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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

Difficult samples

This section will run samples that contain problems that the BERT-based transformer will first solve. Finally, we will end with an intractable sample.

Let’s start with a complex sample that the BERT-based transformer can analyze.

Sample 4

Sample 4 takes us into more tricky SRL territory. The sample separates Alice from the verb liked, creating a long-term dependency that has to jump over whose husband went jogging every Sunday.

The sentence is:

Alice, whose husband went jogging every Sunday, liked to go to a dancing class in the meantime.

A human can isolate Alice and find the predicate:

Alice liked to go to a dancing class in the meantime.

Can the BERT model find the predicate like us?

Let’s find out by first running the code in SRL.ipynb:

prediction=predictor.predict(
    sentence="Alice, whose husband went jogging every Sunday, liked to go to a dancing class in the meantime."
)
head(prediction)
...
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