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\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth}
\begin{tabular}{lccccccccccc}
\toprule
\multirow{2}{*}{\textbf{Model}} & \multicolumn{5}{c}{\textbf{Binary%2-way
}} & & \multicolumn{5}{c}{\textbf{3-way}} \\

\cmidrule{2-6} \cmidrule{8-12}
& \textbf{HM-R} & \textbf{NusaX} & \textbf{African} & \textbf{CS} & \textbf{AVERAGE} & & \textbf{HM-R} & \textbf{NusaX} & \textbf{African} & \textbf{CS} & \textbf{AVERAGE} \\
\midrule
XGLM (2.9B) & 59.66 & 49.34 & 42.50 & 52.61 & 51.03 & & 38.09 & 33.47 & 25.72 & 50.08 & 36.84 \\
BLOOMZ (3B) & \underline{77.82} & \underline{69.85} & \underline{54.92} & 45.89 & 62.12 & & 48.43 & \underline{48.89} & 33.81 & 35.85 & 41.74 \\
GPT--3.5 (175B) & 77.50 & 63.90 & 53.82 & \underline{73.66} & \underline{67.22} & & \underline{67.65} & 48.50 & \underline{38.13} & \underline{50.41} & \underline{51.17} \\
\midrule
\textbf{mBERT$_\text{Base}$ (110M)} & & & & & & & & & & & \\
$+$ SST (sentence-level data) & 76.87 & 74.96 & 76.56 & 74.50 & 90.72 & & 96.89 & 98.94 & 97.80 & 94.85 & 92.12 \\
$+$ ML Lex.\ & 74.57 & \underline{67.92} & 57.79 & 69.43 & 67.43 & & \underline{55.72} & \underline{44.18} & 35.08 & 60.14 & {48.78} \\
$+$ ML Lex.\ $+$ Panlex & \underline{74.93} & 66.71 & \underline{58.99} & 71.58 & \underline{68.05} & & 55.42 & 43.47 & \underline{35.13} & \underline{61.27} & \underline{48.82} \\
$+$ ML Lex.\ $+$ Panlex $+$ Filtering & 74.74 & 63.95 & 58.00 & \underline{71.88} & 67.14 & & 54.21 & 39.18 & 33.42 & 58.81 & 46.40 \\
\midrule
\textbf{XLM-R$_\text{Base}$ (270M)} & & & & & & & & & & & \\
$+$ SST (sentence-level data) & \underline{85.51} & 59.50 & 56.59 & 49.84 & 62.86 & & 68.27 & 41.34 & 35.83 & 36.30 & 45.43 \\
$+$ ML Lex.\ & 82.13 & 70.94 & 61.45 & 62.44 & 69.24 & & 60.52 & 33.85 & 36.31 & 38.84 & 42.38 \\
$+$ ML Lex.\ $+$ Panlex & 82.74 & \underline{73.59} & \underline{63.10} & 63.97 & 70.85 & & 58.81 & \underline{46.34} & \underline{41.95} & \underline{60.31} & \underline{51.85} \\
$+$ ML Lex.\ $+$ Panlex $+$ Filtering & 82.88 & 73.47 & 63.99 & \underline{72.50} & \underline{73.21} & & \underline{61.28} & 33.33 & 34.87 & 40.13 & 42.40 \\
\midrule
\textbf{XLM-R$_\text{Large}$ (550M)} & & & & & & & & & & & \\
$+$ SST (sentence-level data) & \underline{\textbf{88.39}} & 73.00 & 61.75 & 54.38 & 69.38 & & \underline{\textbf{70.57}} & 49.63 & 37.39 & 36.71 & 48.58 \\
$+$ ML Lex.\ & 84.55 & \underline{78.01} & \underline{66.16} & 72.53 & \underline{75.31} & & 61.78 & 45.95 & 41.84 & 63.52 & 53.27 \\
$+$ ML Lex.\ $+$ Panlex & 84.89 & 70.79 & 63.85 & 76.47 & 74.00 & & 64.38 & \underline{52.84} & 42.47 & \underline{64.73} & \underline{\textbf{56.10}} \\
