Approach pre-trained deep learning models with caution

It seems like using these pre-trained models have become a new standard for industry best practices. After all, why wouldn't you take advantage of a model that's been trained on more data and compute than you could ever muster by yourself? Advances within the NLP space have also encouraged the use of pre-trained language models like GPT and GPT-2, AllenNLP's ELMo, Google's BERT, and Sebastian Ruder and Jeremy Howard's ULMFiT (for an excellent over of these models, see this TOPBOTs post). One common technique for leveraging pretrained models is feature extraction, where you're retrieving intermediate representations produced by the pretrained model and using those representations as inputs for a new model. These final fully-connected layers are generally assumed to capture information that is relevant for solving a new task.