\begin{table*}[t] %跨雙欄
\centering
\renewcommand{\arraystretch}{1.10}
\setlength{\tabcolsep}{6pt}

\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline % 畫一條水平線
\multirow{2}{*}{Model (\#para)} &
\multicolumn{6}{c|}{Subtask 2: Polarization Type Classification (22 Languages / Test Set)} \\
\cline{2-7}
& Political & Racial/Ethnic & Religious & Gender/Sexual & Other & Macro F1 \\
\hline
Gemma-3 (27B)             & 0.6930 & 0.5579 & 0.6535 & 0.5453 & 0.4268 & 0.5753 \\ \hline
Mistral-small-3.2 (24B)   & 0.6669 & 0.5340 & 0.5782 & 0.4942 & 0.4102 & 0.5367 \\ \hline
Phi-4 (14B)               & 0.6452 & 0.5040 & 0.5685 & 0.4614 & 0.3281 & 0.5014 \\ \hline
Stacking Ensemble         & \textbf{0.7233} & \textbf{0.6006} & \textbf{0.6671} & \textbf{0.5817} & \textbf{0.4813} & \textbf{0.6108} \\ \hline
\end{tabular}}

\caption{Fine-tuned SLM results on the test set of Subtask 2. Each label score and Macro F1 are macro-F1 averaged over 22 languages.}

\label{tab:subtask2_test_22lang}
\end{table*}