$+$ ML Lex.\ $+$ Panlex $+$ Filtering & 84.20 & 66.75 & 62.28 & \underline{\textbf{78.32}} & 72.89 & & 64.74 & 46.67 & \underline{43.20} & 59.59 & 53.55 \\
\midrule
\textbf{mBART$_\text{Large}$ (600M)} & & & & & & & & & & & \\
$+$ SST (sentence-level data) & \underline{85.41} & 65.41 & 59.62 & 62.61 & 68.26 & & \underline{66.58} & 31.80 & 27.99 & 31.52 & 39.47 \\
$+$ ML Lex.\ & 83.26 & \underline{72.26} & \underline{62.89} & 74.10 & \underline{73.13} & & 61.25 & \underline{41.88} & \underline{39.18} & \underline{54.43} & \underline{49.19} \\
$+$ ML Lex.\ $+$ Panlex & 81.97 & 74.86 & 62.74 & 65.00 & 71.14 & & 61.16 & 35.76 & 31.61 & 49.51 & 44.51 \\
$+$ ML Lex.\ $+$ Panlex $+$ Filtering & 80.66 & 61.71 & 58.28 & \underline{78.02} & 69.67 & & 57.48 & 30.50 & 30.24 & 40.77 & 39.75 \\
\midrule
\textbf{mT5$_\text{Base}$ (580M)} & & & & & & & & & & & \\
$+$ SST (sentence-level data) & \underline{83.16} & 55.33 & 57.18 & 48.39 & 61.02 & & \underline{62.37} & 35.58 & 37.59 & 31.04 & 41.64 \\
$+$ ML Lex.\ & 81.29 & \underline{75.84} & 67.45 & 73.63 & 74.55 & & 59.27 & \underline{51.63} & 43.88 & \underline{60.04} & \underline{53.71} \\
$+$ ML Lex.\ $+$ Panlex & 79.57 & 71.37 & 66.81 & 75.60 & 73.34 & & 57.14 & 50.22 & \underline{44.62} & 58.51 & 52.62 \\
$+$ ML Lex.\ $+$ Panlex $+$ Filtering & 82.24 & 75.52 & \underline{67.52} & \underline{76.33} & \underline{75.40} & & 61.72 & 45.92 & 44.45 & 59.09 & 52.79 \\
\midrule
\textbf{mT5$_\text{Large}$} (1B) & & & & & & & & & & & \\
$+$ SST (sentence-level data) & \underline{84.74} & 60.68 & 58.67 & 47.91 & 63.00 & & 48.05 & 31.67 & 31.53 & 24.75 & 34.00 \\
$+$ ML Lex.\ & 83.69 & \underline{\textbf{78.26}} & 69.28 & 72.62 & 75.96 & & \underline{62.15} & 51.84 & 44.42 & 61.43 & 54.96 \\
$+$ ML Lex.\ $+$ Panlex & 82.78 & 76.70 & \underline{\textbf{70.05}} & 75.32 & \underline{\textbf{76.21}} & & 59.59 & \underline{\textbf{53.96}} & \underline{\textbf{45.12}} & \underline{62.05} & \underline{55.18} \\
$+$ ML Lex.\ $+$ Panlex $+$ Filtering & 81.35 & 73.37 & 68.04 & \underline{75.43} & 74.54 & & 59.52 & 46.37 & 42.75 & 60.54 & 52.29 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Table 4: Full zero-shot results. The \underline{underlined} score indicates the highest performance within the respective group, while scores in \textbf{bold} indicate the best global performance. ``HM-R'' = high/medium-resource languages, excluding English, ``CS'' = code-switched text, and ``ML Lex.''\ indicates the multilingual NRC-VAD lexicon. ``SST (sentence-level data)'' is cross-lingual zero-shot transfer that is trained on English sentence-level sentiment data.}
\end{table}
\end{document}