---
tags:
- feature-extraction
- sentence-similarity
- sentence-transformers
- transformers
- mteb
license: gemma
model-index:
- name: bge-multilingual-gemma2
results:
- task:
type: Retrieval
dataset:
type: mteb/nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: main_score
value: 38.11433513284057
- type: ndcg_at_1
value: 48.45201238390093
- type: ndcg_at_3
value: 44.451438575534574
- type: ndcg_at_5
value: 41.13929990797894
- type: ndcg_at_10
value: 38.11433513284057
- type: ndcg_at_100
value: 35.36065387898559
- type: ndcg_at_1000
value: 44.01125752781003
- type: map_at_1
value: 5.638004398054564
- type: map_at_3
value: 10.375632572339333
- type: map_at_5
value: 11.820531148202422
- type: map_at_10
value: 14.087436978063389
- type: map_at_100
value: 18.25397463114958
- type: map_at_1000
value: 19.868440221606203
- type: precision_at_1
value: 49.84520123839009
- type: precision_at_3
value: 41.89886480908153
- type: precision_at_5
value: 35.356037151702814
- type: precision_at_10
value: 28.513931888544857
- type: precision_at_100
value: 9.337461300309604
- type: precision_at_1000
value: 2.210216718266251
- type: recall_at_1
value: 5.638004398054564
- type: recall_at_3
value: 11.938154656310312
- type: recall_at_5
value: 14.06183119422843
- type: recall_at_10
value: 18.506397834147705
- type: recall_at_100
value: 35.96995569451433
- type: recall_at_1000
value: 68.31771509404795
- task:
type: Retrieval
dataset:
type: mteb/msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: main_score
value: 45.70688915742828
- type: ndcg_at_1
value: 26.002865329512893
- type: ndcg_at_3
value: 37.49665652114275
- type: ndcg_at_5
value: 41.684045067615834
- type: ndcg_at_10
value: 45.70688915742828
- type: ndcg_at_100
value: 51.08932609519671
- type: ndcg_at_1000
value: 51.98806137292924
- type: map_at_1
value: 25.35219675262655
- type: map_at_3
value: 34.39549506526583
- type: map_at_5
value: 36.74936326010824
- type: map_at_10
value: 38.44429852488596
- type: map_at_100
value: 39.60260286311527
- type: map_at_1000
value: 39.64076154054021
- type: precision_at_1
value: 26.002865329512893
- type: precision_at_3
value: 15.840496657115954
- type: precision_at_5
value: 11.647564469914684
- type: precision_at_10
value: 7.1275071633243705
- type: precision_at_100
value: 0.9782234957019871
- type: precision_at_1000
value: 0.10565902578797497
- type: recall_at_1
value: 25.35219675262655
- type: recall_at_3
value: 45.78438395415474
- type: recall_at_5
value: 55.83213944603631
- type: recall_at_10
value: 68.08500477554918
- type: recall_at_100
value: 92.55133715377269
- type: recall_at_1000
value: 99.29083094555875
- task:
type: Retrieval
dataset:
type: mteb/fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: main_score
value: 60.04205769404706
- type: ndcg_at_1
value: 59.25925925925925
- type: ndcg_at_3
value: 55.96637679199298
- type: ndcg_at_5
value: 56.937223390223956
- type: ndcg_at_10
value: 60.04205769404706
- type: ndcg_at_100
value: 66.01619664462949
- type: ndcg_at_1000
value: 67.59651529720728
- type: map_at_1
value: 31.5081163692275
- type: map_at_3
value: 45.7486689836227
- type: map_at_5
value: 48.944906602314
- type: map_at_10
value: 51.85427043799874
- type: map_at_100
value: 53.92920237379484
- type: map_at_1000
value: 54.04694438963671
- type: precision_at_1
value: 59.25925925925925
- type: precision_at_3
value: 37.44855967078195
- type: precision_at_5
value: 26.913580246913547
- type: precision_at_10
value: 16.52777777777774
- type: precision_at_100
value: 2.2962962962962754
- type: precision_at_1000
value: 0.2566358024691334
- type: recall_at_1
value: 31.5081163692275
- type: recall_at_3
value: 50.71759045138676
- type: recall_at_5
value: 57.49321152098932
- type: recall_at_10
value: 67.36356750245642
- type: recall_at_100
value: 88.67335767798735
- type: recall_at_1000
value: 97.83069725199356
- task:
type: Retrieval
dataset:
type: mteb/scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: main_score
value: 26.93150756480961
- type: ndcg_at_1
value: 30.8
- type: ndcg_at_3
value: 25.048085553386628
- type: ndcg_at_5
value: 22.351207380852305
- type: ndcg_at_10
value: 26.93150756480961
- type: ndcg_at_100
value: 37.965486832874014
- type: ndcg_at_1000
value: 43.346046425140244
- type: map_at_1
value: 6.238333333333366
- type: map_at_3
value: 11.479166666666679
- type: map_at_5
value: 14.215999999999983
- type: map_at_10
value: 16.774632936507945
- type: map_at_100
value: 20.148869158557293
- type: map_at_1000
value: 20.528644104490823
- type: precision_at_1
value: 30.8
- type: precision_at_3
value: 23.466666666666736
- type: precision_at_5
value: 19.899999999999967
- type: precision_at_10
value: 14.069999999999938
- type: precision_at_100
value: 2.9770000000000065
- type: precision_at_1000
value: 0.42569999999999486
- type: recall_at_1
value: 6.238333333333366
- type: recall_at_3
value: 14.29333333333338
- type: recall_at_5
value: 20.206666666666628
- type: recall_at_10
value: 28.573333333333224
- type: recall_at_100
value: 60.43666666666675
- type: recall_at_1000
value: 86.3649999999997
- task:
type: Retrieval
dataset:
type: mteb/fever
name: MTEB FEVER
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: main_score
value: 90.38165339181239
- type: ndcg_at_1
value: 84.86348634863486
- type: ndcg_at_3
value: 88.98667069230609
- type: ndcg_at_5
value: 89.86028996734895
- type: ndcg_at_10
value: 90.38165339181239
- type: ndcg_at_100
value: 90.99655378684439
- type: ndcg_at_1000
value: 91.15536362599602
- type: map_at_1
value: 78.8556296105801
- type: map_at_3
value: 86.24061810942983
- type: map_at_5
value: 86.94776680048933
- type: map_at_10
value: 87.26956235873007
- type: map_at_100
value: 87.47986397174834
- type: map_at_1000
value: 87.4897076664281
- type: precision_at_1
value: 84.86348634863486
- type: precision_at_3
value: 34.02340234023296
- type: precision_at_5
value: 21.10411041104359
- type: precision_at_10
value: 10.828082808282083
- type: precision_at_100
value: 1.1381638163816703
- type: precision_at_1000
value: 0.11662166216622569
- type: recall_at_1
value: 78.8556296105801
- type: recall_at_3
value: 92.34465708475605
- type: recall_at_5
value: 94.58010682020583
- type: recall_at_10
value: 96.10713452297611
- type: recall_at_100
value: 98.31672452959585
- type: recall_at_1000
value: 99.25967001462051
- task:
type: Retrieval
dataset:
type: mteb/arguana
name: MTEB ArguAna
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: main_score
value: 77.36555747844541
- type: ndcg_at_1
value: 57.681365576102415
- type: ndcg_at_3
value: 72.01664798084765
- type: ndcg_at_5
value: 75.26345973082836
- type: ndcg_at_10
value: 77.36555747844541
- type: ndcg_at_100
value: 78.15567833673768
- type: ndcg_at_1000
value: 78.16528851292641
- type: map_at_1
value: 57.681365576102415
- type: map_at_3
value: 68.59886201991475
- type: map_at_5
value: 70.38051209103858
- type: map_at_10
value: 71.26684955632336
- type: map_at_100
value: 71.4637216600468
- type: map_at_1000
value: 71.46414501573332
- type: precision_at_1
value: 57.681365576102415
- type: precision_at_3
value: 27.287814129919084
- type: precision_at_5
value: 17.965860597439132
- type: precision_at_10
value: 9.623044096728066
- type: precision_at_100
value: 0.995732574679925
- type: precision_at_1000
value: 0.09964438122332549
- type: recall_at_1
value: 57.681365576102415
- type: recall_at_3
value: 81.86344238975818
- type: recall_at_5
value: 89.82930298719772
- type: recall_at_10
value: 96.23044096728307
- type: recall_at_100
value: 99.57325746799431
- type: recall_at_1000
value: 99.6443812233286
- task:
type: Retrieval
dataset:
type: mteb/scifact
name: MTEB SciFact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: main_score
value: 72.0465439956427
- type: ndcg_at_1
value: 58.666666666666664
- type: ndcg_at_3
value: 66.84566274610046
- type: ndcg_at_5
value: 69.46578881873717
- type: ndcg_at_10
value: 72.0465439956427
- type: ndcg_at_100
value: 74.25705461923272
- type: ndcg_at_1000
value: 74.63689058493014
- type: map_at_1
value: 55.59444444444445
- type: map_at_3
value: 63.71851851851852
- type: map_at_5
value: 65.5362962962963
- type: map_at_10
value: 66.84112433862435
- type: map_at_100
value: 67.36269426417417
- type: map_at_1000
value: 67.37568665562833
- type: precision_at_1
value: 58.666666666666664
- type: precision_at_3
value: 26.444444444444425
- type: precision_at_5
value: 17.66666666666672
- type: precision_at_10
value: 9.866666666666706
- type: precision_at_100
value: 1.0966666666666596
- type: precision_at_1000
value: 0.11266666666666675
- type: recall_at_1
value: 55.59444444444445
- type: recall_at_3
value: 72.72777777777777
- type: recall_at_5
value: 79.31666666666666
- type: recall_at_10
value: 86.75
- type: recall_at_100
value: 96.66666666666667
- type: recall_at_1000
value: 99.66666666666667
- task:
type: Retrieval
dataset:
type: mteb/trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: main_score
value: 64.26928884606035
- type: ndcg_at_1
value: 63.0
- type: ndcg_at_3
value: 64.18432764386345
- type: ndcg_at_5
value: 64.73235515799435
- type: ndcg_at_10
value: 64.26928884606035
- type: ndcg_at_100
value: 52.39807133285409
- type: ndcg_at_1000
value: 52.19937563361241
- type: map_at_1
value: 0.18483494997310454
- type: map_at_3
value: 0.5139705769331114
- type: map_at_5
value: 0.8245601222717243
- type: map_at_10
value: 1.5832530269558573
- type: map_at_100
value: 9.664760850102393
- type: map_at_1000
value: 25.568347406468334
- type: precision_at_1
value: 70.0
- type: precision_at_3
value: 71.33333333333333
- type: precision_at_5
value: 71.60000000000001
- type: precision_at_10
value: 70.99999999999996
- type: precision_at_100
value: 55.140000000000015
- type: precision_at_1000
value: 23.857999999999997
- type: recall_at_1
value: 0.18483494997310454
- type: recall_at_3
value: 0.5584287301859913
- type: recall_at_5
value: 0.9489025953807098
- type: recall_at_10
value: 1.9023711039425688
- type: recall_at_100
value: 13.596810701594226
- type: recall_at_1000
value: 50.92058432920189
- task:
type: Retrieval
dataset:
type: mteb/climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: main_score
value: 39.37204193531481
- type: ndcg_at_1
value: 35.11400651465798
- type: ndcg_at_3
value: 32.36672790229743
- type: ndcg_at_5
value: 34.79369234162357
- type: ndcg_at_10
value: 39.37204193531481
- type: ndcg_at_100
value: 47.544500439419124
- type: ndcg_at_1000
value: 50.305733346049855
- type: map_at_1
value: 15.516829533116216
- type: map_at_3
value: 23.73669923995656
- type: map_at_5
value: 26.43208469055373
- type: map_at_10
value: 28.912036175309773
- type: map_at_100
value: 31.413762299240894
- type: map_at_1000
value: 31.596796093997014
- type: precision_at_1
value: 35.11400651465798
- type: precision_at_3
value: 24.994571118349487
- type: precision_at_5
value: 19.231270358305956
- type: precision_at_10
value: 12.690553745928165
- type: precision_at_100
value: 2.1576547231270466
- type: precision_at_1000
value: 0.2676221498371306
- type: recall_at_1
value: 15.516829533116216
- type: recall_at_3
value: 29.994571118349512
- type: recall_at_5
value: 37.14223669923993
- type: recall_at_10
value: 47.29207383279043
- type: recall_at_100
value: 74.37133550488598
- type: recall_at_1000
value: 89.41585233441913
- task:
type: Retrieval
dataset:
type: mteb/hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: main_score
value: 83.26282954330777
- type: ndcg_at_1
value: 87.5489534098582
- type: ndcg_at_3
value: 78.7646435855166
- type: ndcg_at_5
value: 81.41629077444277
- type: ndcg_at_10
value: 83.26282954330777
- type: ndcg_at_100
value: 85.2771369900158
- type: ndcg_at_1000
value: 85.77519303747493
- type: map_at_1
value: 43.7744767049291
- type: map_at_3
value: 73.4661264911093
- type: map_at_5
value: 75.7169705154168
- type: map_at_10
value: 76.89183627536043
- type: map_at_100
value: 77.53680315727078
- type: map_at_1000
value: 77.5649311522075
- type: precision_at_1
value: 87.5489534098582
- type: precision_at_3
value: 51.74881836596788
- type: precision_at_5
value: 33.13977042539127
- type: precision_at_10
value: 17.492234976369023
- type: precision_at_100
value: 1.9030384875084312
- type: precision_at_1000
value: 0.19679945982446267
- type: recall_at_1
value: 43.7744767049291
- type: recall_at_3
value: 77.62322754895341
- type: recall_at_5
value: 82.84942606347063
- type: recall_at_10
value: 87.4611748818366
- type: recall_at_100
