We made use of the scikit-learn package for the computation of the calibration curve and a min-max scaling for the cosine similarity scores, which is more formally shown in Equation (I1): In Figure , we showed the quantile-based calibration plot. However, it can be valuable to look at the calibration curve for uniform binning for extra insights. According to the displayed data in this plot, if the score exceeds 0.929, the pipeline has a 100% probability of returning the correct score.