ULMFiT: Universal Language Model Fine-Tuning for Text Classification

Abstract

While inductive transfer learning has revolutionized computer vision, current approaches to natural language processing still need training from the ground up and task-specific adjustments. As a powerful transfer learning approach applicable to any NLP activity, we provide Universal Language Model Fine-tuning (ULMFiT) and outline essential strategies for language model fine-tuning. With an error reduction of 18–24% on most datasets, our technique considerably surpasses the state-of-the-art on six text categorization tasks. Additionally, it achieves the same level of performance as training on 100 times more data with only 100 annotated examples. We have made our pretrained models and code publicly available.