Extractive and Abstractive QnA System

• Developed a question-answering system for efficient information retrieval, utilizing both abstractive and extractive approaches
to generate and extract answers from Wikipedia’s history section.
• For the abstractive model, employed ELI5 BART with the Pinecone vector database to perform semantic search and generate
concise, human-like responses with a BLEU score of 0.85.
• Implemented an extractive model using the Transformers library and a question-answering pipeline to identify precise answer spans within the text, reaching a 90% accuracy rate in answer extraction.