alabnii/sciclaimeval-shared-task huggingface dataset:
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\textbf{Hate} & \textbf{Emo} & \textbf{HB} & \textbf{TD} & \textbf{PS} & \textbf{Ll1} & \textbf{Ll8} & \textbf{\#} \\ \cline{1-8}
\hlgreen{0} & \hlred{-1} & .80 & .84 & .83 & .85 & .94 & 523\\
\hlgreen{0} & \hlyellow{0} & .74 & .69 &.75 & .81 & .78 & 121\\
\hlgreen{0} & \hlgreen{1} & .91 & .85 & .95 & .94 & .98 & 811\\\cline{1-8}
\hlred{1} & \hlred{-1} & .77 & .58 & .79 & .87 & .92 & 2,976 \\
\hlred{1} & \hlyellow{0} & .50 & .50 & .50 & .50 & .50 & 4\\
\hlred{1} & \hlgreen{1} & .00 & .00 & .00 & .00 & .00 & 3\\
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\caption{Table 4: Classification accuracy of HateBERT, ToxDect-roberta, Perspective API, and Llama Guard 3 1/8B on \textsc{GPT-HateCheck} grouped by the hatefulness label (hate) and the polarity of the detected emotions (emo). We highlight the ``positive'' labels in \hlgreen{green} (``non-hateful'' and positive emotions) and ``negative'' labels in \hlred{red}. The ambiguous emotions are highlighted in \hlyellow{yellow}.}
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