Universal Language Model Fine-tuning for Text Classification
Jeremy Howard, Sebastian Ruder · 2018
Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch.We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model.Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 18-24% on the majority of datasets.Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100× more data.We opensource our pretrained models and code 1 .