metadata
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- sentence-embedding
- mteb
- mteb
model-index:
- name: e433e634850d125d8b85bee76db3a3b6a0c3bf56
results:
- task:
type: Clustering
dataset:
type: lyon-nlp/alloprof
name: MTEB AlloProfClusteringP2P
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: v_measure
value: 56.88600728743999
- type: v_measures
value:
- 0.5396081553520281
- 0.6022872403200437
- 0.5515205944691852
- 0.5595772885785736
- 0.5632413941951575
- task:
type: Clustering
dataset:
type: lyon-nlp/alloprof
name: MTEB AlloProfClusteringS2S
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: v_measure
value: 38.199527329051804
- type: v_measures
value:
- 0.42157254138936706
- 0.36882298663461527
- 0.3134327610337458
- 0.40391031391690396
- 0.3832775043562133
- task:
type: Reranking
dataset:
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
name: MTEB AlloprofReranking
config: default
split: test
revision: 65393d0d7a08a10b4e348135e824f385d420b0fd
metrics:
- type: map
value: 68.73372257500206
- type: mrr
value: 70.07434479260904
- type: nAUC_map_diff1
value: 50.95933484071007
- type: nAUC_map_max
value: 13.75463910519138
- type: nAUC_mrr_diff1
value: 50.494303783447656
- type: nAUC_mrr_max
value: 14.460935217916187
- task:
type: Retrieval
dataset:
type: lyon-nlp/alloprof
name: MTEB AlloprofRetrieval
config: default
split: test
revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
metrics:
- type: map_at_1
value: 21.675
- type: map_at_10
value: 32.274
- type: map_at_100
value: 33.316
- type: map_at_1000
value: 33.387
- type: map_at_20
value: 32.864
- type: map_at_3
value: 29.166999999999998
- type: map_at_5
value: 30.946
- type: mrr_at_1
value: 21.675302245250432
- type: mrr_at_10
value: 32.274309839076714
- type: mrr_at_100
value: 33.31571024590564
- type: mrr_at_1000
value: 33.3868130424392
- type: mrr_at_20
value: 32.863978562081925
- type: mrr_at_3
value: 29.16666666666669
- type: mrr_at_5
value: 30.94559585492234
- type: nauc_map_at_1000_diff1
value: 34.85808309940442
- type: nauc_map_at_1000_max
value: 31.058801579682825
- type: nauc_map_at_100_diff1
value: 34.842898344470846
- type: nauc_map_at_100_max
value: 31.077561464904342
- type: nauc_map_at_10_diff1
value: 34.6773118480208
- type: nauc_map_at_10_max
value: 30.8489850780642
- type: nauc_map_at_1_diff1
value: 40.65773695743684
- type: nauc_map_at_1_max
value: 28.766036921254617
- type: nauc_map_at_20_diff1
value: 34.73935242577166
- type: nauc_map_at_20_max
value: 31.03143938077287
- type: nauc_map_at_3_diff1
value: 35.12059625476991
- type: nauc_map_at_3_max
value: 30.48787855768291
- type: nauc_map_at_5_diff1
value: 34.73453235094986
- type: nauc_map_at_5_max
value: 30.3860304682398
- type: nauc_mrr_at_1000_diff1
value: 34.85808309940442
- type: nauc_mrr_at_1000_max
value: 31.058801579682825
- type: nauc_mrr_at_100_diff1
value: 34.842898344470846
- type: nauc_mrr_at_100_max
value: 31.077561464904342
- type: nauc_mrr_at_10_diff1
value: 34.6773118480208
- type: nauc_mrr_at_10_max
value: 30.8489850780642
- type: nauc_mrr_at_1_diff1
value: 40.65773695743684
- type: nauc_mrr_at_1_max
value: 28.766036921254617
- type: nauc_mrr_at_20_diff1
value: 34.73935242577166
- type: nauc_mrr_at_20_max
value: 31.03143938077287
- type: nauc_mrr_at_3_diff1
value: 35.12059625476991
- type: nauc_mrr_at_3_max
value: 30.48787855768291
- type: nauc_mrr_at_5_diff1
value: 34.73453235094986
- type: nauc_mrr_at_5_max
value: 30.3860304682398
- type: nauc_ndcg_at_1000_diff1
value: 34.04342467121623
- type: nauc_ndcg_at_1000_max
value: 32.311398352704686
- type: nauc_ndcg_at_100_diff1
value: 33.67278941726764
- type: nauc_ndcg_at_100_max
value: 33.0229606203184
- type: nauc_ndcg_at_10_diff1
value: 32.93808280492078
- type: nauc_ndcg_at_10_max
value: 32.07111775221638
- type: nauc_ndcg_at_1_diff1
value: 40.65773695743684
- type: nauc_ndcg_at_1_max
value: 28.766036921254617
- type: nauc_ndcg_at_20_diff1
value: 33.141323431064585
- type: nauc_ndcg_at_20_max
value: 32.76436962238286
- type: nauc_ndcg_at_3_diff1
value: 33.77769745974645
- type: nauc_ndcg_at_3_max
value: 31.072988073016912
- type: nauc_ndcg_at_5_diff1
value: 33.091582792245696
