# Universal Language Model Fine-tuning for Text Classification

**Authors:** Jeremy Howard, Sebastian Ruder  
**arXiv:** [1801.06146](https://arxiv.org/abs/1801.06146)  
**Date:** 2018-01-18  
**Summary page:** https://www.summarizepaper.com/fr/paper/1801.06146v5/

## Abstract

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 100x more data. We open-source our pretrained models and code.