Patoh254code/Swahili_QA_system

# 🧠 **Swahili Question Answering (QA) System** ## 📌 **Overview** This project fine-tunes the multilingual transformer model mT5-small for the task of sequence to sequence question answering in Swahili. The goal is to build a robust QA system that can accurately provide answers to natural-language questions using Swahili context passages. This initiative supports the advancement of NLP tools in underrepresented African languages, helping make intelligent systems more inclusive. ## 🔍 **1.0 Business Understanding** In an age of rapid digital transformation, equitable access to AI-driven tools is critical for inclusive knowledge sharing. Despite being spoken by over 100 million people across East and Central Africa, Swahili remains vastly underrepresented in the field of Natural Language Processing (NLP). This imbalance limits the development of intelligent systems capable of processing Swahili text, particularly in the domain of question answering (QA). The absence of such systems creates barriers in accessing information related to education, public services, and healthcare for Swahili-speaking communities. This project aims to bridge this gap by building a Swahili QA system that leverages machine learning to provide accurate, real-time answers to fact-based questions. By doing so, we contribute to the digital inclusion of one of Africa’s most widely spoken languages and support broader access to knowledge for Swahili users. ### ⚠️ **1.1 Challenges** Key challenges include: 1. Limited availability of large-scale annotated Swahili QA datasets 2. Scarcity of pre-trained NLP models optimized for Swahili language tasks 3. High computational costs associated with training deep learning models 4. Linguistic variability and dialectal differences within Swahili 5. Difficulty in achieving high accuracy across diverse domains (e.g., education, health) ### 💡 **1.2 Proposed Solution** To address these challenges, we propose: 1. Fine-tuning multilingual transformer mode …