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-small
  results:
  - task:
      type: Clustering
    dataset:
      type: lyon-nlp/alloprof
      name: MTEB AlloProfClusteringP2P
      config: default
      split: test
      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
    metrics:
    - type: v_measure
      value: 63.19030822769444
    - type: v_measures
      value: [0.5938891912573394, 0.6171518411959, 0.6042518292029612, 0.6626602879382325, 0.6471224639325329]
  - task:
      type: Clustering
    dataset:
      type: lyon-nlp/alloprof
      name: MTEB AlloProfClusteringS2S
      config: default
      split: test
      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
    metrics:
    - type: v_measure
      value: 41.32807908087869
    - type: v_measures
      value: [0.3351458197856525, 0.48472823318531566, 0.4631757871803168, 0.43580166532679027, 0.4219041689415661]
  - 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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    - type: mrr
      value: 69.59097513501659
    - type: nAUC_map_diff1
      value: 47.92767021121887
    - type: nAUC_map_max
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    - type: nAUC_mrr_diff1
      value: 47.823670000503014
    - type: nAUC_mrr_max
      value: 10.266197221615979
  - task:
      type: Retrieval
    dataset:
      type: lyon-nlp/alloprof
      name: MTEB AlloprofRetrieval
      config: default
      split: test
      revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
    metrics:
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      value: 23.23
    - type: map_at_10
      value: 33.513999999999996
    - type: map_at_100
      value: 34.554
    - type: map_at_1000
      value: 34.621
    - type: map_at_20
      value: 34.129
    - type: map_at_3
      value: 30.526999999999997
    - type: map_at_5
      value: 32.107
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    - type: mrr_at_100
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    - type: mrr_at_1000
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    - type: mrr_at_20
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      value: 44.566
    - type: ndcg_at_1000
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      value: 41.467
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      value: 32.993
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      value: 35.839
    - type: precision_at_1
      value: 23.23
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      value: 3.318
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      value: 13.385
    - type: precision_at_5
      value: 9.413
    - type: recall_at_1
      value: 23.23
    - type: recall_at_10
      value: 57.42699999999999
    - type: recall_at_100
      value: 83.11699999999999
    - type: recall_at_1000
      value: 97.75500000000001
    - type: recall_at_20
      value: 66.364
    - type: recall_at_3
      value: 40.155
    - type: recall_at_5
      value: 47.064
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (fr)
      config: fr
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 37.102000000000004
    - type: f1
      value: 36.48213245522153
    - type: f1_weighted
      value: 36.48213245522153
  - 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: 8.372
    - type: map_at_100
      value: 9.522
    - type: map_at_1000
      value: 9.645
    - type: map_at_20
      value: 8.987
    - type: map_at_3
      value: 7.132
    - type: map_at_5
      value: 7.763000000000001
    - type: mrr_at_1
      value: 5.405405405405405
    - type: mrr_at_10
      value: 8.37247962247962
    - type: mrr_at_100
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    - type: mrr_at_1000
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    - type: mrr_at_20
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    - type: mrr_at_5
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      value: 27.027
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      value: 9.91
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      value: 12.613
  - task:
      type: Clustering
    dataset:
      type: lyon-nlp/clustering-hal-s2s
      name: MTEB HALClusteringS2S
      config: default
      split: test
      revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
    metrics:
    - type: v_measure
      value: 23.773322308353517
    - type: v_measures
      value: [0.28321300949906897, 0.26004472642751963, 0.25956951558284086, 0.24123195304666292, 0.22207486085944725]
  - task:
      type: Clustering
    dataset:
      type: reciTAL/mlsum
      name: MTEB MLSUMClusteringP2P
      config: default
      split: test
      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
    metrics:
    - type: v_measure
      value: 44.10635133884183
    - type: v_measures
      value: [0.4352021194557699, 0.4616076200837005, 0.4407517544208635, 0.4387026615402928, 0.40575306284000634]
  - task:
      type: Clustering
    dataset:
      type: reciTAL/mlsum
      name: MTEB MLSUMClusteringS2S
