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

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

Steps 7b-8: Importing and defining the model

We will now activate the interaction with the model with interactive_conditional_samples.py.

We need to import three modules that are also in /content/gpt-2/src:

import model, sample, encoder

The three programs are:

model.py defines the model’s structure: the hyperparameters, the multi-attention tf.matmul operations, the activation functions, and all the other properties.
sample.py processes the interaction and controls the sample that will be generated. It makes sure that the tokens are more meaningful.
Softmax values can sometimes be blurry, like looking at an image in low definition. sample.py contains a variable named temperature that will make the values sharper, increasing the higher probabilities and softening the lower ones.

sample.py can activate Top-k sampling. Top-k sampling sorts the probability distribution of a predicted sequence. The higher probability values of the head of...