Learn how to build a generative question-answering system using open-source AI and Python in this comprehensive tutorial. Explore the architecture of generative question-answering, which focuses on creating multi-sentence answers to open-ended questions by searching large document stores and synthesizing relevant information. Follow along as the instructor guides you through data preprocessing, text embedding and indexing, implementing the BART text generation model, and querying the system. Gain hands-on experience in asking questions and interpreting results, while understanding the key concepts behind this powerful natural language processing technique.
Overview
Syllabus
What is generative AI and Q&A?
Generative question-answering architecture
Getting code and prerequisites
Data preprocessing
Embedding and indexing text
BART text generation model
Querying with generative question-answering
Asking questions and getting results
Final notes
Taught by
James Briggs
