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

Book Description

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)

中文：

书名：NLP变压器简介: 用拥抱面库和模型解决问题

使用拥抱面库获得变压器架构的动手介绍。这本书解释了变形金刚是如何改变人工智能领域的，特别是在自然语言处理领域。

本书涵盖了变压器体系结构及其在自然语言处理 (NLP) 中的相关性。首先介绍NLP，然后将语言模型从n-gram发展到基于Transformer的体系结构。接下来，它提供了一些使用Google colab引擎的基本变形金刚示例。然后，介绍了拥抱人脸生态系统及其提供的不同库和模型。展望未来，它通过一些示例解释了诸如Google BERT之类的语言模型，然后使用不同的语言模型来深入研究拥抱面部API，以解决诸如句子分类，情感分析，摘要和文本生成之类的任务。

完成NLP的Transformers简介后，您将了解Transformer的概念，并能够使用拥抱面库解决问题。您将学到什么

• 理解语言模型及其在NLP和NLU中的重要性 (自然语言理解)
• 通过实际示例掌握变压器架构
• 在基于Transformer的语言模型中使用拥抱面库
• 基于Transformer架构在Python中创建简单代码生成器

这本书是谁的数据科学家和软件开发人员有兴趣发展他们的技能在NLP和NLU (自然语言理解)