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-document-embedding
  results:
  - task:
      type: Clustering
    dataset:
      type: lyon-nlp/alloprof
      name: MTEB AlloProfClusteringP2P
      config: default
      split: test
      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
    metrics:
    - type: v_measure
      value: 55.52298673909706
    - type: v_measures
      value: [0.5198748380785404, 0.5562521099012603, 0.5322986254464575, 0.5722250987615152, 0.532932258758668]
  - task:
      type: Clustering
    dataset:
      type: lyon-nlp/alloprof
      name: MTEB AlloProfClusteringS2S
      config: default
      split: test
      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
    metrics:
    - type: v_measure
      value: 35.802733348353094
    - type: v_measures
      value: [0.37359796790048144, 0.36376421464272285, 0.37524966704915225, 0.3749296797757371, 0.36673700158106576]
  - 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: 73.10088493122083
    - type: mrr
      value: 74.33452929243086
    - type: nAUC_map_diff1
      value: 56.63750231223696
    - type: nAUC_map_max
      value: 27.066268470355492
    - type: nAUC_mrr_diff1
      value: 55.33487252773409
    - type: nAUC_mrr_max
      value: 27.328424865584367
  - task:
      type: Retrieval
    dataset:
      type: lyon-nlp/alloprof
      name: MTEB AlloprofRetrieval
      config: default
      split: test
      revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
    metrics:
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      value: 28.282
    - type: map_at_10
      value: 38.805
    - type: map_at_100
      value: 39.804
    - type: map_at_1000
      value: 39.859
    - type: map_at_20
      value: 39.428999999999995
    - type: map_at_3
      value: 35.838
    - type: map_at_5
      value: 37.537
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      value: 28.281519861830745
    - type: mrr_at_10
      value: 38.805171752063
    - type: mrr_at_100
      value: 39.80444636265999
    - type: mrr_at_1000
      value: 39.858722309770464
    - type: mrr_at_20
      value: 39.42867574383368
    - type: mrr_at_3
      value: 35.83765112262528
    - type: mrr_at_5
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    - type: ndcg_at_1
      value: 28.282
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      value: 44.421
    - type: ndcg_at_100
      value: 49.447
    - type: ndcg_at_1000
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      value: 46.671
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      value: 38.289
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      value: 41.349999999999994
    - type: precision_at_1
      value: 28.282
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    - type: precision_at_100
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    - type: precision_at_1000
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      value: 3.558
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    - type: precision_at_5
      value: 10.561
    - type: recall_at_1
      value: 28.282
    - type: recall_at_10
      value: 62.306
    - type: recall_at_100
      value: 86.226
    - type: recall_at_1000
      value: 98.489
    - type: recall_at_20
      value: 71.15700000000001
    - type: recall_at_3
      value: 45.379999999999995
    - type: recall_at_5
      value: 52.807
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (fr)
      config: fr
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 44.10999999999999
    - type: f1
      value: 42.00584553745547
    - type: f1_weighted
      value: 42.005845537455485
  - task:
      type: Retrieval
    dataset:
      type: maastrichtlawtech/bsard
      name: MTEB BSARDRetrieval
      config: default
      split: test
      revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
    metrics:
    - type: map_at_1
      value: 4.955
    - type: map_at_10
      value: 9.103
    - type: map_at_100
      value: 9.998999999999999
    - type: map_at_1000
      value: 10.136000000000001
    - type: map_at_20
      value: 9.554
    - type: map_at_3
      value: 7.4319999999999995
    - type: map_at_5
      value: 7.95
    - type: mrr_at_1
      value: 4.954954954954955
    - type: mrr_at_10
      value: 9.102852852852852
    - type: mrr_at_100
      value: 9.999215850941926
    - type: mrr_at_1000
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    - type: mrr_at_20
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    - type: mrr_at_3
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    - type: mrr_at_5
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      value: 14.655819915811785
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    - type: recall_at_3
      value: 10.811
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  - task:
