A Comparative Study of Pretrained Language Models on Thai Social Text Categorization
7: Classifier Fine-tuning Results. Our models are compared to other models: the baseline that predicts the most frequent label, the latest Kaggle competition winner, and public github repositories. The public leaderboard and private leaderboard are calculated with approximately 30% and 70% of the test data respectively.
['Model', 'Wisesight (Acc.) Private 8.535827pt', 'Wisesight (Acc.) Public 8.535827pt', 'Wongnai (Acc.) Private 8.535827pt', 'Wongnai (Acc.) Public']
BERT with our pretraining data outperforms all existing models on the private set of Wongnai and Wisesight and obtains 0.9% and 3.2% respective absolute accuracy improvement over the state-of-the-art. Absolute accuracy improvements on all models and tasks are obtained when pretrained with our Thai Social Media data instead of the Thai Wiki Dump.