IndoBERT-Relevancy: A Context-Conditioned Relevancy Classifier for Indonesian Text
This work addresses the largely unexplored task of relevancy classification for Bahasa Indonesia, providing a high-performing model for Indonesian NLP applications.
The paper tackled the problem of relevancy classification for Indonesian text, which requires reasoning about the relationship between a topical context and a candidate text, and introduced IndoBERT-Relevancy, a model that achieved an F1 score of 0.948 and 96.5% accuracy on a novel dataset of 31,360 labeled pairs.
Determining whether a piece of text is relevant to a given topic is a fundamental task in natural language processing, yet it remains largely unexplored for Bahasa Indonesia. Unlike sentiment analysis or named entity recognition, relevancy classification requires the model to reason about the relationship between two inputs simultaneously: a topical context and a candidate text. We introduce IndoBERT-Relevancy, a context-conditioned relevancy classifier built on IndoBERT Large (335M parameters) and trained on a novel dataset of 31,360 labeled pairs spanning 188 topics. Through an iterative, failure-driven data construction process, we demonstrate that no single data source is sufficient for robust relevancy classification, and that targeted synthetic data can effectively address specific model weaknesses. Our final model achieves an F1 score of 0.948 and an accuracy of 96.5%, handling both formal and informal Indonesian text. The model is publicly available at HuggingFace.