value: 95.15192437542201
- type: recall_at_1000
value: 98.39972991222147
- task:
type: Retrieval
dataset:
type: mteb/nq
name: MTEB NQ
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: main_score
value: 71.44670934705796
- type: ndcg_at_1
value: 54.026651216685984
- type: ndcg_at_3
value: 65.1267452491225
- type: ndcg_at_5
value: 68.6696802020747
- type: ndcg_at_10
value: 71.44670934705796
- type: ndcg_at_100
value: 73.74642927386503
- type: ndcg_at_1000
value: 73.90908268307331
- type: map_at_1
value: 48.50086906141366
- type: map_at_3
value: 61.07691193510995
- type: map_at_5
value: 63.36580243337187
- type: map_at_10
value: 64.74485498782997
- type: map_at_100
value: 65.34329174534082
- type: map_at_1000
value: 65.35107870745652
- type: precision_at_1
value: 54.026651216685984
- type: precision_at_3
value: 28.437620702974996
- type: precision_at_5
value: 19.20625724217861
- type: precision_at_10
value: 10.67207415990753
- type: precision_at_100
value: 1.1987253765932955
- type: precision_at_1000
value: 0.12143684820393259
- type: recall_at_1
value: 48.50086906141366
- type: recall_at_3
value: 73.19428350714561
- type: recall_at_5
value: 81.19689069138664
- type: recall_at_10
value: 89.04741212823485
- type: recall_at_100
value: 98.58053302433372
- type: recall_at_1000
value: 99.75376593279258
- task:
type: Retrieval
dataset:
type: mteb/quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: main_score
value: 90.03760323006117
- type: ndcg_at_1
value: 83.53
- type: ndcg_at_3
value: 87.53800795646302
- type: ndcg_at_5
value: 88.92909168525203
- type: ndcg_at_10
value: 90.03760323006117
- type: ndcg_at_100
value: 91.08558507332712
- type: ndcg_at_1000
value: 91.1430039358834
- type: map_at_1
value: 72.61760432018744
- type: map_at_3
value: 83.8457060028347
- type: map_at_5
value: 85.6228412692169
- type: map_at_10
value: 86.67700531365115
- type: map_at_100
value: 87.29851728827602
- type: map_at_1000
value: 87.31014621733333
- type: precision_at_1
value: 83.53
- type: precision_at_3
value: 38.33666666667159
- type: precision_at_5
value: 25.12599999999881
- type: precision_at_10
value: 13.629999999998683
- type: precision_at_100
value: 1.5431999999999773
- type: precision_at_1000
value: 0.15671999999997974
- type: recall_at_1
value: 72.61760432018744
- type: recall_at_3
value: 89.06736052932686
- type: recall_at_5
value: 93.09634203522849
- type: recall_at_10
value: 96.35128012894234
- type: recall_at_100
value: 99.7740237858541
- type: recall_at_1000
value: 99.99690476190477
- task:
type: Retrieval
dataset:
type: mteb/webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: main_score
value: 30.2563523019649
- type: ndcg_at_1
value: 37.755102040816325
- type: ndcg_at_3
value: 34.45349994459905
- type: ndcg_at_5
value: 32.508805919063086
- type: ndcg_at_10
value: 30.2563523019649
- type: ndcg_at_100
value: 40.538336664503746
- type: ndcg_at_1000
value: 52.2066951614923
- type: map_at_1
value: 2.75537988273998
- type: map_at_3
value: 6.011397290504469
- type: map_at_5
value: 8.666495836494098
- type: map_at_10
value: 12.17701515007822
- type: map_at_100
value: 18.789086471205852
- type: map_at_1000
value: 20.42972375502502
- type: precision_at_1
value: 40.816326530612244
- type: precision_at_3
value: 35.37414965986394
- type: precision_at_5
value: 32.244897959183675
- type: precision_at_10
value: 26.93877551020408
- type: precision_at_100
value: 8.163265306122451
- type: precision_at_1000
value: 1.5979591836734703
- type: recall_at_1
value: 2.75537988273998
- type: recall_at_3
value: 7.254270324385098
- type: recall_at_5
value: 11.580137100328589
- type: recall_at_10
value: 18.745232816450553
- type: recall_at_100
value: 50.196809658622755
- type: recall_at_1000
value: 85.87317364148332
- task:
type: Retrieval
dataset:
type: mteb/dbpedia
name: MTEB DBPedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: main_score
value: 51.36940792375597
- type: ndcg_at_1
value: 65.125
- type: ndcg_at_3
value: 55.3967569049025
- type: ndcg_at_5
value: 53.09668587926677
- type: ndcg_at_10
value: 51.36940792375597
- type: ndcg_at_100
value: 56.69623269243084
- type: ndcg_at_1000
value: 63.481061270842
- type: map_at_1
value: 10.265595545755545
- type: map_at_3
value: 16.776544233350698
- type: map_at_5
value: 20.184523605272798
- type: map_at_10
value: 24.772797659849264
- type: map_at_100
value: 36.72689012514183
- type: map_at_1000
value: 38.73869985105569
- type: precision_at_1
value: 77.5
- type: precision_at_3
value: 59.75000000000003
- type: precision_at_5
value: 52.849999999999994
- type: precision_at_10
value: 42.47499999999995
- type: precision_at_100
value: 13.614999999999993
- type: precision_at_1000
value: 2.500749999999998
- type: recall_at_1
value: 10.265595545755545
- type: recall_at_3
value: 17.819804963534246
- type: recall_at_5
value: 22.46124219601634
- type: recall_at_10
value: 30.44583516613163
- type: recall_at_100
value: 63.84118006287797
- type: recall_at_1000
value: 85.06450356093833
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: main_score
value: 47.93921415959017
- type: ndcg_at_1
value: 36.526219490536015
- type: ndcg_at_3
value: 42.35099043224295
- type: ndcg_at_5
value: 44.989685312964156
- type: ndcg_at_10
value: 47.93921415959017
- type: ndcg_at_100
value: 53.05390282389675
- type: ndcg_at_1000
value: 54.776052731794266
- type: map_at_1
value: 30.818605279548184
- type: map_at_3
value: 38.363350019087974
- type: map_at_5
value: 40.295203936887226
- type: map_at_10
value: 41.81978941662592
- type: map_at_100
value: 43.13300727554278
- type: map_at_1000
value: 43.2351061120207
- type: precision_at_1
value: 36.526219490536015
- type: precision_at_3
value: 19.550515857206346
- type: precision_at_5
value: 13.958783060831967
- type: precision_at_10
value: 8.498592395773393
- type: precision_at_100
value: 1.3024888941713948
- type: precision_at_1000
value: 0.1630253057414617
- type: recall_at_1
value: 30.818605279548184
- type: recall_at_3
value: 45.9132085981904
- type: recall_at_5
value: 52.6851323959227
- type: recall_at_10
value: 61.39718618970463
- type: recall_at_100
value: 83.30757187969981
- type: recall_at_1000
value: 94.9192024147964
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 89.47761194029852
- type: accuracy_stderr
value: 1.6502495811564162
- type: ap
value: 62.20813715457866
- type: ap_stderr
value: 3.7902166647587854
- type: f1
value: 84.91493292274734
- type: f1_stderr
value: 1.9572239640276208
- type: main_score
value: 89.47761194029852
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 96.89569999999999
- type: accuracy_stderr
value: 0.6886368582206464
- type: ap
value: 95.38531339207739
- type: ap_stderr
value: 0.9009257949898158
- type: f1
value: 96.8941935264779
- type: f1_stderr
value: 0.6908609132985931
- type: main_score
value: 96.89569999999999
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 61.602000000000004
- type: accuracy_stderr
value: 1.4532019818318436
- type: f1
value: 60.96100449021481
- type: f1_stderr
value: 1.8031398419765765
- type: main_score
value: 61.602000000000004
task:
type: Classification
- dataset:
config: default
name: MTEB ArxivClusteringP2P
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: main_score
value: 54.906319409992
- type: v_measure
value: 54.906319409992
- type: v_measure_std
value: 14.382682652951683
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: main_score
value: 50.27779516565727
- type: v_measure
value: 50.27779516565727
- type: v_measure_std
value: 14.463711418590636
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: map
value: 64.59457317979604
- type: mrr
value: 78.05214791364376
- type: main_score
value: 64.59457317979604
task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: cosine_pearson
value: 86.5833945335644
- type: cosine_spearman
value: 85.74472483606
- type: manhattan_pearson
value: 85.07748703871708
- type: manhattan_spearman
value: 85.1459160110718
- type: euclidean_pearson
value: 85.14704290043478
- type: euclidean_spearman
value: 85.10073425868336
- type: main_score
value: 85.74472483606
task:
type: STS
- dataset:
config: default
name: MTEB Banking77Classification
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
- type: accuracy
value: 92.53246753246755
- type: accuracy_stderr
value: 0.5488837781559508
- type: f1
value: 92.5143182074032
- type: f1_stderr
value: 0.5657577980223147
- type: main_score
value: 92.53246753246755
task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
- type: main_score
value: 52.64099497480452
- type: v_measure
value: 52.64099497480452
- type: v_measure_std
value: 1.081892399559334
task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
- type: main_score
value: 49.1972734308178
- type: v_measure
value: 49.1972734308178
- type: v_measure_std
value: 0.9081245477708283
task:
type: Clustering
- dataset:
config: default
name: MTEB EmotionClassification
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
split: test
type: mteb/emotion
metrics:
- type: accuracy
value: 92.975
- type: accuracy_stderr
value: 0.5287958017987677
- type: f1
value: 89.29755895896542
- type: f1_stderr
value: 0.6485027046025079
- type: main_score
value: 92.975
task:
type: Classification
- dataset:
config: default
name: MTEB ImdbClassification
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
split: test
type: mteb/imdb
metrics:
- type: accuracy
value: 96.66480000000001
- type: accuracy_stderr
value: 0.45673204398202666
- type: ap
value: 95.33843919456118
- type: ap_stderr
value: 0.6449846039754393
- type: f1
value: 96.6637668164617
- type: f1_stderr
value: 0.45793673051468287
- type: main_score
value: 96.66480000000001
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 98.61149110807114
- type: accuracy_stderr
value: 0.469748178253266
- type: f1
value: 98.4685511007568
- type: f1_stderr
value: 0.51636776728259
- type: main_score
value: 98.61149110807114
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 95.51299589603283
- type: accuracy_stderr
value: 0.3591676911539482
- type: f1
value: 85.2464691439773
- type: f1_stderr
value: 0.9234502856695337
- type: main_score
value: 95.51299589603283
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 82.04774714189644
- type: accuracy_stderr
value: 0.7288818520309376
- type: f1
value: 79.28060657840692
- type: f1_stderr
value: 0.6872008571781982
- type: main_score
value: 82.04774714189644
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveScenarioClassification (en)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 84.40147948890383
- type: accuracy_stderr
value: 1.2939587629143627
- type: f1
value: 83.97779287582267
- type: f1_stderr
value: 0.9970599222060901
- type: main_score
value: 84.40147948890383
task:
type: Classification
- dataset:
config: default
name: MTEB MedrxivClusteringP2P
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
metrics:
- type: main_score
value: 45.80879120838561
- type: v_measure
value: 45.80879120838561
- type: v_measure_std
value: 1.257800489264564
task:
type: Clustering
- dataset:
config: default
name: MTEB MedrxivClusteringS2S
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
metrics:
- type: main_score
value: 44.106849261042505
- type: v_measure
value: 44.106849261042505
- type: v_measure_std
value: 1.4347344477874981
task:
type: Clustering
- dataset:
config: default
name: MTEB MindSmallReranking
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
split: test
type: mteb/mind_small
metrics:
- type: map
value: 31.794062752995345
- type: mrr
value: 32.98581714772614
- type: main_score
value: 31.794062752995345
task:
type: Reranking
- dataset:
config: default
name: MTEB RedditClustering
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
split: test
type: mteb/reddit-clustering
metrics:
- type: main_score
value: 56.03342473834434
- type: v_measure
value: 56.03342473834434
- type: v_measure_std
value: 5.972192613803461
task:
type: Clustering
- dataset:
config: default
name: MTEB RedditClusteringP2P
revision: 282350215ef01743dc01b456c7f5241fa8937f16
split: test
type: mteb/reddit-clustering-p2p
metrics:
- type: main_score
value: 65.83156688381274
- type: v_measure
value: 65.83156688381274
- type: v_measure_std
value: 14.180225112120162
task:
type: Clustering
- dataset:
config: default
name: MTEB SICK-R
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
split: test
type: mteb/sickr-sts
metrics:
- type: cosine_pearson
value: 84.15759544348467
- type: cosine_spearman
