Yes
BOOLEAN
jiant System Overview ::: jiant Components Tasks: Tasks have references to task data, methods for processing data, references to classifier heads, and methods for calculating performance metrics, and making predictions.
2003.02249
Introduction </s> This Ġpaper Ġintroduces Ġj iant , Ġan Ġopen Ġsource Ġtool kit Ġthat Ġallows Ġresearchers Ġto Ġquickly Ġexperiment Ġon Ġa Ġwide Ġarray Ġof ĠNL U Ġtasks , Ġusing Ġstate - of - the - art ĠN LP Ġmodels , Ġand Ġconduct Ġexperiments Ġon Ġprobing , Ġtransfer Ġlearning , Ġand Ġmultit ask Ġtraining . Ġj iant Ġsupports Ġmany Ġstate - of - the - art ĠTrans former - based Ġmodels Ġimplemented Ġby ĠHug ging face 's ĠTransformers Ġpackage , Ġas Ġwell Ġas Ġnon - Trans former Ġmodels Ġsuch Ġas ĠBi L ST Ms . </s> Pack ages Ġand Ġlibraries Ġlike ĠHug ging Face 's ĠTransformers ĠB IB REF 0 Ġand ĠAllen N LP ĠB IB REF 1 Ġhave Ġaccelerated Ġthe Ġprocess Ġof Ġexperimenting Ġand Ġiter ating Ġon ĠN LP Ġmodels Ġby Ġboth Ġabstract ing Ġout Ġimplementation Ġdetails , Ġand Ġsimpl ifying Ġthe Ġmodel Ġtraining Ġpipeline . Ġj iant Ġextends Ġthe Ġcapabilities Ġof Ġboth Ġtool k its Ġby Ġpresenting Ġa Ġwrapper Ġthat Ġimplements Ġa Ġvariety Ġof Ġcomplex Ġexperimental Ġpipelines Ġin Ġa Ġscalable Ġand Ġeasily Ġcontro ll able Ġsetting . Ġj iant Ġcontains Ġa Ġtask Ġbank Ġof Ġover Ġ50 Ġtasks , Ġincluding Ġall Ġthe Ġtasks Ġpresented Ġin ĠGL UE ĠB IB REF 2 , ĠSuper GL UE ĠB IB REF 3 , Ġthe Ġedge - pro bing Ġsuite ĠB IB REF 4 , Ġand Ġthe ĠSent E val Ġprobing Ġsuite ĠB IB REF 5 , Ġas Ġwell Ġas Ġother Ġindividual Ġtasks Ġincluding ĠCC G Ġsuper tag ging ĠB IB REF 6 , ĠSocial IQ A ĠB IB REF 7 , Ġand ĠCommons ense Q A ĠB IB REF 8 . Ġj iant Ġis Ġalso Ġthe Ġofficial Ġbaseline Ġcode base Ġfor Ġthe ĠSuper GL UE Ġbenchmark . </s> j iant 's Ġcore Ġdesign Ġprinciples Ġare : </s> E ase Ġof Ġuse : Ġj iant Ġshould Ġallow Ġusers Ġto Ġrun Ġa Ġvariety Ġof Ġexperiments Ġusing Ġstate - of - the - art Ġmodels Ġvia Ġan Ġeasy Ġto Ġuse Ġconfiguration - driven Ġinterface . Ġj iant Ġshould Ġalso Ġprovide Ġfeatures Ġthat Ġsupport Ġcorrect Ġand Ġreprodu cible Ġexperiments , Ġincluding Ġlogging Ġand Ġsaving Ġand Ġrestoring Ġmodel Ġstate . </s> Availability Ġof ĠNL U Ġtasks : Ġj iant Ġshould Ġmaintain Ġand Ġcontinue Ġto Ġgrow Ġa Ġcollection Ġof Ġtasks Ġuseful Ġfor ĠNL U Ġresearch , Ġespecially Ġpopular Ġevaluation Ġtasks Ġand Ġtasks Ġcommonly Ġused Ġin Ġpret raining Ġand Ġtransfer Ġlearning . </s> Availability Ġof Ġcutting - edge Ġmodels : Ġj iant Ġshould Ġmake Ġimplementations Ġof Ġstate - of - the - art Ġmodels Ġavailable Ġfor Ġexperimentation . </s> Open Ġsource : Ġj iant Ġshould Ġbe Ġfree Ġto Ġuse , Ġand Ġeasy Ġto Ġcontribute Ġto . </s> Early Ġversions Ġof Ġj iant Ġhave Ġalready Ġbeen Ġused Ġin Ġmultiple Ġworks , Ġincluding Ġprobing Ġanalyses ĠB IB REF 4 , ĠB IB REF 9 , ĠB IB REF 10 , ĠB IB REF 11 , ĠB IB REF 12 , ĠB IB REF 13 , Ġtransfer Ġlearning Ġexperiments ĠB IB REF 14 , ĠB IB REF 15 , Ġand Ġdataset Ġand Ġbenchmark Ġconstruction ĠB IB REF 3 , ĠB IB REF 2 , ĠB IB REF 16 . </s> Background </s> Transfer Ġlearning Ġis Ġan Ġarea Ġof Ġresearch Ġthat Ġuses Ġknowledge Ġfrom Ġpret rained Ġmodels Ġto Ġtransfer Ġto Ġnew Ġtasks . ĠIn Ġrecent Ġyears , ĠTrans former - based Ġmodels Ġlike ĠB ERT ĠB IB REF 17 Ġand ĠT 5 ĠB IB REF 18 Ġhave Ġyielded Ġstate - of - the - art Ġresults Ġon Ġthe Ġlion 's Ġshare Ġof Ġbenchmark Ġtasks Ġfor Ġlanguage Ġunderstanding Ġthrough Ġpret raining Ġand Ġtransfer , Ġoften Ġpaired Ġwith Ġsome Ġform Ġof Ġmultit ask Ġlearning . </s> j iant Ġenables Ġa Ġvariety Ġof Ġcomplex Ġtraining Ġpipelines Ġthrough Ġsimple Ġconfiguration Ġchanges , Ġincluding Ġmulti - task Ġtraining ĠB IB REF 19 , ĠB IB REF 20 Ġand Ġpret raining , Ġas Ġwell Ġas Ġthe Ġsequential Ġfine - tun ing Ġapproach Ġfrom ĠST IL Ts ĠB IB REF 15 . ĠIn ĠST IL Ts , Ġintermediate Ġtask Ġtraining Ġtakes Ġa Ġpret rained Ġmodel Ġlike ĠEL Mo Ġor ĠB ERT , Ġand Ġapplies Ġsupplementary Ġtraining Ġon Ġa Ġset Ġof Ġintermediate Ġtasks , Ġbefore Ġfinally Ġperforming Ġsingle - task Ġtraining Ġon Ġadditional Ġdownstream Ġtasks . </s> j iant ĠSystem ĠOverview Ġ: :: ĠRequirements </s> j iant Ġv 1 . 3 . 0 Ġrequires ĠPython Ġ3 . 5 Ġor Ġlater . Ġj iant Ġcan Ġbe Ġinstalled Ġvia Ġpip , Ġor Ġcl oned Ġand Ġinstalled Ġfrom ĠGitHub . Ġj iant 's Ġcore Ġdependencies Ġare ĠPy Tor ch ĠB IB REF 21 , ĠAllen N LP ĠB IB REF 1 , Ġand ĠHug ging Face 's ĠTransformers ĠB IB REF 0 . Ġj iant Ġis Ġreleased Ġunder Ġthe ĠMIT ĠLicense ĠB IB REF 22 . </s> j iant ĠSystem ĠOverview Ġ: :: Ġj iant ĠComponents </s> T asks : ĠT asks Ġhave Ġreferences Ġto Ġtask Ġdata , Ġmethods Ġfor Ġprocessing Ġdata , Ġreferences Ġto Ġclass ifier Ġheads , Ġand Ġmethods Ġfor Ġcalculating Ġperformance Ġmetrics , Ġand Ġmaking Ġpredictions . </s> Sent ence ĠEnc oder : ĠSent ence Ġenc od ers Ġmap Ġfrom Ġthe Ġindexed Ġexamples Ġto Ġa Ġsentence - level Ġrepresentation . ĠSent ence Ġenc od ers Ġcan Ġinclude Ġan Ġinput Ġmodule Ġ( e . g ., ĠTrans former Ġmodels , ĠEL Mo , Ġor Ġword Ġembed d ings ), Ġfollowed Ġby Ġan Ġoptional Ġsecond Ġlayer Ġof Ġencoding Ġ( usually Ġa ĠBi L ST M ). ĠExamples Ġof Ġpossible Ġsentence Ġenc oder Ġconfigurations Ġinclude ĠB ERT , ĠEL Mo Ġfollowed Ġby Ġa ĠBi L ST M , ĠB ERT Ġwith Ġa Ġvariety Ġof Ġpool ing Ġand Ġaggregation Ġmet th ods , Ġor Ġa Ġbag Ġof Ġwords Ġmodel . </s> Task - Specific ĠOutput ĠHeads : ĠTask - specific Ġoutput Ġmodules Ġmap Ġrepresentations Ġfrom Ġsentence Ġenc od ers Ġto Ġoutputs Ġspecific Ġto Ġa Ġtask , Ġe . g . Ġentail ment / neutral / cont rad iction Ġfor ĠNL I Ġtasks , Ġor Ġtags Ġfor Ġpart - of - speech Ġtagging . ĠThey Ġalso Ġinclude Ġlogic Ġfor Ġcomputing Ġthe Ġcorresponding Ġloss Ġfor Ġtraining Ġ( e . g . Ġcross - ent ropy ). </s> Tr ainer : ĠTrain ers Ġmanage Ġthe Ġcontrol Ġflow Ġfor Ġthe Ġtraining Ġand Ġvalidation Ġloop Ġfor Ġexperiments . ĠThey Ġsample Ġbatches Ġfrom Ġone Ġor Ġmore Ġtasks , Ġperform Ġforward Ġand Ġbackward Ġpasses , Ġcalculate Ġtraining Ġmetrics , Ġevaluate Ġon Ġa Ġvalidation Ġset , Ġand Ġsave Ġcheckpoints . ĠUsers Ġcan Ġspecify Ġexperiment - specific Ġparameters Ġsuch Ġas Ġlearning Ġrate , Ġbatch Ġsize , Ġand Ġmore . </s> Config : ĠConfig Ġfiles Ġor Ġflags Ġare Ġdefined Ġin ĠH OC ON Ġformat . ĠConfig s Ġspecify Ġparameters Ġfor Ġj iant Ġexperiments Ġincluding Ġchoices Ġof Ġtasks , Ġsentence Ġenc oder , Ġand Ġtraining Ġroutine . </s> Config s Ġare Ġj iant 's Ġprimary Ġuser Ġinterface . ĠT asks Ġand Ġmodeling Ġcomponents Ġare Ġdesigned Ġto Ġbe Ġmodular , Ġwhile Ġj iant 's Ġpipeline Ġis Ġa Ġmon olithic , Ġconfiguration - driven Ġdesign Ġintended Ġto Ġfacilitate Ġa Ġnumber Ġof Ġcommon Ġwork flows Ġoutlined Ġin ĠSEC REF 17 . </s> j iant ĠSystem ĠOverview Ġ: :: Ġj iant ĠPipeline ĠOverview </s> j iant 's Ġcore Ġpipeline Ġconsists Ġof Ġthe Ġfive Ġstages Ġdescribed Ġbelow Ġand Ġillustrated Ġin ĠFigure ĠFIG REF 16 : </s> A Ġconfig Ġor Ġmultiple Ġconfig s Ġdefining Ġan Ġexperiment Ġare Ġinterpreted . ĠUsers Ġcan