| --- |
| library_name: sentence-transformers |
| pipeline_tag: sentence-similarity |
| tags: |
| - sentence-transformers |
| - feature-extraction |
| - sentence-similarity |
| - transformers |
| - sentence-embedding |
| - mteb |
| model-index: |
| - name: bilingual-embedding-large |
| results: |
| - task: |
| type: Clustering |
| dataset: |
| type: lyon-nlp/alloprof |
| name: MTEB AlloProfClusteringP2P |
| config: default |
| split: test |
| revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b |
| metrics: |
| - type: v_measure |
| value: 65.3004467686438 |
| - type: v_measures |
| value: [0.632560011824588, 0.6345771823814063, 0.6333686484625257, 0.6508206816667124, 0.6378451181543632] |
| - task: |
| type: Clustering |
| dataset: |
| type: lyon-nlp/alloprof |
| name: MTEB AlloProfClusteringS2S |
| config: default |
| split: test |
| revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b |
| metrics: |
| - type: v_measure |
| value: 55.3684183324479 |
| - type: v_measures |
| value: [0.5262468095085737, 0.586151012721014, 0.5192907959178751, 0.5610730679809162, 0.6360060059791816] |
| - task: |
| type: Reranking |
| dataset: |
| type: lyon-nlp/mteb-fr-reranking-alloprof-s2p |
| name: MTEB AlloprofReranking |
| config: default |
| split: test |
| revision: 65393d0d7a08a10b4e348135e824f385d420b0fd |
| metrics: |
| - type: map |
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| value: 22.29736406333825 |
| - task: |
| type: Retrieval |
| dataset: |
| type: lyon-nlp/alloprof |
| name: MTEB AlloprofRetrieval |
| config: default |
| split: test |
| revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd |
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| - type: map_at_5 |
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| - type: recall_at_1 |
| value: 30.009000000000004 |
| - type: recall_at_10 |
| value: 66.753 |
| - type: recall_at_100 |
| value: 89.076 |
| - type: recall_at_1000 |
| value: 99.007 |
| - type: recall_at_20 |
| value: 75.043 |
| - type: recall_at_3 |
| value: 49.007 |
| - type: recall_at_5 |
| value: 56.821999999999996 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (fr) |
| config: fr |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
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| value: 43.422 |
| - type: f1 |
| value: 41.92216694262306 |
| - type: f1_weighted |
| value: 41.92216694262306 |
| - task: |
| type: Retrieval |
| dataset: |
| type: maastrichtlawtech/bsard |
| name: MTEB BSARDRetrieval |
| config: default |
| split: test |
| revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59 |
| metrics: |
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| value: 10.811 |
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| value: 31.982 |
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| value: 17.568 |
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| value: 22.072 |
| - task: |
| type: Clustering |
| dataset: |
| type: lyon-nlp/clustering-hal-s2s |
| name: MTEB HALClusteringS2S |
| config: default |
| split: test |
| revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915 |
| metrics: |
| - type: v_measure |
| value: 26.26502535631247 |
| - type: v_measures |
| value: [0.30893096531878045, 0.27408569069152805, 0.2872676670832888, 0.26871778422889214, 0.2421329238735192] |
| - task: |
| type: Clustering |
| dataset: |
| type: reciTAL/mlsum |
| name: MTEB MLSUMClusteringP2P |
| config: fr |
| split: test |
| revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 |
| metrics: |
| - type: v_measure |
| value: 42.60059039120384 |
| - type: v_measures |
| value: [0.4248169037837413, 0.44678284494908554, 0.4386784796938775, 0.41609051956546156, 0.37929269357080225] |
| - task: |
| type: Clustering |
| dataset: |
| type: reciTAL/mlsum |
| name: MTEB MLSUMClusteringS2S |
| config: fr |
| split: test |
| revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 |
| metrics: |
| - type: v_measure |
| value: 42.92324222522204 |
| - type: v_measures |
| value: [0.4320945601805418, 0.43467886343873713, 0.4345273113581795, 0.4277842446367462, 0.381555432691925] |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (fr) |
| config: fr |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 88.33385530848732 |
| - type: f1 |
| value: 88.36975245849551 |
| - type: f1_weighted |