value: 82.66085892322664
- type: manhattan_pearson
value: 82.27257241990692
- type: manhattan_spearman
value: 82.57752467555896
- type: euclidean_pearson
value: 82.20795646456065
- type: euclidean_spearman
value: 82.51008729416401
- type: main_score
value: 82.66085892322664
task:
type: STS
- dataset:
config: default
name: MTEB STS12
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cosine_pearson
value: 84.3406321391237
- type: cosine_spearman
value: 77.71091257651071
- type: manhattan_pearson
value: 81.25784268400994
- type: manhattan_spearman
value: 77.98426383345507
- type: euclidean_pearson
value: 81.25641851462917
- type: euclidean_spearman
value: 77.93254971878063
- type: main_score
value: 77.71091257651071
task:
type: STS
- dataset:
config: default
name: MTEB STS13
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cosine_pearson
value: 86.1528398894769
- type: cosine_spearman
value: 87.44662352358895
- type: manhattan_pearson
value: 86.92164570802663
- type: manhattan_spearman
value: 86.9132692625668
- type: euclidean_pearson
value: 87.00156426580821
- type: euclidean_spearman
value: 86.98750068631274
- type: main_score
value: 87.44662352358895
task:
type: STS
- dataset:
config: default
name: MTEB STS14
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cosine_pearson
value: 83.32782491176253
- type: cosine_spearman
value: 83.48313793311584
- type: manhattan_pearson
value: 82.60528063429948
- type: manhattan_spearman
value: 83.10434862310481
- type: euclidean_pearson
value: 82.68016090104034
- type: euclidean_spearman
value: 83.14418662406631
- type: main_score
value: 83.48313793311584
task:
type: STS
- dataset:
config: default
name: MTEB STS15
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cosine_pearson
value: 86.31535441436343
- type: cosine_spearman
value: 87.63145141246594
- type: manhattan_pearson
value: 86.95972711389149
- type: manhattan_spearman
value: 86.9849824463052
- type: euclidean_pearson
value: 86.95391575487379
- type: euclidean_spearman
value: 86.97613682266213
- type: main_score
value: 87.63145141246594
task:
type: STS
- dataset:
config: default
name: MTEB STS16
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cosine_pearson
value: 83.43854397443079
- type: cosine_spearman
value: 86.70176531845136
- type: manhattan_pearson
value: 85.82302317064868
- type: manhattan_spearman
value: 86.36561734213241
- type: euclidean_pearson
value: 85.80127366135169
- type: euclidean_spearman
value: 86.34803859754834
- type: main_score
value: 86.70176531845136
task:
type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 90.38940955877999
- type: cosine_spearman
value: 91.18282119920893
- type: manhattan_pearson
value: 91.31823663739615
- type: manhattan_spearman
value: 90.67257321731341
- type: euclidean_pearson
value: 91.30318753138528
- type: euclidean_spearman
value: 90.69044765693836
- type: main_score
value: 91.18282119920893
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 69.33936467780947
- type: cosine_spearman
value: 69.02345807358802
- type: manhattan_pearson
value: 70.11799452953082
- type: manhattan_spearman
value: 68.55450923481405
- type: euclidean_pearson
value: 70.10857680491809
- type: euclidean_spearman
value: 68.44610245708984
- type: main_score
value: 69.02345807358802
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cosine_pearson
value: 85.97288135509513
- type: cosine_spearman
value: 87.25208310840168
- type: manhattan_pearson
value: 86.3786471501451
- type: manhattan_spearman
value: 86.71177136523868
- type: euclidean_pearson
value: 86.40522339296625
- type: euclidean_spearman
value: 86.73930576508816
- type: main_score
value: 87.25208310840168
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map
value: 87.60324164489178
- type: mrr
value: 96.30331904841708
- type: main_score
value: 87.60324164489178
task:
type: Reranking
- dataset:
config: default
name: MTEB SprintDuplicateQuestions
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: cos_sim_accuracy
value: 99.6920792079208
- type: cos_sim_accuracy_threshold
value: 90.36337347155474
- type: cos_sim_ap
value: 90.93952679056765
- type: cos_sim_f1
value: 83.10700706137968
- type: cos_sim_f1_threshold
value: 90.36337347155474
- type: cos_sim_precision
value: 90.96313912009512
- type: cos_sim_recall
value: 76.5
- type: dot_accuracy
value: 99.54554455445545
- type: dot_accuracy_threshold
value: 2876800.0
- type: dot_ap
value: 84.01112287735286
- type: dot_f1
value: 75.7622739018088
- type: dot_f1_threshold
value: 2820800.0
- type: dot_precision
value: 78.39572192513369
- type: dot_recall
value: 73.3
- type: euclidean_accuracy
value: 99.6930693069307
- type: euclidean_accuracy_threshold
value: 7718.054017089397
- type: euclidean_ap
value: 91.1257568881301
- type: euclidean_f1
value: 83.09022150189087
- type: euclidean_f1_threshold
value: 7817.08324628535
- type: euclidean_precision
value: 90.36427732079906
- type: euclidean_recall
value: 76.9
- type: manhattan_accuracy
value: 99.6920792079208
- type: manhattan_accuracy_threshold
value: 364735.19654273987
- type: manhattan_ap
value: 91.2326885940691
- type: manhattan_f1
value: 83.36008560727663
- type: manhattan_f1_threshold
value: 375395.8945572376
- type: manhattan_precision
value: 89.64326812428078
- type: manhattan_recall
value: 77.9
- type: max_accuracy
value: 99.6930693069307
- type: max_ap
value: 91.2326885940691
- type: max_f1
value: 83.36008560727663
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: main_score
value: 66.2095300942637
- type: v_measure
value: 66.2095300942637
- type: v_measure_std
value: 3.214369679617631
task:
type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
- type: main_score
value: 45.74307000935057
- type: v_measure
value: 45.74307000935057
- type: v_measure_std
value: 1.5352466748569888
task:
type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
- type: map
value: 54.90337951829123
- type: mrr
value: 56.12889663441134
- type: main_score
value: 54.90337951829123
task:
type: Reranking
- dataset:
config: default
name: MTEB SummEval
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: cosine_pearson
value: 31.0669308484832
- type: cosine_spearman
value: 31.19637421540861
- type: dot_pearson
value: 30.62326176666765
- type: dot_spearman
value: 30.42135737502967
- type: main_score
value: 31.19637421540861
task:
type: Summarization
- dataset:
config: default
name: MTEB ToxicConversationsClassification
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy
value: 87.34339999999999
- type: accuracy_stderr
value: 1.838245696309393
- type: ap
value: 33.536584790435406
- type: ap_stderr
value: 2.276373512492581
- type: f1
value: 72.47307082324448
- type: f1_stderr
value: 1.9964640292072542
- type: main_score
value: 87.34339999999999
task:
type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy
value: 78.86247877758915
- type: accuracy_stderr
value: 1.1273253738982443
- type: f1
value: 79.14666244848874
- type: f1_stderr
value: 1.1532640958036497
- type: main_score
value: 78.86247877758915
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: main_score
value: 70.44270836680788
- type: v_measure
value: 70.44270836680788
- type: v_measure_std
value: 1.5185423698266132
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: cos_sim_accuracy
value: 87.74512725755498
- type: cos_sim_accuracy_threshold
value: 82.34941560483547
- type: cos_sim_ap
value: 79.6389274210382
- type: cos_sim_f1
value: 71.76319176319176
- type: cos_sim_f1_threshold
value: 80.1523829249257
- type: cos_sim_precision
value: 70.0502512562814
- type: cos_sim_recall
value: 73.56200527704485
- type: dot_accuracy
value: 85.13441020444657
- type: dot_accuracy_threshold
value: 2220800.0
- type: dot_ap
value: 71.67080150823449
- type: dot_f1
value: 66.18984119287187
- type: dot_f1_threshold
value: 2086400.0
- type: dot_precision
value: 61.224489795918366
- type: dot_recall
value: 72.0316622691293
- type: euclidean_accuracy
value: 87.69148238660071
- type: euclidean_accuracy_threshold
value: 9221.50036619459
- type: euclidean_ap
value: 79.65326151280289
- type: euclidean_f1
value: 71.7903489983621
- type: euclidean_f1_threshold
value: 10313.528386219872
- type: euclidean_precision
value: 68.70026525198939
- type: euclidean_recall
value: 75.17150395778364
- type: manhattan_accuracy
value: 87.74512725755498
- type: manhattan_accuracy_threshold
value: 444289.1119837761
- type: manhattan_ap
value: 79.67744645365104
- type: manhattan_f1
value: 71.94423699278066
- type: manhattan_f1_threshold
value: 491676.24004781246
- type: manhattan_precision
value: 68.0961357210179
- type: manhattan_recall
value: 76.2532981530343
- type: max_accuracy
value: 87.74512725755498
- type: max_ap
value: 79.67744645365104
- type: max_f1
value: 71.94423699278066
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: cos_sim_accuracy
value: 89.5544688943222
- type: cos_sim_accuracy_threshold
value: 81.58909533293946
- type: cos_sim_ap
value: 86.95174990178396
- type: cos_sim_f1
value: 79.1543756145526
- type: cos_sim_f1_threshold
value: 80.08573448087095
- type: cos_sim_precision
value: 77.78355879292404
- type: cos_sim_recall
value: 80.5743763473976
- type: dot_accuracy
value: 88.60752124810804
- type: dot_accuracy_threshold
value: 2136000.0
- type: dot_ap
value: 84.26724775947629
- type: dot_f1
value: 77.67666146985243
- type: dot_f1_threshold
value: 2064000.0
- type: dot_precision
value: 73.40505721921468
- type: dot_recall
value: 82.47613181398214
- type: euclidean_accuracy
value: 89.5370046959289
- type: euclidean_accuracy_threshold
value: 9750.113991666478
- type: euclidean_ap
value: 86.99393092403776
- type: euclidean_f1
value: 79.07167337207571
- type: euclidean_f1_threshold
value: 10338.095928500366
- type: euclidean_precision
value: 76.59497690531177
- type: euclidean_recall
value: 81.71388974437943
- type: manhattan_accuracy
value: 89.57581402569178
- type: manhattan_accuracy_threshold
value: 463812.92815208435
- type: manhattan_ap
value: 87.00849868076658
- type: manhattan_f1
value: 79.08583576933297
- type: manhattan_f1_threshold
value: 482453.35128605366
- type: manhattan_precision
value: 78.00494270950348
- type: manhattan_recall
value: 80.19710502001848
- type: max_accuracy
value: 89.57581402569178
- type: max_ap
value: 87.00849868076658
- type: max_f1
value: 79.1543756145526
task:
type: PairClassification
- dataset:
config: default
name: MTEB AFQMC
revision: b44c3b011063adb25877c13823db83bb193913c4
split: validation
type: C-MTEB/AFQMC
metrics:
- type: cosine_pearson
value: 45.108559635369325
- type: cosine_spearman
value: 47.172833128216176
- type: manhattan_pearson
value: 45.75443077564791
- type: manhattan_spearman
value: 47.13974146235398
- type: euclidean_pearson
value: 45.78921257223492
- type: euclidean_spearman
value: 47.177095238278625
- type: main_score
value: 47.172833128216176
task:
type: STS
- dataset:
config: default
name: MTEB ATEC
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
split: test
type: C-MTEB/ATEC
metrics:
- type: cosine_pearson
value: 48.304409578388466
- type: cosine_spearman
value: 50.75006977697012
- type: manhattan_pearson
value: 52.688818756177035
- type: manhattan_spearman
value: 50.739214155741095
- type: euclidean_pearson
value: 52.71788557204978
- type: euclidean_spearman
value: 50.77895730336448
- type: main_score
value: 50.75006977697012
task:
type: STS
- dataset:
config: zh
name: MTEB AmazonReviewsClassification (zh)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 54.339999999999996
- type: accuracy_stderr
value: 1.6518837731511269
- type: f1
value: 53.37316538790502
- type: f1_stderr
value: 1.6112926272861336
- type: main_score
value: 54.339999999999996
task:
type: Classification
- dataset:
config: default
name: MTEB BQ
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
split: test
type: C-MTEB/BQ
metrics:
- type: cosine_pearson
value: 59.62831218167518
- type: cosine_spearman
value: 62.02213472473759
- type: manhattan_pearson
value: 61.122261197018176
- type: manhattan_spearman
value: 62.208780520694454
- type: euclidean_pearson
value: 61.17827629627213
- type: euclidean_spearman
value: 62.266859648664244
- type: main_score
value: 62.02213472473759
task:
type: STS
- dataset:
config: default
name: MTEB CLSClusteringP2P
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
split: test
type: C-MTEB/CLSClusteringP2P
metrics:
- type: main_score
value: 54.64518394835408
- type: v_measure
value: 54.64518394835408
- type: v_measure_std
