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Binary Classification on AdvGLUE (test)

0.701QNLI Accuracy

Dropout

Updated 3mo ago

Evaluation Results

Method	Links
Dropout 2023.05		0.701	0.633	0.709	0.499	0.636
SimPLE 2023.05		0.701	0.681	0.738	0.516	0.659
SETRED 2023.05		0.698	0.695	0.699	0.509	0.65
SimPLE 2023.05		0.696	0.544	0.623	0.588	0.613
Dropout 2023.05		0.692	0.578	0.619	0.573	0.616
SETRED 2023.05		0.68	0.565	0.626	0.589	0.615
Baseline-ST 2023.05		0.658	0.704	0.684	0.509	0.639
Baseline-ST 2023.05		0.649	0.606	0.609	0.566	0.608
RoBERTa-Sup 2023.05		0.621	0.526	0.617	0.599	0.591
DeBERTa-Sup 2023.05		0.615	0.641	0.667	0.426	0.587
DeBERTa-Cat 2023.05		0.608	0.474	0.506	0.561	0.537
DeBERTa 2023.05		0.579	0.604	0.79	0.578	0.638
RoBERTa 2023.05		0.525	0.454	0.628	0.585	0.548
CT 2023.05		0.496	0.407	0.462	0.392	0.439
R3F 2023.05		0.475	0.406	0.501	0.385	0.442
MT 2023.05		0.475	0.415	0.525	0.513	0.482
BERT 2023.05		0.398	0.379	0.405	0.33	0.378