README.md

metadata

library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  - sentence-embedding
  - mteb
  - mteb
model-index:
  - name: bilingual-embedding-base
    results:
      - task:
          type: Clustering
        dataset:
          type: lyon-nlp/alloprof
          name: MTEB AlloProfClusteringP2P
          config: default
          split: test
          revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
        metrics:
          - type: v_measure
            value: 64.71347977413274
          - type: v_measures
            value:
              - 0.6276406100583347
              - 0.6380984027637511
              - 0.6322659733117469
              - 0.652473343931587
              - 0.6411502615838207
      - task:
          type: Clustering
        dataset:
          type: lyon-nlp/alloprof
          name: MTEB AlloProfClusteringS2S
          config: default
          split: test
          revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
        metrics:
          - type: v_measure
            value: 45.56886694296516
          - type: v_measures
            value:
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              - 0.4218168523895086
              - 0.419693469863105
              - 0.4981808644314091
              - 0.4546120704986696
      - task:
          type: Reranking
        dataset:
          type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
          name: MTEB AlloprofReranking
          config: default
          split: test
          revision: 65393d0d7a08a10b4e348135e824f385d420b0fd
        metrics:
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          - type: mrr
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          - type: nAUC_mrr_diff1
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          - type: nAUC_mrr_max
            value: 11.900785644756448
      - task:
          type: Retrieval
        dataset:
          type: lyon-nlp/alloprof
          name: MTEB AlloprofRetrieval
          config: default
          split: test
          revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
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            value: 35.519
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            value: 36.667
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            value: 36.199
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            value: 34.121
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            value: 24.956999999999997
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          - type: recall_at_1000
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          - type: recall_at_20
            value: 69.257
          - type: recall_at_3
            value: 41.623
          - type: recall_at_5
            value: 49.092999999999996
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (fr)
          config: fr
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 40.354
          - type: f1
            value: 38.96127209677864
          - type: f1_weighted
            value: 38.96127209677863
      - task:
          type: Retrieval
        dataset:
          type: maastrichtlawtech/bsard
          name: MTEB BSARDRetrieval
          config: default
          split: test
          revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
        metrics:
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          - type: map_at_100
            value: 10.852
          - type: map_at_1000
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      - task:
          type: Clustering
        dataset:
          type: lyon-nlp/clustering-hal-s2s
          name: MTEB HALClusteringS2S
          config: default
          split: test
          revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
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      - task:
          type: Clustering
        dataset:
          type: reciTAL/mlsum
          name: MTEB MLSUMClusteringP2P
          config: fr
          split: test
          revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
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          type: reciTAL/mlsum
          name: MTEB MLSUMClusteringS2S
          config: fr
          split: test
          revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
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      - task:
          type: Classification
        dataset:
          type: mteb/mtop_domain
          name: MTEB MTOPDomainClassification (fr)
          config: fr
          split: test
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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      - task:
          type: Classification
        dataset:
          type: mteb/mtop_intent
          name: MTEB MTOPIntentClassification (fr)
          config: fr
          split: test
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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          - type: accuracy
            value: 59.129345443156886
          - type: f1
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          - type: f1_weighted
            value: 61.735222244513906
      - task:
          type: Classification
        dataset:
          type: mteb/masakhanews
          name: MTEB MasakhaNEWSClassification (fra)
          config: fra
          split: test
          revision: 18193f187b92da67168c655c9973a165ed9593dd
        metrics:
          - type: accuracy
            value: 77.44075829383887
          - type: f1
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          - type: f1_weighted
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      - task:
          type: Clustering
        dataset:
          type: masakhane/masakhanews
          name: MTEB MasakhaNEWSClusteringP2P (fra)
          config: fra
          split: test
          revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
        metrics:
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          - type: v_measures
            value:
              - 1
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              - 0.1918473189330316
              - 0.9180419176341081
              - 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: 46.09882977546196
          - type: v_measures
            value:
              - 1
              - 0
              - 0.4881603944307428
              - 0.1341399033659235
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      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (fr)
          config: fr
          split: test
          revision: 4672e20407010da34463acc759c162ca9734bca6
        metrics:
          - type: accuracy
            value: 64.98655010087424
          - type: f1
            value: 62.07892690857404
          - type: f1_weighted
            value: 64.04916798028313
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_scenario
          name: MTEB MassiveScenarioClassification (fr)
          config: fr
          split: test
          revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
        metrics:
          - type: accuracy
            value: 71.72494956287828
          - type: f1
            value: 70.7613627592262
          - type: f1_weighted
            value: 71.59023734198762
      - task:
          type: Retrieval
        dataset:
          type: jinaai/mintakaqa
          name: MTEB MintakaRetrieval (fr)
          config: fr
          split: test
          revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
        metrics:
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            value: 15.643
          - type: map_at_10
            value: 23.263
          - type: map_at_100
            value: 24.282
          - type: map_at_1000
            value: 24.386
