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Transformers for Natural Language Processing

You're reading from  Transformers for Natural Language Processing

Product type Book
Published in Jan 2021
Publisher Packt
ISBN-13 9781800565791
Pages 384 pages
Edition 1st Edition
Languages
Author (1):
Denis Rothman Denis Rothman
Profile icon Denis Rothman
Toc

Table of Contents (16) Chapters close

Preface 1. Getting Started with the Model Architecture of the Transformer 2. Fine-Tuning BERT Models 3. Pretraining a RoBERTa Model from Scratch 4. Downstream NLP Tasks with Transformers 5. Machine Translation with the Transformer 6. Text Generation with OpenAI GPT-2 and GPT-3 Models 7. Applying Transformers to Legal and Financial Documents for AI Text Summarization 8. Matching Tokenizers and Datasets 9. Semantic Role Labeling with BERT-Based Transformers 10. Let Your Data Do the Talking: Story, Questions, and Answers 11. Detecting Customer Emotions to Make Predictions 12. Analyzing Fake News with Transformers 13. Other Books You May Enjoy
14. Index
Appendix: Answers to the Questions

References

  • Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, 2017, Attention Is All You Need: https://arxiv.org/abs/1706.03762
  • Hugging Face Transformer Usage: https://huggingface.co/transformers/usage.html
  • Manuel Romero Notebook with link to explanations by Raimi Karim: https://colab.research.google.com/drive/1rPk3ohrmVclqhH7uQ7qys4oznDdAhpzF
  • Google language research: https://research.google/teams/language/
  • Google Brain Trax documentation: https://trax-ml.readthedocs.io/en/latest/
  • Hugging Face research: https://huggingface.co/transformers/index.html
  • The Annotated Transformer: http://nlp.seas.harvard.edu/2018/04/03/attention.html
  • Jay Alammar, The Illustrated Transformer: http://jalammar.github.io/illustrated-transformer/
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