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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
Author (1):
Denis Rothman Denis Rothman
Profile icon Denis Rothman
Toc

Table of Contents (16) Chapters close

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

T5 Bill of Rights Sample

The following sample, taken from the Bill of Rights, is more difficult because it expressed the precise rights of a person.

Open Summarizing_Text_V2.ipynb, a copy of the Summarizing_Text_with_T5.ipynb notebook we used in Chapter 7, Applying Transformers to Legal and Financial Documents for AI Text Summarization.

We will first run T5 without making any changes.

Summarizing the Bill of Rights, version 1

In this section, we will enter the same text we did in Chapter 7, Applying Transformers to Legal and Financial Documents for AI Text Summarization:

#Bill of Rights,V
text ="""
No person shall be held to answer for a capital, or otherwise infamous crime, unless on a presentment or indictment of a Grand Jury, exceptin cases arising in the land or naval forces, or in the Militia, when in actual service in time of War or public danger; nor shall any person be subject for the same offense to be twice put in jeopardy of life or limb...
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