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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

Chapter 6, Text Generation with OpenAI GPT-2 and GPT-3 Models

  1. A zero-shot method trains the parameters once. (True/False)

    False. No the parameters of the model are first trained through as many episodes as necessary. Zero-shot means that downstream tasks are performed without additional fine-tuning.

  2. Gradient updates are performed when running zero-shot models. (True/False)

    False.

  3. GPT models only have a decoder stack. (True/False)

    True.

  4. It is impossible to train a 117M GPT model on a local machine. (True/False)

    False. We trained one in this chapter.

  5. It is impossible to train the GPT-2 model with a specific dataset. (True/False)

    False. We trained one in this chapter.

  6. A GPT-2 model cannot be conditioned to generate text. (True/False)

    False. We implemented this in this chapter.

  7. A GPT-2 model can analyze the context of input and produce completion content. (True/False)

    True.

  8. We cannot interact with...
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