Adjusting DistilBERT for Question Answering Tasks in Elixir

Jan Świątek provides an in-depth explanation on how to fine-tune the DistilBERT model for extractive question answering, showcasing how to process datasets and adjust the model's architecture. He goes into detail about machine learning concepts, natural language processing (NLP), and the transformer architecture. The guide includes Elixir code snippets for loading models, tokenizing data, and conducting training. Various stages of data processing like flattening and tokenization are explained. The training process, including loss function and evaluation metrics, is described comprehensively, emphasizing practical applications with Elixir and Bumblebee libraries.