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SOTA
Natural Language Inference
Natural Language Inference On Multinli
Natural Language Inference On Multinli
Metrics
Matched
Mismatched
Results
Performance results of various models on this benchmark
Columns
Model Name
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.
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Natural Language Inference On Multinli | SOTA | HyperAI