HyperAI超神经

Paraphrase Identification On Quora Question

评估指标

Accuracy

评测结果

各个模型在此基准测试上的表现结果

模型名称
Accuracy
Paper TitleRepository
MwAN 89.12Multiway Attention Networks for Modeling Sentence Pairs
XLNet-Large (ensemble)90.3XLNet: Generalized Autoregressive Pretraining for Language Understanding
RoBERTa-large 355M + Entailment as Few-shot Learner-Entailment as Few-Shot Learner
ERNIE-ERNIE: Enhanced Language Representation with Informative Entities
ASA + BERT-base-Adversarial Self-Attention for Language Understanding
TRANS-BLSTM88.28TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding-
RealFormer91.34RealFormer: Transformer Likes Residual Attention
SplitEE-S-SplitEE: Early Exit in Deep Neural Networks with Split Computing
SMART-BERT-SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
MT-DNN89.6Multi-Task Deep Neural Networks for Natural Language Understanding
GenSen87.01Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
ASA + RoBERTa-Adversarial Self-Attention for Language Understanding
DIIN89.06Natural Language Inference over Interaction Space
FNet-Large-FNet: Mixing Tokens with Fourier Transforms
1-3[0.8pt/2pt] Random80Self-Explaining Structures Improve NLP Models
StructBERTRoBERTa ensemble90.7StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding-
BERT-Base-Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning-
BiMPM88.17Bilateral Multi-Perspective Matching for Natural Language Sentences
FreeLB74.8SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
BERT-LARGE-BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
0 of 31 row(s) selected.