Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781801072311
Length 200 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Tadej Magajna Tadej Magajna
Author Profile Icon Tadej Magajna
Tadej Magajna
Arrow right icon
View More author details
Toc

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

Hyperparameter tuning in Python

Let's get a taste of what hyperparameter tuning looks like in practice. We will use one of the most popular Python hyperparameter optimization libraries called Hyperopt. First, let's get a general idea of how to use it in practice.

Hyperopt is a Python library that provides an easy-to-use API that requires the following three objects:

  • A search space
  • An objective function
  • An optimization method

Let's look at each of these requirements in detail:

  • Search space is simply the space within which the optimizer will search for different hyperparameter options. The library provides the following parameter expressions:
    • Categorical parameters – Parameter values that are purely categorical and can even be non-scalar (they do not need to be a number). They are provided via the hyperopt.hp.choice method.
    • Integer parameters – Integer value parameters obtained via methods such as hyperopt.hp.quniform or hyperopt...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image