Ġchoose Ġand Ġconfigure Ġmodels , Ġtasks , Ġand Ġstages Ġof Ġtraining Ġand Ġevaluation . </s> The Ġtasks Ġand Ġsentence Ġenc oder Ġare Ġprepared : </s> The Ġtask Ġdata Ġis Ġloaded , Ġtoken ized , Ġand Ġindexed , Ġand Ġ( option ally ) Ġthe Ġpre process ed Ġtask Ġobjects Ġare Ġserial ized Ġand Ġcached . ĠIn Ġthis Ġprocess , ĠAllen N LP Ġis Ġused Ġto Ġcreate Ġthe Ġvocabulary Ġand Ġindex Ġthe Ġtoken ized Ġdata . </s> The Ġsentence Ġenc oder Ġis Ġconstructed Ġand Ġ( option ally ) Ġpret rained Ġweights Ġare Ġloaded . </s> The Ġtask - specific Ġoutput Ġheads Ġare Ġcreated Ġfor Ġeach Ġtask , Ġand Ġtask Ġheads Ġare Ġattached Ġto Ġa Ġcommon Ġsentence Ġenc oder . ĠOption ally , Ġdifferent Ġtasks Ġcan Ġshare Ġthe Ġsame Ġoutput Ġhead , Ġas Ġin ĠB IB REF 20 . </s> Option ally , Ġin Ġthe Ġintermediate Ġphase Ġthe Ġtrainer Ġsamples Ġbatches Ġrandomly Ġfrom Ġone Ġor Ġmore Ġtasks , Ġand Ġtrains Ġthe Ġshared Ġmodel . </s> Option ally , Ġin Ġthe Ġtarget Ġtraining Ġphase , Ġa Ġcopy Ġof Ġthe Ġmodel Ġis Ġconfigured Ġand Ġtrained Ġor Ġfine - tun ed Ġfor Ġeach Ġtarget Ġtask Ġseparately . </s> Option ally , Ġthe Ġmodel Ġis Ġevaluated Ġon Ġthe Ġvalidation Ġand / or Ġtest Ġsets Ġof Ġthe Ġtarget Ġtasks . </s> j iant ĠSystem ĠOverview Ġ: :: ĠTask Ġand ĠModel Ġresources Ġin Ġj iant </s> j iant Ġsupports Ġover Ġ50 Ġtasks . ĠTask Ġtypes Ġinclude Ġclassification , Ġregression , Ġsequence Ġgeneration , Ġtagging , Ġand Ġspan Ġprediction . Ġj iant Ġfocuses Ġon ĠNL U Ġtasks Ġlike ĠMN LI ĠB IB REF 24 , ĠCommons ense Q A ĠB IB REF 8 , Ġthe ĠWin og rad ĠSche ma ĠChallenge ĠB IB REF 25 , Ġand ĠS Qu AD ĠB IB REF 26 . ĠA Ġfull Ġinventory Ġof Ġtasks Ġand Ġtask Ġvariants Ġis Ġavailable Ġin Ġthe Ġj iant / t asks Ġmodule . </s> j iant Ġprovides Ġsupport Ġfor Ġcutting - edge Ġsentence Ġenc oder Ġmodels , Ġincluding Ġsupport Ġfor ĠHug ging face 's ĠTransformers . ĠSupported Ġmodels Ġinclude : ĠB ERT ĠB IB REF 17 , ĠRo BER Ta ĠB IB REF 27 , ĠXL Net ĠB IB REF 28 , ĠXL M ĠB IB REF 29 , ĠG PT ĠB IB REF 30 , ĠG PT - 2 ĠB IB REF 31 , ĠAL BER T ĠB IB REF 32 Ġand ĠEL Mo ĠB IB REF 33 . Ġj iant Ġalso Ġsupports Ġthe Ġfrom - sc ratch Ġtraining Ġof Ġ( bid irection al ) ĠL ST Ms ĠB IB REF 34 Ġand Ġdeep Ġbag Ġof Ġwords Ġmodels ĠB IB REF 35 , Ġas Ġwell Ġas Ġsyntax - aware Ġmodels Ġsuch Ġas ĠPR PN ĠB IB REF 36 Ġand ĠON - L ST M ĠB IB REF 37 . Ġj iant Ġalso Ġsupports Ġword Ġembed d ings Ġsuch Ġas ĠGl o Ve ĠB IB REF 38 . </s> j iant ĠSystem ĠOverview Ġ: :: ĠUser ĠInterface </s> j iant Ġexperiments Ġcan Ġbe Ġrun Ġwith Ġa Ġsimple ĠCLI : </s> j iant Ġprovides Ġdefault Ġconfig Ġfiles Ġthat Ġallow Ġrunning Ġmany Ġexperiments Ġwithout Ġmodifying Ġsource Ġcode . </s> j iant Ġalso Ġprovides Ġbaseline Ġconfig Ġfiles Ġthat Ġcan Ġserve Ġas Ġa Ġstarting Ġpoint Ġfor Ġmodel Ġdevelopment Ġand Ġevaluation Ġagainst ĠGL UE ĠB IB REF 2 Ġand ĠSuper GL UE ĠB IB REF 3 Ġbenchmarks . </s> More Ġadvanced Ġconfigurations Ġcan Ġbe Ġdeveloped Ġby Ġcomposing Ġmultiple Ġconfigurations Ġfiles Ġand Ġoverr ides . ĠFigure ĠFIG REF 29 Ġshows Ġa Ġconfig Ġfile Ġthat Ġoverr ides Ġa Ġdefault Ġconfig , Ġdefining Ġan Ġexperiment Ġthat Ġuses ĠB ERT Ġas Ġthe Ġsentence Ġenc oder . ĠThis Ġconfig Ġincludes Ġan Ġexample Ġof Ġa Ġtask - specific Ġconfiguration , Ġwhich Ġcan Ġbe Ġoverr idden Ġin Ġanother Ġconfig Ġfile Ġor Ġvia Ġa Ġcommand Ġline Ġoverride . </s> j iant Ġalso Ġimplements Ġthe Ġoption Ġto Ġprovide Ġcommand Ġline Ġoverr ides Ġwith Ġa Ġflag . ĠThis Ġoption Ġmakes Ġit Ġeasy Ġto Ġwrite Ġscripts Ġthat Ġlaunch Ġj iant Ġexperiments Ġover Ġa Ġrange Ġof Ġparameters , Ġfor Ġexample Ġwhile Ġperforming Ġgrid Ġsearch Ġacross Ġhyper param eters . Ġj iant Ġusers Ġhave Ġsuccessfully Ġrun Ġlarge - scale Ġexperiments Ġlaunching Ġhundreds Ġof Ġruns Ġon Ġboth ĠKu ber net es Ġand ĠSl ur m . </s> j iant ĠSystem ĠOverview Ġ: :: ĠExample Ġj iant ĠUse ĠCases Ġand ĠOptions </s> Here Ġwe Ġhighlight Ġsome Ġexample Ġuse Ġcases Ġand Ġkey Ġcorresponding Ġj iant Ġconfig Ġoptions Ġrequired Ġin Ġthese Ġexperiments : </s> Fine - t une ĠB ERT Ġon ĠSW AG ĠB IB REF 39 Ġand ĠSQU AD ĠB IB REF 26 , Ġthen Ġfine - t une Ġon ĠHell a Sw ag ĠB IB REF 40 : </s> Train Ġa Ġprobing Ġclass ifier Ġover Ġa Ġfrozen ĠB ERT Ġmodel , Ġas Ġin ĠB IB REF 9 : </s> Compare Ġperformance Ġof ĠGl o Ve ĠB IB REF 38 Ġembed d ings Ġusing Ġa ĠBi L ST M : </s> E val uate ĠAL BER T ĠB IB REF 32 Ġon Ġthe ĠMN LI ĠB IB REF 24 Ġtask : </s> j iant ĠSystem ĠOverview Ġ: :: Ġj iant ĠDeploy ment ĠEn vironments </s> j iant Ġruns Ġon Ġconsumer - grade Ġhardware Ġor Ġin Ġcluster Ġenvironments Ġwith Ġor Ġwithout ĠCU DA ĠGPUs . ĠThe Ġj iant Ġrepository Ġalso Ġcontains Ġdocumentation Ġand Ġconfiguration Ġfiles Ġdemonstrating Ġhow Ġto Ġdeploy Ġj iant Ġin ĠKu ber net es Ġclusters Ġon ĠGoogle ĠKu ber net es ĠEngine . </s> j iant ĠSystem ĠOverview Ġ: :: ĠLog ging Ġand ĠMet ric ĠTracking </s> j iant Ġgenerates Ġcustom Ġlog Ġfiles Ġthat Ġcapture Ġexperimental Ġconfigurations , Ġtraining Ġand Ġevaluation Ġmetrics , Ġand Ġrelevant Ġrun - time Ġinformation . </s> j iant Ġalso Ġgenerates ĠT ensor Board Ġevent Ġfiles ĠB IB REF 41 Ġfor Ġtraining Ġand Ġevaluation Ġmetric Ġtracking . ĠT ensor Board Ġevent Ġfiles Ġcan Ġbe Ġvisual ized Ġusing Ġthe ĠT ensor Board ĠScal ars ĠDash board . </s> j iant ĠSystem ĠOverview Ġ: :: ĠOptim izations Ġand ĠOther ĠFeatures </s> j iant Ġimplements Ġfeatures Ġthat Ġimprove Ġrun Ġstability Ġand Ġefficiency : </s> j iant Ġimplements Ġcheckpoint ing Ġoptions Ġdesigned Ġto Ġoffer Ġefficient Ġearly Ġstopping Ġand Ġto Ġshow Ġconsistent Ġbehavior Ġwhen Ġrestart ing Ġafter Ġan Ġinterruption . </s> j iant Ġcaches Ġpre process ed Ġtask Ġdata Ġto Ġspeed Ġup Ġreuse Ġacross Ġexperiments Ġwhich Ġshare Ġcommon Ġdata Ġresources Ġand Ġartifacts . </s> j iant Ġimplements Ġgradient Ġaccumulation Ġand Ġmulti - GPU , Ġwhich Ġenables Ġtraining Ġon Ġlarger Ġbatches Ġthan Ġcan Ġfit Ġin Ġmemory Ġfor Ġa Ġsingle ĠGPU . </s> j iant Ġsupports Ġoutput ting Ġpredictions Ġin Ġa Ġformat Ġready Ġfor ĠGL UE Ġand ĠSuper GL UE Ġbenchmark Ġsubmission . </s> j iant ĠSystem ĠOverview Ġ: :: ĠExt ens ibility </s> j iant 's Ġdesign Ġoffers Ġconven iences Ġthat Ġreduce Ġthe Ġneed Ġto Ġmodify Ġcode Ġwhen Ġmaking Ġchanges : </s> j iant 's Ġtask Ġregistry Ġmakes Ġit Ġeasy Ġto Ġdefine Ġa Ġnew Ġversion Ġof Ġan Ġexisting Ġtask Ġusing Ġdifferent Ġdata . ĠOnce Ġthe Ġnew Ġtask Ġis Ġdefined Ġin Ġthe Ġtask Ġregistry , Ġthe Ġtask Ġis Ġavailable Ġas Ġan Ġoption Ġin Ġj iant 's Ġconfig . </s> j iant 's Ġsentence Ġenc oder Ġand Ġtask Ġoutput Ġhead Ġabstract ions Ġallow Ġfor Ġeasy Ġsupport Ġof Ġnew Ġsentence Ġenc od ers . </s> In Ġuse Ġcases Ġrequiring Ġthe Ġintroduction Ġof Ġa Ġnew Ġtask , Ġusers Ġcan Ġuse Ġclass Ġinheritance Ġto Ġbuild Ġon Ġa Ġnumber Ġof Ġavailable Ġparent Ġtask Ġtypes Ġincluding Ġclassification , Ġtagging , Ġspan Ġprediction , Ġspan Ġclassification , Ġsequence Ġgeneration , Ġregression , Ġranking , Ġand Ġmultiple Ġchoice Ġtask Ġclasses . ĠFor Ġthese Ġtask Ġtypes , Ġcorresponding Ġtask - specific Ġoutput Ġheads Ġare Ġalready Ġimplemented . </s> More Ġthan Ġ30 Ġresearchers Ġand Ġdevelopers Ġfrom Ġmore Ġthan Ġ5 Ġinstitutions Ġhave Ġcontributed Ġcode Ġto Ġthe Ġj iant Ġproject . Ġj iant 's Ġmaintain ers Ġwelcome Ġpull Ġrequests Ġthat Ġintroduce Ġnew Ġtasks Ġor Ġsentence Ġenc oder Ġcomponents , Ġand Ġpull Ġrequest Ġare Ġactively Ġreviewed . ĠThe Ġj iant Ġrepository 's Ġcontinuous Ġintegration Ġsystem Ġrequires Ġthat Ġall Ġpull Ġrequests Ġpass Ġunit Ġand Ġintegration Ġtests Ġand Ġmeet ĠBlack Ġcode Ġformatting Ġrequirements . </s> j iant ĠSystem ĠOverview Ġ: :: ĠLim itations </s> While Ġj iant Ġis Ġquite Ġflexible Ġin Ġthe Ġpipelines Ġthat Ġcan Ġbe Ġspecified Ġthrough Ġconfig s , Ġand Ġsome Ġcomponents Ġare Ġhighly Ġmodular Ġ( e . g ., Ġtasks , Ġsentence Ġenc od ers , Ġand Ġoutput Ġheads ), Ġmodification Ġof Ġthe Ġpipeline Ġcode Ġcan Ġbe Ġdifficult . ĠFor Ġexample , Ġtraining Ġin Ġmore Ġthan Ġtwo Ġphases Ġwould Ġrequire Ġmodifying Ġthe Ġtrainer Ġcode . ĠMaking Ġmulti - stage Ġtraining Ġconfigurations Ġmore Ġflexible Ġis Ġon Ġj iant 's Ġdevelopment Ġroadmap . </s> j iant ĠSystem ĠOverview Ġ: :: Ġj iant ĠDevelopment ĠRoad map </s> j iant Ġis Ġactively Ġbeing Ġdeveloped . ĠThe Ġj iant Ġproject Ġhas Ġpriorit ized Ġcontinuing Ġto Ġadd Ġsupport Ġfor Ġnew ĠTrans former Ġmodels Ġand Ġadding Ġtasks Ġthat Ġare Ġcommonly Ġused Ġfor Ġpret raining Ġand Ġevaluation Ġin ĠNL U , Ġincluding Ġsequence - to - sequence Ġtasks . </s> Additionally , Ġthere Ġare Ġplans Ġto Ġmake Ġj iant 's Ġtraining Ġphase Ġconfiguration Ġoptions Ġmore Ġflexible Ġto Ġallow Ġtraining Ġin Ġmore Ġthan Ġtwo Ġphases , Ġand Ġto Ġcontinue Ġto Ġref actor Ġj iant 's Ġcode Ġto Ġkeep Ġj iant Ġflexible Ġto Ġtrack Ġdevelopments Ġin ĠNL U Ġresearch . </s> Bench mark ĠExper iments </s> To Ġbenchmark Ġj iant , Ġwe Ġperform Ġa Ġset Ġof Ġexperiments Ġthat Ġreproduce Ġexternal Ġresults Ġfor Ġsingle Ġfine - tun ing Ġand Ġtransfer Ġlearning Ġexperiments . Ġj iant Ġhas Ġbeen Ġbenchmark ed Ġextensively Ġin Ġboth Ġpublished Ġand Ġongoing Ġwork Ġon Ġa Ġmajority Ġof Ġthe Ġimplemented Ġtasks . </s> We Ġbenchmark Ġsingle - task Ġfine - tun ing Ġconfigurations Ġusing ĠCommons ense Q A ĠB IB REF 8 Ġand ĠSocial IQ A ĠB IB REF 7 . ĠOn ĠCommons ense Q A Ġwith Ġ$\ math rm Ġ{ Ro BER Ta } _ \ math rm Ġ{ L AR GE } $, Ġj iant Ġachieves Ġan Ġaccuracy Ġof Ġ0 . 7 22 , Ġcomparable Ġto Ġ0 . 7 21 Ġreported Ġby ĠB IB REF 27 . ĠOn ĠSocial IQ A Ġwith ĠB ERT - large , Ġj iant Ġachieves Ġa Ġdev Ġset Ġaccuracy Ġof Ġ0 . 658 , Ġcomparable Ġto Ġ0 . 66 Ġreported Ġin ĠB IB REF 7 . </s> Next , Ġwe Ġbenchmark Ġj iant 's Ġtransfer Ġlearning Ġregime . ĠWe Ġperform Ġtransfer Ġexperiments Ġfrom ĠMN LI Ġto ĠB ool Q Ġwith ĠB ERT - large . ĠIn Ġthis Ġconfiguration ĠB IB REF 42 Ġdemonstrated Ġan Ġaccuracy Ġimprovement Ġof Ġ0 . 78 Ġto Ġ0 . 82 Ġon Ġthe Ġdev Ġset , Ġand Ġj iant Ġachieves Ġan Ġimprovement Ġof Ġ0 . 78 Ġto Ġ0 . 80 . </s> Conclusion </s> j iant Ġprovides Ġa Ġconfiguration - driven Ġinterface Ġfor Ġdefining Ġtransfer Ġlearning Ġand Ġrepresentation Ġlearning Ġexperiments Ġusing Ġa Ġbank Ġof Ġover Ġ50 ĠNL U Ġtasks , Ġcutting - edge Ġsentence Ġenc oder Ġmodels , Ġand Ġmulti - task Ġand Ġmulti - stage Ġtraining Ġprocedures . ĠFurther , Ġj iant Ġis Ġshown Ġto Ġbe Ġable Ġto Ġreplicate Ġpublished Ġperformance Ġon Ġvarious ĠNL U Ġtasks . </s> j iant 's Ġmodular Ġdesign Ġof Ġtask Ġand Ġsentence Ġenc oder Ġcomponents Ġmake Ġit Ġpossible Ġfor Ġusers Ġto Ġquickly Ġand Ġeasily Ġexperiment Ġwith Ġa Ġlarge Ġnumber Ġof Ġtasks , Ġmodels , Ġand Ġparameter Ġconfigurations , Ġwithout Ġediting Ġsource Ġcode . Ġj iant 's Ġdesign Ġalso Ġmakes Ġit Ġeasy Ġto Ġadd Ġnew Ġtasks , Ġand Ġj iant 's Ġarchitecture Ġmakes Ġit Ġconvenient Ġto Ġextend Ġj iant Ġto Ġsupport Ġnew Ġsentence Ġenc od ers . </s> j iant Ġcode Ġis Ġopen Ġsource , Ġand Ġj iant Ġinvites Ġcontributors Ġto Ġopen Ġissues Ġor Ġsubmit Ġpull Ġrequest Ġto Ġthe Ġj iant Ġproject Ġrepository : Ġhttps :// github . com / ny u - m ll / j iant . </s> Acknowled gments </s> K ather in ĠYu , ĠJan ĠH ula , ĠPatrick ĠXia , ĠR agh u ĠP app ag ari , ĠSh uning ĠJin , ĠR . ĠThomas ĠMcCoy , ĠRoma ĠPatel , ĠYing h ui ĠHuang , ĠEd ou ard ĠGrave , ĠNaj ou ng ĠKim , ĠThib ault ĠF Ã© v ry , ĠBerlin ĠChen , ĠNik ita ĠN ang ia , ĠAn had ĠMoh an aney , ĠKath ar ina ĠK ann , ĠSh ik ha ĠBord ia , ĠNicolas ĠPat ry , ĠDavid ĠBent on , Ġand ĠEllie ĠPav l ick Ġhave Ġcontributed Ġsubstantial Ġengineering Ġassistance Ġto Ġthe Ġproject . </s> The Ġearly Ġdevelopment Ġof Ġj iant Ġtook Ġat Ġthe Ġ2018 ĠFrederick ĠJ eline k ĠMemorial ĠSummer ĠWorkshop Ġon ĠSpeech Ġand ĠLanguage ĠTechnologies , Ġand Ġwas Ġsupported Ġby ĠJohns ĠHopkins ĠUniversity Ġwith Ġunrestricted Ġgifts Ġfrom ĠAmazon , ĠFacebook , ĠGoogle , ĠMicrosoft Ġand ĠMits ub ishi ĠElectric ĠResearch ĠLaboratories . </s> Sub sequent Ġdevelopment Ġwas Ġpossible Ġin Ġpart Ġby Ġa Ġdonation Ġto ĠNYU Ġfrom ĠEric Ġand ĠWendy ĠSchmidt Ġmade Ġby Ġrecommendation Ġof Ġthe ĠSchmidt ĠFut ures Ġprogram , Ġby Ġsupport Ġfrom ĠInt uit ĠInc ., Ġand Ġby Ġsupport Ġfrom ĠSamsung ĠResearch Ġunder Ġthe Ġproject ĠImpro ving ĠDeep ĠLearning Ġusing ĠLat ent ĠStructure . ĠWe Ġgrate fully Ġacknowledge Ġthe Ġsupport Ġof ĠNVIDIA ĠCorporation Ġwith Ġthe Ġdonation Ġof Ġa ĠTitan ĠV ĠGPU Ġused Ġat ĠNYU Ġin Ġthis Ġwork . ĠAlex ĠWang 's Ġwork Ġon Ġthe Ġproject Ġis Ġsupported Ġby Ġthe ĠNational ĠScience ĠFoundation ĠGraduate ĠResearch ĠFellowship ĠProgram Ġunder ĠGrant ĠNo . ĠD GE Ġ134 25 36 . ĠAny Ġopinions , Ġfindings , Ġand Ġconclusions Ġor Ġrecommendations Ġexpressed Ġin Ġthis Ġmaterial Ġare Ġthose Ġof Ġthe Ġauthor ( s ) Ġand Ġdo Ġnot Ġnecessarily Ġreflect Ġthe Ġviews Ġof Ġthe ĠNational ĠScience ĠFoundation . ĠY ada ĠPru ks ach at kun 's Ġwork Ġon Ġthe Ġproject Ġis Ġsupported Ġin Ġpart Ġby Ġthe ĠMoore - S lo an ĠData ĠScience ĠEnvironment Ġas Ġpart Ġof Ġthe ĠNYU ĠData ĠScience ĠServices Ġinitiative . ĠSam ĠBowman 's Ġwork Ġon Ġj iant Ġduring ĠSummer Ġ2019 Ġtook Ġplace Ġin Ġhis Ġcapacity Ġas Ġa Ġvisiting Ġresearcher Ġat ĠGoogle .
