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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

Training custom sequence labeling models

In this section, we will be looking at the process, syntax, objects, and methods involved in training custom sequence labeling models in Flair. If you read the previous sections of this chapter and understood the contents, you're in luck. Once you understand the underlying concepts of neural networks, have a GPU-equipped rig running, and are familiar with the most common parameters, the actual training process is actually fairly straightforward.

The process can be broken down into the following steps:

  1. Loading a tagged corpus
  2. Loading the tag dictionary
  3. Building the embedding stack
  4. Initializing the SequenceTagger object
  5. Training the model

Each of these steps requires only a few lines of code. To best understand the code, let's cover it as part of a practical example of training a PoS tagger. For example, let's pretend there are no pre-trained English taggers in Flair and attempt to train a PoS tagger...

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