alabnii/sciclaimeval-shared-task huggingface dataset:
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\begin{table}
\centering
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\begin{tabular}{lcc}
\toprule \textbf{Model} & \makecell{\textbf{F1} \\ \textbf{\textsc{Docile} labeled}} & \makecell{\textbf{F1} \\ \textbf{\textsc{Payslips}}} \\
\midrule \multicolumn{3}{l}{\textbf{Pre-training on \textsc{IIT-CDIP}}} \\
\midrule $\textsc{LayoutLM}_{\textrm{BASE}}$ & \boldmath{$58.35\pm1.63$} & \boldmath{$64.74\pm2.92$} \\
\midrule \multicolumn{3}{l}{\textbf{Pre-training on \textsc{Docile}}} \\
\midrule $\textsc{LayoutLM}_{\textrm{BASE}}$ & \underline{$58.30\pm1.52$} & \underline{$62.31\pm5.13$} \\
$\textsc{LayoutLM}_{\textrm{6 layers}}$ & $57.38\pm1.38$ & $61.80\pm3.12$ \\
$\textsc{LayoutLM}_{\textrm{2 layers}}$ & $53.89\pm1.03$ & $54.61\pm3.71$ \\
$\textsc{LayoutLM}_{\textrm{1 layer}}$ & $51.12\pm1.53$ & $45.08\pm3.31$ \\
\bottomrule
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\caption{Table 2: F1 scores for named-entity recognition using different pre-training
and fine-tuning datasets. Results are averaged on 100 runs with different
seeds.}
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