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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. BERT stands for Bidirectional Encoder Representations from Transformers. (True/False)
  2. BERT is a two-step framework. Step 1 is pretraining. Step 2 is fine-tuning. (True/False)
  3. Fine-tuning a BERT model implies training parameters from scratch. (True/False)
  4. BERT only pretrains using all downstream tasks. (True/False)
  5. BERT pretrains with Masked Language Modeling (MLM). (True/False)
  6. BERT pretrains with Next Sentence Predictions (NSP). (True/False)
  7. BERT pretrains mathematical functions. (True/False)
  8. A question-answer task is a downstream task. (True/False)
  9. A BERT pretraining model does not require tokenization. (True/False)
  10. Fine-tuning a BERT model takes less time than pretraining. (True/False)
You have been reading a chapter from
Transformers for Natural Language Processing
Published in: Jan 2021
Publisher: Packt
ISBN-13: 9781800565791
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