      config: default
      split: test
      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
    metrics:
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      value: 43.6574557237274
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      value: [0.431013360873005, 0.45456972088535785, 0.4474907746228345, 0.4318507494303067, 0.3857692351737129]
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (fr)
      config: fr
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 86.08518634512997
    - type: f1
      value: 86.01437763316983
    - type: f1_weighted
      value: 86.03483392539235
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (fr)
      config: fr
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 61.23081741309113
    - type: f1
      value: 44.76981337966934
    - type: f1_weighted
      value: 64.86577219367403
  - task:
      type: Classification
    dataset:
      type: mteb/masakhanews
      name: MTEB MasakhaNEWSClassification (fra)
      config: fra
      split: test
      revision: 18193f187b92da67168c655c9973a165ed9593dd
    metrics:
    - type: accuracy
      value: 78.34123222748815
    - type: f1
      value: 74.19808376161188
    - type: f1_weighted
      value: 78.465165305135
  - task:
      type: Clustering
    dataset:
      type: masakhane/masakhanews
      name: MTEB MasakhaNEWSClusteringP2P (fra)
      config: fra
      split: test
      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
    metrics:
    - type: v_measure
      value: 58.6260598345145
    - type: v_measures
      value: [1.0, 0.052551289167589395, 0.4126124221990744, 0.690570263898874, 0.7755690164601873]
  - task:
      type: Clustering
    dataset:
      type: masakhane/masakhanews
      name: MTEB MasakhaNEWSClusteringS2S (fra)
      config: fra
      split: test
      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
    metrics:
    - type: v_measure
      value: 53.47058992788083
    - type: v_measures
      value: [1.0, 0.06287268264858063, 0.6712730568122484, 0.2169401386066275, 0.7224436183265853]
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (fr)
      config: fr
      split: test
      revision: 4672e20407010da34463acc759c162ca9734bca6
    metrics:
    - type: accuracy
      value: 65.48755884330868
    - type: f1
      value: 63.42516904610099
    - type: f1_weighted
      value: 65.62227422445625
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (fr)
      config: fr
      split: test
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
    metrics:
    - type: accuracy
      value: 69.27370544720914
    - type: f1
      value: 68.92639289886843
    - type: f1_weighted
      value: 69.39025426049528
  - task:
      type: Retrieval
    dataset:
      type: jinaai/mintakaqa
      name: MTEB MintakaRetrieval (fr)
      config: fr
      split: test
      revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
    metrics:
    - type: map_at_1
      value: 13.718
    - type: map_at_10
      value: 21.404
    - type: map_at_100
      value: 22.371
    - type: map_at_1000
      value: 22.493
    - type: map_at_20
      value: 21.972
    - type: map_at_3
      value: 19.192
    - type: map_at_5
      value: 20.366999999999997
    - type: mrr_at_1
      value: 13.718263718263717
    - type: mrr_at_10
      value: 21.40396890396889
    - type: mrr_at_100
      value: 22.370673563176524
    - type: mrr_at_1000
      value: 22.49316476646458
    - type: mrr_at_20
      value: 21.971667600361627
    - type: mrr_at_3
      value: 19.191919191919162
    - type: mrr_at_5
      value: 20.36718536718532
    - type: nauc_map_at_1000_diff1
      value: 18.66186772676608
    - type: nauc_map_at_1000_max
      value: 32.45794220535016
    - type: nauc_map_at_100_diff1
      value: 18.620993318615394
    - type: nauc_map_at_100_max
      value: 32.44820369787587
    - type: nauc_map_at_10_diff1
      value: 18.884049513804037
    - type: nauc_map_at_10_max
      value: 32.77552431144882
    - type: nauc_map_at_1_diff1
      value: 25.49080673895181
    - type: nauc_map_at_1_max
      value: 29.25311987317655
    - type: nauc_map_at_20_diff1
      value: 18.704330878419075
    - type: nauc_map_at_20_max
      value: 32.55837988078994
    - type: nauc_map_at_3_diff1
      value: 20.095857563209314
    - type: nauc_map_at_3_max
      value: 32.93191322461617
    - type: nauc_map_at_5_diff1
      value: 19.281813313113396
    - type: nauc_map_at_5_max
      value: 32.786844756856475
    - type: nauc_mrr_at_1000_diff1
      value: 18.66186763232669
    - type: nauc_mrr_at_1000_max
      value: 32.45794216874754
    - type: nauc_mrr_at_100_diff1
      value: 18.620993318615394
    - type: nauc_mrr_at_100_max
      value: 32.44820369787587
    - type: nauc_mrr_at_10_diff1
      value: 18.884049513804037
    - type: nauc_mrr_at_10_max
      value: 32.77552431144882
    - type: nauc_mrr_at_1_diff1
      value: 25.49080673895181
    - type: nauc_mrr_at_1_max
      value: 29.25311987317655
    - type: nauc_mrr_at_20_diff1
      value: 18.704330878419075
    - type: nauc_mrr_at_20_max