value: 1.2745946640208072
task:
type: Clustering
- dataset:
config: default
name: MTEB CLSClusteringS2S
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
split: test
type: C-MTEB/CLSClusteringS2S
metrics:
- type: main_score
value: 63.68323477729556
- type: v_measure
value: 63.68323477729556
- type: v_measure_std
value: 1.740918833098302
task:
type: Clustering
- dataset:
config: default
name: MTEB CMedQAv1
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
split: test
type: C-MTEB/CMedQAv1-reranking
metrics:
- type: map
value: 84.61500884703916
- type: mrr
value: 87.01424603174604
- type: main_score
value: 84.61500884703916
task:
type: Reranking
- dataset:
config: default
name: MTEB CMedQAv2
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
split: test
type: C-MTEB/CMedQAv2-reranking
metrics:
- type: map
value: 85.60137988993483
- type: mrr
value: 87.96857142857142
- type: main_score
value: 85.60137988993483
task:
type: Reranking
- dataset:
config: default
name: MTEB CmedqaRetrieval
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
split: dev
type: C-MTEB/CmedqaRetrieval
metrics:
- type: map_at_1
value: 24.191
- type: map_at_10
value: 35.819
- type: map_at_100
value: 37.639
- type: map_at_1000
value: 37.775
- type: map_at_3
value: 32.045
- type: map_at_5
value: 34.008
- type: mrr_at_1
value: 36.684
- type: mrr_at_10
value: 44.769
- type: mrr_at_100
value: 45.754
- type: mrr_at_1000
value: 45.809
- type: mrr_at_3
value: 42.465
- type: mrr_at_5
value: 43.696
- type: ndcg_at_1
value: 36.834
- type: ndcg_at_10
value: 42.208
- type: ndcg_at_100
value: 49.507
- type: ndcg_at_1000
value: 51.834
- type: ndcg_at_3
value: 37.416
- type: ndcg_at_5
value: 39.152
- type: precision_at_1
value: 36.834
- type: precision_at_10
value: 9.357
- type: precision_at_100
value: 1.5310000000000001
- type: precision_at_1000
value: 0.183
- type: precision_at_3
value: 21.08
- type: precision_at_5
value: 15.068999999999999
- type: recall_at_1
value: 24.191
- type: recall_at_10
value: 52.078
- type: recall_at_100
value: 82.548
- type: recall_at_1000
value: 98.017
- type: recall_at_3
value: 37.484
- type: recall_at_5
value: 43.187
- type: main_score
value: 42.208
task:
type: Retrieval
- dataset:
config: default
name: MTEB Cmnli
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
split: validation
type: C-MTEB/CMNLI
metrics:
- type: cos_sim_accuracy
value: 81.98436560432953
- type: cos_sim_accuracy_threshold
value: 67.33228049687503
- type: cos_sim_ap
value: 90.13312662430796
- type: cos_sim_f1
value: 83.2163938077737
- type: cos_sim_f1_threshold
value: 64.44945196171463
- type: cos_sim_precision
value: 79.45555082943429
- type: cos_sim_recall
value: 87.350946925415
- type: dot_accuracy
value: 80.50511124473843
- type: dot_accuracy_threshold
value: 1736000.0
- type: dot_ap
value: 88.76136186445322
- type: dot_f1
value: 81.75838631878973
- type: dot_f1_threshold
value: 1681600.0
- type: dot_precision
value: 76.96594427244582
- type: dot_recall
value: 87.18728080430208
- type: euclidean_accuracy
value: 82.21286831028262
- type: euclidean_accuracy_threshold
value: 13240.938473272565
- type: euclidean_ap
value: 90.14863232280865
- type: euclidean_f1
value: 83.277292086976
- type: euclidean_f1_threshold
value: 13667.852165734186
- type: euclidean_precision
value: 79.97847147470398
- type: euclidean_recall
value: 86.85994856207621
- type: manhattan_accuracy
value: 82.21286831028262
- type: manhattan_accuracy_threshold
value: 629412.1389746666
- type: manhattan_ap
value: 90.03868533208357
- type: manhattan_f1
value: 83.15683870248579
- type: manhattan_f1_threshold
value: 649621.3114321232
- type: manhattan_precision
value: 79.46314443971026
- type: manhattan_recall
value: 87.21066167874679
- type: max_accuracy
value: 82.21286831028262
- type: max_ap
value: 90.14863232280865
- type: max_f1
value: 83.277292086976
task:
type: PairClassification
- dataset:
config: default
name: MTEB CovidRetrieval
revision: 1271c7809071a13532e05f25fb53511ffce77117
split: dev
type: C-MTEB/CovidRetrieval
metrics:
- type: map_at_1
value: 65.595
- type: map_at_10
value: 73.717
- type: map_at_100
value: 74.134
- type: map_at_1000
value: 74.143
- type: map_at_3
value: 71.97
- type: map_at_5
value: 73.11800000000001
- type: mrr_at_1
value: 65.648
- type: mrr_at_10
value: 73.618
- type: mrr_at_100
value: 74.02499999999999
- type: mrr_at_1000
value: 74.033
- type: mrr_at_3
value: 71.865
- type: mrr_at_5
value: 73.04
- type: ndcg_at_1
value: 65.753
- type: ndcg_at_10
value: 77.458
- type: ndcg_at_100
value: 79.46
- type: ndcg_at_1000
value: 79.666
- type: ndcg_at_3
value: 73.988
- type: ndcg_at_5
value: 76.038
- type: precision_at_1
value: 65.753
- type: precision_at_10
value: 8.999
- type: precision_at_100
value: 0.9939999999999999
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 26.765
- type: precision_at_5
value: 17.092
- type: recall_at_1
value: 65.595
- type: recall_at_10
value: 89.041
- type: recall_at_100
value: 98.31400000000001
- type: recall_at_1000
value: 99.895
- type: recall_at_3
value: 79.768
- type: recall_at_5
value: 84.66799999999999
- type: main_score
value: 77.458
task:
type: Retrieval
- dataset:
config: default
name: MTEB DuRetrieval
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
split: dev
type: C-MTEB/DuRetrieval
metrics:
- type: map_at_1
value: 27.248
- type: map_at_10
value: 84.303
- type: map_at_100
value: 86.866
- type: map_at_1000
value: 86.888
- type: map_at_3
value: 58.658
- type: map_at_5
value: 74.265
- type: mrr_at_1
value: 92.2
- type: mrr_at_10
value: 94.733
- type: mrr_at_100
value: 94.767
- type: mrr_at_1000
value: 94.768
- type: mrr_at_3
value: 94.492
- type: mrr_at_5
value: 94.627
- type: ndcg_at_1
value: 92.2
- type: ndcg_at_10
value: 90.462
- type: ndcg_at_100
value: 92.562
- type: ndcg_at_1000
value: 92.757
- type: ndcg_at_3
value: 89.44800000000001
- type: ndcg_at_5
value: 88.683
- type: precision_at_1
value: 92.2
- type: precision_at_10
value: 42.980000000000004
- type: precision_at_100
value: 4.851
- type: precision_at_1000
value: 0.49
- type: precision_at_3
value: 80.233
- type: precision_at_5
value: 67.95
- type: recall_at_1
value: 27.248
- type: recall_at_10
value: 91.46600000000001
- type: recall_at_100
value: 98.566
- type: recall_at_1000
value: 99.557
- type: recall_at_3
value: 60.671
- type: recall_at_5
value: 78.363
- type: main_score
value: 90.462
task:
type: Retrieval
- dataset:
config: default
name: MTEB EcomRetrieval
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
split: dev
type: C-MTEB/EcomRetrieval
metrics:
- type: map_at_1
value: 54.7
- type: map_at_10
value: 64.574
- type: map_at_100
value: 65.144
- type: map_at_1000
value: 65.156
- type: map_at_3
value: 62.333000000000006
- type: map_at_5
value: 63.63799999999999
- type: mrr_at_1
value: 54.7
- type: mrr_at_10
value: 64.603
- type: mrr_at_100
value: 65.172
- type: mrr_at_1000
value: 65.184
- type: mrr_at_3
value: 62.383
- type: mrr_at_5
value: 63.683
- type: ndcg_at_1
value: 54.7
- type: ndcg_at_10
value: 69.298
- type: ndcg_at_100
value: 71.81
- type: ndcg_at_1000
value: 72.117
- type: ndcg_at_3
value: 64.72099999999999
- type: ndcg_at_5
value: 67.071
- type: precision_at_1
value: 54.7
- type: precision_at_10
value: 8.41
- type: precision_at_100
value: 0.9530000000000001
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 23.867
- type: precision_at_5
value: 15.459999999999999
- type: recall_at_1
value: 54.7
- type: recall_at_10
value: 84.1
- type: recall_at_100
value: 95.3
- type: recall_at_1000
value: 97.7
- type: recall_at_3
value: 71.6
- type: recall_at_5
value: 77.3
- type: main_score
value: 69.298
task:
type: Retrieval
- dataset:
config: default
name: MTEB IFlyTek
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
split: validation
type: C-MTEB/IFlyTek-classification
metrics:
- type: accuracy
value: 49.942285494420936
- type: accuracy_stderr
value: 0.9218275144833329
- type: f1
value: 41.32381790374152
- type: f1_stderr
value: 0.8291507105327707
- type: main_score
value: 49.942285494420936
task:
type: Classification
- dataset:
config: default
name: MTEB JDReview
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
split: test
type: C-MTEB/JDReview-classification
metrics:
- type: accuracy
value: 88.91181988742964
- type: accuracy_stderr
value: 1.952391767940518
- type: ap
value: 60.18509628974178
- type: ap_stderr
value: 4.273060966573582
- type: f1
value: 84.02722221827027
- type: f1_stderr
value: 2.238197243395083
- type: main_score
value: 88.91181988742964
task:
type: Classification
- dataset:
config: default
name: MTEB LCQMC
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
split: test
type: C-MTEB/LCQMC
metrics:
- type: cosine_pearson
value: 68.32691294171383
- type: cosine_spearman
value: 75.95458618586729
- type: manhattan_pearson
value: 74.37198807732018
- type: manhattan_spearman
value: 75.99352157963375
- type: euclidean_pearson
value: 74.36294627886716
- type: euclidean_spearman
value: 75.98632511635132
- type: main_score
value: 75.95458618586729
task:
type: STS
- dataset:
config: default
name: MTEB MMarcoReranking
revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
split: dev
type: C-MTEB/Mmarco-reranking
metrics:
- type: map
value: 35.4327533126161
- type: mrr
value: 34.61507936507937
- type: main_score
value: 35.4327533126161
task:
type: Reranking
- dataset:
config: default
name: MTEB MMarcoRetrieval
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
split: dev
type: C-MTEB/MMarcoRetrieval
metrics:
- type: map_at_1
value: 72.652
- type: map_at_10
value: 81.396
- type: map_at_100
value: 81.597
- type: map_at_1000
value: 81.60300000000001
- type: map_at_3
value: 79.757
- type: map_at_5
value: 80.798
- type: mrr_at_1
value: 75.01400000000001
- type: mrr_at_10
value: 81.842
- type: mrr_at_100
value: 82.025
- type: mrr_at_1000
value: 82.03099999999999
- type: mrr_at_3
value: 80.45400000000001
- type: mrr_at_5
value: 81.345
- type: ndcg_at_1
value: 74.98599999999999
- type: ndcg_at_10
value: 84.70100000000001
- type: ndcg_at_100
value: 85.568
- type: ndcg_at_1000
value: 85.721
- type: ndcg_at_3
value: 81.64099999999999
- type: ndcg_at_5
value: 83.375
- type: precision_at_1
value: 74.98599999999999
- type: precision_at_10
value: 10.049
- type: precision_at_100
value: 1.047
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 30.458000000000002
- type: precision_at_5
value: 19.206
- type: recall_at_1
value: 72.652
- type: recall_at_10
value: 94.40899999999999
- type: recall_at_100
value: 98.241
- type: recall_at_1000
value: 99.42
- type: recall_at_3
value: 86.354
- type: recall_at_5
value: 90.472
- type: main_score
value: 84.70100000000001
task:
type: Retrieval
- dataset:
config: zh-CN
name: MTEB MassiveIntentClassification (zh-CN)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 78.19098856758575
- type: accuracy_stderr
value: 0.6325028678427684
- type: f1
value: 74.80611425574001
- type: f1_stderr
value: 0.9021806207904779
- type: main_score
value: 78.19098856758575
task:
type: Classification
- dataset:
config: zh-CN
name: MTEB MassiveScenarioClassification (zh-CN)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 82.58238063214526
- type: accuracy_stderr
value: 1.0999970213165273
- type: f1
value: 81.94734854057064
- type: f1_stderr
value: 1.248633855872851
- type: main_score
value: 82.58238063214526
task:
type: Classification
- dataset:
config: default
name: MTEB MedicalRetrieval
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
split: dev
type: C-MTEB/MedicalRetrieval
metrics:
- type: map_at_1
value: 53.7
- type: map_at_10
value: 59.184000000000005
- type: map_at_100
value: 59.754
- type: map_at_1000
value: 59.8
- type: map_at_3
value: 57.833
- type: map_at_5
value: 58.548
- type: mrr_at_1
value: 54.0
- type: mrr_at_10
value: 59.352000000000004
- type: mrr_at_100
value: 59.926
- type: mrr_at_1000
value: 59.971
- type: mrr_at_3
value: 57.99999999999999
- type: mrr_at_5
value: 58.714999999999996
- type: ndcg_at_1
value: 53.7
- type: ndcg_at_10
value: 62.022
- type: ndcg_at_100
value: 65.038
- type: ndcg_at_1000
value: 66.366
- type: ndcg_at_3
value: 59.209
- type: ndcg_at_5
value: 60.51299999999999
- type: precision_at_1
value: 53.7
- type: precision_at_10
value: 7.1
- type: precision_at_100
value: 0.856
- type: precision_at_1000
value: 0.096
- type: precision_at_3
value: 21.067
- type: precision_at_5
value: 13.28
- type: recall_at_1
value: 53.7
- type: recall_at_10
value: 71.0
- type: recall_at_100
