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\title{Supplementary Material}

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\begin{document}
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\section{Equation for BM25}

BM25 score is calculated as:

\begin{equation} \label{eq:bm25} 
\resizebox{.95\hsize}{!}{\text{BM25}(D,x) = \sum_{i=1}^{n} \text{idf}(x_i) \frac{\text{tf}(x_i, D)(k_1 + 1)}{\text{tf}(x_i, D) + k_1(1 - b + b \frac{|D|}{\text{avgdl}})}}
\end{equation}

% \begin{equation}
%     \boldsymbol{z}_{wide}^{(k)} = \text{BM25}(\boldsymbol{D}_k, \boldsymbol{X})
% \end{equation}
where $x_i$ is the $i$th item in text x; $\boldsymbol{D}_k$ is  the label description for class $k$; \textit{idf} and \textit{tf} are inverse document frequency and text frequency for $x_i$ in $D_k$ respectively; \textit{avgdl} is the average description length; $k_1$ and $b$ are hyper parameters.

\section{Binary Cross Entropy Function}

\begin{equation} \label{eq:bm25} 
\resizebox{.9\hsize}{!}{
$\text{CE}(\hat{y}, y) = - \left[y \log(\hat{y}) + (1-y)\log(1-\hat{y}) \right]$
}
\end{equation}

\end{document}