      value: 32.55837988078994
    - type: nauc_mrr_at_3_diff1
      value: 20.095857563209314
    - type: nauc_mrr_at_3_max
      value: 32.93191322461617
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      value: 19.281813313113396
    - type: nauc_mrr_at_5_max
      value: 32.786844756856475
    - type: nauc_ndcg_at_1000_diff1
      value: 16.291266125191186
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      value: 32.03483412880716
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      value: 15.08155959648069
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      value: 31.74628993952365
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      value: 16.457288503185854
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      value: 33.34322548472455
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      value: 25.49080673895181
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      value: 15.847885101378232
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      value: 18.77312834653236
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      value: 33.76341797807492
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      value: 17.46839695168085
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      value: 33.52006824258854
    - type: nauc_precision_at_1000_diff1
      value: -1.1586826170737583
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      value: 19.93551888234813
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      value: 27.2930149786862
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      value: 10.566658459623403
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      value: 8.479138077976014
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      value: 32.113399922346744
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      value: 27.293014978686276
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    - type: nauc_recall_at_20_diff1
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      value: 32.113399922346744
    - type: nauc_recall_at_3_diff1
      value: 15.622074491261479
    - type: nauc_recall_at_3_max
      value: 35.725010828159924
    - type: nauc_recall_at_5_diff1
      value: 13.229128818765403
    - type: nauc_recall_at_5_max
      value: 35.14850024280548
    - type: ndcg_at_1
      value: 13.718
    - type: ndcg_at_10
      value: 25.576
    - type: ndcg_at_100
      value: 30.537999999999997
    - type: ndcg_at_1000
      value: 34.364
    - type: ndcg_at_20
      value: 27.619
    - type: ndcg_at_3
      value: 20.924
    - type: ndcg_at_5
      value: 23.046
    - type: precision_at_1
      value: 13.718
    - type: precision_at_10
      value: 3.894
    - type: precision_at_100
      value: 0.628
    - type: precision_at_1000
      value: 0.094
    - type: precision_at_20
      value: 2.348
    - type: precision_at_3
      value: 8.64
    - type: precision_at_5
      value: 6.216
    - type: recall_at_1
      value: 13.718
    - type: recall_at_10
      value: 38.943
    - type: recall_at_100
      value: 62.775999999999996
    - type: recall_at_1000
      value: 94.10300000000001
    - type: recall_at_20
      value: 46.97
    - type: recall_at_3
      value: 25.921
    - type: recall_at_5
      value: 31.080999999999996
  - task:
      type: PairClassification
    dataset:
      type: GEM/opusparcus
      name: MTEB OpusparcusPC (fr)
      config: fr
      split: test
      revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
    metrics:
      - type: cos_sim_accuracy
        value: 81.81198910081744
      - type: cos_sim_ap
        value: 92.92034333454589
      - type: cos_sim_f1
        value: 87.20651653090561
      - type: cos_sim_precision
        value: 84.25925925925926
      - type: cos_sim_recall
        value: 90.36742800397219
      - type: dot_accuracy
        value: 81.06267029992752
      - type: dot_ap
        value: 92.19923182286357
      - type: dot_f1
        value: 87.23307587460246
      - type: dot_precision
        value: 80.40200995025126
      - type: dot_recall
        value: 95.33267130089378
      - type: euclidean_accuracy
        value: 81.06267029992752
      - type: euclidean_ap
        value: 92.58456772515232
      - type: euclidean_f1
        value: 86.94835680751173
      - type: euclidean_precision
        value: 82.45770258236865
      - type: euclidean_recall
        value: 91.95630585898709
      - type: manhattan_accuracy
        value: 80.92643051771117
      - type: manhattan_ap
        value: 92.47972548332238
      - type: manhattan_f1
        value: 86.88372093023257
      - type: manhattan_precision
        value: 81.71478565179353
      - type: manhattan_recall
        value: 92.75074478649454
      - type: max_accuracy
        value: 81.81198910081744
      - type: max_ap
        value: 92.92034333454589
      - type: max_f1
        value: 87.23307587460246
  - 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: 60.550000000000004
    - type: cos_sim_ap
      value: 58.1865824487652
