Universal Language Model Fine-tuning for Text Classification
参考论文：Universal Language Model Fine-tuning for Text Classification 迁移学习最早是应用于机器视觉方向的，迁移学习是从general到specific的过程，在general过程中在大型语料库中进行预训练(pre-training),在specific过程中在specific task领域中进行微调(fine-tunni
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- 1. Universal Language Model Fine-tuning for Text Classification
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- 4. 文献阅读笔记—Universal Language Model Fine-tuning for Text Classification
- 5. 将迁移学习用于文本分类 《 Universal Language Model Fine-tuning for Text Classification》
- 6. CNNs for Text Classification
- 7. Recurrent Convolutional Neural Networks for Text Classification
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相关文章
- 1. Universal Language Model Fine-tuning for Text Classification
- 2. 【论文阅读笔记】Universal Language Model Fine-tuning for Text Classification
- 3. “Universal Language Model Fine-tuning for Text Classification”论文解读
- 4. 文献阅读笔记—Universal Language Model Fine-tuning for Text Classification
- 5. 将迁移学习用于文本分类 《 Universal Language Model Fine-tuning for Text Classification》
- 6. CNNs for Text Classification
- 7. Recurrent Convolutional Neural Networks for Text Classification
- 8. Adversarial Multi-task Learning for Text Classification
- 9. Enriching Pre-trained Language Model with Entity Information for Relation Classification论文阅读
- 10. 论文浅尝 | Enriching Pre-trained Language Model with Entity Information for Relation Classification