      type: Clustering
    dataset:
      type: lyon-nlp/clustering-hal-s2s
      name: MTEB HALClusteringS2S
      config: default
      split: test
      revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
    metrics:
    - type: v_measure
      value: 23.137623974622365
    - type: v_measures
      value: [0.2802068838665942, 0.2565274984774815, 0.25245022445056786, 0.22595460950575297, 0.20177741591393913]
  - task:
      type: Clustering
    dataset:
      type: reciTAL/mlsum
      name: MTEB MLSUMClusteringP2P
      config: default
      split: test
      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
    metrics:
    - type: v_measure
      value: 40.31003279146524
    - type: v_measures
      value: [0.3943651322813771, 0.4189000344922205, 0.4101443880670743, 0.3832149080991847, 0.37602613534689566]
  - task:
      type: Clustering
    dataset:
      type: reciTAL/mlsum
      name: MTEB MLSUMClusteringS2S
      config: default
      split: test
      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
    metrics:
    - type: v_measure
      value: 40.04524841336757
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      value: [0.39835449199860185, 0.405905613221237, 0.40326782414397255, 0.40882879348632284, 0.3683302592759367]
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_domain
      name: MTEB MTOPDomainClassification (fr)
      config: fr
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 87.82023175696837
    - type: f1
      value: 87.58287510797385
    - type: f1_weighted
      value: 87.75645870762435
  - task:
      type: Classification
    dataset:
      type: mteb/mtop_intent
      name: MTEB MTOPIntentClassification (fr)
      config: fr
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 58.628249295333546
    - type: f1
      value: 42.22070573172825
    - type: f1_weighted
      value: 60.62087995743649
  - task:
      type: Classification
    dataset:
      type: mteb/masakhanews
      name: MTEB MasakhaNEWSClassification (fra)
      config: fra
      split: test
      revision: 18193f187b92da67168c655c9973a165ed9593dd
    metrics:
    - type: accuracy
      value: 69.81042654028435
    - type: f1
      value: 66.05811881796396
    - type: f1_weighted
      value: 70.34901566149948
  - task:
      type: Clustering
    dataset:
      type: masakhane/masakhanews
      name: MTEB MasakhaNEWSClusteringP2P (fra)
      config: fra
      split: test
      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
    metrics:
    - type: v_measure
      value: 45.02712178986078
    - type: v_measures
      value: [1.0, 0.23955793240111928, 0.7158920010774062, 0.036391635653837, 0.25951452036067674]
  - task:
      type: Clustering
    dataset:
      type: masakhane/masakhanews
      name: MTEB MasakhaNEWSClusteringS2S (fra)
      config: fra
      split: test
      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
    metrics:
    - type: v_measure
      value: 30.38607254306223
    - type: v_measures
      value: [1.0, 0.01936507478006705, 0.19876372667844472, 0.17182595867380823, 0.12934886702079137]
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (fr)
      config: fr
      split: test
      revision: 4672e20407010da34463acc759c162ca9734bca6
    metrics:
    - type: accuracy
      value: 66.13651647612645
    - type: f1
      value: 64.42898347709598
    - type: f1_weighted
      value: 65.01442547020224
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (fr)
      config: fr
      split: test
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
    metrics:
    - type: accuracy
      value: 72.73705447209144
    - type: f1
      value: 72.09285609231057
    - type: f1_weighted
      value: 72.34295244611339
  - task:
      type: Retrieval
    dataset:
      type: jinaai/mintakaqa
      name: MTEB MintakaRetrieval (fr)
      config: fr
      split: test
      revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
    metrics:
    - type: map_at_1
      value: 13.677
    - type: map_at_10
      value: 21.044
    - type: map_at_100
      value: 22.012
    - type: map_at_1000
      value: 22.125
    - type: map_at_20
      value: 21.573999999999998
    - type: map_at_3
      value: 18.857
    - type: map_at_5
      value: 19.936999999999998
    - type: mrr_at_1
      value: 13.677313677313677
    - type: mrr_at_10
      value: 21.043933543933505
    - type: mrr_at_100
      value: 22.012160523798318
    - type: mrr_at_1000
      value: 22.124555014776913
    - type: mrr_at_20
      value: 21.574199922074904
    - type: mrr_at_3
      value: 18.857493857493825
    - type: mrr_at_5
      value: 19.93652743652738
    - type: nauc_map_at_1000_diff1
      value: 20.63087633823352
    - type: nauc_map_at_1000_max
      value: 31.753246807516362
    - type: nauc_map_at_100_diff1
