Xris001/swahili-ai-data-evaluation

Repository containing sample evaluation matrix datasets, pairwise LLM comparison logs, and Swahili localization frameworks for AI data annotation workflows. # Swahili AI Data Training & Evaluation Repository Welcome to my repository dedicated to Large Language Model (LLM) fine-tuning alignment, linguistic data annotation, and quality assurance framework samples specifically for the Swahili-English language pair. ## 📁 Repository Contents & Data Structures ### 1. Pairwise-Evaluation/ * Contains structured test sets evaluating competing model outputs against target human user instructions. * Includes localized evaluation schemas emphasizing standard grammar parameters, regional idioms, and natural tonal context (Sanifu vs. Regional variants). ### 2. Annotation-Matrices/ * Log files showcasing string error tagging, content safety classification flags, and contextual severity metrics (Minor, Moderate, Major language errors). * Focuses on structural noun-adjective agreements, verb prefix logic handling, and syntax validation for natural language processing (NLP). ### 3. Localization-Frameworks/ * Reference guides tracking direct translation patterns versus contextually accurate localization for technical digital user interfaces (UI/UX). ## 🚀 Core Competencies Demonstrated * **RLHF Data Preparation:** Experienced in human-in-the-loop validation tasks to enhance LLM alignment and cultural conversational safety metrics. * **Strict Parameter Adherence:** Executing quality checks based cleanly on defined external system constraints, maintaining structural and semantic data consistency. * **Bilingual Analysis:** Native fluency in Swahili coupled with professional-tier English comprehension to seamlessly bridge localization gaps.