Dataset creation and benchmarking for Kashmiri news snippet classification using fine-tuned transformer and LLM models in a low resource setting.

ABSTRACT: Kashmiri language, recognized as one of the low-resource languages, has rich cultural heritage but remains underexplored in NLP due to lack of resources and datasets. The proposed research addresses this gap by creating a dataset of 15,036 news snippets for the task of Kashmiri news snippets classification, created through the translation of English news snippets into Kashmiri using the Microsoft Bing translation tool. These snippets are manually refined to ensure domain specificity, covering ten categories: Medical, Politics, Sports, Tourism, Education, Art and Craft, Environment, Entertainment, Technology, and Culture. Various machine learning, deep learning, transformer-models, and LLMs are explored for text classification. Among the models experimented for classification, fine-tuned Pa

SUBMITTER: Deyar DU 

PROVIDER: S-EPMC12630696 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature