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Mastering spaCy

You're reading from   Mastering spaCy An end-to-end practical guide to implementing NLP applications using the Python ecosystem

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781800563353
Length 356 pages
Edition 1st Edition
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Author (1):
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Duygu Altınok Duygu Altınok
Author Profile Icon Duygu Altınok
Duygu Altınok
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Getting Started with spaCy
2. Chapter 1: Getting Started with spaCy FREE CHAPTER 3. Chapter 2: Core Operations with spaCy 4. Section 2: spaCy Features
5. Chapter 3: Linguistic Features 6. Chapter 4: Rule-Based Matching 7. Chapter 5: Working with Word Vectors and Semantic Similarity 8. Chapter 6: Putting Everything Together: Semantic Parsing with spaCy 9. Section 3: Machine Learning with spaCy
10. Chapter 7: Customizing spaCy Models 11. Chapter 8: Text Classification with spaCy 12. Chapter 9: spaCy and Transformers 13. Chapter 10: Putting Everything Together: Designing Your Chatbot with spaCy 14. Other Books You May Enjoy

Transformers and TensorFlow

In this section, we'll dive into transformers code with TensorFlow. Pre-trained transformer models are provided to the developer community as open source by many organizations, including Google (https://github.com/google-research/bert), Facebook (https://github.com/pytorch/fairseq/blob/master/examples/language_model/README.md), and HuggingFace (https://github.com/huggingface/transformers). All the listed organizations offer pre-trained models and nice interfaces to integrate transformers into our Python code. The interfaces are compatible with either PyTorch or Tensorflow or both.

Throughout this chapter, we'll be using HuggingFace's pre-trained transformers and their TensorFlow interface to the transformer models. HuggingFace is an AI company with a focus on NLP and quite devoted to open source. In the next section, we'll take a closer look at what is available in HuggingFace Transformers.

HuggingFace Transformers

In the first...

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