- type: nauc_ndcg_at_5_max
value: 30.92378976230745
- type: nauc_precision_at_1000_diff1
value: 33.74743287990321
- type: nauc_precision_at_1000_max
value: 60.08005213097628
- type: nauc_precision_at_100_diff1
value: 28.869275501873236
- type: nauc_precision_at_100_max
value: 46.35483380447927
- type: nauc_precision_at_10_diff1
value: 27.910043146581497
- type: nauc_precision_at_10_max
value: 36.07399824307888
- type: nauc_precision_at_1_diff1
value: 40.65773695743684
- type: nauc_precision_at_1_max
value: 28.766036921254617
- type: nauc_precision_at_20_diff1
value: 28.144265629196163
- type: nauc_precision_at_20_max
value: 39.60361579056115
- type: nauc_precision_at_3_diff1
value: 30.31893725671278
- type: nauc_precision_at_3_max
value: 32.63695126407254
- type: nauc_precision_at_5_diff1
value: 28.699678130380235
- type: nauc_precision_at_5_max
value: 32.37908851919098
- type: nauc_recall_at_1000_diff1
value: 33.74743287990342
- type: nauc_recall_at_1000_max
value: 60.080052130975346
- type: nauc_recall_at_100_diff1
value: 28.869275501873247
- type: nauc_recall_at_100_max
value: 46.35483380447917
- type: nauc_recall_at_10_diff1
value: 27.910043146581508
- type: nauc_recall_at_10_max
value: 36.07399824307888
- type: nauc_recall_at_1_diff1
value: 40.65773695743684
- type: nauc_recall_at_1_max
value: 28.766036921254617
- type: nauc_recall_at_20_diff1
value: 28.14426562919617
- type: nauc_recall_at_20_max
value: 39.60361579056118
- type: nauc_recall_at_3_diff1
value: 30.318937256712804
- type: nauc_recall_at_3_max
value: 32.63695126407256
- type: nauc_recall_at_5_diff1
value: 28.699678130380224
- type: nauc_recall_at_5_max
value: 32.37908851919102
- type: ndcg_at_1
value: 21.675
- type: ndcg_at_10
value: 38.06
- type: ndcg_at_100
value: 43.491
- type: ndcg_at_1000
value: 45.432
- type: ndcg_at_20
value: 40.217000000000006
- type: ndcg_at_3
value: 31.642
- type: ndcg_at_5
value: 34.837
- type: precision_at_1
value: 21.675
- type: precision_at_10
value: 5.652
- type: precision_at_100
value: 0.827
- type: precision_at_1000
value: 0.098
- type: precision_at_20
value: 3.253
- type: precision_at_3
value: 12.939
- type: precision_at_5
value: 9.309000000000001
- type: recall_at_1
value: 21.675
- type: recall_at_10
value: 56.52
- type: recall_at_100
value: 82.729
- type: recall_at_1000
value: 98.1
- type: recall_at_20
value: 65.069
- type: recall_at_3
value: 38.817
- type: recall_at_5
value: 46.546
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 43.51
- type: f1
value: 41.3284674671926
- type: f1_weighted
value: 41.3284674671926
- task:
type: Retrieval
dataset:
type: maastrichtlawtech/bsard
name: MTEB BSARDRetrieval
config: default
split: test
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
metrics:
- type: map_at_1
value: 5.405
- type: map_at_10
value: 9.008
- type: map_at_100
value: 9.932
- type: map_at_1000
value: 10.042
- type: map_at_20
value: 9.389
- type: map_at_3
value: 7.883
- type: map_at_5
value: 8.626000000000001
- type: mrr_at_1
value: 5.405405405405405
- type: mrr_at_10
value: 9.007579007579007
- type: mrr_at_100
value: 9.931517094611667
- type: mrr_at_1000
value: 10.0416462267215
- type: mrr_at_20
value: 9.38869595990339
- type: mrr_at_3
value: 7.882882882882883
- type: mrr_at_5
value: 8.626126126126126
- type: nauc_map_at_1000_diff1
value: 23.53549434486455
- type: nauc_map_at_1000_max
value: 9.977010641647402
- type: nauc_map_at_100_diff1
value: 23.50007884241435
- type: nauc_map_at_100_max
value: 9.984274734441085
- type: nauc_map_at_10_diff1
value: 24.69444512826233
- type: nauc_map_at_10_max
value: 9.726162724771594
- type: nauc_map_at_1_diff1
value: 40.88188899137848
- type: nauc_map_at_1_max
value: 12.044739470755896
- type: nauc_map_at_20_diff1
value: 23.833757177107557
- type: nauc_map_at_20_max
value: 9.94328216894336
- type: nauc_map_at_3_diff1
value: 28.320570164876653
- type: nauc_map_at_3_max
value: 11.195397944839767
- type: nauc_map_at_5_diff1
value: 25.86894200735248
- type: nauc_map_at_5_max
value: 8.43950569758736
- type: nauc_mrr_at_1000_diff1
value: 23.53549434486455
- type: nauc_mrr_at_1000_max
value: 9.977010641647402
- type: nauc_mrr_at_100_diff1