| value: 88.310383667222 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (fr) |
| config: fr |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 62.84685248982148 |
| - type: f1 |
| value: 44.420122133882366 |
| - type: f1_weighted |
| value: 65.2728620649712 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/masakhanews |
| name: MTEB MasakhaNEWSClassification (fra) |
| config: fra |
| split: test |
| revision: 18193f187b92da67168c655c9973a165ed9593dd |
| metrics: |
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| value: 80.56872037914692 |
| - type: f1 |
| value: 77.28557364601339 |
| - type: f1_weighted |
| value: 80.51403795220486 |
| - task: |
| type: Clustering |
| dataset: |
| type: masakhane/masakhanews |
| name: MTEB MasakhaNEWSClusteringP2P (fra) |
| config: fra |
| split: test |
| revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
| metrics: |
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| value: 71.29428035967938 |
| - type: v_measures |
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| - task: |
| type: Clustering |
| dataset: |
| type: masakhane/masakhanews |
| name: MTEB MasakhaNEWSClusteringS2S (fra) |
| config: fra |
| split: test |
| revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
| metrics: |
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| - type: v_measures |
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| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (fr) |
| config: fr |
| split: test |
| revision: 4672e20407010da34463acc759c162ca9734bca6 |
| metrics: |
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| value: 66.80564895763281 |
| - type: f1 |
| value: 64.35238995318795 |
| - type: f1_weighted |
| value: 65.7206181780162 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (fr) |
| config: fr |
| split: test |
| revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8 |
| metrics: |
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| - type: f1 |
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| - type: f1_weighted |
| value: 72.81793917434213 |
| - task: |
| type: Retrieval |
| dataset: |
| type: jinaai/mintakaqa |
| name: MTEB MintakaRetrieval (fr) |
| config: fr |
| split: test |
| revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e |
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| value: 28.88414927603794 |
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| value: 25.109200109200113 |
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| - type: ndcg_at_1 |
| value: 18.96 |
| - type: ndcg_at_10 |
| value: 32.617000000000004 |
| - type: ndcg_at_100 |
| value: 37.974000000000004 |
| - type: ndcg_at_1000 |
| value: 40.65 |
| - type: ndcg_at_20 |
| value: 34.888000000000005 |
| - type: ndcg_at_3 |
| value: 27.106 |
| - type: ndcg_at_5 |
| value: 29.614 |
| - type: precision_at_1 |
| value: 18.96 |
| - type: precision_at_10 |
| value: 4.824 |
| - type: precision_at_100 |
| value: 0.738 |
| - type: precision_at_1000 |
| value: 0.096 |
| - type: precision_at_20 |
| value: 2.858 |
| - type: precision_at_3 |
| value: 10.961 |
| - type: precision_at_5 |
| value: 7.789 |
| - type: recall_at_1 |
| value: 18.96 |
| - type: recall_at_10 |
| value: 48.239 |
| - type: recall_at_100 |
| value: 73.833 |
| - type: recall_at_1000 |
| value: 95.82300000000001 |
| - type: recall_at_20 |
| value: 57.166 |
| - type: recall_at_3 |
| value: 32.883 |
| - type: recall_at_5 |
| value: 38.943 |
| - task: |
| type: PairClassification |
| dataset: |
| type: GEM/opusparcus |
| name: MTEB OpusparcusPC (fr) |
| config: fr |
| split: test |
| revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a |
| metrics: |
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| value: 84.80926430517711 |
| - type: cos_sim_ap |
| value: 94.76661922683681 |
| - type: cos_sim_f1 |
| value: 89.31480594154289 |
| - type: cos_sim_precision |
| value: 86.29629629629629 |
| - type: cos_sim_recall |
| value: 92.55213505461768 |
| - type: dot_accuracy |
| value: 84.80926430517711 |
| - type: dot_ap |
| value: 94.76663088644299 |
| - type: dot_f1 |
| value: 89.31480594154289 |
| - type: dot_precision |
| value: 86.29629629629629 |
| - type: dot_recall |
| value: 92.55213505461768 |
| - type: euclidean_accuracy |
| value: 84.80926430517711 |
| - type: euclidean_ap |
| value: 94.76661922683681 |
| - type: euclidean_f1 |
| value: 89.31480594154289 |
