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Transformers for Natural Language Processing

You're reading from Transformers for Natural Language Processing

Product type Book

Published in Jan 2021

Publisher Packt

ISBN-13 9781800565791

Pages 384 pages

Edition 1st Edition

Languages

Concepts

Mobile Application Development

Author (1):

Denis Rothman

Table of Contents (16) Chapters

Preface

1. Getting Started with the Model Architecture of the Transformer

2. Fine-Tuning BERT Models

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 4, Downstream NLP Tasks with Transformers

Machine intelligence uses the same data as humans to make predictions. (True/False)
False. For NLU, humans have access to more information through their senses. Machine intelligence relies on what humans provide for all types of media.
SuperGLUE is more difficult than GLUE for NLP models. (True/False)
True.
BoolQ expects a binary answer. (True/False)
True.
WiC stands for Words in Context. (True/False)
True.
Recognizing Textual Entailment (RTE) detects if one sequence entails another sequence. (True/False)
True.
A Winograd Schema predicts if a verb is spelled correctly. (True/False)
False. Winograd schemas mostly apply to pronoun disambiguation.
Transformer models now occupy the top ranks of GLUE and SuperGLUE. (True/False)
True.
Human Baseline Standards are not defined once and for all. They were made tougher to attain by SuperGLUE. (True...

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Authors (1)

Denis Rothman

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.

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