Natural Language Inference On Qnli

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name	Accuracy	Paper Title	Repository
FNet-Large	85%	FNet: Mixing Tokens with Fourier Transforms	-
RealFormer	91.89%	RealFormer: Transformer Likes Residual Attention	-
Nyströmformer	88.7%	Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention	-
Q-BERT (Shen et al., 2020)	93.0	Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT	-
DeBERTaV3large	96%	DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing	-
Charformer-Tall	91.0%	Charformer: Fast Character Transformers via Gradient-based Subword Tokenization	-
ELECTRA	95.4%	-	-
SMART-BERT	-	SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization	-
data2vec	91.1%	data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language	-
SpanBERT	94.3%	SpanBERT: Improving Pre-training by Representing and Predicting Spans	-
24hBERT	90.6	How to Train BERT with an Academic Budget	-
ASA + RoBERTa	93.6%	Adversarial Self-Attention for Language Understanding	-
PSQ (Chen et al., 2020)	94.5	A Statistical Framework for Low-bitwidth Training of Deep Neural Networks	-
TRANS-BLSTM	94.08%	TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding	-
T5-Small	90.3%	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer	-
ASA + BERT-base	91.4%	Adversarial Self-Attention for Language Understanding	-
ALICE	99.2%	SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization	-
T5-Base	93.7%	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer	-
T5-11B	96.7%	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer	-
ERNIE	91.3%	ERNIE: Enhanced Language Representation with Informative Entities	-

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