| - type: euclidean_precision |
| value: 86.29629629629629 |
| - type: euclidean_recall |
| value: 92.55213505461768 |
| - type: manhattan_accuracy |
| value: 84.94550408719346 |
| - type: manhattan_ap |
| value: 94.78582392571815 |
| - type: manhattan_f1 |
| value: 89.33912204534491 |
| - type: manhattan_precision |
| value: 86.86679174484052 |
| - type: manhattan_recall |
| value: 91.95630585898709 |
| - type: max_accuracy |
| value: 84.94550408719346 |
| - type: max_ap |
| value: 94.78582392571815 |
| - type: max_f1 |
| value: 89.33912204534491 |
| - 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: 64.5 |
| - type: cos_sim_ap |
| value: 64.51219412005997 |
| - type: cos_sim_f1 |
| value: 62.84885828198622 |
| - type: cos_sim_precision |
| value: 46.713362068965516 |
| - type: cos_sim_recall |
| value: 96.01328903654485 |
| - type: dot_accuracy |
| value: 64.5 |
| - type: dot_ap |
| value: 64.50290830259848 |
| - type: dot_f1 |
| value: 62.84885828198622 |
| - type: dot_precision |
| value: 46.713362068965516 |
| - type: dot_recall |
| value: 96.01328903654485 |
| - type: euclidean_accuracy |
| value: 64.5 |
| - type: euclidean_ap |
| value: 64.51219412005995 |
| - type: euclidean_f1 |
| value: 62.84885828198622 |
| - type: euclidean_precision |
| value: 46.713362068965516 |
| - type: euclidean_recall |
| value: 96.01328903654485 |
| - type: manhattan_accuracy |
| value: 64.55 |
| - type: manhattan_ap |
| value: 64.54022554293084 |
| - type: manhattan_f1 |
| value: 62.836363636363636 |
| - type: manhattan_precision |
| value: 46.778559826746076 |
| - type: manhattan_recall |
| value: 95.68106312292359 |
| - type: max_accuracy |
| value: 64.55 |
| - type: max_ap |
| value: 64.54022554293084 |
| - type: max_f1 |
| value: 62.84885828198622 |
| - task: |
| type: STS |
| dataset: |
| type: Lajavaness/SICK-fr |
| name: MTEB SICKFr |
| config: default |
| split: test |
| revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.15315949054092 |
| - type: cos_sim_spearman |
| value: 79.19701933507372 |
| - type: euclidean_pearson |
| value: 82.68441006897395 |
| - type: euclidean_spearman |
| value: 79.1963186010215 |
| - type: manhattan_pearson |
| value: 82.6725500567899 |
| - type: manhattan_spearman |
| value: 79.13255295711785 |
| - 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: 83.13328685349694 |
| - type: cos_sim_spearman |
| value: 84.64291479319418 |
| - type: euclidean_pearson |
| value: 83.28605886303359 |
| - type: euclidean_spearman |
| value: 84.64291479319418 |
| - type: manhattan_pearson |
| value: 83.01485484058145 |
| - type: manhattan_spearman |
| value: 84.35826862976153 |
| - 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: 86.15391910168253 |
| - type: cos_sim_spearman |
| value: 87.0224186207858 |
| - type: euclidean_pearson |
| value: 86.04463800957714 |
| - type: euclidean_spearman |
| value: 87.02424394489165 |
| - type: manhattan_pearson |
| value: 86.03126279628441 |
| - type: manhattan_spearman |
| value: 86.99427177229043 |
| - task: |
| type: Summarization |
| dataset: |
| type: lyon-nlp/summarization-summeval-fr-p2p |
| name: MTEB SummEvalFr |
| config: default |
| split: test |
| revision: b385812de6a9577b6f4d0f88c6a6e35395a94054 |
| metrics: |
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| value: 31.415083738613355 |
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| value: 30.301784303588285 |
| - type: dot_pearson |
| value: 31.415089981266963 |
| - type: dot_spearman |
| value: 30.286152348575108 |
| - 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: mrr |
| value: 85.95238095238095 |
| - type: nAUC_map_diff1 |
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| - type: nAUC_map_max |
| value: 19.806028833551718 |
| - type: nAUC_mrr_diff1 |
| value: 70.42176052252755 |
| - type: nAUC_mrr_max |
| value: 19.806028833551718 |
| - 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: 69.0 |
| - type: map_at_10 |
| value: 79.668 |
| - type: map_at_100 |
| value: 79.791 |
| - type: map_at_1000 |
| value: 79.791 |
| - type: map_at_20 |
| value: 79.751 |
| - type: map_at_3 |
| value: 78.167 |
| - type: map_at_5 |
| value: 79.067 |
| - type: mrr_at_1 |
| value: 69.0 |
| - type: mrr_at_10 |
| value: 79.66785714285714 |
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| value: 79.7911904761905 |
| - type: mrr_at_1000 |
