The Stanford Question Answering Dataset, or SQuAD for short, is a popular dataset for training and evaluating question answering systems. SQuAD contains a large collection of questions and their corresponding answer spans, along with the context paragraph from which the answers can be found. SQuAD has been widely used in the natural language processing (NLP) community as a benchmark for evaluating the performance of question answering systems.
SQuAD 2.0 is the latest version of the SQuAD dataset, released in 2018. Unlike the original SQuAD, which only contains questions that have a definite answer in the context, SQuAD 2.0 includes additional questions that either have an unanswerable question or require reasoning beyond the given context. This makes SQuAD 2.0 a more challenging dataset for question answering systems, as they must not only extract the correct answer but also determine when a question is unanswerable or requires additional knowledge beyond the given context.
In this notebook, we will explore the SQuAD 2.0 dataset and develop a question answering model using deep learning techniques. We will begin by loading the dataset and preprocessing the data, followed by training, evaluating and comparing our models.