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Python Natural Language Processing Cookbook

You're reading from   Python Natural Language Processing Cookbook Over 60 recipes for building powerful NLP solutions using Python and LLM libraries

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781803245744
Length 312 pages
Edition 2nd Edition
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Concepts
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Authors (2):
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Saurabh Chakravarty Saurabh Chakravarty
Author Profile Icon Saurabh Chakravarty
Saurabh Chakravarty
Zhenya Antić Zhenya Antić
Author Profile Icon Zhenya Antić
Zhenya Antić
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Toc

Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Learning NLP Basics 2. Chapter 2: Playing with Grammar FREE CHAPTER 3. Chapter 3: Representing Text – Capturing Semantics 4. Chapter 4: Classifying Texts 5. Chapter 5: Getting Started with Information Extraction 6. Chapter 6: Topic Modeling 7. Chapter 7: Visualizing Text Data 8. Chapter 8: Transformers and Their Applications 9. Chapter 9: Natural Language Understanding 10. Chapter 10: Generative AI and Large Language Models 11. Index 12. Other Books You May Enjoy

Using a zero-shot classifier

In this recipe, we will classify a sentence using a zero-shot classifier. There are instances where we do not have the luxury of training a classifier from scratch or using a model that has been trained as per the labels of our data. Zero-shot classification can be used in such scenarios for any team to get up and running quickly. The zero in the terminology means that the classifier has not seen any data (zero samples precisely) from the target dataset that will be used for inference.

Getting ready

As part of this recipe, we will use the pipeline module from the transformers package. You can use the 8.4_Zero_shot_classification.ipynb notebook from the code site if you need to work from an existing notebook.

How to do it...

In this recipe, we will use a couple of sentences and classify them. We will use our own set of labels for these sentences. We will use the facebook/bart-large-mnli model for this recipe. This model is suitable for the task...

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