8 февраля 2023 г. 7:41
Команда SberDevices
Ссылка на модель https://huggingface.co/xlm-roberta-large
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 |
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.
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}.
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 |
Датасет | Speed | RAM |
---|---|---|
LiDiRus | - | - |
RCB | - | - |
PARus | - | - |
MuSeRC | - | - |
TERRa | - | - |
RUSSE | - | - |
RWSD | - | - |
DaNetQA | - | - |
RuCoS | - | - |