针对印尼语设计的文本相关性分类模型，可精准判断文本与主题的相关性。
IndoBERT-Relevancy: A Context-Conditioned Relevancy Classifier for Indonesian Text
- 基于IndoBERT Large构建上下文感知的分类器，融合多源数据训练
- 在188个主题上达到96.5%准确率和0.948 F1分数
- 适用于正式与非正式印尼语，适合信息筛选与检索场景
判断文本是否与特定主题相关是自然语言处理中的基础任务，但在印度尼西亚语中仍研究不足。与情感分析或命名实体识别不同，相关性分类需同时理解主题上下文与候选文本之间的关系。我们提出IndoBERT-Relevancy，基于拥有3350万参数的IndoBERT Large模型，使用包含31,360对标注样本的新数据集（覆盖188个主题）进行训练。通过迭代式、故障驱动的数据构建过程，我们发现单一数据源无法支撑鲁棒的相关性分类，而针对性合成数据可有效弥补模型缺陷。最终模型在测试中实现0.948的F1分数与96.5%的准确率，能处理正式与非正式印尼语文本。该模型已公开发布于HuggingFace。
原文摘要 · Abstract (English)
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.
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