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Natural Language Processing with Flair

You're reading from   Natural Language Processing with Flair A practical guide to understanding and solving NLP problems with Flair

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781801072311
Length 200 pages
Edition 1st Edition
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Author (1):
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Tadej Magajna Tadej Magajna
Author Profile Icon Tadej Magajna
Tadej Magajna
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Table of Contents (15) Chapters Close

Preface 1. Part 1: Understanding and Solving NLP with Flair
2. Chapter 1: Introduction to Flair FREE CHAPTER 3. Chapter 2: Flair Base Types 4. Chapter 3: Embeddings in Flair 5. Chapter 4: Sequence Tagging 6. Part 2: Deep Dive into Flair – Training Custom Models
7. Chapter 5: Training Sequence Labeling Models 8. Chapter 6: Hyperparameter Optimization in Flair 9. Chapter 7: Train Your Own Embeddings 10. Chapter 8: Text Classification in Flair 11. Part 3: Real-World Applications with Flair
12. Chapter 9: Deploying and Using Models in Production 13. Chapter 10: Hands-On Exercise – Building a Trading Bot with Flair 14. Other Books You May Enjoy

Understanding hyperparameter tuning

When first faced with a long list of model training parameters and their possible values, you might think that in order to successfully train a model, you need a special superpower that helps you pick the right parameter for the right scenario. This isn't necessarily true. While experience may help you narrow down the set of possible hyperparameters, there usually isn't a reliable way of knowing with certainty what the best hyperparameter is in advance.

Let's imagine the simplest possible scenario – a sequence tagging model trainer that receives a single parameter – say, a learning rate. This is generally a value between 0 (exclusive) and 1. To create a set of possible hyperparameter values, we simply discretize the range into a set of 10 possible hyperparameter values: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]. We can then perform the most trivial type of hyperparameter optimization by training 10 different...

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