README.md

metadata

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: 62.16774435322237
          - type: v_measures
            value:
              - 0.60677169174265
              - 0.6420496407630484
              - 0.5831486687495704
              - 0.6103877439594066
              - 0.6135248698273804
      - task:
          type: Clustering
        dataset:
          type: lyon-nlp/alloprof
          name: MTEB AlloProfClusteringS2S
          config: default
          split: test
          revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
        metrics:
          - type: v_measure
            value: 45.11926529002472
          - type: v_measures
            value:
              - 0.4690142817423344
              - 0.48186364883143407
              - 0.40961058482586177
              - 0.4555301647788916
              - 0.49555829107195404
      - 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
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          - type: nAUC_mrr_diff1
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          - type: nAUC_mrr_max
            value: 12.945500349888256
      - task:
          type: Retrieval
        dataset:
          type: lyon-nlp/alloprof
          name: MTEB AlloprofRetrieval
          config: default
          split: test
          revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
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            value: 34.146
          - type: map_at_100
            value: 35.223
          - type: map_at_1000
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            value: 34.809
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            value: 30.865
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            value: 32.75
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            value: 33.151
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            value: 23.877000000000002
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          - type: precision_at_5
            value: 9.594
          - type: recall_at_1
            value: 23.877000000000002
          - type: recall_at_10
            value: 58.29
          - type: recall_at_100
            value: 83.981
          - type: recall_at_1000
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          - type: recall_at_20
            value: 68.048
          - type: recall_at_3
            value: 39.767
          - type: recall_at_5
            value: 47.971000000000004
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (fr)
          config: fr
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 36.476000000000006
          - type: f1
            value: 36.03335046550013
          - type: f1_weighted
            value: 36.03335046550013
      - task:
          type: Retrieval
        dataset:
          type: maastrichtlawtech/bsard
          name: MTEB BSARDRetrieval
          config: default
          split: test
          revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
        metrics:
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            value: 4.505
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          - type: map_at_100
            value: 7.7170000000000005
          - type: map_at_1000
            value: 7.872
          - type: map_at_20
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          - type: map_at_3
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          - type: map_at_5
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      - task:
          type: Clustering
        dataset:
          type: lyon-nlp/clustering-hal-s2s
          name: MTEB HALClusteringS2S
          config: default
          split: test
          revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
        metrics:
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          - type: v_measures
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              - 0.2513025353387455
              - 0.2510650794590461
              - 0.20351221275456768
      - task:
          type: Clustering
        dataset:
          type: reciTAL/mlsum
          name: MTEB MLSUMClusteringP2P
          config: fr
          split: test
          revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
        metrics:
          - type: v_measure
            value: 43.301348316325736
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              - 0.45186708688635496
              - 0.42148885803149255
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      - task:
          type: Clustering
        dataset:
          type: reciTAL/mlsum
          name: MTEB MLSUMClusteringS2S
          config: fr
          split: test
          revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
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              - 0.39675619790062994
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      - task:
          type: Classification
        dataset:
          type: mteb/mtop_domain
          name: MTEB MTOPDomainClassification (fr)
          config: fr
          split: test
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
        metrics:
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            value: 84.19041653617289
          - type: f1
            value: 84.31104471084487
          - type: f1_weighted
            value: 84.10618363737768
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_intent
          name: MTEB MTOPIntentClassification (fr)
          config: fr
          split: test
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
        metrics:
          - type: accuracy
            value: 58.3463827121829
          - type: f1
            value: 43.088632109848426
          - type: f1_weighted
            value: 62.107376647577894
      - task:
          type: Classification
        dataset:
          type: mteb/masakhanews
          name: MTEB MasakhaNEWSClassification (fra)
          config: fra
          split: test
          revision: 18193f187b92da67168c655c9973a165ed9593dd
        metrics:
          - type: accuracy
            value: 78.43601895734596
          - type: f1
            value: 74.45529808417628
          - type: f1_weighted
            value: 78.62574024040258
      - task:
          type: Clustering
        dataset:
          type: masakhane/masakhanews
          name: MTEB MasakhaNEWSClusteringP2P (fra)
          config: fra
          split: test
          revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
        metrics:
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            value: 52.87571544597074
          - type: v_measures
            value:
              - 1
              - 0.0363409432192075
              - 0.14130554872026818
              - 0.690570263898874
              - 0.7755690164601873
      - task:
          type: Clustering
        dataset:
          type: masakhane/masakhanews
          name: MTEB MasakhaNEWSClusteringS2S (fra)
          config: fra
          split: test
          revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
        metrics:
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            value: 53.93220782443
          - type: v_measures
            value:
              - 1
              - 0.08628485862349315
              - 0.4767520746594815
              - 0.41112983961194033
              - 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: 64.57296570275723
          - type: f1
            value: 62.03302453288864
