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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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Transformers and Their Applications

In this chapter, we will learn about transformers and how to apply them to perform various NLP tasks. Typical tasks in the NLP domain involve loading and processing data so that it can be used downstream seamlessly. Once the data is read, another task is that of transforming the data into a form that the various models can use. Once the data is transformed into the requisite format, we use it to perform the actual tasks, such as classification, text generation, and language translation.

Here is a list of the recipes in this chapter:

  • Loading a dataset
  • Tokenizing the text in your dataset
  • Using the tokenized text to perform classification with Transformer models
  • Using different Transformer models based on different requirements
  • Generating text by taking a cue from an initial starting sentence
  • Translating text between different languages using pre-trained Transformer models
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Python Natural Language Processing Cookbook
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