Introduction This paper introduces jiant, an open source toolkit that allows researchers to quickly experiment on a wide array of NLU tasks, using state-of-the-art NLP models, and conduct experiments on probing, transfer learning, and multitask training. jiant supports many state-of-the-art Transformer-based models implemented by Huggingface's Transformers package, as well as non-Transformer models such as BiLSTMs. Packages and libraries like HuggingFace's Transformers BIBREF0 and AllenNLP BIBREF1 have accelerated the process of experimenting and iterating on NLP models by both abstracting out implementation details, and simplifying the model training pipeline. jiant extends the capabilities of both toolkits by presenting a wrapper that implements a variety of complex experimental pipelines in a scalable and easily controllable setting. jiant contains a task bank of over 50 tasks, including all the tasks presented in GLUE BIBREF2, SuperGLUE BIBREF3, the edge-probing suite BIBREF4, and the SentEval probing suite BIBREF5, as well as other individual tasks including CCG supertagging BIBREF6, SocialIQA BIBREF7, and CommonsenseQA BIBREF8. jiant is also the official baseline codebase for the SuperGLUE benchmark. jiant's core design principles are: Ease of use: jiant should allow users to run a variety of experiments using state-of-the-art models via an easy to use configuration-driven interface. jiant should also provide features that support correct and reproducible experiments, including logging and saving and restoring model state. Availability of NLU tasks: jiant should maintain and continue to grow a collection of tasks useful for NLU research, especially popular evaluation tasks and tasks commonly used in pretraining and transfer learning. Availability of cutting-edge models: jiant should make implementations of state-of-the-art models available for experimentation. Open source: jiant should be free to use, and easy to contribute to. Early versions of jiant have already been used in multiple works, including probing analyses BIBREF4, BIBREF9, BIBREF10, BIBREF11, BIBREF12, BIBREF13, transfer learning experiments BIBREF14, BIBREF15, and dataset and benchmark construction BIBREF3, BIBREF2, BIBREF16. Background Transfer learning is an area of research that uses knowledge from pretrained models to transfer to new tasks. In recent years, Transformer-based models like BERT BIBREF17 and T5 BIBREF18 have yielded state-of-the-art results on the lion's share of benchmark tasks for language understanding through pretraining and transfer, often paired with some form of multitask learning. jiant enables a variety of complex training pipelines through simple configuration changes, including multi-task training BIBREF19, BIBREF20 and pretraining, as well as the sequential fine-tuning approach from STILTs BIBREF15. In STILTs, intermediate task training takes a pretrained model like ELMo or BERT, and applies supplementary training on a set of intermediate tasks, before finally performing single-task training on additional downstream tasks. jiant System Overview ::: Requirements jiant v1.3.0 requires Python 3.5 or later. jiant can be installed via pip, or cloned and installed from GitHub. jiant's core dependencies are PyTorch BIBREF21, AllenNLP BIBREF1, and HuggingFace's Transformers BIBREF0. jiant is released under the MIT License BIBREF22. jiant System Overview ::: jiant Components Tasks: Tasks have references to task data, methods for processing data, references to classifier heads, and methods for calculating performance metrics, and making predictions. Sentence Encoder: Sentence encoders map from the indexed examples to a sentence-level representation. Sentence encoders can include an input module (e.g., Transformer models, ELMo, or word embeddings), followed by an optional second layer of encoding (usually a BiLSTM). Examples of possible sentence encoder configurations include BERT, ELMo followed by a BiLSTM, BERT with a variety of pooling and aggregation metthods, or a bag of words model. Task-Specific Output Heads: Task-specific output modules map representations from sentence encoders to outputs specific to a task, e.g. entailment/neutral/contradiction for NLI tasks, or tags for part-of-speech tagging. They also include logic for computing the corresponding loss for training (e.g. cross-entropy). Trainer: Trainers manage the control flow for the training and validation loop for experiments. They sample batches from one or more tasks, perform forward and backward passes, calculate training metrics, evaluate on a validation set, and save checkpoints. Users can specify experiment-specific parameters such as learning rate, batch size, and more. Config: Config files or flags are defined in HOCON format. Configs specify parameters for jiant experiments including choices of tasks, sentence encoder, and training routine. Configs are jiant's primary user interface. Tasks and modeling components are designed to be modular, while jiant's pipeline is a monolithic, configuration-driven design intended to facilitate a number of common workflows outlined in SECREF17. jiant System Overview ::: jiant Pipeline Overview jiant's core pipeline consists of the five stages described below and illustrated in Figure FIGREF16: A config or multiple configs defining an experiment are interpreted. Users can choose and configure models, tasks, and stages of training and evaluation. The tasks and sentence encoder are prepared: The task data is loaded, tokenized, and indexed, and (optionally) the preprocessed task objects are serialized and cached. In this process, AllenNLP is used to create the vocabulary and index the tokenized data. The sentence encoder is constructed and (optionally) pretrained weights are loaded. The task-specific output heads are created for each task, and task heads are attached to a common sentence encoder. Optionally, different tasks can share the same output head, as in BIBREF20. Optionally, in the intermediate phase the trainer samples batches randomly from one or more tasks, and trains the shared model. Optionally, in the target training phase, a copy of the model is configured and trained or fine-tuned for each target task separately. Optionally, the model is evaluated on the validation and/or test sets of the target tasks. jiant System Overview ::: Task and Model resources in jiant jiant supports over 50 tasks. Task types include classification, regression, sequence generation, tagging, and span prediction. jiant focuses on NLU tasks like MNLI BIBREF24, CommonsenseQA BIBREF8, the Winograd Schema Challenge BIBREF25, and SQuAD BIBREF26. A full inventory of tasks and task variants is available in the jiant/tasks module. jiant provides support for cutting-edge sentence encoder models, including support for Huggingface's Transformers. Supported models include: BERT BIBREF17, RoBERTa BIBREF27, XLNet BIBREF28, XLM BIBREF29, GPT BIBREF30, GPT-2 BIBREF31, ALBERT BIBREF32 and ELMo BIBREF33. jiant also supports the from-scratch training of (bidirectional) LSTMs BIBREF34 and deep bag of words models BIBREF35, as well as syntax-aware models such as PRPN BIBREF36 and ON-LSTM BIBREF37. jiant also supports word embeddings such as GloVe BIBREF38. jiant System Overview ::: User Interface jiant experiments can be run with a simple CLI: jiant provides default config files that allow running many experiments without modifying source code. jiant also provides baseline config files that can serve as a starting point for model development and evaluation against GLUE BIBREF2 and SuperGLUE BIBREF3 benchmarks. More advanced configurations can be developed by composing multiple configurations files and overrides. Figure FIGREF29 shows a config file that overrides a default