      value: 20.602874174259885
    - type: nauc_map_at_100_max
      value: 31.74109681792161
    - type: nauc_map_at_10_diff1
      value: 20.82028964049537
    - type: nauc_map_at_10_max
      value: 32.082751313883705
    - type: nauc_map_at_1_diff1
      value: 27.838566854973656
    - type: nauc_map_at_1_max
      value: 32.0217755083183
    - type: nauc_map_at_20_diff1
      value: 20.685607874578192
    - type: nauc_map_at_20_max
      value: 31.89765440964895
    - type: nauc_map_at_3_diff1
      value: 22.385335765437958
    - type: nauc_map_at_3_max
      value: 32.47346568889047
    - type: nauc_map_at_5_diff1
      value: 21.173253596770003
    - type: nauc_map_at_5_max
      value: 32.2528418460596
    - type: nauc_mrr_at_1000_diff1
      value: 20.63087633823352
    - type: nauc_mrr_at_1000_max
      value: 31.753246807516362
    - type: nauc_mrr_at_100_diff1
      value: 20.602874174259885
    - type: nauc_mrr_at_100_max
      value: 31.74109681792161
    - type: nauc_mrr_at_10_diff1
      value: 20.82028964049537
    - type: nauc_mrr_at_10_max
      value: 32.082751313883705
    - type: nauc_mrr_at_1_diff1
      value: 27.838566854973656
    - type: nauc_mrr_at_1_max
      value: 32.0217755083183
    - type: nauc_mrr_at_20_diff1
      value: 20.685607874578192
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      value: 31.89765440964895
    - type: nauc_mrr_at_3_diff1
      value: 22.385335765437958
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      value: 32.47346568889047
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      value: 21.173253596770003
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      value: 32.2528418460596
    - type: nauc_ndcg_at_1000_diff1
      value: 18.08460876388022
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      value: 30.282810360048217
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      value: 29.66409825853174
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      value: 18.23254548133648
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      value: 27.838566854973656
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      value: 32.0217755083183
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      value: 17.769003159911446
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      value: 32.363993132409526
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      value: 19.00106027591966
    - type: nauc_ndcg_at_5_max
      value: 31.962682994281664
    - type: nauc_precision_at_1000_diff1
      value: -0.439767274118902
    - type: nauc_precision_at_1000_max
      value: 12.247737195943136
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      value: 5.574224743755663
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      value: 20.625486141114006
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      value: 12.116438700823444
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      value: 32.0217755083183
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      value: 28.101643683820228
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      value: 28.101643683820228
    - type: nauc_recall_at_3_diff1
      value: 17.57508308178443
    - type: nauc_recall_at_3_max
      value: 32.042579480429
    - type: nauc_recall_at_5_diff1
      value: 13.870976762193543
    - type: nauc_recall_at_5_max
      value: 31.186621554981787
    - type: ndcg_at_1
      value: 13.677
    - type: ndcg_at_10
      value: 25.191000000000003
    - type: ndcg_at_100
      value: 30.379
    - type: ndcg_at_1000
      value: 33.961999999999996
    - type: ndcg_at_20
      value: 27.1
    - type: ndcg_at_3
      value: 20.546
    - type: ndcg_at_5
      value: 22.505
    - type: precision_at_1
      value: 13.677
    - type: precision_at_10
      value: 3.853
    - type: precision_at_100
      value: 0.639
    - type: precision_at_1000
      value: 0.093
    - type: precision_at_20
      value: 2.3009999999999997
    - type: precision_at_3
      value: 8.477
    - type: precision_at_5
      value: 6.0440000000000005
    - type: recall_at_1
      value: 13.677
    - type: recall_at_10
      value: 38.534
    - type: recall_at_100
      value: 63.922999999999995
    - type: recall_at_1000
      value: 93.407
    - type: recall_at_20
      value: 46.028000000000006
    - type: recall_at_3
      value: 25.430000000000003
    - type: recall_at_5
      value: 30.220999999999997
  - task:
      type: PairClassification
    dataset:
      type: GEM/opusparcus
      name: MTEB OpusparcusPC (fr)
      config: fr
      split: test
      revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
    metrics:
    - type: cos_sim_accuracy
      value: 82.9700272479564
    - type: cos_sim_ap
      value: 93.15021785539084
    - type: cos_sim_f1
      value: 87.97316722568279