    - type: cos_sim_f1
      value: 62.491349480968864
    - type: cos_sim_precision
      value: 45.44539506794162
    - type: cos_sim_recall
      value: 100.0
    - type: dot_accuracy
      value: 56.49999999999999
    - type: dot_ap
      value: 49.511525626044474
    - type: dot_f1
      value: 62.76595744680852
    - type: dot_precision
      value: 46.165884194053206
    - type: dot_recall
      value: 98.00664451827242
    - type: euclidean_accuracy
      value: 60.199999999999996
    - type: euclidean_ap
      value: 58.003058708335246
    - type: euclidean_f1
      value: 62.491349480968864
    - type: euclidean_precision
      value: 45.44539506794162
    - type: euclidean_recall
      value: 100.0
    - type: manhattan_accuracy
      value: 60.199999999999996
    - type: manhattan_ap
      value: 58.02420001567834
    - type: manhattan_f1
      value: 62.491349480968864
    - type: manhattan_precision
      value: 45.44539506794162
    - type: manhattan_recall
      value: 100.0
    - type: max_accuracy
      value: 60.550000000000004
    - type: max_ap
      value: 58.1865824487652
    - type: max_f1
      value: 62.76595744680852
  - task:
      type: STS
    dataset:
      type: Lajavaness/SICK-fr
      name: MTEB SICKFr
      config: default
      split: test
      revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
    metrics:
    - type: cos_sim_pearson
      value: 81.35988610550564
    - type: cos_sim_spearman
      value: 74.30702501405389
    - type: euclidean_pearson
      value: 77.98265846914386
    - type: euclidean_spearman
      value: 74.28309779423242
    - type: manhattan_pearson
      value: 77.91611618952486
    - type: manhattan_spearman
      value: 74.09543847416339
  - 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: 82.87930229178569
    - type: cos_sim_spearman
      value: 82.91122500126046
    - type: euclidean_pearson
      value: 82.30161381658885
    - type: euclidean_spearman
      value: 82.80157531184477
    - type: manhattan_pearson
      value: 82.59746592491155
    - type: manhattan_spearman
      value: 82.91620907805208
  - 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: 83.44206580073515
    - type: cos_sim_spearman
      value: 83.29855460437528
    - type: euclidean_pearson
      value: 82.28885833656986
    - type: euclidean_spearman
      value: 83.17545506016941
    - type: manhattan_pearson
      value: 82.16568250036501
    - type: manhattan_spearman
      value: 83.0743139221437
  - task:
      type: Summarization
    dataset:
      type: lyon-nlp/summarization-summeval-fr-p2p
      name: MTEB SummEvalFr
      config: default
      split: test
      revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
    metrics:
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      value: 31.280485627024635
    - type: cos_sim_spearman
      value: 32.33005962500831
    - type: dot_pearson
      value: 30.158348138782753
    - type: dot_spearman
      value: 30.392045689426418
  - 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: 81.67777777777776
    - type: mrr
      value: 81.67777777777776
    - type: nAUC_map_diff1
      value: 59.89472485574524
    - type: nAUC_map_max
      value: 8.215249384307162
    - type: nAUC_mrr_diff1
      value: 59.89472485574524
    - type: nAUC_mrr_max
      value: 8.215249384307162
  - 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: 72.887
    - type: map_at_100
      value: 73.181
    - type: map_at_1000
      value: 73.181
    - type: map_at_20
      value: 73.16
    - type: map_at_3
      value: 70.667
    - type: map_at_5
      value: 71.56700000000001
    - type: mrr_at_1
      value: 62.0
    - type: mrr_at_10
      value: 72.88690476190479
    - type: mrr_at_100
      value: 73.18055555555557
    - type: mrr_at_1000
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    - type: mrr_at_20
      value: 73.15972222222224
    - type: mrr_at_3
      value: 70.66666666666667
    - type: mrr_at_5
      value: 71.56666666666666
    - type: nauc_map_at_1000_diff1
      value: 47.52352042312832
    - type: nauc_map_at_1000_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:
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    dataset:
      type: mteb/amazon_polarity
      name: MTEB AmazonPolarityClassification
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
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  - task:
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      type: mteb/amazon_reviews_multi
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      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:
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    dataset:
      type: mteb/askubuntudupquestions-reranking
      name: MTEB AskUbuntuDupQuestions
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
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  - task:
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      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:
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    dataset:
