  With the advancement of deep learning technology, neural networks have
demonstrated their excellent ability to provide accurate predictions in many
tasks. However, a lack of consideration for neural network calibration will not
gain trust from humans, even for high-accuracy models. In this regard, the gap
between the confidence of the model's predictions and the actual correctness
likelihood must be bridged to derive a well-calibrated model. In this paper, we
introduce the Neural Clamping Toolkit, the first open-source framework designed
to help developers employ state-of-the-art model-agnostic calibrated models.
Furthermore, we provide animations and interactive sections in the
demonstration to familiarize researchers with calibration in neural networks. A
Colab tutorial on utilizing our toolkit is also introduced.
