T5 based context-aware Answer Generator - TURB phase 1
This repository contains a notebook that demonstrates how to use the T5 model to generate answers based on context and question. T5 is a text-to-text transfer transformer that can be fine-tuned on various natural language processing tasks, such as summarization, translation, question answering, and more. In this notebook, we show how to train and fine-tune a T5 model on the SQuADv2 dataset, which consists of questions and answers based on Wikipedia articles. We also show how to use the trained model to generate answers for new questions given a context.
This use case can be useful for virtual reality applications, where users can interact with a virtual environment and ask questions about it. For example, a user can explore a historical site and ask questions about its history, culture, or architecture. The T5 model can then generate answers based on the information provided by the virtual environment. This can enhance the user's immersion and learning experience.
All instructions regarding installation, model output, finetuning, etc are mentioned in the notebook.