    - type: cos_sim_precision
      value: 85.0
    - type: cos_sim_recall
      value: 91.16186693147964
    - type: dot_accuracy
      value: 82.9700272479564
    - type: dot_ap
      value: 93.15021785539084
    - type: dot_f1
      value: 87.97316722568279
    - type: dot_precision
      value: 85.0
    - type: dot_recall
      value: 91.16186693147964
    - type: euclidean_accuracy
      value: 82.9700272479564
    - type: euclidean_ap
      value: 93.15015081441638
    - type: euclidean_f1
      value: 87.97316722568279
    - type: euclidean_precision
      value: 85.0
    - type: euclidean_recall
      value: 91.16186693147964
    - type: manhattan_accuracy
      value: 82.56130790190735
    - type: manhattan_ap
      value: 93.14590481820592
    - type: manhattan_f1
      value: 87.86729857819905
    - type: manhattan_precision
      value: 84.04351767905711
    - type: manhattan_recall
      value: 92.05561072492552
    - type: max_accuracy
      value: 82.9700272479564
    - type: max_ap
      value: 93.15021785539084
    - type: max_f1
      value: 87.97316722568279
  - task:
      type: PairClassification
    dataset:
      type: google-research-datasets/paws-x
      name: MTEB PawsX (fr)
      config: fr
      split: test
      revision: 8a04d940a42cd40658986fdd8e3da561533a3646
    metrics:
    - type: cos_sim_accuracy
      value: 64.14999999999999
    - type: cos_sim_ap
      value: 63.43794001840604
    - type: cos_sim_f1
      value: 62.59187620889749
    - type: cos_sim_precision
      value: 48.097502972651604
    - type: cos_sim_recall
      value: 89.59025470653378
    - type: dot_accuracy
      value: 64.14999999999999
    - type: dot_ap
      value: 63.52400235031554
    - type: dot_f1
      value: 62.59187620889749
    - type: dot_precision
      value: 48.097502972651604
    - type: dot_recall
      value: 89.59025470653378
    - type: euclidean_accuracy
      value: 64.14999999999999
    - type: euclidean_ap
      value: 63.43794001840604
    - type: euclidean_f1
      value: 62.59187620889749
    - type: euclidean_precision
      value: 48.097502972651604
    - type: euclidean_recall
      value: 89.59025470653378
    - type: manhattan_accuracy
      value: 64.2
    - type: manhattan_ap
      value: 63.46163243480347
    - type: manhattan_f1
      value: 62.540021344717175
    - type: manhattan_precision
      value: 46.069182389937104
    - type: manhattan_recall
      value: 97.34219269102991
    - type: max_accuracy
      value: 64.2
    - type: max_ap
      value: 63.52400235031554
    - type: max_f1
      value: 62.59187620889749
  - task:
      type: STS
    dataset:
      type: Lajavaness/SICK-fr
      name: MTEB SICKFr
      config: default
      split: test
      revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
    metrics:
    - type: cos_sim_pearson
      value: 85.12347242597652
    - type: cos_sim_spearman
      value: 79.80580538857501
    - type: euclidean_pearson
      value: 82.03127787921382
    - type: euclidean_spearman
      value: 79.80580538857501
    - type: manhattan_pearson
      value: 82.02795155003601
    - type: manhattan_spearman
      value: 79.7808784011127
  - 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: 82.34462624659417
    - type: cos_sim_spearman
      value: 82.83867899462683
    - type: euclidean_pearson
      value: 80.00679113308384
    - type: euclidean_spearman
      value: 82.83867899462683
    - type: manhattan_pearson
      value: 79.97582730301362
    - type: manhattan_spearman
      value: 82.95718926500541
  - task:
      type: STS
    dataset:
      type: mteb/stsb_multi_mt
      name: MTEB STSBenchmarkMultilingualSTS (fr)
      config: fr
      split: test
      revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
    metrics:
    - type: cos_sim_pearson
      value: 86.0897698618904
    - type: cos_sim_spearman
      value: 86.58814894229229
    - type: euclidean_pearson
      value: 85.53992615842806
    - type: euclidean_spearman
      value: 86.58814894229229
    - type: manhattan_pearson
      value: 85.4985023034774
    - type: manhattan_spearman
      value: 86.50239881298486
  - 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: 30.458145110977753
    - type: cos_sim_spearman
      value: 31.624715940109265
    - type: dot_pearson
      value: 30.458145236239915
    - type: dot_spearman
      value: 31.624715940109265
  - 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: 87.60277777777777
    - type: mrr
      value: 87.60277777777777
    - type: nAUC_map_diff1
      value: 63.877496103879814
    - type: nAUC_map_max
      value: -4.8943605546581725
    - type: nAUC_mrr_diff1
      value: 63.877496103879814
    - type: nAUC_mrr_max
      value: -4.8943605546581725
  - 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: 67.0
