  Although Indonesian is known to be the fourth most frequently used language
over the internet, the research progress on this language in the natural
language processing (NLP) is slow-moving due to a lack of available resources.
In response, we introduce the first-ever vast resource for the training,
evaluating, and benchmarking on Indonesian natural language understanding
(IndoNLU) tasks. IndoNLU includes twelve tasks, ranging from single sentence
classification to pair-sentences sequence labeling with different levels of
complexity. The datasets for the tasks lie in different domains and styles to
ensure task diversity. We also provide a set of Indonesian pre-trained models
(IndoBERT) trained from a large and clean Indonesian dataset Indo4B collected
from publicly available sources such as social media texts, blogs, news, and
websites. We release baseline models for all twelve tasks, as well as the
framework for benchmark evaluation, and thus it enables everyone to benchmark
their system performances.
