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

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 ParsBERT-Uncased emerged as the best-performing transformer model, achieving an F1 score of 0.98. This work not only contributes a valuable dataset for Kashmiri but also identifies effective methodologies for accurate news snippet classification in the Kashmiri language. This research developed an essential dataset, which to our best belief, is the first attempt at creating a manually labelled corpus for the Kashmiri language and also devised an architecture using the best combination of embeddings, algorithms, and transformer-models for accurate text classification. It contributes significantly to the field of NLP for this underrepresented language.