    - type: map_at_10
      value: 78.47800000000001
    - type: map_at_100
      value: 78.616
    - type: map_at_1000
      value: 78.616
    - type: map_at_20
      value: 78.52799999999999
    - type: map_at_3
      value: 77.833
    - type: map_at_5
      value: 78.033
    - type: mrr_at_1
      value: 67.0
    - type: mrr_at_10
      value: 78.47777777777777
    - type: mrr_at_100
      value: 78.61609758846066
    - type: mrr_at_1000
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    - type: mrr_at_20
      value: 78.52777777777777
    - type: mrr_at_3
      value: 77.83333333333331
    - type: mrr_at_5
      value: 78.03333333333333
    - type: nauc_map_at_1000_diff1
      value: 54.76919250379753
    - type: nauc_map_at_1000_max
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    - type: ndcg_at_1
      value: 67.0
    - type: ndcg_at_10
      value: 82.864
    - type: ndcg_at_100
      value: 83.672
    - type: ndcg_at_1000
      value: 83.672
    - type: ndcg_at_20
      value: 83.092
    - type: ndcg_at_3
      value: 81.464
    - type: ndcg_at_5
      value: 81.851
    - type: precision_at_1
      value: 67.0
    - type: precision_at_10
      value: 9.6
    - type: precision_at_100
      value: 1.0
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_20
      value: 4.8500000000000005
    - type: precision_at_3
      value: 30.667
    - type: precision_at_5
      value: 18.6
    - type: recall_at_1
      value: 67.0
    - type: recall_at_10
      value: 96.0
    - type: recall_at_100
      value: 100.0
    - type: recall_at_1000
      value: 100.0
    - type: recall_at_20
      value: 97.0
    - type: recall_at_3
      value: 92.0
    - type: recall_at_5
      value: 93.0
  - task:
      type: Retrieval
    dataset:
      type: jinaai/xpqa
      name: MTEB XPQARetrieval (fr)
      config: fr
      split: test
      revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
    metrics:
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      value: 40.038000000000004
    - type: map_at_10
      value: 62.409000000000006
    - type: map_at_100
      value: 63.63999999999999
    - type: map_at_1000
      value: 63.693
    - type: map_at_20
      value: 63.165000000000006
    - type: map_at_3
      value: 55.364999999999995
    - type: map_at_5
      value: 59.95399999999999
    - type: mrr_at_1
      value: 62.88384512683578
    - type: mrr_at_10
      value: 70.414944794117
    - type: mrr_at_100
      value: 70.85679259651413
    - type: mrr_at_1000
      value: 70.8680806615119
    - type: mrr_at_20
      value: 70.69824986774621
    - type: mrr_at_3
      value: 68.04628393413438
    - type: mrr_at_5
      value: 69.65509568313303
    - type: nauc_map_at_1000_diff1
      value: 47.58306966781138
    - type: nauc_map_at_1000_max
      value: 49.99853404950863
    - type: nauc_map_at_100_diff1
      value: 47.5473544905194
    - type: nauc_map_at_100_max
      value: 49.98683021023155
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      value: 47.443327641163705
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      value: 49.31862257934493
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      value: 55.93203426614159
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      value: 27.467436111704224
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      value: 47.454162467793985
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      value: 49.715459382963765
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      value: 48.910525378486874
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      value: 42.13319318718595
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      value: 48.56545403298638
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      value: 47.311811085622445
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      value: 56.739822956274224
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      value: 58.274212468278854
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      value: 56.7308210328899
    - type: nauc_mrr_at_100_max
      value: 58.27250671019899
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      value: 56.647228471816405
    - type: nauc_mrr_at_10_max
      value: 58.210342990657495
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      value: 58.618266167104046
    - type: nauc_mrr_at_1_max
      value: 58.55438607166539
    - type: nauc_mrr_at_20_diff1
      value: 56.63534799976597
    - type: nauc_mrr_at_20_max
      value: 58.17181317797869
    - type: nauc_mrr_at_3_diff1
      value: 56.815531582264825
    - type: nauc_mrr_at_3_max
      value: 58.32821204695344
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      value: 56.79122022985127
    - type: nauc_mrr_at_5_max
      value: 58.20366609452701