config, defining an experiment that uses BERT as the sentence encoder. This config includes an example of a task-specific configuration, which can be overridden in another config file or via a command line override. jiant also implements the option to provide command line overrides with a flag. This option makes it easy to write scripts that launch jiant experiments over a range of parameters, for example while performing grid search across hyperparameters. jiant users have successfully run large-scale experiments launching hundreds of runs on both Kubernetes and Slurm. jiant System Overview ::: Example jiant Use Cases and Options Here we highlight some example use cases and key corresponding jiant config options required in these experiments: Fine-tune BERT on SWAG BIBREF39 and SQUAD BIBREF26, then fine-tune on HellaSwag BIBREF40: Train a probing classifier over a frozen BERT model, as in BIBREF9: Compare performance of GloVe BIBREF38 embeddings using a BiLSTM: Evaluate ALBERT BIBREF32 on the MNLI BIBREF24 task: jiant System Overview ::: jiant Deployment Environments jiant runs on consumer-grade hardware or in cluster environments with or without CUDA GPUs. The jiant repository also contains documentation and configuration files demonstrating how to deploy jiant in Kubernetes clusters on Google Kubernetes Engine. jiant System Overview ::: Logging and Metric Tracking jiant generates custom log files that capture experimental configurations, training and evaluation metrics, and relevant run-time information. jiant also generates TensorBoard event files BIBREF41 for training and evaluation metric tracking. TensorBoard event files can be visualized using the TensorBoard Scalars Dashboard. jiant System Overview ::: Optimizations and Other Features jiant implements features that improve run stability and efficiency: jiant implements checkpointing options designed to offer efficient early stopping and to show consistent behavior when restarting after an interruption. jiant caches preprocessed task data to speed up reuse across experiments which share common data resources and artifacts. jiant implements gradient accumulation and multi-GPU, which enables training on larger batches than can fit in memory for a single GPU. jiant supports outputting predictions in a format ready for GLUE and SuperGLUE benchmark submission. jiant System Overview ::: Extensibility jiant's design offers conveniences that reduce the need to modify code when making changes: jiant's task registry makes it easy to define a new version of an existing task using different data. Once the new task is defined in the task registry, the task is available as an option in jiant's config. jiant's sentence encoder and task output head abstractions allow for easy support of new sentence encoders. In use cases requiring the introduction of a new task, users can use class inheritance to build on a number of available parent task types including classification, tagging, span prediction, span classification, sequence generation, regression, ranking, and multiple choice task classes. For these task types, corresponding task-specific output heads are already implemented. More than 30 researchers and developers from more than 5 institutions have contributed code to the jiant project. jiant's maintainers welcome pull requests that introduce new tasks or sentence encoder components, and pull request are actively reviewed. The jiant repository's continuous integration system requires that all pull requests pass unit and integration tests and meet Black code formatting requirements. jiant System Overview ::: Limitations While jiant is quite flexible in the pipelines that can be specified through configs, and some components are highly modular (e.g., tasks, sentence encoders, and output heads), modification of the pipeline code can be difficult. For example, training in more than two phases would require modifying the trainer code. Making multi-stage training configurations more flexible is on jiant's development roadmap. jiant System Overview ::: jiant Development Roadmap jiant is actively being developed. The jiant project has prioritized continuing to add support for new Transformer models and adding tasks that are commonly used for pretraining and evaluation in NLU, including sequence-to-sequence tasks. Additionally, there are plans to make jiant's training phase configuration options more flexible to allow training in more than two phases, and to continue to refactor jiant's code to keep jiant flexible to track developments in NLU research. Benchmark Experiments To benchmark jiant, we perform a set of experiments that reproduce external results for single fine-tuning and transfer learning experiments. jiant has been benchmarked extensively in both published and ongoing work on a majority of the implemented tasks. We benchmark single-task fine-tuning configurations using CommonsenseQA BIBREF8 and SocialIQA BIBREF7. On CommonsenseQA with $\mathrm {RoBERTa}_\mathrm {LARGE}$, jiant achieves an accuracy of 0.722, comparable to 0.721 reported by BIBREF27. On SocialIQA with BERT-large, jiant achieves a dev set accuracy of 0.658, comparable to 0.66 reported in BIBREF7. Next, we benchmark jiant's transfer learning regime. We perform transfer experiments from MNLI to BoolQ with BERT-large. In this configuration BIBREF42 demonstrated an accuracy improvement of 0.78 to 0.82 on the dev set, and jiant achieves an improvement of 0.78 to 0.80. Conclusion jiant provides a configuration-driven interface for defining transfer learning and representation learning experiments using a bank of over 50 NLU tasks, cutting-edge sentence encoder models, and multi-task and multi-stage training procedures. Further, jiant is shown to be able to replicate published performance on various NLU tasks. jiant's modular design of task and sentence encoder components make it possible for users to quickly and easily experiment with a large number of tasks, models, and parameter configurations, without editing source code. jiant's design also makes it easy to add new tasks, and jiant's architecture makes it convenient to extend jiant to support new sentence encoders. jiant code is open source, and jiant invites contributors to open issues or submit pull request to the jiant project repository: https://github.com/nyu-mll/jiant. Acknowledgments Katherin Yu, Jan Hula, Patrick Xia, Raghu Pappagari, Shuning Jin, R. Thomas McCoy, Roma Patel, Yinghui Huang, Edouard Grave, Najoung Kim, Thibault Févry, Berlin Chen, Nikita Nangia, Anhad Mohananey, Katharina Kann, Shikha Bordia, Nicolas Patry, David Benton, and Ellie Pavlick have contributed substantial engineering assistance to the project. The early development of jiant took at the 2018 Frederick Jelinek Memorial Summer Workshop on Speech and Language Technologies, and was supported by Johns Hopkins University with unrestricted gifts from Amazon, Facebook, Google, Microsoft and Mitsubishi Electric Research Laboratories. Subsequent development was possible in part by a donation to NYU from Eric and Wendy Schmidt made by recommendation of the Schmidt Futures program, by support from Intuit Inc., and by support from Samsung Research under the project Improving Deep Learning using Latent Structure. We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan V GPU used at NYU in this work. Alex Wang's work on the project is supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE 1342536. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Yada Pruksachatkun's work on the project is supported in part by the Moore-Sloan Data Science Environment as part of the NYU Data Science Services initiative. Sam Bowman's work on jiant during Summer 2019 took place in his capacity as a visiting researcher at Google.
