HyperAI
HyperAI초신경
홈
플랫폼
문서
뉴스
연구 논문
튜토리얼
데이터셋
백과사전
SOTA
LLM 모델
GPU 랭킹
컨퍼런스
전체 검색
소개
서비스 약관
개인정보 처리방침
한국어
HyperAI
HyperAI초신경
Toggle Sidebar
전체 사이트 검색...
⌘
K
Command Palette
Search for a command to run...
플랫폼
홈
SOTA
자연어 추론
Natural Language Inference On Multinli
Natural Language Inference On Multinli
평가 지표
Matched
Mismatched
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
Matched
Mismatched
Paper Title
UnitedSynT5 (3B)
92.6
-
First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI
Turing NLR v5 XXL 5.4B (fine-tuned)
92.6
92.4
-
T5-XXL 11B (fine-tuned)
92.0
-
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
T5
92.0
91.7
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
T5-3B
91.4
91.2
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
ALBERT
91.3
-
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Adv-RoBERTa ensemble
91.1
90.7
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding
DeBERTa (large)
91.1
91.1
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
RoBERTa
90.8
-
RoBERTa: A Robustly Optimized BERT Pretraining Approach
XLNet (single model)
90.8
-
XLNet: Generalized Autoregressive Pretraining for Language Understanding
RoBERTa-large 355M (MLP quantized vector-wise, fine-tuned)
90.2
-
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
T5-Large
89.9
-
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
PSQ (Chen et al., 2020)
89.9
-
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
UnitedSynT5 (335M)
89.8
-
First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI
ERNIE 2.0 Large
88.7
88.8
ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
SpanBERT
88.1
-
SpanBERT: Improving Pre-training by Representing and Predicting Spans
ASA + RoBERTa
88
-
Adversarial Self-Attention for Language Understanding
BERT-Large
88
88
FNet: Mixing Tokens with Fourier Transforms
MT-DNN-ensemble
87.9
87.4
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding
Q-BERT (Shen et al., 2020)
87.8
-
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT
0 of 67 row(s) selected.
Previous
Next
Natural Language Inference On Multinli | SOTA | HyperAI초신경