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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In this chapter, we analyzed the difference between the human language representation process and the way machine intelligence has to perform transduction. We saw that transformers must rely on the outputs of our incredibly complex thought process expressed in written language. Language remains the most precise way to express a massive amount of information. The machine has no senses and must convert speech to text to extract meaning from raw datasets.

We then explored how to measure the performance of multi-task transformers. Transformers' ability to obtain top ranking results for downstream tasks is unique in the history of NLP. We went through the tough SuperGLUE tasks that brought transformers up to the top ranks of the GLUE and SuperGLUE leaderboards.

BoolQ, CB, WiC, and the many other tasks we covered are by no means easy to process, even for humans. We went through an example of several downstream tasks that show the difficulty transformer models must face...