You're reading from Transformers for Natural Language Processing Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4

Product type Paperback

Published in Mar 2022

Publisher Packt

ISBN-13 9781803247335

Length 602 pages

Edition 2nd Edition

Languages

Processing

Tools

Processing

Concepts

Mobile Application Development

Author (1):

Denis Rothman

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Table of Contents (25) Chapters

Preface

1. What are Transformers?

2. Getting Started with the Architecture of the Transformer Model FREE CHAPTER

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

Implementing GPUs in code

PyTorch, among other languages and frameworks, manages GPUs. PyTorch contains tensors just as TensorFlow does. A tensor may look like NumPy np.arrays(). However, NumPy is not fit for parallel processing. Tensors use the parallel processing features of GPUs.

Tensors open the doors to distributed data over GPUs in PyTorch, among other frameworks: https://pytorch.org/tutorials/intermediate/ddp_tutorial.html

In the Chapter03 notebook, BERT_Fine_Tuning_Sentence_Classification_GPU.ipynb, we used CUDA (Compute Unified Device Architecture) to communicate with NVIDIA GPUs. CUDA is an NVIDIA platform for general computing on GPUs. Specific instructions can be added to our source code. For more, see https://developer.nvidia.com/cuda-zone.

In the Chapter03 notebook, we used CUDA instructions to transfer our model and data to NVIDIA GPUs. PyTorch has an instruction to specify the device we wish to use: torch.device.

For more, see https://pytorch.org/docs...

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You're reading from Transformers for Natural Language Processing Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4

Table of Contents (25) Chapters

Implementing GPUs in code

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Authors (1)