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Transformers for Natural Language Processing

You're reading from Transformers for Natural Language Processing Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more

Product type Paperback

Published in Jan 2021

Publisher Packt

ISBN-13 9781800565791

Length 384 pages

Edition 1st Edition

Languages

Processing

Tools

BERT

Concepts

Mobile Application Development

Author (1):

Denis Rothman

View More author details

Table of Contents (16) Chapters

Preface

1. Getting Started with the Model Architecture of the Transformer

2. Fine-Tuning BERT Models FREE CHAPTER

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

Method 2: SRL first

The transformer could not find who was driving to go to Las Vegas and thought it was the Nat King Cole instead of Jo and Maria.

What went wrong? Can we see what the transformers think and obtain an explanation? To find out, let's go back to semantic role modeling. If necessary, take a few minutes to review Chapter 9, Semantic Role Labeling with BERT-Based Transformers.

Let's run the same sequence on AllenNLP, https://demo.allennlp.org/reading-comprehension, in the Semantic Role Labeling section to obtain a visual representation of the verb "drove" in our sequence:

Figure 10.2: EER Semantic Role Labeling (SRL)

We can see the problem. The argument of the verb "driving" is "they." There is no relationship established between "they" and "Jo" and "Maria." It seems that the inference could be made.

Transformer models keep evolving. The output might vary; however, the...

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Authors (1)

Denis Rothman

Denis Rothman

Denis Rothman graduated from Sorbonne University and Paris-Diderot University, and as a student, he wrote and registered a patent for one of the earliest word2vector embeddings and word piece tokenization solutions. He started a company focused on deploying AI and went on to author one of the first AI cognitive NLP chatbots, applied as a language teaching tool for Moët et Chandon (part of LVMH) and more. Denis rapidly became an expert in explainable AI, incorporating interpretable, acceptance-based explanation data and interfaces into solutions implemented for major corporate projects in the aerospace, apparel, and supply chain sectors. His core belief is that you only really know something once you have taught somebody how to do it.

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