Papers › Universal Language Model Fine-tuning for Text Classification
Universal Language Model Fine-tuning for Text Classification
Jeremy Howard, Sebastian Ruder
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.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sentiment Analysis | IMDb | ULMFiT | Accuracy | 95.4 | #16 of 49 | Archive leaderboard | report |
| Sentiment Analysis | Yelp Binary classification | ULMFiT | Error | 2.16 | #7 of 20 | Archive leaderboard | report |
| Sentiment Analysis | Yelp Fine-grained classification | ULMFiT | Error | 29.98 | #5 of 17 | Archive leaderboard | report |
| Text Classification | AG News | ULMFiT | Error | 5.01 | #4 of 24 | Archive leaderboard | report |
| Text Classification | DBpedia | ULMFiT | Error | 0.80 | #6 of 21 | Archive leaderboard | report |
| Text Classification | TREC-6 | ULMFiT | Error | 3.6 | #5 of 19 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: Discriminative Fine-Tuning, Slanted Triangular Learning Rates, ULMFiT
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