Methodology

Question-answering is mainly presented as an NLP exercise involving a transformer and a dataset containing the ready-to-ask questions and answering those questions. The transformer is trained to answer the questions asked in this closed environment.

However, in more complex situations, reliable transformer model implementations require customized methods.

Transformers and methods

A perfect and efficient universal transformer model for question-answering or any other NLP task does not exist. The best model for a project is the one that produces the best outputs for a specific dataset and task.

The method outperforms models in many cases. For example, a suitable method with an average model often will produce more efficient results than a flawed method with an excellent model.

In this chapter, we will run DistilBERT, ELECTRA, and RoBERTa models. Some produce better performances than others.

However, performance does not guarantee a result in a critical...