      type: mteb/banking77
      name: MTEB Banking77Classification
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
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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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  - task:
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    dataset:
      type: mteb/biorxiv-clustering-s2s
      name: MTEB BiorxivClusteringS2S
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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
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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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  - 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
    metrics:
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    - type: map_at_1000
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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_100
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    - type: precision_at_1000
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    - type: precision_at_3
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    - type: precision_at_5
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    - type: recall_at_1
      value: 3.5680000000000005
    - type: recall_at_10
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    - type: recall_at_100
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    - type: recall_at_1000
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    - type: recall_at_20
      value: 23.122
    - type: recall_at_3
      value: 8.948
    - type: recall_at_5
      value: 11.858
  - task:
      type: STS
    dataset:
      type: mteb/sickr-sts
      name: MTEB SICK-R
      config: default
      split: test
      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
    metrics:
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      value: 83.0786740980213
    - type: cos_sim_spearman
      value: 74.64910820402831
    - type: euclidean_pearson
      value: 79.40680658618808
    - type: euclidean_spearman
      value: 74.04786370197291
    - type: manhattan_pearson
      value: 79.30290796130608
    - type: manhattan_spearman
      value: 73.86543081865257
  - task:
      type: STS
    dataset:
      type: mteb/sts12-sts
      name: MTEB STS12
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 87.14143764866938
    - type: cos_sim_spearman
      value: 79.39117869636218
    - type: euclidean_pearson
      value: 82.27893672472992
    - type: euclidean_spearman
      value: 78.12857266398304
    - type: manhattan_pearson
      value: 82.40958626880706
    - type: manhattan_spearman
      value: 78.2460736745845
  - task:
      type: STS
    dataset:
      type: mteb/sts13-sts
      name: MTEB STS13
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
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      value: 84.98565298864834
    - type: cos_sim_spearman
      value: 85.3226077419183
    - type: euclidean_pearson
      value: 83.36095201234602
    - type: euclidean_spearman
      value: 83.44580751011605
    - type: manhattan_pearson
      value: 83.26944531709971
    - type: manhattan_spearman
      value: 83.3511641574103
  - task:
      type: STS
    dataset:
      type: mteb/sts14-sts
      name: MTEB STS14
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
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      value: 86.15642283009589
    - type: cos_sim_spearman
      value: 83.89978896960656
    - type: euclidean_pearson
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    - type: euclidean_spearman
      value: 82.70615194483753
    - type: manhattan_pearson
      value: 84.82154011079453
    - type: manhattan_spearman
      value: 82.61620436539884
  - task:
      type: STS
    dataset:
      type: mteb/sts15-sts
      name: MTEB STS15
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
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      value: 87.7730685270548
    - type: cos_sim_spearman
      value: 88.46744045180212
    - type: euclidean_pearson
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    - type: euclidean_spearman
      value: 87.32502541228249
    - type: manhattan_pearson
      value: 87.06217303693649
    - type: manhattan_spearman
      value: 87.24696449513658
  - task:
      type: STS
    dataset:
      type: mteb/sts16-sts
      name: MTEB STS16
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
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      value: 83.0653949018384
    - type: cos_sim_spearman
      value: 84.43898725124001
    - type: euclidean_pearson
      value: 83.46057253146975
    - type: euclidean_spearman
      value: 83.70938571051141
    - type: manhattan_pearson
      value: 83.48079890307652
    - type: manhattan_spearman
      value: 83.75548841452152
  - 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
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    - type: manhattan_pearson