value: 23.50007884241435
- type: nauc_mrr_at_100_max
value: 9.984274734441085
- type: nauc_mrr_at_10_diff1
value: 24.69444512826233
- type: nauc_mrr_at_10_max
value: 9.726162724771594
- type: nauc_mrr_at_1_diff1
value: 40.88188899137848
- type: nauc_mrr_at_1_max
value: 12.044739470755896
- type: nauc_mrr_at_20_diff1
value: 23.833757177107557
- type: nauc_mrr_at_20_max
value: 9.94328216894336
- type: nauc_mrr_at_3_diff1
value: 28.320570164876653
- type: nauc_mrr_at_3_max
value: 11.195397944839767
- type: nauc_mrr_at_5_diff1
value: 25.86894200735248
- type: nauc_mrr_at_5_max
value: 8.43950569758736
- type: nauc_ndcg_at_1000_diff1
value: 15.939402272339343
- type: nauc_ndcg_at_1000_max
value: 10.076089125537772
- type: nauc_ndcg_at_100_diff1
value: 16.12740122067642
- type: nauc_ndcg_at_100_max
value: 10.39935154464689
- type: nauc_ndcg_at_10_diff1
value: 20.455941061369295
- type: nauc_ndcg_at_10_max
value: 9.350349883274461
- type: nauc_ndcg_at_1_diff1
value: 40.88188899137848
- type: nauc_ndcg_at_1_max
value: 12.044739470755896
- type: nauc_ndcg_at_20_diff1
value: 18.267195122936364
- type: nauc_ndcg_at_20_max
value: 10.211299135510837
- type: nauc_ndcg_at_3_diff1
value: 26.453038443158267
- type: nauc_ndcg_at_3_max
value: 10.628723618231271
- type: nauc_ndcg_at_5_diff1
value: 22.815939702854084
- type: nauc_ndcg_at_5_max
value: 6.308794763068443
- type: nauc_precision_at_1000_diff1
value: -7.915540524594587
- type: nauc_precision_at_1000_max
value: 10.441250503021037
- type: nauc_precision_at_100_diff1
value: 2.7415108070462253
- type: nauc_precision_at_100_max
value: 11.957692005514204
- type: nauc_precision_at_10_diff1
value: 12.731449206012213
- type: nauc_precision_at_10_max
value: 9.218464561250887
- type: nauc_precision_at_1_diff1
value: 40.88188899137848
- type: nauc_precision_at_1_max
value: 12.044739470755896
- type: nauc_precision_at_20_diff1
value: 8.658189595700664
- type: nauc_precision_at_20_max
value: 11.571072137198621
- type: nauc_precision_at_3_diff1
value: 22.7637681983756
- type: nauc_precision_at_3_max
value: 9.361635703809425
- type: nauc_precision_at_5_diff1
value: 17.02002973192349
- type: nauc_precision_at_5_max
value: 1.8844406919262011
- type: nauc_recall_at_1000_diff1
value: -7.915540524594531
- type: nauc_recall_at_1000_max
value: 10.441250503021028
- type: nauc_recall_at_100_diff1
value: 2.741510807046166
- type: nauc_recall_at_100_max
value: 11.957692005514156
- type: nauc_recall_at_10_diff1
value: 12.731449206012224
- type: nauc_recall_at_10_max
value: 9.218464561250883
- type: nauc_recall_at_1_diff1
value: 40.88188899137848
- type: nauc_recall_at_1_max
value: 12.044739470755896
- type: nauc_recall_at_20_diff1
value: 8.65818959570063
- type: nauc_recall_at_20_max
value: 11.571072137198572
- type: nauc_recall_at_3_diff1
value: 22.763768198375587
- type: nauc_recall_at_3_max
value: 9.361635703809409
- type: nauc_recall_at_5_diff1
value: 17.02002973192351
- type: nauc_recall_at_5_max
value: 1.8844406919262173
- type: ndcg_at_1
value: 5.405
- type: ndcg_at_10
value: 11.045
- type: ndcg_at_100
value: 16.724
- type: ndcg_at_1000
value: 20.325
- type: ndcg_at_20
value: 12.42
- type: ndcg_at_3
value: 8.746
- type: ndcg_at_5
value: 10.065
- type: precision_at_1
value: 5.405
- type: precision_at_10
value: 1.757
- type: precision_at_100
value: 0.468
- type: precision_at_1000
value: 0.077
- type: precision_at_20
value: 1.149
- type: precision_at_3
value: 3.7539999999999996
- type: precision_at_5
value: 2.883
- type: recall_at_1
value: 5.405
- type: recall_at_10
value: 17.568
- type: recall_at_100
value: 46.847
- type: recall_at_1000
value: 76.577
- type: recall_at_20
value: 22.973
- type: recall_at_3
value: 11.261000000000001
- type: recall_at_5
value: 14.414
- task:
type: Clustering
dataset:
type: lyon-nlp/clustering-hal-s2s
name: MTEB HALClusteringS2S
config: default
split: test
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
metrics:
- type: v_measure
value: 24.495384349905265
- type: v_measures
value:
- 0.2850587858600384
- 0.274086904447773
- 0.2446866774990972
- 0.26946100959565517
- 0.24156528297396174
- task:
type: Clustering
dataset:
type: reciTAL/mlsum
