Much of software-engineering research relies on the naturalness of code, the fact that code, in small code snippets, is repetitive and can be predicted using statistical language models like n-gram. Although powerful, training such models on large code corpus is tedious, time-consuming and sensitive to code patterns (and practices) encountered during training. Consequently, these models are often trained on a small corpora and estimate the language naturalness that is relative to a specific style of programming or type of project. To overcome these issues, we propose using pre-trained language models to infer code naturalness. Pre-trained models are often built on big data, are easy to use in an out-of-the-box way and include powerful learning associations mechanisms. Our key idea is to quantify code naturalness through its predictability, by using state-of-the-art generative pre-trained language models. To this end, we infer naturalness by masking (omitting) code tokens, one at a time, of code-sequences, and checking the models' ability to predict them. To this end, we evaluate three different predictability metrics; a) measuring the number of exact matches of the predictions, b) computing the embedding similarity between the original and predicted code, i.e., similarity at the vector space, and c) computing the confidence of the model when doing the token completion task irrespective of the outcome. We implement this workflow, named CodeBERT-nt, and evaluate its capability to prioritize buggy lines over non-buggy ones when ranking code based on its naturalness. Our results, on 2510 buggy versions of 40 projects from the SmartShark dataset, show that CodeBERT-nt outperforms both, random-uniform and complexity-based ranking techniques, and yields comparable results (slightly better) than the n-gram models.