value: 85.6
- type: recall_at_1000
value: 96.3
- type: recall_at_3
value: 63.2
- type: recall_at_5
value: 66.4
- type: main_score
value: 62.022
task:
type: Retrieval
- dataset:
config: default
name: MTEB MultilingualSentiment
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
split: validation
type: C-MTEB/MultilingualSentiment-classification
metrics:
- type: accuracy
value: 78.91333333333334
- type: accuracy_stderr
value: 1.0834307648494321
- type: f1
value: 78.881433228092
- type: f1_stderr
value: 1.122457277013712
- type: main_score
value: 78.91333333333334
task:
type: Classification
- dataset:
config: default
name: MTEB Ocnli
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
split: validation
type: C-MTEB/OCNLI
metrics:
- type: cos_sim_accuracy
value: 76.39415268002165
- type: cos_sim_accuracy_threshold
value: 68.98242139321592
- type: cos_sim_ap
value: 83.20687440058073
- type: cos_sim_f1
value: 78.4351145038168
- type: cos_sim_f1_threshold
value: 65.47409929698304
- type: cos_sim_precision
value: 71.54046997389034
- type: cos_sim_recall
value: 86.80042238648363
- type: dot_accuracy
value: 74.60747157552788
- type: dot_accuracy_threshold
value: 1737600.0
- type: dot_ap
value: 79.78938545919723
- type: dot_f1
value: 76.92307692307692
- type: dot_f1_threshold
value: 1652800.0
- type: dot_precision
value: 67.90622473726758
- type: dot_recall
value: 88.70116156283
- type: euclidean_accuracy
value: 76.34001082837032
- type: euclidean_accuracy_threshold
value: 12597.299662420446
- type: euclidean_ap
value: 83.60222701792158
- type: euclidean_f1
value: 78.77947295423024
- type: euclidean_f1_threshold
value: 13639.653702639469
- type: euclidean_precision
value: 70.06578947368422
- type: euclidean_recall
value: 89.96832101372756
- type: manhattan_accuracy
value: 76.23172712506768
- type: manhattan_accuracy_threshold
value: 587601.2824743986
- type: manhattan_ap
value: 83.51813426548178
- type: manhattan_f1
value: 78.6654135338346
- type: manhattan_f1_threshold
value: 639711.1931562424
- type: manhattan_precision
value: 70.87214225232854
- type: manhattan_recall
value: 88.3843717001056
- type: max_accuracy
value: 76.39415268002165
- type: max_ap
value: 83.60222701792158
- type: max_f1
value: 78.77947295423024
task:
type: PairClassification
- dataset:
config: default
name: MTEB OnlineShopping
revision: e610f2ebd179a8fda30ae534c3878750a96db120
split: test
type: C-MTEB/OnlineShopping-classification
metrics:
- type: accuracy
value: 94.59
- type: accuracy_stderr
value: 0.8971621926942733
- type: ap
value: 93.01229797205905
- type: ap_stderr
value: 1.0519542956523058
- type: f1
value: 94.58077736915268
- type: f1_stderr
value: 0.8954928292768671
- type: main_score
value: 94.59
task:
type: Classification
- dataset:
config: default
name: MTEB PAWSX
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
split: test
type: C-MTEB/PAWSX
metrics:
- type: cosine_pearson
value: 24.341872875292857
- type: cosine_spearman
value: 30.570037022875436
- type: manhattan_pearson
value: 31.41015320258418
- type: manhattan_spearman
value: 30.604526098895114
- type: euclidean_pearson
value: 31.400038084432175
- type: euclidean_spearman
value: 30.61062265273698
- type: main_score
value: 30.570037022875436
task:
type: STS
- dataset:
config: default
name: MTEB QBQTC
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
split: test
type: C-MTEB/QBQTC
metrics:
- type: cosine_pearson
value: 36.61757468091905
- type: cosine_spearman
value: 38.981417359835504
- type: manhattan_pearson
value: 37.971127169578764
- type: manhattan_spearman
value: 39.55028286687854
- type: euclidean_pearson
value: 37.96983777648438
- type: euclidean_spearman
value: 39.542856511171784
- type: main_score
value: 38.981417359835504
task:
type: STS
- dataset:
config: zh
name: MTEB STS22 (zh)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 68.29834902017382
- type: cosine_spearman
value: 68.6823378297782
- type: manhattan_pearson
value: 68.47336169904406
- type: manhattan_spearman
value: 69.08033223619941
- type: euclidean_pearson
value: 68.38785956191622
- type: euclidean_spearman
value: 68.97973814449657
- type: main_score
value: 68.6823378297782
task:
type: STS
- dataset:
config: default
name: MTEB STSB
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
split: test
type: C-MTEB/STSB
metrics:
- type: cosine_pearson
value: 80.60572958563593
- type: cosine_spearman
value: 80.87063761195603
- type: manhattan_pearson
value: 79.30174059269083
- type: manhattan_spearman
value: 80.02203618135883
- type: euclidean_pearson
value: 79.3314553444783
- type: euclidean_spearman
value: 80.04556415585255
- type: main_score
value: 80.87063761195603
task:
type: STS
- dataset:
config: default
name: MTEB T2Reranking
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
split: dev
type: C-MTEB/T2Reranking
metrics:
- type: map
value: 67.47921173708028
- type: mrr
value: 77.9396513739777
- type: main_score
value: 67.47921173708028
task:
type: Reranking
- dataset:
config: default
name: MTEB T2Retrieval
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
split: dev
type: C-MTEB/T2Retrieval
metrics:
- type: map_at_1
value: 28.021
- type: map_at_10
value: 79.149
- type: map_at_100
value: 82.613
- type: map_at_1000
value: 82.67099999999999
- type: map_at_3
value: 55.665
- type: map_at_5
value: 68.46900000000001
- type: mrr_at_1
value: 91.106
- type: mrr_at_10
value: 93.372
- type: mrr_at_100
value: 93.44200000000001
- type: mrr_at_1000
value: 93.445
- type: mrr_at_3
value: 92.99300000000001
- type: mrr_at_5
value: 93.24900000000001
- type: ndcg_at_1
value: 91.106
- type: ndcg_at_10
value: 86.259
- type: ndcg_at_100
value: 89.46600000000001
- type: ndcg_at_1000
value: 90.012
- type: ndcg_at_3
value: 87.574
- type: ndcg_at_5
value: 86.283
- type: precision_at_1
value: 91.106
- type: precision_at_10
value: 42.742999999999995
- type: precision_at_100
value: 5.029999999999999
- type: precision_at_1000
value: 0.516
- type: precision_at_3
value: 76.593
- type: precision_at_5
value: 64.243
- type: recall_at_1
value: 28.021
- type: recall_at_10
value: 85.184
- type: recall_at_100
value: 95.79299999999999
- type: recall_at_1000
value: 98.547
- type: recall_at_3
value: 57.233000000000004
- type: recall_at_5
value: 71.628
- type: main_score
value: 86.259
task:
type: Retrieval
- dataset:
config: default
name: MTEB TNews
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
split: validation
type: C-MTEB/TNews-classification
metrics:
- type: accuracy
value: 50.255
- type: accuracy_stderr
value: 0.9341868121526873
- type: f1
value: 48.65080322457893
- type: f1_stderr
value: 0.9391547591179161
- type: main_score
value: 50.255
task:
type: Classification
- dataset:
config: default
name: MTEB ThuNewsClusteringP2P
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
split: test
type: C-MTEB/ThuNewsClusteringP2P
metrics:
- type: main_score
value: 64.32076022871308
- type: v_measure
value: 64.32076022871308
- type: v_measure_std
value: 0.7190996709617924
task:
type: Clustering
- dataset:
config: default
name: MTEB ThuNewsClusteringS2S
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
split: test
type: C-MTEB/ThuNewsClusteringS2S
metrics:
- type: main_score
value: 54.57080911705562
- type: v_measure
value: 54.57080911705562
- type: v_measure_std
value: 1.5185826402845883
task:
type: Clustering
- dataset:
config: default
name: MTEB VideoRetrieval
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
split: dev
type: C-MTEB/VideoRetrieval
metrics:
- type: map_at_1
value: 63.1
- type: map_at_10
value: 73.137
- type: map_at_100
value: 73.539
- type: map_at_1000
value: 73.546
- type: map_at_3
value: 71.467
- type: map_at_5
value: 72.552
- type: mrr_at_1
value: 63.3
- type: mrr_at_10
value: 73.238
- type: mrr_at_100
value: 73.64
- type: mrr_at_1000
value: 73.64699999999999
- type: mrr_at_3
value: 71.56700000000001
- type: mrr_at_5
value: 72.652
- type: ndcg_at_1
value: 63.1
- type: ndcg_at_10
value: 77.397
- type: ndcg_at_100
value: 79.11399999999999
- type: ndcg_at_1000
value: 79.305
- type: ndcg_at_3
value: 74.031
- type: ndcg_at_5
value: 75.976
- type: precision_at_1
value: 63.1
- type: precision_at_10
value: 9.049999999999999
- type: precision_at_100
value: 0.98
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 27.133000000000003
- type: precision_at_5
value: 17.22
- type: recall_at_1
value: 63.1
- type: recall_at_10
value: 90.5
- type: recall_at_100
value: 98.0
- type: recall_at_1000
value: 99.5
- type: recall_at_3
value: 81.39999999999999
- type: recall_at_5
value: 86.1
- type: main_score
value: 77.397
task:
type: Retrieval
- dataset:
config: default
name: MTEB Waimai
revision: 339287def212450dcaa9df8c22bf93e9980c7023
split: test
type: C-MTEB/waimai-classification
metrics:
- type: accuracy
value: 89.26
- type: accuracy_stderr
value: 1.44651304867948
- type: ap
value: 75.17154345788362
- type: ap_stderr
value: 2.7356371110082565
- type: f1
value: 87.94016849813178
- type: f1_stderr
value: 1.3897605039980534
- type: main_score
value: 89.26
task:
type: Classification
- dataset:
config: default
name: MTEB AlloProfClusteringP2P
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: main_score
value: 71.20310003742769
- type: v_measure
value: 71.20310003742769
- type: v_measure_std
value: 2.3682783706448687
task:
type: Clustering
- dataset:
config: default
name: MTEB AlloProfClusteringS2S
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: main_score
value: 59.64232194434788
- type: v_measure
value: 59.64232194434788
- type: v_measure_std
value: 2.4292956011867557
task:
type: Clustering
- dataset:
config: default
name: MTEB AlloprofReranking
revision: 65393d0d7a08a10b4e348135e824f385d420b0fd
split: test
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
metrics:
- type: main_score
value: 78.62041803111894
- type: map
value: 78.62041803111894
- type: mrr
value: 79.82309057762426
- type: nAUC_map_diff1
value: 58.23586953459263
- type: nAUC_map_max
value: 16.162821346484357
- type: nAUC_map_std
value: 20.727030444422525
- type: nAUC_mrr_diff1
value: 57.89675675999501
- type: nAUC_mrr_max
value: 17.188359535738417
- type: nAUC_mrr_std
value: 20.121404571879598
task:
type: Reranking
- dataset:
config: default
name: MTEB AlloprofRetrieval
revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
split: test
type: lyon-nlp/alloprof
metrics:
- type: main_score
value: 58.499
- type: map_at_1
value: 40.371
- type: map_at_10
value: 52.337
- type: map_at_100
value: 53.04
- type: map_at_1000
value: 53.065
- type: map_at_20
value: 52.772
- type: map_at_3
value: 49.201
- type: map_at_5
value: 51.025
- type: mrr_at_1
value: 40.3713298791019
- type: mrr_at_10
value: 52.322165337061755
- type: mrr_at_100
value: 53.02092832847133
- type: mrr_at_1000
value: 53.04594680215603
- type: mrr_at_20
value: 52.750849914358135
- type: mrr_at_3
value: 49.150834772596475
- type: mrr_at_5
value: 50.998848589522275
- type: nauc_map_at_1000_diff1
value: 44.71946249374932
- type: nauc_map_at_1000_max
value: 28.074204125714193
- type: nauc_map_at_1000_std
value: -5.1319087890196275
- type: nauc_map_at_100_diff1
value: 44.71140286780233
- type: nauc_map_at_100_max
value: 28.09677884622645
- type: nauc_map_at_100_std
value: -5.116353867480612
- type: nauc_map_at_10_diff1
value: 44.737968596047736
- type: nauc_map_at_10_max
value: 28.103186472557184
- type: nauc_map_at_10_std
value: -5.258817287329683
- type: nauc_map_at_1_diff1
value: 47.48389890056789
- type: nauc_map_at_1_max
value: 24.803734709402654
- type: nauc_map_at_1_std
value: -6.504759899363267
- type: nauc_map_at_20_diff1
value: 44.67268454863271
- type: nauc_map_at_20_max
value: 28.068912295976933
- type: nauc_map_at_20_std
value: -5.1971060419801836
- type: nauc_map_at_3_diff1
value: 44.59399231542881
- type: nauc_map_at_3_max
value: 27.097806786915502
- type: nauc_map_at_3_std
value: -5.957120508111229
- type: nauc_map_at_5_diff1
value: 44.549807218619236
- type: nauc_map_at_5_max
value: 28.03902312965202
- type: nauc_map_at_5_std
value: -5.279585300980128
- type: nauc_mrr_at_1000_diff1
value: 44.70183532803094
- type: nauc_mrr_at_1000_max
value: 28.08833759937601
- type: nauc_mrr_at_1000_std
value: -5.097929115475795
- type: nauc_mrr_at_100_diff1
value: 44.693824401340684
- type: nauc_mrr_at_100_max
value: 28.110898009292296
- type: nauc_mrr_at_100_std
value: -5.082401300601749
- type: nauc_mrr_at_10_diff1
value: 44.74052791862188
- type: nauc_mrr_at_10_max
value: 28.125378341430725