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| value: 79.75119047619049 |
| - type: mrr_at_3 |
| value: 78.16666666666666 |
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| value: 79.06666666666666 |
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| name: MTEB SICK-R |
| config: default |
| split: test |
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| - task: |
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| name: MTEB STS12 |
| config: default |
| split: test |
| revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| metrics: |
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| name: MTEB STS13 |
| config: default |
| split: test |
| revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
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| - task: |
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| name: MTEB STS14 |
| config: default |
| split: test |
| revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
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| name: MTEB STS15 |
| config: default |
| split: test |
| revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
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| - task: |
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| dataset: |
| type: mteb/sts16-sts |
| name: MTEB STS16 |
| config: default |
| split: test |
| revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
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| - task: |
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| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (en-en) |
| config: en-en |
| split: test |
| revision: faeb762787bd10488a50c8b5be4a3b82e411949c |
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| - task: |
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| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (en) |
| config: en |
| split: test |
| revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 |
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| - task: |
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| name: MTEB STSBenchmark |
| config: default |
| split: test |
| revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
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| name: MTEB SciDocsRR |
| config: default |
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| - task: |
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| name: MTEB SciFact |
| config: default |
| split: test |
| revision: 0228b52cf27578f30900b9e5271d331663a030d7 |
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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_3 |
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| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/sprintduplicatequestions-pairclassification |
| name: MTEB SprintDuplicateQuestions |
| config: default |
| split: test |
| revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| metrics: |
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| value: 92.56973112464647 |
| - type: cos_sim_f1 |
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| value: 83.36483931947069 |
| - type: cos_sim_recall |
| value: 88.2 |
| - type: dot_accuracy |
| value: 99.7089108910891 |
| - type: dot_ap |
| value: 92.56973112464647 |
| - type: dot_f1 |
| value: 85.71428571428572 |
| - type: dot_precision |
| value: 83.36483931947069 |
| - type: dot_recall |
| value: 88.2 |
| - type: euclidean_accuracy |
| value: 99.7089108910891 |
| - type: euclidean_ap |
| value: 92.56973112464647 |
| - type: euclidean_f1 |
| value: 85.71428571428572 |
| - type: euclidean_precision |
| value: 83.36483931947069 |
| - type: euclidean_recall |
| value: 88.2 |
| - type: manhattan_accuracy |
| value: 99.71089108910891 |
| - type: manhattan_ap |
| value: 92.61210920251231 |
| - type: manhattan_f1 |
| value: 85.67335243553008 |
| - type: manhattan_precision |
| value: 81.99268738574041 |
| - type: manhattan_recall |
| value: 89.7 |
| - type: max_accuracy |
| value: 99.71089108910891 |
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| value: 86.97025689932646 |
| - type: dot_ap |
| value: 77.0656524331512 |
| - type: dot_f1 |
| value: 70.32217308907138 |
| - type: dot_precision |
| value: 67.46666666666667 |
| - type: dot_recall |
| value: 73.43007915567283 |
| - type: euclidean_accuracy |
| value: 86.97025689932646 |