          - type: map_at_20
            value: 23.810000000000002
          - type: map_at_3
            value: 20.857
          - type: map_at_5
            value: 22.127
          - type: mrr_at_1
            value: 15.642915642915641
          - type: mrr_at_10
            value: 23.26313326313326
          - type: mrr_at_100
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          - type: mrr_at_1000
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          - type: mrr_at_20
            value: 23.809503335080308
          - type: mrr_at_3
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          - type: mrr_at_5
            value: 22.126672126672087
          - type: nauc_map_at_1000_diff1
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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_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
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          - type: recall_at_3
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          - type: recall_at_5
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      - task:
          type: PairClassification
        dataset:
          type: GEM/opusparcus
          name: MTEB OpusparcusPC (fr)
          config: fr
          split: test
          revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
        metrics:
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            value: 82.08446866485014
          - 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
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          - type: dot_precision
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          - type: dot_recall
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          - type: euclidean_accuracy
            value: 82.08446866485014
          - type: euclidean_ap
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          - type: euclidean_f1
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          - type: euclidean_precision
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          - type: euclidean_recall
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          - type: manhattan_accuracy
            value: 82.15258855585832
          - type: manhattan_ap
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          - type: manhattan_f1
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          - type: manhattan_precision
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          - type: manhattan_recall
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          - type: max_accuracy
            value: 82.15258855585832
          - type: max_ap
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          - type: max_f1
            value: 87.36740597878496
      - task:
          type: PairClassification
        dataset:
          type: google-research-datasets/paws-x
          name: MTEB PawsX (fr)
          config: fr
          split: test
          revision: 8a04d940a42cd40658986fdd8e3da561533a3646
        metrics:
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            value: 60.85
          - type: cos_sim_ap
            value: 60.37878110577144
          - type: cos_sim_f1
            value: 62.67978803936413
          - type: cos_sim_precision
            value: 47.61357101782634
          - type: cos_sim_recall
            value: 91.69435215946844
          - type: dot_accuracy
            value: 60.85
          - type: dot_ap
            value: 60.282876235548
          - type: dot_f1
            value: 62.67978803936413
          - type: dot_precision
            value: 47.61357101782634
          - type: dot_recall
            value: 91.69435215946844
          - type: euclidean_accuracy
            value: 60.85
          - type: euclidean_ap
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          - type: euclidean_f1
            value: 62.67978803936413
          - type: euclidean_precision
            value: 47.61357101782634
          - type: euclidean_recall
            value: 91.69435215946844
          - type: manhattan_accuracy
            value: 61
          - type: manhattan_ap
            value: 60.32592616688752
          - type: manhattan_f1
            value: 62.70871985157699
          - type: manhattan_precision
            value: 47.154017857142854
          - type: manhattan_recall
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          - type: max_accuracy
            value: 61
          - type: max_ap
            value: 60.37878110577144
          - type: max_f1
            value: 62.70871985157699
      - task:
          type: STS
        dataset:
          type: Lajavaness/SICK-fr
          name: MTEB SICKFr
          config: default
          split: test
          revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
        metrics:
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            value: 82.41331853949572
          - type: cos_sim_spearman
            value: 75.4815169821101
          - type: euclidean_pearson
            value: 79.81380205322172
          - type: euclidean_spearman
            value: 75.48076002322844
          - type: manhattan_pearson
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          - type: manhattan_spearman
            value: 75.51770448845875
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (fr)
          config: fr
          split: test
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
        metrics:
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            value: 78.63723398201921
          - type: cos_sim_spearman
            value: 81.1413239539243
          - 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:
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        dataset:
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          name: MTEB STSBenchmarkMultilingualSTS (fr)
          config: fr
          split: test
          revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
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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:
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        dataset:
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          name: MTEB SummEvalFr
          config: default
          split: test
          revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
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          - type: dot_pearson
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          - type: dot_spearman
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      - task:
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          name: MTEB SyntecReranking
          config: default
          split: test
          revision: daf0863838cd9e3ba50544cdce3ac2b338a1b0ad
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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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          config: en
          split: test
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              - 0.4778925279162627
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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
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          - type: nauc_map_at_1000_diff1
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      - task:
          type: STS
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          name: MTEB SICK-R
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          split: test
          revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
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          - type: euclidean_spearman
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          config: default
          split: test
