  The emergence of pre-trained models has significantly impacted Natural
Language Processing (NLP) and Computer Vision to relational datasets.
Traditionally, these models are assessed through fine-tuned downstream tasks.
However, this raises the question of how to evaluate these models more
efficiently and more effectively. In this study, we explore a novel approach
where we leverage the meta-features associated with each entity as a source of
worldly knowledge and employ entity representations from the models. We propose
using the consistency between these representations and the meta-features as a
metric for evaluating pre-trained models. Our method's effectiveness is
demonstrated across various domains, including models with relational datasets,
large language models and image models.