name: MTEB MLSUMClusteringP2P
config: default
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: v_measure
value: 41.7878688793447
- type: v_measures
value:
- 0.4201324393825989
- 0.4205306567437461
- 0.4221300501395374
- 0.4210735177933313
- 0.38124298228695813
- task:
type: Clustering
dataset:
type: reciTAL/mlsum
name: MTEB MLSUMClusteringS2S
config: default
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: v_measure
value: 41.54533473611554
- type: v_measures
value:
- 0.3978917671338969
- 0.42610299599987944
- 0.4152131658150196
- 0.40558711021249855
- 0.38327501252308305
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (fr)
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 85.33041027247104
- type: f1
value: 85.4043088703478
- type: f1_weighted
value: 85.22086763441686
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (fr)
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 59.01346695897275
- type: f1
value: 41.296845063208316
- type: f1_weighted
value: 61.793813202867696
- task:
type: Classification
dataset:
type: mteb/masakhanews
name: MTEB MasakhaNEWSClassification (fra)
config: fra
split: test
revision: 18193f187b92da67168c655c9973a165ed9593dd
metrics:
- type: accuracy
value: 72.60663507109004
- type: f1
value: 68.67522100429781
- type: f1_weighted
value: 72.75616093668002
- task:
type: Clustering
dataset:
type: masakhane/masakhanews
name: MTEB MasakhaNEWSClusteringP2P (fra)
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 49.17691007381563
- type: v_measures
value:
- 1
- 0.033833191750480725
- 0.5707463198244268
- 0.1318223737892885
- 0.7224436183265853
- task:
type: Clustering
dataset:
type: masakhane/masakhanews
name: MTEB MasakhaNEWSClusteringS2S (fra)
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 26.9350763881635
- type: v_measures
value:
- 1
- 0.0002883507347309009
- 0.18259625098776155
- 0.025306110065234755
- 0.1385631076204479
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fr)
config: fr
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 65.1546738399462
- type: f1
value: 62.81367149102006
- type: f1_weighted
value: 64.45478181518959
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (fr)
config: fr
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
- type: accuracy
value: 69.94283792871553
- type: f1
value: 69.3387310036327
- type: f1_weighted
value: 69.77979200675047
- task:
type: Retrieval
dataset:
type: jinaai/mintakaqa
name: MTEB MintakaRetrieval (fr)
config: fr
split: test
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
metrics:
- type: map_at_1
value: 14.536999999999999
- type: map_at_10
value: 22.972
- type: map_at_100
value: 24.046
- type: map_at_1000
value: 24.15
- type: map_at_20
value: 23.56
- type: map_at_3
value: 20.639
- type: map_at_5
value: 21.886
- type: mrr_at_1
value: 14.537264537264537
- type: mrr_at_10
value: 22.97172172172171
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type: PairClassification
dataset:
type: GEM/opusparcus
name: MTEB OpusparcusPC (fr)
config: fr
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
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value: 87.37233054781802
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value: 81.74386920980926
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value: 82.04010462074979
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value: 81.74386920980926
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value: 93.18281680904117
- type: max_f1
value: 87.37864077669903
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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: 61.1
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value: 60.75603519868964
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value: 62.78646780647509
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value: 46.74972914409534
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- type: dot_accuracy
value: 61.1
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value: 60.74807680023078
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value: 62.78646780647509
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value: 46.74972914409534