| - type: euclidean_ap |
| value: 77.06564828845742 |
| - type: euclidean_f1 |
| value: 70.32217308907138 |
| - type: euclidean_precision |
| value: 67.46666666666667 |
| - type: euclidean_recall |
| value: 73.43007915567283 |
| - type: manhattan_accuracy |
| value: 86.90469094593789 |
| - type: manhattan_ap |
| value: 76.94347285253252 |
| - type: manhattan_f1 |
| value: 70.18523217457499 |
| - type: manhattan_precision |
| value: 67.59530791788856 |
| - type: manhattan_recall |
| value: 72.98153034300792 |
| - type: max_accuracy |
| value: 86.97025689932646 |
| - type: max_ap |
| value: 77.0656524331512 |
| - type: max_f1 |
| value: 70.32217308907138 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twitterurlcorpus-pairclassification |
| name: MTEB TwitterURLCorpus |
| config: default |
| split: test |
| revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| metrics: |
| - type: cos_sim_accuracy |
| value: 89.64567081926495 |
| - type: cos_sim_ap |
| value: 87.19162831580245 |
| - type: cos_sim_f1 |
| value: 79.67696578577352 |
| - type: cos_sim_precision |
| value: 74.92033358193775 |
| - type: cos_sim_recall |
| value: 85.07853403141361 |
| - type: dot_accuracy |
| value: 89.64567081926495 |
| - type: dot_ap |
| value: 87.19162304433766 |
| - type: dot_f1 |
| value: 79.67696578577352 |
| - type: dot_precision |
| value: 74.92033358193775 |
| - type: dot_recall |
| value: 85.07853403141361 |
| - type: euclidean_accuracy |
| value: 89.64567081926495 |
| - type: euclidean_ap |
| value: 87.19162847931055 |
| - type: euclidean_f1 |
| value: 79.67696578577352 |
| - type: euclidean_precision |
| value: 74.92033358193775 |
| - type: euclidean_recall |
| value: 85.07853403141361 |
| - type: manhattan_accuracy |
| value: 89.67283735009897 |
| - type: manhattan_ap |
| value: 87.19033616510255 |
| - type: manhattan_f1 |
| value: 79.67444226437031 |
| - type: manhattan_precision |
| value: 75.43690656391908 |
| - type: manhattan_recall |
| value: 84.41638435478903 |
| - type: max_accuracy |
| value: 89.67283735009897 |
| - type: max_ap |
| value: 87.19162847931055 |
| - type: max_f1 |
| value: 79.67696578577352 |
| license: apache-2.0 |
| language: |
| - fr |
| - en |
| --- |
| |
| # [bilingual-embedding-large](https://huggingface.co/Lajavaness/bilingual-embedding-large) |
|
|
| Bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-large), a pre-trained language model based on the [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-large) architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language. |
|
|
|
|
| ## Full Model Architecture |
| ``` |
| SentenceTransformer( |
| (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BilingualModel |
| (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
| (2): Normalize() |
| ) |
| ``` |
|
|
| ## Training and Fine-tuning process |
| #### Stage 1: NLI Training |
| - Dataset: [(SNLI+XNLI) for english+french] |
| - Method: Training using Multi-Negative Ranking Loss. This stage focused on improving the model's ability to discern and rank nuanced differences in sentence semantics. |
| ### Stage 3: Continued Fine-tuning for Semantic Textual Similarity on STS Benchmark |
| - Dataset: [STSB-fr and en] |
| - Method: Fine-tuning specifically for the semantic textual similarity benchmark using Siamese BERT-Networks configured with the 'sentence-transformers' library. |
| ### Stage 4: Advanced Augmentation Fine-tuning |
| - Dataset: STSB with generate [silver sample from gold sample](https://www.sbert.net/examples/training/data_augmentation/README.html) |
| - Method: Employed an advanced strategy using [Augmented SBERT](https://arxiv.org/abs/2010.08240) with Pair Sampling Strategies, integrating both Cross-Encoder and Bi-Encoder models. This stage further refined the embeddings by enriching the training data dynamically, enhancing the model's robustness and accuracy. |
|
|
|
|
| ## Usage: |
|
|
| Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
|
|
| ``` |
| pip install -U sentence-transformers |
| ``` |
|
|
| Then you can use the model like this: |
|
|
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| sentences = ["Paris est une capitale de la France", "Paris is a capital of France"] |
| |
| model = SentenceTransformer('Lajavaness/bilingual-embedding-large', 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} |