Does jiant involve datasets for the 50 NLU tasks?
b637d6393ef3af7462917b81861531022b291933
Does Ġj iant Ġinvolve Ġdatasets Ġfor Ġthe Ġ50 ĠNL U Ġtasks ?
Yes
<s> Does Ġj iant Ġinvolve Ġdatasets Ġfor Ġthe Ġ50 ĠNL U Ġtasks ? </s> Introduction </s> This Ġpaper Ġintroduces Ġj iant , Ġan Ġopen Ġsource Ġtool kit Ġthat Ġallows Ġresearchers Ġto Ġquickly Ġexperiment Ġon Ġa Ġwide Ġarray Ġof ĠNL U Ġtasks , Ġusing Ġstate - of - the - art ĠN LP Ġmodels , Ġand Ġconduct Ġexperiments Ġon Ġprobing , Ġtransfer Ġlearning , Ġand Ġmultit ask Ġtraining . Ġj iant Ġsupports Ġmany Ġstate - of - the - art ĠTrans former - based Ġmodels Ġimplemented Ġby ĠHug ging face 's ĠTransformers Ġpackage , Ġas Ġwell Ġas Ġnon - Trans former Ġmodels Ġsuch Ġas ĠBi L ST Ms . </s> Pack ages Ġand Ġlibraries Ġlike ĠHug ging Face 's ĠTransformers ĠB IB REF 0 Ġand ĠAllen N LP ĠB IB REF 1 Ġhave Ġaccelerated Ġthe Ġprocess Ġof Ġexperimenting Ġand Ġiter ating Ġon ĠN LP Ġmodels Ġby Ġboth Ġabstract ing Ġout Ġimplementation Ġdetails , Ġand Ġsimpl ifying Ġthe Ġmodel Ġtraining Ġpipeline . Ġj iant Ġextends Ġthe Ġcapabilities Ġof Ġboth Ġtool k its Ġby Ġpresenting Ġa Ġwrapper Ġthat Ġimplements Ġa Ġvariety Ġof Ġcomplex Ġexperimental Ġpipelines Ġin Ġa Ġscalable Ġand Ġeasily Ġcontro ll able Ġsetting . Ġj iant Ġcontains Ġa Ġtask Ġbank Ġof Ġover Ġ50 Ġtasks , Ġincluding Ġall Ġthe Ġtasks Ġpresented Ġin ĠGL UE ĠB IB REF 2 , ĠSuper GL UE ĠB IB REF 3 , Ġthe Ġedge - pro bing Ġsuite ĠB IB REF 4 , Ġand Ġthe ĠSent E val Ġprobing Ġsuite ĠB IB REF 5 , Ġas Ġwell Ġas Ġother Ġindividual Ġtasks Ġincluding ĠCC G Ġsuper tag ging ĠB IB REF 6 , ĠSocial IQ A ĠB IB REF 7 , Ġand ĠCommons ense Q A ĠB IB REF 8 . Ġj iant Ġis Ġalso Ġthe Ġofficial Ġbaseline Ġcode base Ġfor Ġthe ĠSuper GL UE Ġbenchmark . </s> j iant 's Ġcore Ġdesign Ġprinciples Ġare : </s> E ase Ġof Ġuse : Ġj iant Ġshould Ġallow Ġusers Ġto Ġrun Ġa Ġvariety Ġof Ġexperiments Ġusing Ġstate - of - the - art Ġmodels Ġvia Ġan Ġeasy Ġto Ġuse Ġconfiguration - driven Ġinterface . Ġj iant Ġshould Ġalso Ġprovide Ġfeatures Ġthat Ġsupport Ġcorrect Ġand Ġreprodu cible Ġexperiments , Ġincluding Ġlogging Ġand Ġsaving Ġand Ġrestoring Ġmodel Ġstate . </s> Availability Ġof ĠNL U Ġtasks : Ġj iant Ġshould Ġmaintain Ġand Ġcontinue Ġto Ġgrow Ġa Ġcollection Ġof Ġtasks Ġuseful Ġfor ĠNL U Ġresearch , Ġespecially Ġpopular Ġevaluation Ġtasks Ġand Ġtasks Ġcommonly Ġused Ġin Ġpret raining Ġand Ġtransfer Ġlearning . </s> Availability Ġof Ġcutting - edge Ġmodels : Ġj iant Ġshould Ġmake Ġimplementations Ġof Ġstate - of - the - art Ġmodels Ġavailable Ġfor Ġexperimentation . </s> Open Ġsource : Ġj iant Ġshould Ġbe Ġfree Ġto Ġuse , Ġand Ġeasy Ġto Ġcontribute Ġto . </s> Early Ġversions Ġof Ġj iant Ġhave Ġalready Ġbeen Ġused Ġin Ġmultiple Ġworks , Ġincluding Ġprobing Ġanalyses ĠB IB REF 4 , ĠB IB REF 9 , ĠB IB REF 10 , ĠB IB REF 11 , ĠB IB REF 12 , ĠB IB REF 13 , Ġtransfer Ġlearning Ġexperiments ĠB IB REF 14 , ĠB IB REF 15 , Ġand Ġdataset Ġand Ġbenchmark Ġconstruction ĠB IB REF 3 , ĠB IB REF 2 , ĠB IB REF 16 . </s> Background </s> Transfer Ġlearning Ġis Ġan Ġarea Ġof Ġresearch Ġthat Ġuses Ġknowledge Ġfrom Ġpret rained Ġmodels Ġto Ġtransfer Ġto Ġnew Ġtasks . ĠIn Ġrecent Ġyears , ĠTrans former - based Ġmodels Ġlike ĠB ERT ĠB IB REF 17 Ġand ĠT 5 ĠB IB REF 18 Ġhave Ġyielded Ġstate - of - the - art Ġresults Ġon Ġthe Ġlion 's Ġshare Ġof Ġbenchmark Ġtasks Ġfor Ġlanguage Ġunderstanding Ġthrough Ġpret raining Ġand Ġtransfer , Ġoften Ġpaired Ġwith Ġsome Ġform Ġof Ġmultit ask Ġlearning . </s> j iant Ġenables Ġa Ġvariety Ġof Ġcomplex Ġtraining Ġpipelines Ġthrough Ġsimple Ġconfiguration Ġchanges , Ġincluding Ġmulti - task Ġtraining ĠB IB REF 19 , ĠB IB REF 20 Ġand Ġpret raining , Ġas Ġwell Ġas Ġthe Ġsequential Ġfine - tun ing Ġapproach Ġfrom ĠST IL Ts ĠB IB REF 15 . ĠIn ĠST IL Ts , Ġintermediate Ġtask Ġtraining Ġtakes Ġa Ġpret rained Ġmodel Ġlike ĠEL Mo Ġor ĠB ERT , Ġand Ġapplies Ġsupplementary Ġtraining Ġon Ġa Ġset Ġof Ġintermediate Ġtasks , Ġbefore Ġfinally Ġperforming Ġsingle - task Ġtraining Ġon Ġadditional Ġdownstream Ġtasks . </s> j iant ĠSystem ĠOverview Ġ: :: ĠRequirements </s> j iant Ġv 1 . 3 . 0 Ġrequires ĠPython Ġ3 . 5 Ġor Ġlater . Ġj iant Ġcan Ġbe Ġinstalled Ġvia Ġpip , Ġor Ġcl oned Ġand Ġinstalled Ġfrom ĠGitHub . Ġj iant 's Ġcore Ġdependencies Ġare ĠPy Tor ch ĠB IB REF 21 , ĠAllen N LP ĠB IB REF 1 , Ġand ĠHug ging Face 's ĠTransformers ĠB IB REF 0 . Ġj iant Ġis Ġreleased Ġunder Ġthe ĠMIT ĠLicense ĠB IB REF 22 . </s> j iant ĠSystem ĠOverview Ġ: :: Ġj iant ĠComponents </s> T asks : ĠT asks Ġhave Ġreferences Ġto Ġtask Ġdata , Ġmethods Ġfor Ġprocessing Ġdata , Ġreferences Ġto Ġclass ifier Ġheads , Ġand Ġmethods Ġfor Ġcalculating Ġperformance Ġmetrics , Ġand Ġmaking Ġpredictions . </s> Sent ence ĠEnc oder : ĠSent ence Ġenc od ers Ġmap Ġfrom Ġthe Ġindexed Ġexamples Ġto Ġa Ġsentence - level Ġrepresentation . ĠSent ence Ġenc od ers Ġcan Ġinclude Ġan Ġinput Ġmodule Ġ( e . g ., ĠTrans former Ġmodels , ĠEL Mo , Ġor Ġword Ġembed d ings ), Ġfollowed Ġby Ġan Ġoptional Ġsecond Ġlayer Ġof Ġencoding Ġ( usually Ġa ĠBi L ST M ). ĠExamples Ġof Ġpossible Ġsentence Ġenc oder Ġconfigurations Ġinclude ĠB ERT , ĠEL Mo Ġfollowed Ġby Ġa ĠBi L ST M , ĠB ERT Ġwith Ġa Ġvariety Ġof Ġpool ing Ġand Ġaggregation Ġmet th ods , Ġor Ġa Ġbag Ġof Ġwords Ġmodel . </s> Task - Specific ĠOutput ĠHeads : ĠTask - specific Ġoutput Ġmodules Ġmap Ġrepresentations Ġfrom Ġsentence Ġenc od ers Ġto Ġoutputs Ġspecific Ġto Ġa Ġtask , Ġe . g . Ġentail ment / neutral / cont rad iction Ġfor ĠNL I Ġtasks , Ġor Ġtags Ġfor Ġpart - of - speech Ġtagging . ĠThey Ġalso Ġinclude Ġlogic Ġfor Ġcomputing Ġthe Ġcorresponding Ġloss Ġfor Ġtraining Ġ( e . g . Ġcross - ent ropy ). </s> Tr ainer : ĠTrain ers Ġmanage Ġthe Ġcontrol Ġflow Ġfor Ġthe Ġtraining Ġand Ġvalidation Ġloop Ġfor Ġexperiments . ĠThey Ġsample Ġbatches Ġfrom Ġone Ġor Ġmore Ġtasks , Ġperform Ġforward Ġand Ġbackward Ġpasses , Ġcalculate Ġtraining Ġmetrics , Ġevaluate Ġon Ġa Ġvalidation Ġset , Ġand Ġsave Ġcheckpoints . ĠUsers Ġcan Ġspecify