\begin{table*}[!ht]
\centering
\small
\begin{tabular}{@{}L{100pt}|C{50pt}|ccc|ccc@{}}
\toprule
\multicolumn{1}{c|}{\textbf{Model}} & \textbf{DisCoDisCo 2021 (BERT)} & \textbf{mBERT} & \textbf{DistilmBERT} & \textbf{XLM} & \textbf{mBERT} & \textbf{DistilmBERT} & \textbf{XLM} \\ \midrule
\multicolumn{1}{c|}{\textbf{Relation direction}} & \textbf{Add. tokens} & & 
 {\textbf{Add. tokens}} & & & {\textbf{Switching units}} & \\ \midrule \arrayrulecolor{gray!30}
% \textbf{No features} & 0.6041 &  &  &  & 0.5836 & 0.5569 & 0.6052 \\ \hline
% \textbf{Used DisCoDisCo features} & 0.6182 &  &  &  & 0.5975 & 0.5724 & 0.6114 \\ \hline
% \textbf{All DisCoDisCo features} & - & 0.6233 & 0.5908 & 0.6395 & 0.6233 & 0.5908 & 0.6395 \\ \hline
% \textbf{Language, Corpus, Framework (LCF)} & - & 0.6176 & 0.5917 & 0.6413 & 0.5834 & 0.5569 & 0.6052 \\ \hline
% \textbf{LCF + Used by DisCoDisCo} & - & 0.6346 & 0.6201 & 0.6591 & 0.6112 & 0.5775 & 0.6288 \\ \hline \arrayrulecolor{black}
% \textbf{LCF + All} & - & 0.6367 & 0.6192 & 0.6553 & 0.6389 & 0.5965 & 0.6351 \\ \bottomrule 
\textbf{No features} & 60.41 & 59.54 & 56.81 & 62.09 & 58.36 & 55.69 & 60.52 \\ \hline
\textbf{Common DisCoDisCo features} & 61.82 & 62.56 & 60.92 & 64.86 & 59.75 & 57.24 & 61.14 \\ \hline
\textbf{All DisCoDisCo features} & - & 63.09 & 60.28 & {64.50} & 62.33 & 59.08 & \textbf{63.95} \\ \hline
\textbf{Language, Corpus, Framework (LCF)} & - & 61.76 & 59.17 & 64.13 & 58.34 & 55.69 & 60.52 \\ \hline
\textbf{LCF + Common} & - & 63.46 & \textbf{62.01} & \textbf{65.91} & 61.12 & 57.75 & 62.88 \\ \hline
\textbf{LCF + All} & - & \textbf{63.67} & 61.92 & 65.53 & \textbf{63.89} & \textbf{59.65} & 63.51 \\ \arrayrulecolor{black} \bottomrule
\end{tabular}
\caption{Average accuracies of the models, reported on the test set. We report the results of the DisCoDisCo system with individual models trained with or without their specific features and the DisCoDisCo relation annotation. For our multilingual models, we report models trained with the DisCoDisCo direction annotation (``Add. tokens'') or the DiscReT direction normalization (``Switching units''). The models were trained with different sets of features or without. In bold are the best scores for each column, so for model and direction fixed. 
}
\label{tab:results-avg-all-models}
\end{table*}


% REMAKE THE TABLE WITH THE TEST RESULTS DUMBASS