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    - type: manhattan_spearman
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  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (en)
      config: en
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    metrics:
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    - type: cos_sim_spearman
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    - type: euclidean_pearson
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    - type: euclidean_spearman
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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:
      type: Reranking
    dataset:
      type: mteb/scidocs-reranking
      name: MTEB SciDocsRR
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
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  - task:
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      name: MTEB SciFact
      config: default
      split: test
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
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      value: 15.333
    - type: recall_at_1
      value: 46.694
    - type: recall_at_10
      value: 79.5
    - type: recall_at_100
      value: 92.767
    - type: recall_at_1000
      value: 99.0
    - type: recall_at_20
      value: 82.956
    - type: recall_at_3
      value: 66.783
    - type: recall_at_5
      value: 69.906
  - task:
      type: PairClassification
    dataset:
      type: mteb/sprintduplicatequestions-pairclassification
      name: MTEB SprintDuplicateQuestions
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
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      value: 99.78118811881188
    - type: cos_sim_ap
      value: 94.38499172210743
    - type: cos_sim_f1
      value: 88.99803536345776
    - type: cos_sim_precision
      value: 87.45173745173746
    - type: cos_sim_recall
      value: 90.60000000000001
    - type: dot_accuracy
      value: 99.68118811881187
    - type: dot_ap
      value: 88.59372518155831
    - type: dot_f1
      value: 83.45323741007195
    - type: dot_precision
      value: 85.83509513742071
    - type: dot_recall
      value: 81.2
    - type: euclidean_accuracy
      value: 99.78019801980199
    - type: euclidean_ap
      value: 94.41961507812081
    - type: euclidean_f1
      value: 88.91098955743412
    - type: euclidean_precision
      value: 88.4272997032641
    - type: euclidean_recall
      value: 89.4
    - type: manhattan_accuracy
      value: 99.78118811881188
    - type: manhattan_ap
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    - type: manhattan_f1
      value: 88.93280632411069
    - type: manhattan_precision
      value: 87.890625
    - type: manhattan_recall
      value: 90.0
    - type: max_accuracy
      value: 99.78118811881188
    - type: max_ap
      value: 94.53929097513269
    - type: max_f1
      value: 88.99803536345776
  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering
      name: MTEB StackExchangeClustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
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  - task:
      type: Clustering
    dataset:
      type: mteb/stackexchange-clustering-p2p
      name: MTEB StackExchangeClusteringP2P
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
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  - task:
      type: Reranking
    dataset:
      type: mteb/stackoverflowdupquestions-reranking
      name: MTEB StackOverflowDupQuestions
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
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    - type: nAUC_mrr_diff1
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  - task:
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      name: MTEB SummEval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
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      value: 29.70675041719688
  - task:
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      name: MTEB TRECCOVID
      config: default
      split: test
      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
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      value: 64.0
    - type: mrr_at_5
      value: 66.8
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    - type: precision_at_1
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    - type: precision_at_1000
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    - type: recall_at_1
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    - type: recall_at_1000
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    - type: recall_at_20
      value: 2.4
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    - type: recall_at_5
      value: 0.739
  - task:
      type: Retrieval
    dataset:
      type: mteb/touche2020
      name: MTEB Touche2020
      config: default
      split: test
      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
    metrics:
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    - type: map_at_1000
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    - type: map_at_20
