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 architecture of OpenAI GPT models

Transformers went from training, fine-tuning, and finally zero-shot models in less than 3 years between the end of 2017 and the first semester of 2020. A zero-shot GPT-3 transformer model requires no fine-tuning. The trained model parameters are not updated for downstream multi-tasks, which opens a new era for NLP/NLU tasks.

In this section, we will first understand the motivation of the OpenAI team that designed GPT models. We will begin by going through the fine-tuning to zero-shot models. Then we will see how to condition a transformer model to generate mind-blowing text completion. Finally, we will explore the architecture of GPT models.

We will first go through the creation process of the OpenAI team.

From fine-tuning to zero-shot models

From the start, OpenAI's research teams, led by Radford et al. (2018), wanted to take transformers from trained models to GPT models. The goal was to train transformers on unlabeled...