InterIIT-DevRev-Domain Specific question answering

This work presents a natural language processing (NLP) based pipeline for question-answering in resource-constrained environments. Our pipeline takes a question and a set of paragraphs as input and uses two state-of-the-art NLP models, BM 250KAPI and Mini LM, to rank and classify the paragraphs based on their relevance to the question. The top-ranked paragraphs are then further processed by a finetuned Mini LM model for question answering that outputs the start and end indices of the answer within the paragraph. The results of this pipeline demonstrate the effectiveness of using advanced NLP techniques for question answering in resource-constrained environments and highlight the potential for further refinement and optimization of the pipeline. This work builds on top of the ongoing effort to develop and improve NLP-based question-answering systems and has the potential to impact a wide range of applications.