearson
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    - type: euclidean_spearman
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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: 78.46103896103897
    - type: f1
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    - type: f1_weighted
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  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-p2p
      name: MTEB BiorxivClusteringP2P
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
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      value: [0.37382307264671194, 0.36301531251106245, 0.3793738872327412, 0.35770158136125185, 0.3555364559997305]
  - task:
      type: Clustering
    dataset:
      type: mteb/biorxiv-clustering-s2s
      name: MTEB BiorxivClusteringS2S
      config: default
      split: test
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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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      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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  - task:
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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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      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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  - task:
      type: Retrieval
    dataset:
      type: mteb/scidocs
      name: MTEB SCIDOCS
      config: default
      split: test
      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
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    - type: map_at_1000
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    - type: map_at_20
      value: 10.781
    - type: map_at_3
      value: 6.973999999999999
    - type: map_at_5
      value: 8.305
    - type: mrr_at_1
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    - type: mrr_at_100
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    - type: mrr_at_1000
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    - type: mrr_at_3
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    - type: mrr_at_5
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    - type: nauc_map_at_1000_diff1
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    - type: precision_at_1
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    - type: precision_at_10
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    - type: precision_at_100
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    - type: precision_at_1000
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    - type: precision_at_20
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    - type: precision_at_3
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    - type: precision_at_5
      value: 12.24
    - type: recall_at_1
      value: 4.077999999999999
    - type: recall_at_10
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    - type: recall_at_100
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    - type: recall_at_1000
      value: 68.845
    - type: recall_at_20
      value: 24.6
    - type: recall_at_3
      value: 8.898
    - type: recall_at_5
      value: 12.413
  - task:
      type: STS
    dataset:
      type: mteb/sickr-sts
      name: MTEB SICK-R
      config: default
      split: test
      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
    metrics:
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      value: 85.7559383087264
    - type: cos_sim_spearman
      value: 79.49310740723678
    - type: euclidean_pearson
      value: 83.65824145595195
    - type: euclidean_spearman
      value: 79.49312535113798
    - type: manhattan_pearson
      value: 83.66988365842141
    - type: manhattan_spearman
      value: 79.47470834406325
  - task:
      type: STS
    dataset:
      type: mteb/sts12-sts
      name: MTEB STS12
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 87.5839985534636
    - type: cos_sim_spearman
      value: 79.59450354665624
    - type: euclidean_pearson
      value: 84.43014644677757
    - type: euclidean_spearman
      value: 79.59390361649422
    - type: manhattan_pearson
      value: 84.29576591448196
    - type: manhattan_spearman
      value: 79.41063925463973
  - task:
      type: STS
    dataset:
      type: mteb/sts13-sts
      name: MTEB STS13
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
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      value: 84.29076815493453
    - type: cos_sim_spearman
      value: 84.70010174539925
    - type: euclidean_pearson
      value: 84.61503525685455
    - type: euclidean_spearman
      value: 84.70010174539925
    - type: manhattan_pearson
      value: 84.49870751490295
    - type: manhattan_spearman
      value: 84.56101687530112
  - task:
      type: STS
    dataset:
      type: mteb/sts14-sts
      name: MTEB STS14
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
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      value: 85.9713152765247
    - type: cos_sim_spearman
      value: 83.91546630697559
    - type: euclidean_pearson
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    - type: euclidean_spearman
      value: 83.915468446163
    - type: manhattan_pearson
      value: 85.10357473564157
    - type: manhattan_spearman
      value: 83.73948507923797
  - task:
      type: STS
    dataset:
      type: mteb/sts15-sts
      name: MTEB STS15
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
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      value: 88.93457583410698
    - type: cos_sim_spearman
      value: 89.87899103812357
    - type: euclidean_pearson
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    - type: euclidean_spearman
      value: 89.87899029911844
    - type: manhattan_pearson
      value: 89.27376081191959
    - type: manhattan_spearman
      value: 89.7855896343813
  - task:
      type: STS
    dataset:
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      name: MTEB STS16
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
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      value: 83.07870989011116
    - type: cos_sim_spearman
      value: 84.87521134248455
    - type: euclidean_pearson
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    - type: euclidean_spearman
      value: 84.87521088458564
    - type: manhattan_pearson
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    - type: manhattan_spearman
      value: 84.75674790631112
  - 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
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    - type: euclidean_pearson
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    - type: euclidean_spearman
      value: 88.69212374652548
    - type: manhattan_pearson
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    - type: manhattan_spearman
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  - task:
      type: STS
    dataset:
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      name: MTEB STS22 (en)
      config: en
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    metrics:
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      value: 64.635158432127
    - type: cos_sim_spearman
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    - type: euclidean_pearson
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    - type: euclidean_spearman
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    - type: manhattan_pearson
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  - task:
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    dataset:
