Новый сабмит xlm-roberta-large (Facebook) finetune

8 февраля 2023 г. 7:41

Команда SberDevices

Ссылка на модель https://huggingface.co/xlm-roberta-large


Результат бейзлайна: 0,654

Датасет Результат Метрика
LiDiRus 0,369 Кор, коэффициент Мэтью
RCB 0,328 / 0,457 F1/Точность
PARus 0,59 Точность
MuSeRC 0,809 / 0,501 F1a/Em
TERRa 0,798 Точность
RUSSE 0,765 Точность
RWSD 0,669 Точность
DaNetQA 0,757 Точность
RuCoS 0,89 / 0,886 F1/EM
Описание модели:

The underlying model (contains 560M parameters) was pre-trained by Facebook on the CC100 multi-lingual dataset (Russian included). We further fine-tune the pre-trained 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) based on validation metrics.


Описание параметров:

Hyper-parameters for fine-tuning xlm-roberta-large: batch size {8, 16, 32, 64}, epochs {20, 30}, lr {1e-06, 2e-06, 1e-05, 3e-05}, lr scheduler {constant, linear}, warmup ratio {0.02, 0.05, 0.1}, weight decay {0, 0.01, 0.1}.

Диагностика: 0,369

Категория Результат
LOGIC 0,1963386058559272
KNOWLEDGE 0,3156834130078425
PREDICATE-ARGUMENT STRUCTURE 0,3777715485318068
LEXICAL SEMANTICS 0,45839855677638014
Lexical Semantics - Lexical Entailment 0,4282616143136151
Lexical Semantics - Morphological Negation 0,45175395145262565
Lexical Semantics - Factivity 0,18428853505018536
Lexical Semantics - Symmetry/Collectivity 0,43852900965351466
Lexical Semantics - Redundancy 0,27348301713730944
Lexical Semantics - Named Entities 0,38949041885226005
Lexical Semantics - Quantifiers 0,501227406091964
Predicate-Argument Structure Core Args 0,40943028340181464
Predicate-Argument Structure Prepositional Phrases 0,5823384109195534
Predicate-Argument Structure Ellipsis/Implicits 0,3263956049169334
Predicate-Argument Structure Anaphora/Coreference 0,26875600982680947
Predicate-Argument Structure Active/Passive 0,1835325870964494
Predicate-Argument Structure Nominalization 0,4687501237868722
Predicate-Argument Structure Genitives/Partitives 0,8660254037844386
Predicate-Argument Structure Datives 0,629940788348712
Predicate-Argument Structure Relative Clauses 0,16265001215808886
Predicate-Argument Structure Coordination Scopes 0,33454829277463405
Predicate-Argument Structure Intersectivity 0,2553606237816764
Predicate-Argument Structure Restrictivity 0,2748737083745107
Logic Negation 0,045531262041544854
Logic Double Negation 0,30151134457776363
Logic Interval/Numbers -0,1050485078938172
Logic Conjuction 0,42163702135578396
Logic Disjunction 0,008695652173913044
Logic Conditionals 0,14285714285714285
Logic Universal 0,4029114820126901
Logic Existential 0,04279604925109129
Logic Temporal 0,2548235957188128
Logic Upward Monotone 0,623033246356214
Logic Downward Monotone -0,06726727939963124
Logic Non-Monotonic 0,09267505241022214
Knowledge Common Sense 0,2876139817629179
Knowledge World Knowledge 0,3387649364472491

Производительность:

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