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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The rise of the Transformer: Attention Is All You Need

In December 2017, Vaswani et al. published their seminal paper, Attention Is All You Need. They performed their work at Google Research and Google Brain. I will refer to the model described in Attention Is All You Need as the "original Transformer model" throughout this chapter and book.

In this section, we will look at the Transformer model they built from the outside. In the following sections, we will explore what is inside each component of the model.

The original Transformer model is a stack of 6 layers. The output of layer l is the input of layer l+1 until the final prediction is reached. There is a 6-layer encoder stack on the left and a 6-layer decoder stack on the right:

Figure 1.2: The architecture of the Transformer

On the left, the inputs enter the encoder side of the Transformer through an attention sub-layer and FeedForward Network (FFN) sub-layer. On the right, the target outputs go...