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    - type: map_at_3
      value: 2.8819999999999997
    - type: map_at_5
      value: 3.859
    - type: mrr_at_1
      value: 18.367346938775512
    - type: mrr_at_10
      value: 30.816326530612244
    - 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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  - task:
      type: Classification
    dataset:
      type: mteb/toxic_conversations_50k
      name: MTEB ToxicConversationsClassification
      config: default
      split: test
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
    metrics:
    - type: accuracy
      value: 62.5244140625
    - type: ap
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    - type: ap_weighted
      value: 11.036738738208067
    - type: f1
      value: 48.178922337841016
    - type: f1_weighted
      value: 70.79346668027127
  - task:
      type: Classification
    dataset:
      type: mteb/tweet_sentiment_extraction
      name: MTEB TweetSentimentExtractionClassification
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 59.60384833050367
    - type: f1
      value: 59.89473957112635
    - type: f1_weighted
      value: 59.21770850754739
  - task:
      type: Clustering
    dataset:
      type: mteb/twentynewsgroups-clustering
      name: MTEB TwentyNewsgroupsClustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
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      value: 40.52017931682707
    - type: v_measures
      value: [0.41056001750265486, 0.4115812168479953, 0.4020131539653664, 0.41845373495523314, 0.3990043371824943]
  - task:
      type: PairClassification
    dataset:
      type: mteb/twittersemeval2015-pairclassification
      name: MTEB TwitterSemEval2015
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 84.19860523335518
    - type: cos_sim_ap
      value: 67.98183780223552
    - type: cos_sim_f1
      value: 63.287574797606474
    - type: cos_sim_precision
      value: 56.98288611874076
    - type: cos_sim_recall
      value: 71.16094986807387
    - type: dot_accuracy
      value: 81.0872027180068
    - type: dot_ap
      value: 57.080616165589994
    - type: dot_f1
      value: 57.184056030487184
    - type: dot_precision
      value: 46.899814157796925
    - type: dot_recall
      value: 73.24538258575198
    - type: euclidean_accuracy
      value: 84.10919711509806
    - type: euclidean_ap
      value: 68.02422564958268
    - type: euclidean_f1
      value: 63.76539589442815
    - type: euclidean_precision
      value: 57.40232312565998
    - type: euclidean_recall
      value: 71.71503957783642
    - type: manhattan_accuracy
      value: 84.06747332657805
    - type: manhattan_ap
      value: 67.74186393843273
    - type: manhattan_f1
      value: 63.57935359382538
    - type: manhattan_precision
      value: 58.55175477565526
    - type: manhattan_recall
      value: 69.55145118733509
    - type: max_accuracy
      value: 84.19860523335518
    - type: max_ap
      value: 68.02422564958268
    - type: max_f1
      value: 63.76539589442815
  - task:
      type: PairClassification
    dataset:
      type: mteb/twitterurlcorpus-pairclassification
      name: MTEB TwitterURLCorpus
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 89.40311250824699
    - type: cos_sim_ap
      value: 86.47944792205789
    - type: cos_sim_f1
      value: 78.80539499036608
    - type: cos_sim_precision
      value: 75.95714285714286
    - type: cos_sim_recall
      value: 81.87557745611333
    - type: dot_accuracy
      value: 87.8468583847557
    - type: dot_ap
      value: 83.05643449341216
    - type: dot_f1
      value: 76.55210439257489
    - type: dot_precision
      value: 73.24330027431948
    - type: dot_recall
      value: 80.17400677548507
    - type: euclidean_accuracy
      value: 89.29250591842279
    - type: euclidean_ap
      value: 86.35499372223612
    - type: euclidean_f1
      value: 78.9011715450439
    - type: euclidean_precision
      value: 75.43009620110948
    - type: euclidean_recall
      value: 82.7071142593163
    - type: manhattan_accuracy
      value: 89.26339892110063
    - type: manhattan_ap
      value: 86.2956040159182
    - type: manhattan_f1
      value: 78.78428904601488
    - type: manhattan_precision
      value: 75.87165775401068
    - type: manhattan_recall
      value: 81.92947336002464
    - type: max_accuracy
      value: 89.40311250824699
    - type: max_ap
      value: 86.47944792205789
    - type: max_f1
      value: 78.9011715450439
license: apache-2.0
language:
    - fr
    - en
---

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

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 [Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384), a pre-trained language model  is built upon [multilingual-e5](https://huggingface.co/intfloat/multilingual-e5-small) architecture. The model utilizes MiniLM 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': 384, '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-small', 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}