Ġexperiment - specific Ġparameters Ġsuch Ġas Ġlearning Ġrate , Ġbatch Ġsize , Ġand Ġmore . </s> Config : ĠConfig Ġfiles Ġor Ġflags Ġare Ġdefined Ġin ĠH OC ON Ġformat . ĠConfig s Ġspecify Ġparameters Ġfor Ġj iant Ġexperiments Ġincluding Ġchoices Ġof Ġtasks , Ġsentence Ġenc oder , Ġand Ġtraining Ġroutine . </s> Config s Ġare Ġj iant 's Ġprimary Ġuser Ġinterface . ĠT asks Ġand Ġmodeling Ġcomponents Ġare Ġdesigned Ġto Ġbe Ġmodular , Ġwhile Ġj iant 's Ġpipeline Ġis Ġa Ġmon olithic , Ġconfiguration - driven Ġdesign Ġintended Ġto Ġfacilitate Ġa Ġnumber Ġof Ġcommon Ġwork flows Ġoutlined Ġin ĠSEC REF 17 . </s> j iant ĠSystem ĠOverview Ġ: :: Ġj iant ĠPipeline ĠOverview </s> j iant 's Ġcore Ġpipeline Ġconsists Ġof Ġthe Ġfive Ġstages Ġdescribed Ġbelow Ġand Ġillustrated Ġin ĠFigure ĠFIG REF 16 : </s> A Ġconfig Ġor Ġmultiple Ġconfig s Ġdefining Ġan Ġexperiment Ġare Ġinterpreted . ĠUsers Ġcan Ġchoose Ġand Ġconfigure Ġmodels , Ġtasks , Ġand Ġstages Ġof Ġtraining Ġand Ġevaluation . </s> The Ġtasks Ġand Ġsentence Ġenc oder Ġare Ġprepared : </s> The Ġtask Ġdata Ġis Ġloaded , Ġtoken ized , Ġand Ġindexed , Ġand Ġ( option ally ) Ġthe Ġpre process ed Ġtask Ġobjects Ġare Ġserial ized Ġand Ġcached . ĠIn Ġthis Ġprocess , ĠAllen N LP Ġis Ġused Ġto Ġcreate Ġthe Ġvocabulary Ġand Ġindex Ġthe Ġtoken ized Ġdata . </s> The Ġsentence Ġenc oder Ġis Ġconstructed Ġand Ġ( option ally ) Ġpret rained Ġweights Ġare Ġloaded . </s> The Ġtask - specific Ġoutput Ġheads Ġare Ġcreated Ġfor Ġeach Ġtask , Ġand Ġtask Ġheads Ġare Ġattached Ġto Ġa Ġcommon Ġsentence Ġenc oder . ĠOption ally , Ġdifferent Ġtasks Ġcan Ġshare Ġthe Ġsame Ġoutput Ġhead , Ġas Ġin ĠB IB REF 20 . </s> Option ally , Ġin Ġthe Ġintermediate Ġphase Ġthe Ġtrainer Ġsamples Ġbatches Ġrandomly Ġfrom Ġone Ġor Ġmore Ġtasks , Ġand Ġtrains Ġthe Ġshared Ġmodel . </s> Option ally , Ġin Ġthe Ġtarget Ġtraining Ġphase , Ġa Ġcopy Ġof Ġthe Ġmodel Ġis Ġconfigured Ġand Ġtrained Ġor Ġfine - tun ed Ġfor Ġeach Ġtarget Ġtask Ġseparately . </s> Option ally , Ġthe Ġmodel Ġis Ġevaluated Ġon Ġthe Ġvalidation Ġand / or Ġtest Ġsets Ġof Ġthe Ġtarget Ġtasks . </s> j iant ĠSystem ĠOverview Ġ: :: ĠTask Ġand ĠModel Ġresources Ġin Ġj iant </s> j iant Ġsupports Ġover Ġ50 Ġtasks . ĠTask Ġtypes Ġinclude Ġclassification , Ġregression , Ġsequence Ġgeneration , Ġtagging , Ġand Ġspan Ġprediction . Ġj iant Ġfocuses Ġon ĠNL U Ġtasks Ġlike ĠMN LI ĠB IB REF 24 , ĠCommons ense Q A ĠB IB REF 8 , Ġthe ĠWin og rad ĠSche ma ĠChallenge ĠB IB REF 25 , Ġand ĠS Qu AD ĠB IB REF 26 . ĠA Ġfull Ġinventory Ġof Ġtasks Ġand Ġtask Ġvariants Ġis Ġavailable Ġin Ġthe Ġj iant / t asks Ġmodule . </s> j iant Ġprovides Ġsupport Ġfor Ġcutting - edge Ġsentence Ġenc oder Ġmodels , Ġincluding Ġsupport Ġfor ĠHug ging face 's ĠTransformers . ĠSupported Ġmodels Ġinclude : ĠB ERT ĠB IB REF 17 , ĠRo BER Ta ĠB IB REF 27 , ĠXL Net ĠB IB REF 28 , ĠXL M ĠB IB REF 29 , ĠG PT ĠB IB REF 30 , ĠG PT - 2 ĠB IB REF 31 , ĠAL BER T ĠB IB REF 32 Ġand ĠEL Mo ĠB IB REF 33 . Ġj iant Ġalso Ġsupports Ġthe Ġfrom - sc ratch Ġtraining Ġof Ġ( bid irection al ) ĠL ST Ms ĠB IB REF 34 Ġand Ġdeep Ġbag Ġof Ġwords Ġmodels ĠB IB REF 35 , Ġas Ġwell Ġas Ġsyntax - aware Ġmodels Ġsuch Ġas ĠPR PN ĠB IB REF 36 Ġand ĠON - L ST M ĠB IB REF 37 . Ġj iant Ġalso Ġsupports Ġword Ġembed d ings Ġsuch Ġas ĠGl o Ve ĠB IB REF 38 . </s> j iant ĠSystem ĠOverview Ġ: :: ĠUser ĠInterface </s> j iant Ġexperiments Ġcan Ġbe Ġrun Ġwith Ġa Ġsimple ĠCLI : </s> j iant Ġprovides Ġdefault Ġconfig Ġfiles Ġthat Ġallow Ġrunning Ġmany Ġexperiments Ġwithout Ġmodifying Ġsource Ġcode . </s> j iant Ġalso Ġprovides Ġbaseline Ġconfig Ġfiles Ġthat Ġcan Ġserve Ġas Ġa Ġstarting Ġpoint Ġfor Ġmodel Ġdevelopment Ġand Ġevaluation Ġagainst ĠGL UE ĠB IB REF 2 Ġand ĠSuper GL UE ĠB IB REF 3 Ġbenchmarks . </s> More Ġadvanced Ġconfigurations Ġcan Ġbe Ġdeveloped Ġby Ġcomposing Ġmultiple Ġconfigurations Ġfiles Ġand Ġoverr ides . ĠFigure ĠFIG REF 29 Ġshows Ġa Ġconfig Ġfile Ġthat Ġoverr ides Ġa Ġdefault Ġconfig , Ġdefining Ġan Ġexperiment Ġthat Ġuses ĠB ERT Ġas Ġthe Ġsentence Ġenc oder . ĠThis Ġconfig Ġincludes Ġan Ġexample Ġof Ġa Ġtask - specific Ġconfiguration , Ġwhich Ġcan Ġbe Ġoverr idden Ġin Ġanother Ġconfig Ġfile Ġor Ġvia Ġa Ġcommand Ġline Ġoverride . </s> j iant Ġalso Ġimplements Ġthe Ġoption Ġto Ġprovide Ġcommand Ġline Ġoverr ides Ġwith Ġa Ġflag . ĠThis Ġoption Ġmakes Ġit Ġeasy Ġto Ġwrite Ġscripts Ġthat Ġlaunch Ġj iant Ġexperiments Ġover Ġa Ġrange Ġof Ġparameters , Ġfor Ġexample Ġwhile Ġperforming Ġgrid Ġsearch Ġacross Ġhyper param eters . Ġj iant Ġusers Ġhave Ġsuccessfully Ġrun Ġlarge - scale Ġexperiments Ġlaunching Ġhundreds Ġof Ġruns Ġon Ġboth ĠKu ber net es Ġand ĠSl ur m . </s> j iant ĠSystem ĠOverview Ġ: :: ĠExample Ġj iant ĠUse ĠCases Ġand ĠOptions </s> Here Ġwe Ġhighlight Ġsome Ġexample Ġuse Ġcases Ġand Ġkey Ġcorresponding Ġj iant Ġconfig Ġoptions Ġrequired Ġin Ġthese Ġexperiments : </s> Fine - t une ĠB ERT Ġon ĠSW AG ĠB IB REF 39 Ġand ĠSQU AD ĠB IB REF 26 , Ġthen Ġfine - t une Ġon ĠHell a Sw ag ĠB IB REF 40 : </s> Train Ġa Ġprobing Ġclass ifier Ġover Ġa Ġfrozen ĠB ERT Ġmodel , Ġas Ġin ĠB IB REF 9 : </s> Compare Ġperformance Ġof ĠGl o Ve ĠB IB REF 38 Ġembed d ings Ġusing Ġa ĠBi L ST M : </s> E val uate ĠAL BER T ĠB IB REF 32 Ġon Ġthe ĠMN LI ĠB IB REF 24 Ġtask : </s> j iant ĠSystem ĠOverview Ġ: :: Ġj iant ĠDeploy ment ĠEn vironments </s> j iant Ġruns Ġon Ġconsumer - grade Ġhardware Ġor Ġin Ġcluster Ġenvironments Ġwith Ġor Ġwithout ĠCU DA ĠGPUs . ĠThe Ġj iant Ġrepository Ġalso Ġcontains Ġdocumentation Ġand Ġconfiguration Ġfiles Ġdemonstrating Ġhow Ġto Ġdeploy Ġj iant Ġin ĠKu ber net es Ġclusters Ġon ĠGoogle ĠKu ber net es ĠEngine . </s> j iant ĠSystem ĠOverview Ġ: :: ĠLog ging Ġand ĠMet ric ĠTracking </s> j iant Ġgenerates Ġcustom Ġlog Ġfiles Ġthat Ġcapture Ġexperimental Ġconfigurations , Ġtraining Ġand Ġevaluation Ġmetrics , Ġand Ġrelevant Ġrun - time Ġinformation . </s> j iant Ġalso Ġgenerates ĠT ensor Board Ġevent Ġfiles ĠB IB REF 41 Ġfor Ġtraining Ġand Ġevaluation Ġmetric Ġtracking . ĠT ensor Board Ġevent Ġfiles Ġcan Ġbe Ġvisual ized Ġusing Ġthe ĠT ensor Board ĠScal ars ĠDash board . </s> j iant ĠSystem ĠOverview Ġ: :: ĠOptim izations Ġand ĠOther ĠFeatures </s> j iant Ġimplements Ġfeatures Ġthat Ġimprove Ġrun Ġstability Ġand Ġefficiency : </s> j iant Ġimplements Ġcheckpoint ing Ġoptions Ġdesigned Ġto Ġoffer Ġefficient Ġearly Ġstopping Ġand Ġto Ġshow Ġconsistent Ġbehavior Ġwhen Ġrestart ing Ġafter Ġan Ġinterruption . </s> j iant Ġcaches Ġpre process ed Ġtask Ġdata Ġto Ġspeed Ġup Ġreuse Ġacross Ġexperiments Ġwhich Ġshare Ġcommon Ġdata Ġresources Ġand Ġartifacts . </s> j iant Ġimplements Ġgradient Ġaccumulation Ġand Ġmulti - GPU , Ġwhich Ġenables Ġtraining Ġon Ġlarger Ġbatches Ġthan Ġcan Ġfit Ġin Ġmemory Ġfor Ġa Ġsingle ĠGPU . </s> j iant Ġsupports Ġoutput ting Ġpredictions Ġin Ġa Ġformat Ġready Ġfor ĠGL UE Ġand ĠSuper GL UE Ġbenchmark Ġsubmission . </s> j iant ĠSystem ĠOverview Ġ: :: ĠExt