- type: nauc_mrr_at_10_std
value: -5.209767905428716
- type: nauc_mrr_at_1_diff1
value: 47.48389890056789
- type: nauc_mrr_at_1_max
value: 24.803734709402654
- type: nauc_mrr_at_1_std
value: -6.504759899363267
- type: nauc_mrr_at_20_diff1
value: 44.65204014980107
- type: nauc_mrr_at_20_max
value: 28.071523791101487
- type: nauc_mrr_at_20_std
value: -5.176680495032765
- type: nauc_mrr_at_3_diff1
value: 44.566371489967835
- type: nauc_mrr_at_3_max
value: 27.138418179089243
- type: nauc_mrr_at_3_std
value: -5.8860676927947715
- type: nauc_mrr_at_5_diff1
value: 44.513022796226025
- type: nauc_mrr_at_5_max
value: 28.037968016529184
- type: nauc_mrr_at_5_std
value: -5.286851060853457
- type: nauc_ndcg_at_1000_diff1
value: 44.31019947897497
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task:
type: Retrieval
- dataset:
config: fr
name: MTEB AmazonReviewsClassification (fr)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
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value: 55.186
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value: 54.46705535013317
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value: 54.46705535013317
- type: main_score
value: 55.186
task:
type: Classification
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config: default
name: MTEB BSARDRetrieval
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
split: test
type: maastrichtlawtech/bsard
metrics:
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task:
type: Retrieval
- dataset:
config: default
name: MTEB HALClusteringS2S
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
split: test
type: lyon-nlp/clustering-hal-s2s
metrics:
- type: main_score
value: 28.18744772954557
- type: v_measure
value: 28.18744772954557
- type: v_measure_std
value: 3.239838057506439
task:
type: Clustering
- dataset:
config: fr
name: MTEB MLSUMClusteringP2P (fr)
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: reciTAL/mlsum
metrics:
- type: main_score
value: 47.75009059283003
- type: v_measure
value: 47.75009059283003
- type: v_measure_std
value: 2.009277732690298
task:
type: Clustering
- dataset:
config: fr
name: MTEB MLSUMClusteringS2S (fr)
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: reciTAL/mlsum
metrics:
- type: main_score
value: 47.46091989113078
- type: v_measure
value: 47.46091989113078
- type: v_measure_std
value: 2.604802270948194
task:
type: Clustering
- dataset:
config: fr
name: MTEB MTOPDomainClassification (fr)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 97.20325712496086
- type: f1
value: 97.05991090368462
- type: f1_weighted
value: 97.20748006323807
- type: main_score
value: 97.20325712496086
task:
type: Classification
- dataset:
config: fr
name: MTEB MTOPIntentClassification (fr)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
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value: 93.07234575634199
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value: 76.54521288506878
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value: 93.6903586431893
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value: 93.07234575634199
task:
type: Classification
- dataset:
config: fra
name: MTEB MasakhaNEWSClassification (fra)
revision: 18193f187b92da67168c655c9973a165ed9593dd
split: test
type: mteb/masakhanews
metrics:
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value: 82.48815165876778
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value: 78.71164464238117
- type: f1_weighted
value: 82.38927389376973
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value: 82.48815165876778
task:
type: Classification
- dataset:
config: fra
name: MTEB MasakhaNEWSClusteringP2P (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
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value: 73.85712952800003
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value: 73.85712952800003
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value: 22.471668299794416
task:
type: Clustering
- dataset:
config: fra
name: MTEB MasakhaNEWSClusteringS2S (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
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value: 67.23960512566751
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value: 67.23960512566751
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value: 24.65079601360142
task:
type: Clustering
- dataset:
config: fr
name: MTEB MassiveIntentClassification (fr)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 79.59986550100874
- type: f1
value: 76.0439154517916
- type: f1_weighted
value: 79.48538292013761
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value: 79.59986550100874
task:
type: Classification
- dataset:
config: fr
name: MTEB MassiveScenarioClassification (fr)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
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value: 82.182246133154
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value: 81.68006668655397
- type: f1_weighted
value: 81.94775072858566
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value: 82.182246133154
task:
type: Classification
- dataset:
config: fr
name: MTEB MintakaRetrieval (fr)
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
split: test
type: jinaai/mintakaqa
metrics:
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value: 62.532
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value: 45.823
- type: map_at_10
value: 57.174
- type: map_at_100
value: 57.735
- type: map_at_1000
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value: 54.716079716079676
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value: 39.48401851944296
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value: 3.9173787361040913
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value: 3.9506714061766557
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value: 46.9370613928509
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value: 29.494641243672216
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value: 47.32607171885759
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value: 12.273738036245142
- type: ndcg_at_1
value: 45.823
- type: ndcg_at_10
value: 62.532
- type: ndcg_at_100
value: 65.298
- type: ndcg_at_1000
value: 66.214
- type: ndcg_at_20
value: 63.82600000000001
- type: ndcg_at_3
value: 57.528999999999996
- type: ndcg_at_5
value: 60.24
- type: precision_at_1
value: 45.823
- type: precision_at_10
value: 7.928
- type: precision_at_100
value: 0.923
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.22
- type: precision_at_3
value: 21.881
- type: precision_at_5
value: 14.438999999999998
- type: recall_at_1
value: 45.823
- type: recall_at_10
value: 79.279
- type: recall_at_100
value: 92.301
- type: recall_at_1000
value: 99.631
- type: recall_at_20
value: 84.398
- type: recall_at_3
value: 65.643
- type: recall_at_5
value: 72.195
task:
type: Retrieval
- dataset:
config: fr
name: MTEB OpusparcusPC (fr)
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
split: test
type: GEM/opusparcus
metrics:
- type: cosine_accuracy
value: 99.90069513406156
- type: cosine_accuracy_threshold
value: 54.45001207375879
- type: cosine_ap
value: 100.0
- type: cosine_f1
value: 99.95032290114257
- type: cosine_f1_threshold
value: 54.45001207375879
- type: cosine_precision
value: 100.0
- type: cosine_recall
value: 99.90069513406156
- type: dot_accuracy
value: 99.90069513406156
- type: dot_accuracy_threshold
value: 1312800.0
- type: dot_ap
value: 100.0
- type: dot_f1
value: 99.95032290114257
- type: dot_f1_threshold
value: 1312800.0
- type: dot_precision
value: 100.0
- type: dot_recall
value: 99.90069513406156
- type: euclidean_accuracy
value: 99.90069513406156
- type: euclidean_accuracy_threshold
value: 15150.791732002876
- type: euclidean_ap
value: 100.0
- type: euclidean_f1
value: 99.95032290114257
- type: euclidean_f1_threshold
value: 15150.791732002876
- type: euclidean_precision
value: 100.0
- type: euclidean_recall
value: 99.90069513406156
- type: main_score
value: 100.0
- type: manhattan_accuracy
value: 99.90069513406156
- type: manhattan_accuracy_threshold
value: 717903.2791554928
- type: manhattan_ap
value: 100.0
- type: manhattan_f1
value: 99.95032290114257
- type: manhattan_f1_threshold
value: 717903.2791554928
- type: manhattan_precision
value: 100.0
- type: manhattan_recall
value: 99.90069513406156
- type: max_ap
value: 100.0
- type: max_f1
value: 99.95032290114257
- type: max_precision
value: 100.0
- type: max_recall
value: 99.90069513406156
- type: similarity_accuracy
value: 99.90069513406156
- type: similarity_accuracy_threshold
value: 54.45001207375879
- type: similarity_ap
value: 100.0
- type: similarity_f1
value: 99.95032290114257
- type: similarity_f1_threshold
value: 54.45001207375879
- type: similarity_precision
value: 100.0
- type: similarity_recall
value: 99.90069513406156
task:
type: PairClassification
- dataset:
config: fr
name: MTEB PawsXPairClassification (fr)
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
split: test
type: google-research-datasets/paws-x
metrics:
- type: cosine_accuracy
value: 67.95
- type: cosine_accuracy_threshold
value: 97.36901285947026
- type: cosine_ap
value: 70.14158727060726
- type: cosine_f1
value: 65.38108356290174
- type: cosine_f1_threshold
value: 94.90683744884689
- type: cosine_precision
value: 55.84313725490196
- type: cosine_recall
value: 78.8482834994463
- type: dot_accuracy
value: 60.5
- type: dot_accuracy_threshold
value: 2606400.0
- type: dot_ap
value: 57.0114505567262
- type: dot_f1
value: 63.29394387001477
- type: dot_f1_threshold
value: 2345600.0
- type: dot_precision
value: 47.4792243767313
- type: dot_recall
value: 94.90586932447398
- type: euclidean_accuracy
value: 68.05
- type: euclidean_accuracy_threshold
value: 3824.99743197985
- type: euclidean_ap
value: 70.01158306654237
- type: euclidean_f1
value: 65.21939953810623
- type: euclidean_f1_threshold
value: 5187.47968966464
- type: euclidean_precision
value: 55.942947702060216
- type: euclidean_recall
value: 78.18383167220377
- type: main_score
value: 70.14158727060726
- type: manhattan_accuracy
value: 68.05
- type: manhattan_accuracy_threshold
value: 191852.34832763672
- type: manhattan_ap
value: 70.01670033904287
- type: manhattan_f1
value: 65.2854511970534
- type: manhattan_f1_threshold
value: 246807.1710705757
- type: manhattan_precision
value: 55.87076438140268
- type: manhattan_recall
value: 78.51605758582502
- type: max_ap
value: 70.14158727060726
- type: max_f1
value: 65.38108356290174
- type: max_precision
value: 55.942947702060216
- type: max_recall
value: 94.90586932447398
- type: similarity_accuracy
value: 67.95
- type: similarity_accuracy_threshold
value: 97.36901285947026
- type: similarity_ap
value: 70.14158727060726
- type: similarity_f1
value: 65.38108356290174
- type: similarity_f1_threshold
value: 94.90683744884689
- type: similarity_precision
value: 55.84313725490196
- type: similarity_recall
value: 78.8482834994463
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICKFr
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
split: test
type: Lajavaness/SICK-fr
metrics:
- type: cosine_pearson
value: 79.79861486027
- type: cosine_spearman
value: 79.3918786992987
- type: euclidean_pearson
value: 77.73226212475764
- type: euclidean_spearman
value: 79.08856888397014
- type: main_score
value: 79.3918786992987
- type: manhattan_pearson
value: 77.8002206650809
- type: manhattan_spearman
value: 79.15284532531264
- type: pearson
value: 79.79861486027
- type: spearman
value: 79.3918786992987
task:
type: STS
- dataset:
config: fr
name: MTEB STS22 (fr)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 83.32314025534286
- type: cosine_spearman
value: 83.2806004701507
- type: euclidean_pearson
value: 81.88040500817269
- type: euclidean_spearman
value: 82.73179823676206
- type: main_score
value: 83.2806004701507
- type: manhattan_pearson
value: 82.0438174605579
- type: manhattan_spearman
value: 83.0253049811576
- type: pearson
value: 83.32314025534286
- type: spearman
value: 83.2806004701507
task:
type: STS
- dataset:
config: fr
name: MTEB STSBenchmarkMultilingualSTS (fr)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics:
- type: cosine_pearson
value: 84.56723075054445
- type: cosine_spearman
value: 85.08759191551403
- type: euclidean_pearson
value: 83.186096744725
- type: euclidean_spearman
value: 84.36958569816491
- type: main_score
value: 85.08759191551403
- type: manhattan_pearson
value: 83.1405072165467
