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Transformers for Natural Language Processing - Second Edition

You're reading from  Transformers for Natural Language Processing - Second Edition

Product type Book
Published in Mar 2022
Publisher Packt
ISBN-13 9781803247335
Pages 602 pages
Edition 2nd Edition
Languages
Concepts
Author (1):
Denis Rothman Denis Rothman
Profile icon Denis Rothman
LinkedIn
Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide.
See other products by Denis Rothman

Table of Contents (25) Chapters

Preface 1. What are Transformers? 2. Getting Started with the Architecture of the Transformer Model 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

Chapter 5, Downstream NLP Tasks with Transformers

  1. Machine intelligence uses the same data as humans to make predictions. (True/False)

    True and False.

    True. In some cases, machine intelligence surpasses humans when processing massive amounts of data to extract meaning and perform a range of tasks that would take centuries for humans to process.

    False. For NLU, humans have access to more information through their senses. Machine intelligence relies on what humans provide for all types of media.

  1. SuperGLUE is more difficult than GLUE for NLP models. (True/False)

    True.

  1. BoolQ expects a binary answer. (True/False)

    True.

  1. WiC stands for Words in Context. (True/False)

    True.

  1. Recognizing Textual Entailment (RTE) detects whether one sequence entails another sequence. (True/False)

    True.

  1. A Winograd schema predicts whether a verb is spelled correctly. (True/False) ...
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Authors (1)

Profile icon Denis Rothman
LinkedIn
Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide.
See other products by Denis Rothman

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