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Transformers for Natural Language Processing

You're reading from   Transformers for Natural Language Processing Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781803247335
Length 602 pages
Edition 2nd Edition
Languages
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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 (25) Chapters Close

Preface 1. What are Transformers? 2. Getting Started with the Architecture of the Transformer Model FREE CHAPTER 3. Fine-Tuning BERT Models 4. Pretraining a RoBERTa Model from Scratch 5. Downstream NLP Tasks with Transformers 6. Machine Translation with the Transformer 7. The Rise of Suprahuman Transformers with GPT-3 Engines 8. Applying Transformers to Legal and Financial Documents for AI Text Summarization 9. Matching Tokenizers and Datasets 10. Semantic Role Labeling with BERT-Based Transformers 11. Let Your Data Do the Talking: Story, Questions, and Answers 12. Detecting Customer Emotions to Make Predictions 13. Analyzing Fake News with Transformers 14. Interpreting Black Box Transformer Models 15. From NLP to Task-Agnostic Transformer Models 16. The Emergence of Transformer-Driven Copilots 17. The Consolidation of Suprahuman Transformers with OpenAI’s ChatGPT and GPT-4 18. Other Books You May Enjoy
19. Index
Appendix I — Terminology of Transformer Models 1. Appendix II — Hardware Constraints for Transformer Models 2. Appendix III — Generic Text Completion with GPT-2 3. Appendix IV — Custom Text Completion with GPT-2 4. Appendix V — Answers to the Questions

Questioning the scope of SRL

We are alone when faced with a real-life project. We have a job to do, and the only people to satisfy are those who asked for that project.

Pragmatism must come first. Technical ideology after.

In the 2020s, former AI ideology and new ideology coexist. By the end of the decade, there will be only one winner merging some of the former into the latter.

This section questions the productivity of SRL and also its motivation through two aspects:

  • The limit of predicate analysis
  • Questioning the use of the term “semantic”

The limit of predicate analysis

SRL relies on predicates. SRL BERT only works as long as you provide a verb. But millions of sentences do not contain verbs.

If you provide SRL BERT in the Semantic Role Labeling section in the AllenNLP demo interface (https://demo.allennlp.org/) with an assertion alone, it works.

But what happens if your assertion is an answer to a question:

    ...
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