          revision: a0d554a64d88156834ff5ae9920b964011b16384
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          - type: euclidean_spearman
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          - type: manhattan_spearman
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          name: MTEB STS13
          config: default
          split: test
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
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          - type: euclidean_spearman
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      - task:
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          name: MTEB STS14
          config: default
          split: test
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
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          - type: euclidean_spearman
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          name: MTEB STS15
          config: default
          split: test
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
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            value: 88.93457583410698
          - type: cos_sim_spearman
            value: 89.87899103812357
          - type: euclidean_pearson
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          - type: euclidean_spearman
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          - type: manhattan_pearson
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      - task:
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          name: MTEB STS16
          config: default
          split: test
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
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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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          config: en-en
          split: test
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
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          - type: euclidean_spearman
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          config: en
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          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
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      - task:
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          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
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      - task:
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          config: default
          split: test
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
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          name: MTEB SprintDuplicateQuestions
          config: default
          split: test
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
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      - task:
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        dataset:
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          revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
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          config: default
          split: test
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
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          config: default
          split: test
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
        metrics:
          - type: v_measure
            value: 44.96874447839597
          - type: v_measures
            value:
              - 0.44089700334011683
              - 0.4808871718296333
              - 0.4655762216311635
              - 0.4108889263207817
              - 0.45449139982441744
      - task:
          type: PairClassification
        dataset:
          type: mteb/twittersemeval2015-pairclassification
          name: MTEB TwitterSemEval2015
          config: default
          split: test
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
        metrics:
          - type: cos_sim_accuracy
            value: 85.83179352685224
          - type: cos_sim_ap
            value: 73.75870454252158
          - type: cos_sim_f1
            value: 67.61786600496278
          - type: cos_sim_precision
            value: 63.81733021077284
          - type: cos_sim_recall
            value: 71.89973614775725
          - type: dot_accuracy
            value: 85.83179352685224
          - type: dot_ap
            value: 73.75870536224079
          - type: dot_f1
            value: 67.61786600496278
          - type: dot_precision
            value: 63.81733021077284
          - type: dot_recall
            value: 71.89973614775725
          - type: euclidean_accuracy
            value: 85.83179352685224
          - type: euclidean_ap
            value: 73.7587242895193
          - type: euclidean_f1
            value: 67.61786600496278
          - type: euclidean_precision
            value: 63.81733021077284
          - type: euclidean_recall
            value: 71.89973614775725
          - type: manhattan_accuracy
            value: 85.91524110389224
          - type: manhattan_ap
            value: 73.77139111004601
          - type: manhattan_f1
            value: 67.52419453632244
          - type: manhattan_precision
            value: 63.023096272581746
          - type: manhattan_recall
            value: 72.71767810026385
          - type: max_accuracy
            value: 85.91524110389224
          - type: max_ap
            value: 73.77139111004601
          - type: max_f1
            value: 67.61786600496278
      - task:
          type: PairClassification
        dataset:
          type: mteb/twitterurlcorpus-pairclassification
          name: MTEB TwitterURLCorpus
          config: default
          split: test
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
        metrics:
          - type: cos_sim_accuracy
            value: 89.2711607870532
          - type: cos_sim_ap
            value: 86.48986874793765
          - type: cos_sim_f1
            value: 79.10280373831775
          - type: cos_sim_precision
            value: 76.87836070338614
          - type: cos_sim_recall
            value: 81.45980905451185
          - type: dot_accuracy
            value: 89.2711607870532
          - type: dot_ap
            value: 86.48986996838993
          - type: dot_f1
            value: 79.10280373831775
          - type: dot_precision
            value: 76.87836070338614
          - type: dot_recall
            value: 81.45980905451185
          - type: euclidean_accuracy
            value: 89.2711607870532
          - type: euclidean_ap
            value: 86.4898691356683
          - type: euclidean_f1
            value: 79.10280373831775
          - type: euclidean_precision
            value: 76.87836070338614
          - type: euclidean_recall
            value: 81.45980905451185
          - type: manhattan_accuracy
            value: 89.2711607870532
          - type: manhattan_ap
            value: 86.46475884590569
          - type: manhattan_f1
            value: 78.9534579927593
          - type: manhattan_precision
            value: 76.61716769286491
          - type: manhattan_recall
            value: 81.43671080997844
          - type: max_accuracy
            value: 89.2711607870532
          - type: max_ap
            value: 86.48986996838993
          - type: max_f1
            value: 79.10280373831775
license: apache-2.0

bilingual-embedding-base

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': 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
• 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-base', trust_remote_code=True)
print(embeddings)

Evaluation

TODO

Citation

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

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

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