data/tables/dev/val_tab_0029.tex

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\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth}
		\begin{tabular}{lccccccccc}
			\toprule
			Methods & SST-2 & MNLI & QNLI & QQP & CoLA & STS-B & MRPC & RTE & Average\\ 
			\midrule
			\multicolumn{10}{c}{\textit{Development}} \\
			BERT$_{\rm{base}}$ & 92.3 & 84.6 & 91.5 & 91.3 & 60.3 & 89.9 & 85.1 & 70.8 & 83.23 \\
			\ \  +\textit{SCAL}$^\dagger$ miao2021simple & 92.8 & 84.1 & 90.9 & 91.4 & 61.7 & - & - & 69.7 & - \\
			\ \  +\textit{SuperT}$^\dagger$ liang2021super & 93.4 & 84.5 & 91.3 & 91.3 & 58.8 & 89.8 & 87.5 & 72.5 & 83.64 \\
			\ \  +\textit{R-Drop}$^\dagger$ wu2021r  & 93.0 & 85.5 & 92.0 & 91.4 & 62.6 & 89.6 & 87.3 & 71.1 & 84.06 \\
			\ \  +\textsc{AD-Drop}  & 93.9 & 85.1 & 92.3 & 91.8 & 64.6 & 90.4 & 88.5 & 75.1 & \textbf{85.21} \\
			\midrule
			RoBERTa$_{\rm{base}}$ & 95.3 & 87.6 & 92.9 & 91.9 & 64.8 & 90.9 & 90.7 & 79.4 & 86.69 \\
			\ \  +\textit{R-Drop} wu2021r & 95.2 & 87.8 & 93.2 & 91.7 & 64.7 & 91.2 & 90.5 & 80.5 & 86.85 \\
			\ \  +\textit{HiddenCut}$^\dagger$ chen2021hiddencut & 95.8 & 88.2 & 93.7 & 92.0 & 66.2 & 91.3 & 92.0 & 83.4 & 87.83 \\
			\ \  +\textsc{AD-Drop}  & 95.8 & 88.0 & 93.5 & 92.0 & 66.8 & 91.4 & 92.2 & 84.1 & \textbf{87.98}\\
			\midrule
			\midrule
			\multicolumn{10}{c}{\textit{Test}} \\
			BERT$_{\rm{base}}$ & 93.6 & 84.7 & 90.4 & 89.3 & 52.8 & 85.6 & 81.4 & 68.4 & 80.78 \\
			\ \  +\textsc{AD-Drop}  & 94.3 & 85.2 & 91.6 & 89.4 & 53.3 & 86.6 & 84.1 & 68.7 & \textbf{81.65} \\
			\midrule
			RoBERTa$_{\rm{base}}$ & 94.8 & 87.5 & 92.8 & 89.6 & 58.3 & 88.7 & 86.3 & 75.1 & 84.14 \\
			\ \  +\textsc{AD-Drop}  & 95.9 & 87.6 & 93.4 & 89.5 & 58.5 & 89.3 & 87.9 & 76.0 & \textbf{84.76} \\
			\bottomrule
	\end{tabular}}
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\caption{Table 1: Overall results of fine-tuned models on the GLUE benchmark. The symbol $\dagger$ denotes results directly taken from the original papers. The best average results are shown in bold.}
\end{table}
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