Book Image

Transformers for Natural Language Processing

By : Denis Rothman
Book Image

Transformers for Natural Language Processing

By: Denis Rothman

Overview of this book

The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.
Table of Contents (16 chapters)
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NLP summarizing tasks extract succinct parts of a text. In this section, we will start by presenting the Hugging Face resources we will use in this chapter. Then we will initialize a T5-large transformer model. Finally, we will see how to use T5 to summarize any type of document, including legal and corporate documents.

Let's begin by using Hugging Face's framework.

Hugging Face

Hugging Face designed a framework to implement Transformers at a higher level. We used Hugging Face to fine-tune a BERT model in Chapter 2, Fine-Tuning BERT Models, and to train a RoBERTa model in Chapter 3, Pretraining a RoBERTa Model from Scratch.

However, we needed to explore other approaches, such as Trax, in Chapter 5, Machine Translation with the Transformer, and OpenAI's GitHub repository in Chapter 6, Text Generation with OpenAI GPT-2 and GPT-3 Models.

In this chapter, we will use Hugging Face's framework again and explain more about...