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Transformers for Natural Language Processing

You're reading from   Transformers for Natural Language Processing Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more

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Product type Paperback
Published in Jan 2021
Publisher Packt
ISBN-13 9781800565791
Length 384 pages
Edition 1st Edition
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Table of Contents (16) Chapters Close

Preface 1. Getting Started with the Model Architecture of the Transformer 2. Fine-Tuning BERT Models FREE CHAPTER 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

Designing a universal text-to-text model

Google's NLP technical revolution started with Vaswani et al. (2017), the original Transformer, in 2017. "Attention is All You Need" toppled 30+ years of artificial intelligence belief in RNNs and CNNs applied to NLP tasks. It took us from the stone age of NLP/NLU to the 21st century in a long-overdue evolution.

Chapter 6, Text Generation with OpenAI GPT-2 and GPT-3 Models, summed up a second revolution that boiled up and erupted between Google's Vaswani et al. (2017) original Transformer and OpenAI's Brown et al. (2020) GPT-3 transformers. The original Transformer was focused on performance to prove that attention was all we needed for NLP/NLU tasks.

OpenAI's second revolution, through GPT-3, focused on taking transformer models from fine-tuning pretrained models to few-shot trained models that required no fine-tuning. The second revolution was to show that a machine can learn a language and apply it to...

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