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      name: MTEB STSBenchmark
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
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  - task:
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    dataset:
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      name: MTEB SciDocsRR
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
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  - task:
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      type: mteb/scifact
      name: MTEB SciFact
      config: default
      split: test
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
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    - type: precision_at_20
      value: 4.833
    - type: precision_at_3
      value: 24.444
    - type: precision_at_5
      value: 16.267
    - type: recall_at_1
      value: 54.678000000000004
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      value: 81.244
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    - type: recall_at_1000
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    - type: recall_at_20
      value: 86.02199999999999
    - type: recall_at_3
      value: 67.878
    - type: recall_at_5
      value: 74.06700000000001
  - task:
      type: PairClassification
    dataset:
      type: mteb/sprintduplicatequestions-pairclassification
      name: MTEB SprintDuplicateQuestions
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
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    - type: cos_sim_f1
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    - type: cos_sim_precision
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    - type: cos_sim_recall
      value: 90.60000000000001
    - type: dot_accuracy
      value: 99.79702970297029
    - type: dot_ap
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    - type: dot_f1
      value: 89.7029702970297
    - type: dot_precision
      value: 88.8235294117647
    - type: dot_recall
      value: 90.60000000000001
    - type: euclidean_accuracy
      value: 99.79702970297029
    - type: euclidean_ap
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    - type: euclidean_f1
      value: 89.7029702970297
    - type: euclidean_precision
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    - type: euclidean_recall
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    - type: manhattan_accuracy
      value: 99.79207920792079
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    - type: manhattan_precision
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      value: 99.79702970297029
    - type: max_ap
      value: 94.75976312657727
    - type: max_f1
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  - task:
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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:
      type: Reranking
    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
    metrics:
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    - type: mrr_at_100
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    - type: mrr_at_1000
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  - task:
      type: Classification
    dataset:
      type: mteb/toxic_conversations_50k
      name: MTEB ToxicConversationsClassification
      config: default
      split: test
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
    metrics:
    - type: accuracy
      value: 63.38378906249999
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    - type: ap_weighted
      value: 11.003129023708196
    - type: f1
      value: 48.57435688911943
    - type: f1_weighted
      value: 71.50552650067205
  - task:
      type: Classification
    dataset:
      type: mteb/tweet_sentiment_extraction
      name: MTEB TweetSentimentExtractionClassification
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 62.467458970005666
    - type: f1
      value: 62.66021402025028
    - type: f1_weighted
      value: 61.71229183506532
  - task:
      type: Clustering
    dataset:
      type: mteb/twentynewsgroups-clustering
      name: MTEB TwentyNewsgroupsClustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
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      value: 44.96874447839597
    - type: v_measures
      value: [0.44089700334011683, 0.4808871718296333, 0.4655762216311635, 0.4108889263207817, 0.45449139982441744]
  - task:
      type: PairClassification
    dataset:
      type: mteb/twittersemeval2015-pairclassification
      name: MTEB TwitterSemEval2015
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 85.83179352685224
    - type: cos_sim_ap
      value: 73.75870454252158
    - type: cos_sim_f1
      value: 67.61786600496278
    - type: cos_sim_precision
      value: 63.81733021077284
    - type: cos_sim_recall
      value: 71.89973614775725
    - type: dot_accuracy
      value: 85.83179352685224
    - type: dot_ap
      value: 73.75870536224079
    - type: dot_f1
      value: 67.61786600496278
    - type: dot_precision
      value: 63.81733021077284
    - type: dot_recall
      value: 71.89973614775725
    - type: euclidean_accuracy
      value: 85.83179352685224
    - type: euclidean_ap
      value: 73.7587242895193
    - type: euclidean_f1
      value: 67.61786600496278
    - type: euclidean_precision
      value: 63.81733021077284
    - type: euclidean_recall
      value: 71.89973614775725
    - type: manhattan_accuracy
      value: 85.91524110389224
    - type: manhattan_ap
      value: 73.77139111004601
    - type: manhattan_f1
      value: 67.52419453632244
    - type: manhattan_precision
      value: 63.023096272581746
    - type: manhattan_recall
      value: 72.71767810026385
    - type: max_accuracy
      value: 85.91524110389224
    - type: max_ap
      value: 73.77139111004601
    - type: max_f1
      value: 67.61786600496278
  - task:
      type: PairClassification
    dataset:
      type: mteb/twitterurlcorpus-pairclassification
      name: MTEB TwitterURLCorpus
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 89.2711607870532
    - type: cos_sim_ap
      value: 86.48986874793765
    - type: cos_sim_f1
      value: 79.10280373831775
    - type: cos_sim_precision
      value: 76.87836070338614
    - type: cos_sim_recall
      value: 81.45980905451185
    - type: dot_accuracy
      value: 89.2711607870532
    - type: dot_ap
      value: 86.48986996838993
    - type: dot_f1
      value: 79.10280373831775
    - type: dot_precision
      value: 76.87836070338614
    - type: dot_recall
      value: 81.45980905451185
    - type: euclidean_accuracy
      value: 89.2711607870532
    - type: euclidean_ap
      value: 86.4898691356683
    - type: euclidean_f1
      value: 79.10280373831775
    - type: euclidean_precision
      value: 76.87836070338614
    - type: euclidean_recall
      value: 81.45980905451185
    - type: manhattan_accuracy
      value: 89.2711607870532
    - type: manhattan_ap
      value: 86.46475884590569
    - type: manhattan_f1
      value: 78.9534579927593
    - type: manhattan_precision
      value: 76.61716769286491
    - type: manhattan_recall
      value: 81.43671080997844
    - type: max_accuracy
      value: 89.2711607870532
    - type: max_ap
      value: 86.48986996838993
    - type: max_f1
      value: 79.10280373831775
license: apache-2.0
---

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

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-base), a pre-trained language model based on the [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-base) 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-base', 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}