How to Train A Question-Answering Machine Learning Model (BERT)

Question-Answering Models are designed to respond to questions using given context. This involves understanding language structure, semantic context, and pinpointing answer locations. The advent of Transformer's self-attention mechanism revolutionized NLP, leading to models like BERT. BERT's architecture includes a ladder of encoder layers that process data in parallel, making it efficient. Trained through Masked Language Modelling and Next Sentence Prediction, BERT is fine-tuned for specific tasks like question answering using datasets like SQuAD2.0. Here, BioBERT, a domain-specific variant, is trained using the Hugging Face library to answer COVID-19 related questions with modified data handling for RAM efficiency.