ens ibility </s> j iant 's Ġdesign Ġoffers Ġconven iences Ġthat Ġreduce Ġthe Ġneed Ġto Ġmodify Ġcode Ġwhen Ġmaking Ġchanges : </s> j iant 's Ġtask Ġregistry Ġmakes Ġit Ġeasy Ġto Ġdefine Ġa Ġnew Ġversion Ġof Ġan Ġexisting Ġtask Ġusing Ġdifferent Ġdata . ĠOnce Ġthe Ġnew Ġtask Ġis Ġdefined Ġin Ġthe Ġtask Ġregistry , Ġthe Ġtask Ġis Ġavailable Ġas Ġan Ġoption Ġin Ġj iant 's Ġconfig . </s> j iant 's Ġsentence Ġenc oder Ġand Ġtask Ġoutput Ġhead Ġabstract ions Ġallow Ġfor Ġeasy Ġsupport Ġof Ġnew Ġsentence Ġenc od ers . </s> In Ġuse Ġcases Ġrequiring Ġthe Ġintroduction Ġof Ġa Ġnew Ġtask , Ġusers Ġcan Ġuse Ġclass Ġinheritance Ġto Ġbuild Ġon Ġa Ġnumber Ġof Ġavailable Ġparent Ġtask Ġtypes Ġincluding Ġclassification , Ġtagging , Ġspan Ġprediction , Ġspan Ġclassification , Ġsequence Ġgeneration , Ġregression , Ġranking , Ġand Ġmultiple Ġchoice Ġtask Ġclasses . ĠFor Ġthese Ġtask Ġtypes , Ġcorresponding Ġtask - specific Ġoutput Ġheads Ġare Ġalready Ġimplemented . </s> More Ġthan Ġ30 Ġresearchers Ġand Ġdevelopers Ġfrom Ġmore Ġthan Ġ5 Ġinstitutions Ġhave Ġcontributed Ġcode Ġto Ġthe Ġj iant Ġproject . Ġj iant 's Ġmaintain ers Ġwelcome Ġpull Ġrequests Ġthat Ġintroduce Ġnew Ġtasks Ġor Ġsentence Ġenc oder Ġcomponents , Ġand Ġpull Ġrequest Ġare Ġactively Ġreviewed . ĠThe Ġj iant Ġrepository 's Ġcontinuous Ġintegration Ġsystem Ġrequires Ġthat Ġall Ġpull Ġrequests Ġpass Ġunit Ġand Ġintegration Ġtests Ġand Ġmeet ĠBlack Ġcode Ġformatting Ġrequirements . </s> j iant ĠSystem ĠOverview Ġ: :: ĠLim itations </s> While Ġj iant Ġis Ġquite Ġflexible Ġin Ġthe Ġpipelines Ġthat Ġcan Ġbe Ġspecified Ġthrough Ġconfig s , Ġand Ġsome Ġcomponents Ġare Ġhighly Ġmodular Ġ( e . g ., Ġtasks , Ġsentence Ġenc od ers , Ġand Ġoutput Ġheads ), Ġmodification Ġof Ġthe Ġpipeline Ġcode Ġcan Ġbe Ġdifficult . ĠFor Ġexample , Ġtraining Ġin Ġmore Ġthan Ġtwo Ġphases Ġwould Ġrequire Ġmodifying Ġthe Ġtrainer Ġcode . ĠMaking Ġmulti - stage Ġtraining Ġconfigurations Ġmore Ġflexible Ġis Ġon Ġj iant 's Ġdevelopment Ġroadmap . </s> j iant ĠSystem ĠOverview Ġ: :: Ġj iant ĠDevelopment ĠRoad map </s> j iant Ġis Ġactively Ġbeing Ġdeveloped . ĠThe Ġj iant Ġproject Ġhas Ġpriorit ized Ġcontinuing Ġto Ġadd Ġsupport Ġfor Ġnew ĠTrans former Ġmodels Ġand Ġadding Ġtasks Ġthat Ġare Ġcommonly Ġused Ġfor Ġpret raining Ġand Ġevaluation Ġin ĠNL U , Ġincluding Ġsequence - to - sequence Ġtasks . </s> Additionally , Ġthere Ġare Ġplans Ġto Ġmake Ġj iant 's Ġtraining Ġphase Ġconfiguration Ġoptions Ġmore Ġflexible Ġto Ġallow Ġtraining Ġin Ġmore Ġthan Ġtwo Ġphases , Ġand Ġto Ġcontinue Ġto Ġref actor Ġj iant 's Ġcode Ġto Ġkeep Ġj iant Ġflexible Ġto Ġtrack Ġdevelopments Ġin ĠNL U Ġresearch . </s> Bench mark ĠExper iments </s> To Ġbenchmark Ġj iant , Ġwe Ġperform Ġa Ġset Ġof Ġexperiments Ġthat Ġreproduce Ġexternal Ġresults Ġfor Ġsingle Ġfine - tun ing Ġand Ġtransfer Ġlearning Ġexperiments . Ġj iant Ġhas Ġbeen Ġbenchmark ed Ġextensively Ġin Ġboth Ġpublished Ġand Ġongoing Ġwork Ġon Ġa Ġmajority Ġof Ġthe Ġimplemented Ġtasks . </s> We Ġbenchmark Ġsingle - task Ġfine - tun ing Ġconfigurations Ġusing ĠCommons ense Q A ĠB IB REF 8 Ġand ĠSocial IQ A ĠB IB REF 7 . ĠOn ĠCommons ense Q A Ġwith Ġ$\ math rm Ġ{ Ro BER Ta } _ \ math rm Ġ{ L AR GE } $, Ġj iant Ġachieves Ġan Ġaccuracy Ġof Ġ0 . 7 22 , Ġcomparable Ġto Ġ0 . 7 21 Ġreported Ġby ĠB IB REF 27 . ĠOn ĠSocial IQ A Ġwith ĠB ERT - large , Ġj iant Ġachieves Ġa Ġdev Ġset Ġaccuracy Ġof Ġ0 . 658 , Ġcomparable Ġto Ġ0 . 66 Ġreported Ġin ĠB IB REF 7 . </s> Next , Ġwe Ġbenchmark Ġj iant 's Ġtransfer Ġlearning Ġregime . ĠWe Ġperform Ġtransfer Ġexperiments Ġfrom ĠMN LI Ġto ĠB ool Q Ġwith ĠB ERT - large . ĠIn Ġthis Ġconfiguration ĠB IB REF 42 Ġdemonstrated Ġan Ġaccuracy Ġimprovement Ġof Ġ0 . 78 Ġto Ġ0 . 82 Ġon Ġthe Ġdev Ġset , Ġand Ġj iant Ġachieves Ġan Ġimprovement Ġof Ġ0 . 78 Ġto Ġ0 . 80 . </s> Conclusion </s> j iant Ġprovides Ġa Ġconfiguration - driven Ġinterface Ġfor Ġdefining Ġtransfer Ġlearning Ġand Ġrepresentation Ġlearning Ġexperiments Ġusing Ġa Ġbank Ġof Ġover Ġ50 ĠNL U Ġtasks , Ġcutting - edge Ġsentence Ġenc oder Ġmodels , Ġand Ġmulti - task Ġand Ġmulti - stage Ġtraining Ġprocedures . ĠFurther , Ġj iant Ġis Ġshown Ġto Ġbe Ġable Ġto Ġreplicate Ġpublished Ġperformance Ġon Ġvarious ĠNL U Ġtasks . </s> j iant 's Ġmodular Ġdesign Ġof Ġtask Ġand Ġsentence Ġenc oder Ġcomponents Ġmake Ġit Ġpossible Ġfor Ġusers Ġto Ġquickly Ġand Ġeasily Ġexperiment Ġwith Ġa Ġlarge Ġnumber Ġof Ġtasks , Ġmodels , Ġand Ġparameter Ġconfigurations , Ġwithout Ġediting Ġsource Ġcode . Ġj iant 's Ġdesign Ġalso Ġmakes Ġit Ġeasy Ġto Ġadd Ġnew Ġtasks , Ġand Ġj iant 's Ġarchitecture Ġmakes Ġit Ġconvenient Ġto Ġextend Ġj iant Ġto Ġsupport Ġnew Ġsentence Ġenc od ers . </s> j iant Ġcode Ġis Ġopen Ġsource , Ġand Ġj iant Ġinvites Ġcontributors Ġto Ġopen Ġissues Ġor Ġsubmit Ġpull Ġrequest Ġto Ġthe Ġj iant Ġproject Ġrepository : Ġhttps :// github . com / ny u - m ll / j iant . </s> Acknowled gments </s> K ather in ĠYu , ĠJan ĠH ula , ĠPatrick ĠXia , ĠR agh u ĠP app ag ari , ĠSh uning ĠJin , ĠR . ĠThomas ĠMcCoy , ĠRoma ĠPatel , ĠYing h ui ĠHuang , ĠEd ou ard ĠGrave , ĠNaj ou ng ĠKim , ĠThib ault ĠF Ã© v ry , ĠBerlin ĠChen , ĠNik ita ĠN ang ia , ĠAn had ĠMoh an aney , ĠKath ar ina ĠK ann , ĠSh ik ha ĠBord ia , ĠNicolas ĠPat ry , ĠDavid ĠBent on , Ġand ĠEllie ĠPav l ick Ġhave Ġcontributed Ġsubstantial Ġengineering Ġassistance Ġto Ġthe Ġproject . </s> The Ġearly Ġdevelopment Ġof Ġj iant Ġtook Ġat Ġthe Ġ2018 ĠFrederick ĠJ eline k ĠMemorial ĠSummer ĠWorkshop Ġon ĠSpeech Ġand ĠLanguage ĠTechnologies , Ġand Ġwas Ġsupported Ġby ĠJohns ĠHopkins ĠUniversity Ġwith Ġunrestricted Ġgifts Ġfrom ĠAmazon , ĠFacebook , ĠGoogle , ĠMicrosoft Ġand ĠMits ub ishi ĠElectric ĠResearch ĠLaboratories . </s> Sub sequent Ġdevelopment Ġwas Ġpossible Ġin Ġpart Ġby Ġa Ġdonation Ġto ĠNYU Ġfrom ĠEric Ġand ĠWendy ĠSchmidt Ġmade Ġby Ġrecommendation Ġof Ġthe ĠSchmidt ĠFut ures Ġprogram , Ġby Ġsupport Ġfrom ĠInt uit ĠInc ., Ġand Ġby Ġsupport Ġfrom ĠSamsung ĠResearch Ġunder Ġthe Ġproject ĠImpro ving ĠDeep ĠLearning Ġusing ĠLat ent ĠStructure . ĠWe Ġgrate fully Ġacknowledge Ġthe Ġsupport Ġof ĠNVIDIA ĠCorporation Ġwith Ġthe Ġdonation Ġof Ġa ĠTitan ĠV ĠGPU Ġused Ġat ĠNYU Ġin Ġthis Ġwork . ĠAlex ĠWang 's Ġwork Ġon Ġthe Ġproject Ġis Ġsupported Ġby Ġthe ĠNational ĠScience ĠFoundation ĠGraduate ĠResearch ĠFellowship ĠProgram Ġunder ĠGrant ĠNo . ĠD GE Ġ134 25 36 . ĠAny Ġopinions , Ġfindings , Ġand Ġconclusions Ġor Ġrecommendations Ġexpressed Ġin Ġthis Ġmaterial Ġare Ġthose Ġof Ġthe Ġauthor ( s ) Ġand Ġdo Ġnot Ġnecessarily Ġreflect Ġthe Ġviews Ġof Ġthe ĠNational ĠScience ĠFoundation . ĠY ada ĠPru ks ach at kun 's Ġwork Ġon Ġthe Ġproject Ġis Ġsupported Ġin Ġpart Ġby Ġthe ĠMoore - S lo an ĠData ĠScience ĠEnvironment Ġas Ġpart Ġof Ġthe ĠNYU ĠData ĠScience ĠServices Ġinitiative . ĠSam ĠBowman 's Ġwork Ġon Ġj iant Ġduring ĠSummer Ġ2019 Ġtook Ġplace Ġin Ġhis Ġcapacity Ġas Ġa Ġvisiting Ġresearcher Ġat ĠGoogle .