--- 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 - 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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 - 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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]] ``` ## 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: MIRACL-nDCG@10

Recall@100: MIRACL-Recall@100

- [**MTEB-fr/pl**](https://huggingface.co/spaces/mteb/leaderboard)

- [**MTEB**](https://huggingface.co/spaces/mteb/leaderboard) MTEB

- [**BEIR**](https://huggingface.co/spaces/mteb/leaderboard) BEIR

- [**C-MTEB**](https://huggingface.co/spaces/mteb/leaderboard) C-MTEB

- [**AIR-Bench**](https://huggingface.co/spaces/AIR-Bench/leaderboard) Long-Doc (en, Recall@10): AIR-Bench_Long-Doc

QA (en&zh, nDCG@10): AIR-Bench_QA

## 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} } ```