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Python Natural Language Processing Cookbook

Python Natural Language Processing Cookbook - Second Edition

By : Zhenya Antić, Saurabh Chakravarty
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Python Natural Language Processing Cookbook

Python Natural Language Processing Cookbook

5 (5)
By: Zhenya Antić, Saurabh Chakravarty

Overview of this book

Harness the power of Natural Language Processing (NLP) to overcome real-world text analysis challenges with this recipe-based roadmap written by two seasoned NLP experts with vast experience transforming various industries with their NLP prowess. You’ll be able to make the most of the latest NLP advancements, including large language models (LLMs), and leverage their capabilities through Hugging Face transformers. Through a series of hands-on recipes, you’ll master essential techniques such as extracting entities and visualizing text data. The authors will expertly guide you through building pipelines for sentiment analysis, topic modeling, and question-answering using popular libraries like spaCy, Gensim, and NLTK. You’ll also learn to implement RAG pipelines to draw out precise answers from a text corpus using LLMs. This second edition expands your skillset with new chapters on cutting-edge LLMs like GPT-4, Natural Language Understanding (NLU), and Explainable AI (XAI)—fostering trust in your NLP models. By the end of this book, you'll be equipped with the skills to apply advanced text processing techniques, use pre-trained transformer models, build custom NLP pipelines to extract valuable insights from text data to drive informed decision-making.
Table of Contents (13 chapters)
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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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Python Natural Language Processing Cookbook
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