SeSoDa: A Compact Context-Rich Sesotho-English Dataset for LoRA Fine-Tuning of SLMs. - Mandla, Motaung; Hill, Graham; Mots’oehli, Moseli (Journal of the Digital Humanities Association of Southern Africa (DHASA), 2025)
We introduce SeSoDa, a multidomain Sesotho(Sa Lesotho)-English dataset of 1,966 prompt-completion pairs that span six categories (nouns, verbs, idioms, quantifiers, grammar rules, usage alerts). SeSoDa documents the morphosyntactic complexity, uncaptured Basotho cultural specificity, and orthographic/phonological differences between Lesotho and South African Sesotho. We created a user-friendly, JSON-style corpus with detailed metadata. This aims to lower the technical barrier for new researchers in Lesotho, helping them advance culture-aware machine translation, linguistic analysis, and cultural preservation using AI. As a proof of concept, we demonstrate SeSoDa’s utility by fine-tuning the TinyLlama-1.1B-Chat model using Low-Rank Adaptation (LoRA) on entirely free Google Colab GPUs and runtime limits. This parameterefficient fine-tuning approach is particularly vital for resource-constrained environments like Lesotho, making advanced NLP model adaptation feasible and accessible without requiring extensive computational resources. We open-source the code for the dataset creation, the baseline model, and the dataset itself. We hope to see both Basotho researchers and developers build on top of our effort.