          - type: f1_weighted
            value: 64.87910807109024
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_scenario
          name: MTEB MassiveScenarioClassification (fr)
          config: fr
          split: test
          revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
        metrics:
          - type: accuracy
            value: 69.04169468728986
          - type: f1
            value: 68.62183120784525
          - type: f1_weighted
            value: 69.14028442566733
      - task:
          type: Retrieval
        dataset:
          type: jinaai/mintakaqa
          name: MTEB MintakaRetrieval (fr)
          config: fr
          split: test
          revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
        metrics:
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            value: 13.514000000000001
          - type: map_at_10
            value: 21.246000000000002
          - type: map_at_100
            value: 22.238
          - type: map_at_1000
            value: 22.352
          - type: map_at_20
            value: 21.814
          - type: map_at_3
            value: 19.014
          - type: map_at_5
            value: 20.173
          - type: mrr_at_1
            value: 13.513513513513514
          - type: mrr_at_10
            value: 21.24569374569373
          - type: mrr_at_100
            value: 22.238167605117905
          - type: mrr_at_1000
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          - type: mrr_at_20
            value: 21.81387155669449
          - type: mrr_at_3
            value: 19.014469014468983
          - type: mrr_at_5
            value: 20.173355173355116
          - type: nauc_map_at_1000_diff1
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            value: 32.29046745508962
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          - type: ndcg_at_1000
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          - type: ndcg_at_3
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          - type: ndcg_at_5
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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
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          - type: recall_at_1
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          - 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: 47.297
          - type: recall_at_3
            value: 26.003
          - type: recall_at_5
            value: 31.122
      - task:
          type: PairClassification
        dataset:
          type: GEM/opusparcus
          name: MTEB OpusparcusPC (fr)
          config: fr
          split: test
          revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
        metrics:
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            value: 80.72207084468664
          - type: cos_sim_ap
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          - type: cos_sim_f1
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          - type: cos_sim_precision
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          - type: cos_sim_recall
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          - type: dot_accuracy
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          - type: dot_ap
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          - type: dot_f1
            value: 87.13503649635037
          - type: dot_precision
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          - type: dot_recall
            value: 94.83614697120159
          - type: euclidean_accuracy
            value: 80.51771117166211
          - type: euclidean_ap
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          - type: euclidean_f1
            value: 86.73050615595076
          - type: euclidean_precision
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          - type: euclidean_recall
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          - type: manhattan_accuracy
            value: 80.3133514986376
          - type: manhattan_ap
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          - type: manhattan_f1
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          - type: manhattan_precision
            value: 80.26981450252951
          - type: manhattan_recall
            value: 94.5382323733863
          - type: max_accuracy
            value: 80.79019073569482
          - type: max_ap
            value: 92.59899042186348
          - type: max_f1
            value: 87.13503649635037
      - 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: 60.75000000000001
          - type: cos_sim_ap
            value: 58.62577310571759
          - type: cos_sim_f1
            value: 62.491349480968864
          - type: cos_sim_precision
            value: 45.44539506794162
          - type: cos_sim_recall
            value: 100
          - type: dot_accuracy
            value: 56.10000000000001
          - type: dot_ap
            value: 50.150973841607005
          - type: dot_f1
            value: 62.491349480968864
          - type: dot_precision
            value: 45.44539506794162
          - type: dot_recall
            value: 100
          - type: euclidean_accuracy
            value: 60.550000000000004
          - type: euclidean_ap
            value: 58.42883888530385
          - type: euclidean_f1
            value: 62.491349480968864
          - type: euclidean_precision
            value: 45.44539506794162
          - type: euclidean_recall
            value: 100
          - type: manhattan_accuracy
            value: 60.5
          - type: manhattan_ap
            value: 58.474590272503214
          - type: manhattan_f1
            value: 62.491349480968864
          - type: manhattan_precision
            value: 45.44539506794162
          - type: manhattan_recall
            value: 100
          - type: max_accuracy
            value: 60.75000000000001
          - type: max_ap
            value: 58.62577310571759
          - type: max_f1
            value: 62.491349480968864
      - task:
          type: STS
        dataset:
          type: Lajavaness/SICK-fr
          name: MTEB SICKFr
          config: default
          split: test
          revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
        metrics:
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            value: 80.76682266570421
          - type: cos_sim_spearman
            value: 72.87656590123949
          - type: euclidean_pearson
            value: 77.19658924853128
          - type: euclidean_spearman
            value: 72.63728770955521
          - type: manhattan_pearson
            value: 77.11028731757044
          - type: manhattan_spearman
            value: 72.4475625515528
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (fr)
          config: fr
          split: test
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
        metrics:
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            value: 81.47132547305752
          - type: cos_sim_spearman
            value: 82.57064642049959
          - type: euclidean_pearson
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          - type: euclidean_spearman
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          - type: manhattan_pearson
            value: 81.21813802112128
          - type: manhattan_spearman
            value: 82.54904875440255
      - task:
          type: STS
        dataset:
          type: mteb/stsb_multi_mt
          name: MTEB STSBenchmarkMultilingualSTS (fr)