    - type: nauc_ndcg_at_1000_diff1
      value: 49.530062263932194
    - type: nauc_ndcg_at_1000_max
      value: 53.473298956705925
    - type: nauc_ndcg_at_100_diff1
      value: 48.95703823297219
    - type: nauc_ndcg_at_100_max
      value: 53.191721124797276
    - type: nauc_ndcg_at_10_diff1
      value: 47.98530786084638
    - type: nauc_ndcg_at_10_max
      value: 51.155857323188016
    - type: nauc_ndcg_at_1_diff1
      value: 58.618266167104046
    - type: nauc_ndcg_at_1_max
      value: 58.55438607166539
    - type: nauc_ndcg_at_20_diff1
      value: 47.95544792051313
    - type: nauc_ndcg_at_20_max
      value: 51.751640167194054
    - type: nauc_ndcg_at_3_diff1
      value: 48.50900656884395
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      value: 50.78667595293348
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      value: 49.496100926859654
    - type: nauc_ndcg_at_5_max
      value: 49.089893886856835
    - type: nauc_precision_at_1000_diff1
      value: -19.085707327488784
    - type: nauc_precision_at_1000_max
      value: 22.16522736611267
    - type: nauc_precision_at_100_diff1
      value: -16.92930793417545
    - type: nauc_precision_at_100_max
      value: 26.119556898620655
    - type: nauc_precision_at_10_diff1
      value: -8.586758571265364
    - type: nauc_precision_at_10_max
      value: 34.29909350105018
    - type: nauc_precision_at_1_diff1
      value: 58.618266167104046
    - type: nauc_precision_at_1_max
      value: 58.55438607166539
    - type: nauc_precision_at_20_diff1
      value: -12.36545815755639
    - type: nauc_precision_at_20_max
      value: 30.779202784243694
    - type: nauc_precision_at_3_diff1
      value: 7.173290556095678
    - type: nauc_precision_at_3_max
      value: 43.244915594569356
    - type: nauc_precision_at_5_diff1
      value: -0.5308831428158323
    - type: nauc_precision_at_5_max
      value: 39.78478615216909
    - type: nauc_recall_at_1000_diff1
      value: 44.67738158424653
    - type: nauc_recall_at_1000_max
      value: 71.12276250795361
    - type: nauc_recall_at_100_diff1
      value: 30.071917991701135
    - type: nauc_recall_at_100_max
      value: 42.226214389979326
    - type: nauc_recall_at_10_diff1
      value: 36.275167481806804
    - type: nauc_recall_at_10_max
      value: 40.16796727800884
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      value: 55.93203426614159
    - type: nauc_recall_at_1_max
      value: 27.467436111704224
    - type: nauc_recall_at_20_diff1
      value: 32.189427460851505
    - type: nauc_recall_at_20_max
      value: 38.926081167758205
    - type: nauc_recall_at_3_diff1
      value: 43.959378195689894
    - type: nauc_recall_at_3_max
      value: 36.441633750156335
    - type: nauc_recall_at_5_diff1
      value: 42.6274479464408
    - type: nauc_recall_at_5_max
      value: 38.9902118898862
    - type: ndcg_at_1
      value: 62.88399999999999
    - type: ndcg_at_10
      value: 68.907
    - type: ndcg_at_100
      value: 72.896
    - type: ndcg_at_1000
      value: 73.721
    - type: ndcg_at_20
      value: 70.738
    - type: ndcg_at_3
      value: 62.731
    - type: ndcg_at_5
      value: 65.191
    - type: precision_at_1
      value: 62.88399999999999
    - type: precision_at_10
      value: 16.101
    - type: precision_at_100
      value: 1.951
    - type: precision_at_1000
      value: 0.20600000000000002
    - type: precision_at_20
      value: 8.705
    - type: precision_at_3
      value: 38.095
    - type: precision_at_5
      value: 27.904
    - type: recall_at_1
      value: 40.038000000000004
    - type: recall_at_10
      value: 79.237
    - type: recall_at_100
      value: 94.17699999999999
    - type: recall_at_1000
      value: 99.466
    - type: recall_at_20
      value: 85.027
    - type: recall_at_3
      value: 60.336
    - type: recall_at_5
      value: 70.122
license: apache-2.0
language:
- fr
metrics:
- pearsonr
- spearmanr
---

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

bilingual-document-embedding is the Embedding Model for document in bilingual language: french and english with context length up to 8096 tokens . This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of [BGE M3](https://huggingface.co/BAAI/bge-m3), a pre-trained language model larged on the [BGE M3](https://huggingface.co/BAAI/bge-m3) 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-document-embedding', trust_remote_code=True)
print(embeddings)

```





## Evaluation

TODO

## Citation
    @article{chen2024bge,
      title={Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation},
      author={Chen, Jianlv and Xiao, Shitao and Zhang, Peitian and Luo, Kun and Lian, Defu and Liu, Zheng},
      journal={arXiv preprint arXiv:2402.03216},
      year={2024}
    }

    @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}