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value: 95.5703211517165
- type: euclidean_accuracy
value: 61.1
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value: 60.756144387817734
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value: 62.78646780647509
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value: 46.74972914409534
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value: 95.5703211517165
- type: manhattan_accuracy
value: 61.150000000000006
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value: 60.685188544775116
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value: 62.7721335268505
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value: 61.150000000000006
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value: 60.756144387817734
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value: 62.78646780647509
- task:
type: STS
dataset:
type: Lajavaness/SICK-fr
name: MTEB SICKFr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
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value: 83.1543597030015
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value: 77.10092303546944
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value: 80.27115846915481
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value: 77.10092516058822
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value: 80.30090425968062
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value: 77.09423647945061
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type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr)
config: fr
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 79.20797144286122
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value: 80.31452099282514
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value: 78.43621396282957
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value: 80.31452099282514
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value: 78.29678738374866
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value: 79.93185465249057
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type: STS
dataset:
type: PhilipMay/stsb_multi_mt
name: MTEB STSBenchmarkMultilingualSTS (fr)
config: fr
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
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value: 84.69215133897265
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value: 83.85371663492563
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value: 84.35617480959016
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value: 83.85857789722276
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value: 84.30794186513978
- 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: 29.187176809104393
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value: 29.65160679657583
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value: 29.18717349611766
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value: 29.65160679657583
- task:
type: Reranking
dataset:
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
name: MTEB SyntecReranking
config: default
split: test
revision: daf0863838cd9e3ba50544cdce3ac2b338a1b0ad
metrics:
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value: 82.76666666666667
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- 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: nan
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- type: precision_at_1
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- type: recall_at_100
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- type: recall_at_1000
value: 100
- type: recall_at_20
value: 100
- type: recall_at_3
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- type: recall_at_5
value: 90
- task:
type: Retrieval
dataset:
type: jinaai/xpqa
name: MTEB XPQARetrieval (fr)
config: fr
split: test
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
metrics:
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- type: map_at_1000
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- type: nauc_map_at_1000_diff1
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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}