          config: fr
          split: test
          revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
        metrics:
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            value: 83.17606773302035
          - 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
            value: 81.8299015346327
      - task:
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        dataset:
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          name: MTEB SummEvalFr
          config: default
          split: test
          revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
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          - type: cos_sim_spearman
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          - type: dot_pearson
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          - type: dot_spearman
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      - 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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          - type: nAUC_mrr_diff1
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      - task:
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        dataset:
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          name: MTEB SyntecRetrieval
          config: default
          split: test
          revision: 19661ccdca4dfc2d15122d776b61685f48c68ca9
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          - type: map_at_1000
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          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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        dataset:
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          name: MTEB RedditClusteringP2P
          config: default
          split: test
          revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
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          - type: v_measures
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          name: MTEB SCIDOCS
          config: default
          split: test
          revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
        metrics:
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          - type: map_at_1000
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            value: 6.598
          - type: map_at_5
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          - type: recall_at_1
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          - 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
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          - type: recall_at_3
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          - type: recall_at_5
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      - task:
          type: STS
        dataset:
          type: mteb/sickr-sts
          name: MTEB SICK-R
          config: default
          split: test
          revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
        metrics:
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          - type: cos_sim_spearman
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          - type: euclidean_pearson
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          - type: euclidean_spearman
            value: 74.04786370197291
          - type: manhattan_pearson
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          - type: manhattan_spearman
            value: 73.86543081865257
      - task:
          type: STS
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          name: MTEB STS12
          config: default
          split: test
          revision: a0d554a64d88156834ff5ae9920b964011b16384
        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
            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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          - 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
            value: 83.3511641574103
      - task:
          type: STS
        dataset:
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          name: MTEB STS14
          config: default
          split: test
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
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            value: 86.15642283009589
          - 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
            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
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          - type: manhattan_pearson
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          - 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
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          - type: euclidean_spearman
            value: 83.70938571051141
          - type: manhattan_pearson
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          - 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
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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:
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          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:
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        dataset:
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          name: MTEB SciDocsRR
          config: default
          split: test
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
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      - task:
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          config: default
          split: test
          revision: 0228b52cf27578f30900b9e5271d331663a030d7
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          name: MTEB SprintDuplicateQuestions
          config: default
          split: test
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
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          name: MTEB StackExchangeClustering
          config: default
          split: test
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          split: test
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      - task:
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      - task:
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        dataset:
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          name: MTEB ToxicConversationsClassification
          config: default
          split: test
          revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
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          config: default
          split: test
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
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      - task:
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          name: MTEB TwentyNewsgroupsClustering
          config: default
          split: test
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
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          - 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

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, a pre-trained language model based on the XLM-RoBERTa 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': 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
• Method: Employed an advanced strategy using Augmented SBERT 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 installed:

pip install -U sentence-transformers

Then you can use the model like this:

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}