Weak supervision has emerged as a promising approach for rapid and
large-scale dataset creation in response to the increasing demand for
accelerated NLP development. By leveraging labeling functions, weak supervision
allows practitioners to generate datasets quickly by creating learned label
models that produce soft-labeled datasets. This paper aims to show how such an
approach can be utilized to build an Indonesian NLP dataset from conservation
news text. We construct two types of datasets: multi-class classification and
sentiment classification. We then provide baseline experiments using various
pretrained language models. These baseline results demonstrate test
performances of 59.79% accuracy and 55.72% F1-score for sentiment
classification, 66.87% F1-score-macro, 71.5% F1-score-micro, and 83.67% ROC-AUC
for multi-class classification. Additionally, we release the datasets and
labeling functions used in this work for further research and exploration.

利用弱监督方法快速构建印尼自然语言处理数据集，通过使用标注函数生成软标签数据集，进行多类别分类和情感分类的基准实验，得到了相应的测试性能结果，并提供了数据集和标注函数以供进一步研究和探索。