Новый сабмит FRED-T5 1.7B (only encoder 760M) finetune

8 февраля 2023 г. 7:42

Команда SberDevices

Ссылка на модель https://huggingface.co/sberbank-ai/FRED-T5-1.7B


Результат бейзлайна: 0,694

Датасет Результат Метрика
LiDiRus 0,421 Кор, коэффициент Мэтью
RCB 0,311 / 0,441 F1/Точность
PARus 0,806 Точность
MuSeRC 0,882 / 0,666 F1a/Em
TERRa 0,831 Точность
RUSSE 0,723 Точность
RWSD 0,669 Точность
DaNetQA 0,735 Точность
RuCoS 0,91 / 0,911 F1/EM
Описание модели:

Here we evaluate encoder-only part of the pretrained FRED-T5-1.7B model (https://russiansuperglue.com/login/submit_info/1936), resulting 760M parameters (2.2 times smaller). We fine-tune the model separately for each RussianSuperGLUE task. For LiDiRus we start with TERRa-finetuned model. For RWSD we use the majority class baseline. For each task we submit best-performing checkpoint (saving each epoch, but more frequently for RCB, PARus and RuCoS) based on validation metrics. No fixes were applied to the datasets. No filters/fixes were applied to datasets.


Описание параметров:

Hyper-parameters for fine-tuning: batch size of 16, epochs {10, 20, 30}, lr {1e-06, 1e-04, 1e-5, 2e-5, 3e-5}, linear lr scheduler, warmup ratio {0.02, 0.05}, weight decay {0, 0.01, 0.1}.

Диагностика: 0,421

Категория Результат
LOGIC 0,25548133714772964
KNOWLEDGE 0,35721247554168717
PREDICATE-ARGUMENT STRUCTURE 0,4530930715114425
LEXICAL SEMANTICS 0,49319873437048684
Lexical Semantics - Lexical Entailment 0,4770239916187289
Lexical Semantics - Morphological Negation 0,39477101697586137
Lexical Semantics - Factivity 0,4226770155886447
Lexical Semantics - Symmetry/Collectivity 0,3243723035407737
Lexical Semantics - Redundancy 0,27348301713730944
Lexical Semantics - Named Entities 0,612056372482123
Lexical Semantics - Quantifiers 0,3157894736842105
Predicate-Argument Structure Core Args 0,5487601413337525
Predicate-Argument Structure Prepositional Phrases 0,6588289607878823
Predicate-Argument Structure Ellipsis/Implicits 0,5260558322946913
Predicate-Argument Structure Anaphora/Coreference 0,3349672436203912
Predicate-Argument Structure Active/Passive 0,2843611155188746
Predicate-Argument Structure Nominalization 0,5017348819226064
Predicate-Argument Structure Genitives/Partitives 0,15724272550828775
Predicate-Argument Structure Datives 0,7637626158259734
Predicate-Argument Structure Relative Clauses 0,3333333333333333
Predicate-Argument Structure Coordination Scopes 0,5091750772173156
Predicate-Argument Structure Intersectivity 0,3973597071195131
Predicate-Argument Structure Restrictivity 0,28741691319281637
Logic Negation 0,12866255886641637
Logic Double Negation 0,21320071635561044
Logic Interval/Numbers 0,010615495921641366
Logic Conjuction 0,5680375574437545
Logic Disjunction 0,16447838793172298
Logic Conditionals 0,07100716024967263
Logic Universal 0,2548235957188128
Logic Existential 0,2058790548922549
Logic Temporal 0,33910215700436014
Logic Upward Monotone 0,821271097469555
Logic Downward Monotone -0,30207927000959933
Logic Non-Monotonic 0,2364331218717302
Knowledge Common Sense 0,3594723992410968
Knowledge World Knowledge 0,3394352270463883

Производительность:

Датасет Speed RAM
LiDiRus - -
RCB - -
PARus - -
MuSeRC - -
TERRa - -
RUSSE - -
RWSD - -
DaNetQA - -
RuCoS - -