Based on paper "Text classification on software requirements specifications using transformer models"

$1.$ Describe the architecture, key features, and how these models $($BERT$,$ DistilBERT, Roberta, ALBERT, and XLNet$)$ process natural language. Emphasis on the ability of these models to understand and produce human$-$like text?

$2.$ How the Transformer model is applied to classify Software Requirements Specification $($SRS$)$ documents, including the process, the model's ability to understand context, and its advantages over traditional methods?

$3.$ Provide a detailed comparison of the tested models $($BERT$,$ DistilBERT, Roberta, ALBERT, XLNet$),$ focusing on their performance metrics, strengths, and weaknesses in the context of SRS document classification?

$4.$ What are the challenges faced by applying this model to SRS classification, including data preparation, model training, and limitations of the Transformer model itself?