- type: manhattan_spearman
value: 84.34227830781155
- type: pearson
value: 84.56723075054445
- type: spearman
value: 85.08759191551403
task:
type: STS
- dataset:
config: default
name: MTEB SummEvalFr
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
split: test
type: lyon-nlp/summarization-summeval-fr-p2p
metrics:
- type: cosine_pearson
value: 31.921764332449115
- type: cosine_spearman
value: 31.260442997631806
- type: dot_pearson
value: 31.585578707631406
- type: dot_spearman
value: 31.479238746310028
- type: main_score
value: 31.260442997631806
- type: pearson
value: 31.921764332449115
- type: spearman
value: 31.260442997631806
task:
type: Summarization
- dataset:
config: default
name: MTEB SyntecReranking
revision: daf0863838cd9e3ba50544cdce3ac2b338a1b0ad
split: test
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
metrics:
- type: main_score
value: 91.83333333333333
- type: map
value: 91.83333333333333
- type: mrr
value: 92.0
- type: nAUC_map_diff1
value: 53.97793263646914
- type: nAUC_map_max
value: 44.264158743282195
- type: nAUC_map_std
value: 14.692218350754885
- type: nAUC_mrr_diff1
value: 54.36926882239366
- type: nAUC_mrr_max
value: 46.43108510296003
- type: nAUC_mrr_std
value: 17.48914092664096
task:
type: Reranking
- dataset:
config: default
name: MTEB SyntecRetrieval
revision: 19661ccdca4dfc2d15122d776b61685f48c68ca9
split: test
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
metrics:
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value: 90.36699999999999
- type: map_at_1
value: 79.0
- type: map_at_10
value: 87.18599999999999
- type: map_at_100
value: 87.18599999999999
- type: map_at_1000
value: 87.18599999999999
- type: map_at_20
value: 87.18599999999999
- type: map_at_3
value: 86.0
- type: map_at_5
value: 86.95
- type: mrr_at_1
value: 79.0
- type: mrr_at_10
value: 87.18611111111112
- type: mrr_at_100
value: 87.18611111111112
- type: mrr_at_1000
value: 87.18611111111112
- type: mrr_at_20
value: 87.18611111111112
- type: mrr_at_3
value: 86.0
- type: mrr_at_5
value: 86.95
- type: nauc_map_at_1000_diff1
value: 63.05539428169271
- type: nauc_map_at_1000_max
value: 45.428107132447124
- type: nauc_map_at_1000_std
value: 13.94507583970834
- type: nauc_map_at_100_diff1
value: 63.05539428169271
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value: 45.428107132447124
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value: 13.94507583970834
- type: nauc_map_at_10_diff1
value: 63.05539428169271
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value: 45.428107132447124
- type: nauc_map_at_10_std
value: 13.94507583970834
- type: nauc_map_at_1_diff1
value: 64.24122923028831
- type: nauc_map_at_1_max
value: 44.34077957053877
- type: nauc_map_at_1_std
value: 9.594344386466878
- type: nauc_map_at_20_diff1
value: 63.05539428169271
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value: 45.428107132447124
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value: 13.94507583970834
- type: nauc_map_at_3_diff1
value: 62.30831315577075
- type: nauc_map_at_3_max
value: 47.33980193586779
- type: nauc_map_at_3_std
value: 16.132624025733
- type: nauc_map_at_5_diff1
value: 63.079622378971834
- type: nauc_map_at_5_max
value: 45.13424437707254
- type: nauc_map_at_5_std
value: 13.730785051570013
- type: nauc_mrr_at_1000_diff1
value: 63.05539428169271
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value: 45.428107132447124
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value: 13.94507583970834
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value: 63.05539428169271
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value: 45.428107132447124
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value: 13.94507583970834
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value: 63.05539428169271
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value: 45.428107132447124
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value: 47.33980193586779
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value: 16.132624025733
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value: 63.079622378971834
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value: 13.730785051570013
- type: nauc_ndcg_at_1000_diff1
value: 62.97376441474187
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value: 45.457846840130586
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value: 64.24122923028831
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value: 95.306
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task:
type: Retrieval
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
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value: 38.21674564144456
- type: f1
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task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics:
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value: 89.0
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value: 69.23076923076923
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value: 87.1877340095262
- type: cosine_precision
value: 67.5
- type: cosine_recall
value: 71.05263157894737
- type: dot_accuracy
value: 88.3
- type: dot_accuracy_threshold
value: 2472000.0
- type: dot_ap
value: 74.26705897704197
- type: dot_f1
value: 66.49874055415617
- type: dot_f1_threshold
value: 2316800.0
- type: dot_precision
value: 63.76811594202898
- type: dot_recall
value: 69.47368421052632
- type: euclidean_accuracy
value: 89.2
- type: euclidean_accuracy_threshold
value: 6878.705188647788
- type: euclidean_ap
value: 78.51718555534579
- type: euclidean_f1
value: 69.54314720812182
- type: euclidean_f1_threshold
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- type: euclidean_precision
value: 67.15686274509804
- type: euclidean_recall
value: 72.10526315789474
- type: main_score
value: 78.51718555534579
- type: manhattan_accuracy
value: 89.2
- type: manhattan_accuracy_threshold
value: 326812.48528957367
- type: manhattan_ap
value: 78.50895632545628
- type: manhattan_f1
value: 69.84924623115577
- type: manhattan_f1_threshold
value: 398102.616417408
- type: manhattan_precision
value: 66.82692307692307
- type: manhattan_recall
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- type: max_ap
value: 78.51718555534579
- type: max_f1
value: 69.84924623115577
- type: max_precision
value: 67.5
- type: max_recall
value: 73.15789473684211
- type: similarity_accuracy
value: 89.0
- type: similarity_accuracy_threshold
value: 95.30268088769837
- type: similarity_ap
value: 78.23422403821777
- type: similarity_f1
value: 69.23076923076923
- type: similarity_f1_threshold
value: 87.1877340095262
- type: similarity_precision
value: 67.5
- type: similarity_recall
value: 71.05263157894737
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics:
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value: 91.04238667979497
- type: cosine_spearman
value: 90.96758456402505
- type: euclidean_pearson
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- type: euclidean_spearman
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- type: main_score
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- type: manhattan_spearman
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- type: pearson
value: 91.04238667979497
- type: spearman
value: 90.96758456402505
task:
type: STS
- dataset:
config: default
name: MTEB DBPedia-PL
revision: 76afe41d9af165cc40999fcaa92312b8b012064a
split: test
type: clarin-knext/dbpedia-pl
metrics:
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task:
type: Retrieval
- dataset:
config: default
name: MTEB 8TagsClustering
revision: None
split: test
type: PL-MTEB/8tags-clustering
metrics:
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value: 50.28804848851286
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value: 50.28804848851286
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task:
type: Clustering
- dataset:
config: default
name: MTEB FiQA-PL
revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e
split: test
type: clarin-knext/fiqa-pl
metrics:
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- type: map_at_100
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- type: map_at_1000
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task:
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name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
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type: mteb/amazon_massive_intent
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task:
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task:
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value: 55.36262550526809
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value: 31.854572843060865
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value: 15.449819317346778
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value: 3.9346798436914625
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value: 5.3792560913820635
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value: 29.662449422215627
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value: 35.59583981361554
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value: 9.138475426366536
- type: ndcg_at_1
value: 38.847
- type: ndcg_at_10
value: 56.854000000000006
- type: ndcg_at_100
value: 60.767
- type: ndcg_at_1000
value: 61.399
- type: ndcg_at_20
value: 58.941
- type: ndcg_at_3
value: 49.576
- type: ndcg_at_5
value: 53.502
- type: precision_at_1
value: 38.847
- type: precision_at_10
value: 9.064
- type: precision_at_100
value: 1.127
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_20
value: 5.038
- type: precision_at_3
value: 22.335
- type: precision_at_5
value: 15.689
- type: recall_at_1
value: 34.514
- type: recall_at_10
value: 76.152
- type: recall_at_100
value: 92.837
- type: recall_at_1000
value: 97.596
- type: recall_at_20
value: 83.77799999999999
- type: recall_at_3
value: 57.484
- type: recall_at_5
value: 66.476
task:
type: Retrieval
- dataset:
config: default
name: MTEB PAC
revision: None
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 67.24297712134376
- type: accuracy_stderr
value: 4.77558207347837
- type: ap
value: 77.38171975466854
- type: ap_stderr
value: 2.5801970175320394
- type: f1
value: 65.21823897814332
- type: f1_stderr
value: 4.317111734308895
- type: main_score
value: 67.24297712134376
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics:
- type: cosine_accuracy
value: 97.95918367346938
- type: cosine_accuracy_threshold
value: 59.87724328133361
- type: cosine_ap
value: 99.24498625606927
- type: cosine_f1
value: 96.6867469879518
- type: cosine_f1_threshold
value: 59.87724328133361
- type: cosine_precision
value: 95.53571428571429
- type: cosine_recall
value: 97.86585365853658
- type: dot_accuracy
value: 98.51576994434137
- type: dot_accuracy_threshold
value: 1574400.0
- type: dot_ap
value: 99.28566232682996
- type: dot_f1
value: 97.57575757575758
- type: dot_f1_threshold
value: 1564800.0
- type: dot_precision
value: 96.98795180722891
- type: dot_recall
value: 98.17073170731707
- type: euclidean_accuracy
value: 97.6808905380334
- type: euclidean_accuracy_threshold
value: 14418.957939643331
- type: euclidean_ap
value: 99.0876340868033
- type: euclidean_f1
value: 96.24060150375941
- type: euclidean_f1_threshold
value: 14442.183182634264
- type: euclidean_precision
value: 94.95548961424333
- type: euclidean_recall
value: 97.5609756097561
- type: main_score
value: 99.28566232682996
- type: manhattan_accuracy
value: 97.86641929499072
- type: manhattan_accuracy_threshold
value: 681802.1857857704
- type: manhattan_ap
value: 99.08465290287205
- type: manhattan_f1
value: 96.52042360060513
- type: manhattan_f1_threshold
value: 681802.1857857704
- type: manhattan_precision
value: 95.7957957957958
- type: manhattan_recall
value: 97.2560975609756
- type: max_ap
value: 99.28566232682996
- type: max_f1
value: 97.57575757575758
- type: max_precision
value: 96.98795180722891
- type: max_recall
value: 98.17073170731707
- type: similarity_accuracy
value: 97.95918367346938
- type: similarity_accuracy_threshold
value: 59.87724328133361
- type: similarity_ap
value: 99.24498625606927
- type: similarity_f1
value: 96.6867469879518
- type: similarity_f1_threshold
value: 59.87724328133361
- type: similarity_precision
value: 95.53571428571429
- type: similarity_recall
value: 97.86585365853658
task:
type: PairClassification
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 90.41551246537396
