IndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian NLP dataset encompassing a broad range of morpho-syntactic, semantic, and discourse analysis competencies. Like GLUE Benchmark, The purpose of IndoLEM is to benchmark progress in Indonesian NLP. The tasks in IndoLEM can be categorized into one of these followings:
Fajri Koto, Afshin, Rahimi, Jey Han Lau, and Timothy Baldwin. IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP. In Proceedings of the 28th COLING, December 2020.