\begin{table*}[hbt!]
    \centering
    \resizebox{0.9\linewidth}{!}{
    \begin{tabular}{lc|cccc}
        \textbf{Model Name} & \textbf{Type} & \textbf{Acc@1} & \textbf{Acc@3} & \textbf{Acc@5} & \textbf{Acc@100}\\
        \toprule
        RoBERT \cite{masala-etal-2020-robert} & \multirow{3}{*}{off-the-shelf} & 44.7 $\pm$ 0.003 & 53.4 $\pm$ 0.003 & 56.9 $\pm$ 0.004 & 75.3 $\pm$ 0.003 \\
        BioClinicalBERT \cite{alsentzer2019clincalbert} &  & 47.7 $\pm$ 0.003 & 56.7 $\pm$ 0.003 & 60.2 $\pm$ 0.002 & 74.9 $\pm$ 0.003\\
        mE5 \cite{wangMultilingualE5Text2024}  &  & 56.8 $\pm$ 0.003 & 69.4 $\pm$ 0.002 & 74.3 $\pm$ 0.002 & 91.3 $\pm$ 0.002\\
        \midrule
        RoBERT \cite{masala-etal-2020-robert} & \multirow{3}{*}{fine-tuned} & 75.9 $\pm$ 0.001 & 89.9 $\pm$ 0.002 & 93.2 $\pm$ 0.000 & 98.9 $\pm$ 0.001\\
        BioClinicalBERT \cite{alsentzer2019clincalbert} &  & 75.7 $\pm$ 0.002 & 89.2 $\pm$ 0.002 & 92.7 $\pm$ 0.002 & 98.9 $\pm$ 0.000\\
        mE5 \cite{wangMultilingualE5Text2024}  &  &\textbf{ 78.8 $\pm$ 0.002} & \textbf{92.2 $\pm$ 0.002} & \textbf{95.0 $\pm$ 0.002} & \textbf{99.5 $\pm$ 0.001}\\
    \end{tabular}
    }
    \caption{Comparison between different types of text embedding models, having entries in the vector store only from the masterlist entries. We obtained the best results using a fine-tuned version of mE5, a general-purpose multi-lingual model. Results are averaged across 5 folds.}
    \label{tab:masterlist}
\end{table*}