- type: f1
value: 89.15361039614409
- type: f1_weighted
value: 90.69893050097603
- type: main_score
value: 90.41551246537396
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 77.77327935222672
- type: f1
value: 61.238079022455636
- type: f1_weighted
value: 80.58753601509183
- type: main_score
value: 77.77327935222672
task:
type: Classification
- dataset:
config: default
name: MTEB PPC
revision: None
split: test
type: PL-MTEB/ppc-pairclassification
metrics:
- type: cos_sim_accuracy
value: 87.2
- type: cos_sim_accuracy_threshold
value: 83.69773167092553
- type: cos_sim_ap
value: 95.43345251568122
- type: cos_sim_f1
value: 89.82785602503913
- type: cos_sim_f1_threshold
value: 81.2116503074739
- type: cos_sim_precision
value: 85.16320474777447
- type: cos_sim_recall
value: 95.03311258278146
- type: dot_accuracy
value: 85.9
- type: dot_accuracy_threshold
value: 2177600.0
- type: dot_ap
value: 92.4192102018206
- type: dot_f1
value: 88.9238020424195
- type: dot_f1_threshold
value: 2163200.0
- type: dot_precision
value: 84.60388639760838
- type: dot_recall
value: 93.70860927152319
- type: euclidean_accuracy
value: 87.5
- type: euclidean_accuracy_threshold
value: 9325.450203438862
- type: euclidean_ap
value: 95.42730698295347
- type: euclidean_f1
value: 89.92747784045125
- type: euclidean_f1_threshold
value: 9325.450203438862
- type: euclidean_precision
value: 87.59811616954474
- type: euclidean_recall
value: 92.3841059602649
- type: manhattan_accuracy
value: 87.5
- type: manhattan_accuracy_threshold
value: 441412.88244724274
- type: manhattan_ap
value: 95.4277447451651
- type: manhattan_f1
value: 89.92747784045125
- type: manhattan_f1_threshold
value: 441412.88244724274
- type: manhattan_precision
value: 87.59811616954474
- type: manhattan_recall
value: 92.3841059602649
- type: max_accuracy
value: 87.5
- type: max_ap
value: 95.43345251568122
- type: max_f1
value: 89.92747784045125
task:
type: PairClassification
- dataset:
config: default
name: MTEB Quora-PL
revision: 0be27e93455051e531182b85e85e425aba12e9d4
split: test
type: clarin-knext/quora-pl
metrics:
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- type: map_at_100
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value: 76.97
- type: map_at_5
value: 78.926
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- type: mrr_at_1000
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- type: nauc_map_at_1000_diff1
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- type: nauc_recall_at_1000_std
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value: 2.9185552604991716
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- type: ndcg_at_1
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- type: ndcg_at_10
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- type: ndcg_at_100
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value: 86.276
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- type: precision_at_1
value: 75.92
- 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_5
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- type: recall_at_1
value: 65.892
- type: recall_at_10
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- type: recall_at_100
value: 99.124
- type: recall_at_1000
value: 99.92699999999999
- type: recall_at_20
value: 96.256
- type: recall_at_3
value: 83.69
- type: recall_at_5
value: 88.783
task:
type: Retrieval
- dataset:
config: default
name: MTEB SCIDOCS-PL
revision: 45452b03f05560207ef19149545f168e596c9337
split: test
type: clarin-knext/scidocs-pl
metrics:
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- type: map_at_1
value: 4.5280000000000005
- type: map_at_10
value: 11.649
- type: map_at_100
value: 14.019
- type: map_at_1000
value: 14.35
- type: map_at_20
value: 12.866
- type: map_at_3
value: 8.35
- type: map_at_5
value: 9.84
- type: mrr_at_1
value: 22.3
- type: mrr_at_10
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- type: mrr_at_100
value: 33.91097016542133
- type: mrr_at_1000
value: 33.96940693754695
- type: mrr_at_20
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- type: mrr_at_3
value: 29.4
- type: mrr_at_5
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- type: nauc_map_at_1000_diff1
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task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics:
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value: 2190400.0
- type: dot_precision
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- type: manhattan_recall
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- type: max_ap
value: 85.97216152106346
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value: 77.9874213836478
- type: max_precision
value: 79.89536621823618
- type: max_recall
value: 82.4074074074074
- type: similarity_accuracy
value: 87.50509580105992
- type: similarity_accuracy_threshold
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- type: similarity_ap
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- type: similarity_f1
value: 77.58919293384136
- type: similarity_f1_threshold
value: 87.10908804245841
- type: similarity_precision
value: 75.52258934592044
- type: similarity_recall
value: 79.77207977207978
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics:
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value: 78.15913085997471
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- type: euclidean_spearman
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- type: main_score
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- type: manhattan_spearman
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- type: pearson
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task:
type: STS
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config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
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task:
type: STS
- dataset:
config: default
name: MTEB SciFact-PL
revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e
split: test
type: clarin-knext/scifact-pl
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task:
type: Retrieval
- dataset:
config: default
name: MTEB TRECCOVID-PL
revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd
split: test
type: clarin-knext/trec-covid-pl
metrics:
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value: 0.20400000000000001
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value: 2.099
- type: map_at_100
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- type: map_at_5
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- type: mrr_at_1
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- type: mrr_at_10
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- type: mrr_at_100
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- type: mrr_at_1000
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value: 54.981
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value: 4.201
- type: recall_at_3
value: 0.699
- type: recall_at_5
value: 1.141
task:
type: Retrieval
---
FlagEmbedding
For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
**BGE-Multilingual-Gemma2** is a LLM-based multilingual embedding model. It is trained on a diverse range of languages and tasks based on [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b). BGE-Multilingual-Gemma2 primarily demonstrates the following advancements:
- Diverse training data: The model's training data spans a broad range of languages, including English, Chinese, Japanese, Korean, French, and more.Additionally, the data covers a variety of task types, such as retrieval, classification, and clustering.
- Outstanding performance: The model exhibits state-of-the-art (SOTA) results on multilingual benchmarks like MIRACL, MTEB-pl, and MTEB-fr. It also achieves excellent performance on other major evaluations, including MTEB, C-MTEB and AIR-Bench.
## 📑 Open-source Plan
- [x] Checkpoint
- [ ] Training Data
We will release the training data of **BGE-Multilingual-Gemma2** in the future.
## Usage
### Using FlagEmbedding
```
git clone https://github.com/FlagOpen/FlagEmbedding.git
cd FlagEmbedding
pip install -e .
```
```python
from FlagEmbedding import FlagLLMModel
queries = ["how much protein should a female eat", "summit define"]
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
model = FlagLLMModel('BAAI/bge-multilingual-gemma2',
query_instruction_for_retrieval="Given a web search query, retrieve relevant passages that answer the query.",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode_queries(queries)
embeddings_2 = model.encode_corpus(documents)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# [[ 0.559 0.01654 ]
# [-0.002575 0.4998 ]]
```
By default, FlagLLMModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
### Using Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
import torch
# Load the model, optionally in float16 precision for faster inference
model = SentenceTransformer("BAAI/bge-multilingual-gemma2", model_kwargs={"torch_dtype": torch.float16})
# Prepare a prompt given an instruction
instruction = 'Given a web search query, retrieve relevant passages that answer the query.'
prompt = f'{instruction}\n'
# Prepare queries and documents
queries = [
'how much protein should a female eat',
'summit define',
]
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
# Compute the query and document embeddings
query_embeddings = model.encode(queries, prompt=prompt)
document_embeddings = model.encode(documents)
# Compute the cosine similarity between the query and document embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.5591, 0.0164],
# [-0.0026, 0.4993]], dtype=torch.float16)
```
### Using HuggingFace Transformers
```python
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'{task_description}\n{query}'
task = 'Given a web search query, retrieve relevant passages that answer the query.'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, 'summit define')
]
# No need to add instructions for documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-multilingual-gemma2')
model = AutoModel.from_pretrained('BAAI/bge-multilingual-gemma2')
model.eval()
max_length = 4096
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt', pad_to_multiple_of=8)
with torch.no_grad():
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
# [[55.92064666748047, 1.6549524068832397], [-0.2698777914047241, 49.95653533935547]]
```
### Usage with infinity
Instructions for usage with [Infinity](https://github.com/michaelfeil/infinity)
```bash
docker run --gpus all -v $PWD/data:/app/.cache -e HF_TOKEN=$HF_TOKEN -p "7997":"7997" \
michaelf34/infinity:0.0.68 \
v2 --model-id BAAI/bge-multilingual-gemma2 --revision "b67c20b19cabf41f74b2aa1469047dade1f42738" --dtype bfloat16 --batch-size 4 --device cuda --engine torch --port 7997 --no-bettertransformer
```
## Evaluation
`bge-multilingual-gemma2` exhibits **state-of-the-art (SOTA) results on benchmarks like MIRACL, MTEB-pl, and MTEB-fr**. It also achieves excellent performance on other major evaluations, including MTEB, C-MTEB and AIR-Bench.
- [**MIRACL**](https://github.com/project-miracl/miracl)
nDCG@10:
Recall@100:
- [**MTEB-fr/pl**](https://huggingface.co/spaces/mteb/leaderboard)
- [**MTEB**](https://huggingface.co/spaces/mteb/leaderboard)
- [**BEIR**](https://huggingface.co/spaces/mteb/leaderboard)
- [**C-MTEB**](https://huggingface.co/spaces/mteb/leaderboard)
- [**AIR-Bench**](https://huggingface.co/spaces/AIR-Bench/leaderboard)
Long-Doc (en, Recall@10):
QA (en&zh, nDCG@10):
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
| :----------------------------------------------------------- | :-----------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
| [BAAI/bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2) | Multilingual | - | A LLM-based multilingual embedding model, trained on a diverse range of languages and tasks. |
| [BAAI/bge-en-icl](https://huggingface.co/BAAI/bge-en-icl) | English | - | A LLM-based dense retriever with in-context learning capabilities can fully leverage the model's potential based on a few shot examples(4096 tokens) | Provide instructions and few-shot examples freely based on the given task. |
| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge-m3,
title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation},
author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},
year={2024},
eprint={2402.03216},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```