  Neutrino Events Reconstruction has always been crucial for IceCube Neutrino
Observatory. In the Kaggle competition "IceCube -- Neutrinos in Deep Ice", many
solutions use Transformer. We present ISeeCube, a pure Transformer model based
on TorchScale (the backbone of BEiT-3). When having relatively same amount of
total trainable parameters, our model outperforms the 2nd place solution. By
using TorchScale, the lines of code drop sharply by about 80% and a lot of new
methods can be tested by simply adjusting configs. We compared two fundamental
models for predictions on a continuous space, regression and classification,
trained with MSE Loss and CE Loss respectively. We also propose a new metric,
overlap ratio, to evaluate the performance of the model. Since the model is
simple enough, it has the potential to be used for more purposes such as energy
reconstruction, and many new methods such as combining it with GraphNeT can be
tested more easily. The code and pretrained models are available at
https://github.com/ChenLi2049/ISeeCube
