RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth}
	\begin{tabular}{c|cc|cc|cc|cc}
		\toprule
		\multirow{2}{*}{Methods} & \multicolumn{2}{c|}{CoLA} & \multicolumn{2}{c|}{STS-B$^\ddagger$} & \multicolumn{2}{c|}{MRPC} & \multicolumn{2}{c}{RTE} \\ 
		& Mcc & Time & Pcc & Time & Acc & Time & Acc & Time \\
		\midrule
		RD & +1.8 & $\times$1.42 & +0.3 & $\times$1.38 & +2.7 & $\times$1.31 & +3.9 & $\times$1.42 \\
		AA & +3.3 & $\times$1.42 & +0.1 & $\times$1.48 & +2.9 & $\times$1.94 & +3.9 & $\times$1.58 \\
		GA & +4.3 & $\times$3.58 & +0.5 & $\times$1.95 & +3.4 & $\times$4.13 & +4.3 & $\times$4.50 \\
		IGA & +3.5 & $\times$3.57 & +0.8 & $\times$1.94 & +3.4 & $\times$4.12 & +3.6 & $\times$4.49 \\
		\bottomrule
	\end{tabular}}	
\end{adjustbox}
\caption{Table 7: Results of performance and computational cost of \textsc{AD-Drop} with different masking strategies (GA, IGA, AA, and RD) relative to the original fine-tuning. The symbol $\ddagger$ means \textsc{AD-Drop} is only applied in the first layer. BERT is chosen as the base model.}
\end{table}