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Mastering PyTorch

You're reading from   Mastering PyTorch Build powerful neural network architectures using advanced PyTorch 1.x features

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781789614381
Length 450 pages
Edition 1st Edition
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Author (1):
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Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
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Table of Contents (20) Chapters Close

Preface 1. Section 1: PyTorch Overview
2. Chapter 1: Overview of Deep Learning using PyTorch FREE CHAPTER 3. Chapter 2: Combining CNNs and LSTMs 4. Section 2: Working with Advanced Neural Network Architectures
5. Chapter 3: Deep CNN Architectures 6. Chapter 4: Deep Recurrent Model Architectures 7. Chapter 5: Hybrid Advanced Models 8. Section 3: Generative Models and Deep Reinforcement Learning
9. Chapter 6: Music and Text Generation with PyTorch 10. Chapter 7: Neural Style Transfer 11. Chapter 8: Deep Convolutional GANs 12. Chapter 9: Deep Reinforcement Learning 13. Section 4: PyTorch in Production Systems
14. Chapter 10: Operationalizing PyTorch Models into Production 15. Chapter 11: Distributed Training 16. Chapter 12: PyTorch and AutoML 17. Chapter 13: PyTorch and Explainable AI 18. Chapter 14: Rapid Prototyping with PyTorch 19. Other Books You May Enjoy

Using a pre-trained GPT-2 model as a text generator

Using the transformers library together with PyTorch, we can load most of the latest advanced transformer models for performing various tasks such as language modeling, text classification, machine translation, and so on. We demonstrated how to do so in Chapter 5, Hybrid Advanced Models.

In this section, we will load the pre-trained GPT-2-based language model. We will then extend this model so that we can use it as a text generator. Then, we will explore the various strategies we can follow to generate text from a pre-trained language model and use PyTorch to demonstrate those strategies.

Out-of-the-box text generation with GPT-2

In the form of an exercise, we will load a pre-trained GPT-2 language model using the transformers library and extend this language model as a text generation model to generate arbitrary yet meaningful texts. We will only show the important parts of the code for demonstration purposes. In order to...

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