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Natural language processing (NLP) is a fast-growing field of artificial intelligence. Since the Transformer [32] was introduced by Google in 2017, a large number of language models such as BERT, GPT, and ELMo have been inspired by this architecture. These models were trained on huge datasets and achieved state-of-the-art results on natural language understanding. However, fine-tuning a pre-trained language model on much smaller datasets for downstream tasks requires a carefully-designed pipeline to mitigate problems of the datasets such as lack of training data and imbalanced data. In this paper, we propose a pipeline to adapt the general-purpose RoBERTa language model to a specific text classification task: Vietnamese Hate Speech Detection. We first tune the PhoBERT<sup>1</sup>[9] on our dataset by re-training the model on the Masked Language Model (MLM) task; then, we employ its encoder for text classification. In order to preserve pre-trained weights while learning new feature representations, we further utilize different training techniques: layer freezing, block-wise learning rate, and label smoothing. Our experiments proved that our proposed pipeline boosts the performance significantly, achieving a new state-of-the-art on Vietnamese Hate Speech Detection (HSD) campaign<sup>2</sup> with 0.7221 F1 score.

From Universal Language Model to Downstream Task: Improving RoBERTa-Based Vietnamese Hate Speech Detection