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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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Toc

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

Questions

  1. T5 models only have encoder stacks like BERT models. (True/False)
  2. T5 models have both encoder and decoder stacks. (True/False)
  3. T5 models use relative positional encoding, not absolute positional encoding. (True/False)
  4. Text-to-text models are only designed for summarization. (True/False)
  5. Text-to-text models apply a prefix to the input sequence that determines the NLP task. (True/False)
  6. T5 models require specific hyperparameters for each task. (True/False)
  7. One of the advantages of text-to-text models is that they use the same hyperparameters for all NLP tasks. (True/False)
  8. T5 transformers do not contain a feedforward network. (True/False)
  9. NLP text summarization works for any text. (True/False)
  10. Hugging Face is a framework that makes transformers easier to implement. (True/False)
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