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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A rational approach to fake news

Transformers are the most powerful NLP tools ever. In this section, we will first define a method that can take two parties engaged in conflict over fake news from an emotional level to a rational level.

We will then use transformer tools and heuristics. We will run transformer samples on gun control and former President Trump's Tweets during the COVID-19 pandemic. We will also describe heuristics that could be implemented with classical functions.

You can implement these transformer NLP tasks or other tasks of your choice. In any case, the roadmap and method can help teachers, parents, friends, co-workers, and anybody seeking the truth. Your work will always be worthwhile!

Let's begin with the roadmap of a rational approach to fake news that includes transformers.

Defining a fake news resolution roadmap

Figure 12.3 defines a roadmap for a rational fake news analysis process. The process contains transformer...