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Get a hands-on introduction to Transformer architecture using the Hugging Face library. This book explains how Transformers are changing the AI domain, particularly in the area of natural language processing.

This book covers Transformer architecture and its relevance in natural language processing (NLP). It starts with an introduction to NLP and a progression of language models from n-grams to a Transformer-based architecture. Next, it offers some basic Transformers examples using the Google colab engine. Then, it introduces the Hugging Face ecosystem and the different libraries and models provided by it. Moving forward, it explains language models such as Google BERT with some examples before providing a deep dive into Hugging Face API using different language models to address tasks such as sentence classification, sentiment analysis, summarization, and text generation.

After completing Introduction to Transformers for NLP, you will understand Transformer concepts and be able to solve problems using the Hugging Face library.

What You Will Learn

• Understand language models and their importance in NLP and NLU (Natural Language Understanding)
• Master Transformer architecture through practical examples
• Use the Hugging Face library in Transformer-based language models
• Create a simple code generator in Python based on Transformer architecture

Who This Book Is ForData Scientists and software developers interested in developing their skills in NLP and NLU (Natural Language Understanding)

Introduction to Transformers for NLP: With the Hugging Face Library and Models to Solve Problems

Chapter 1: Introduction to Language ModelsChapter Goal: History and introduction to language modelsSub-topics:-What is a language model -Evolution of language models from n-grams to now Transformer based models-High-level intro to Google BERT Chapter 2: TransformersChapter Goal: Introduction to Transformers and their architectureSub-topics: Introduction to Transformers-Deep dive into Transformer architecture and how attention plays a key role in Transformers-How Transformer realizes tasks like sentiment analysis, Q&A, sentence masking, etc.  Chapter 3: Intro to Hugging Face libraryChapter Goal: Gives an introduction to Hugging Face libraries and how they are used in achieving NLP tasksSub-topics:-What is Hugging Face, and how its emerge as a relevant library for various data sets and models related to NLP-Creating simple Hugging Face applications for NLP tasks like sentiment analysis, sentence masking, etc.-Play around with different models available in the IT space.  Chapter 4: Code GeneratorChapter Goal: Cover an example of a code generator using Transformer architecture.Sub-topics:-Creating a simple code generator wherein user input is text in NLP like sorting a given array of numbers. -The generator will take the user text and generate Python code or YAML (yet another markup language)file as an example for Kubernetes-Deploying the model on the cloud as a service in Kubernetes  Chapter 5: Transformer Based Applications  Chapter Goal: Summary of the topics around Transformers, Hugging Face libraries, and their usage.Subtopics:-Summary of Transformer based applications and language models. -Summarize Hugging Face libraries and why how they are relevant in NLP.