The comprehensive results for the other models are shown in Tables and, illustrating that LumiNet achieves a Spearman correlation coefficient higher than 0.5 across various data partitioning scenarios, achieving state-of-the-art results in five partitioning methods. This suggests the robust applicability of the models across different systems, with the structural knowledge gained from pre-training exhibiting significant transferability. However, a notable challenge lies in the models' inconsistency in maintaining stable performance across diverse data partitioning scenarios.