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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Building KantaiBERT from scratch

We will build KantaiBERT in 15 steps from scratch and then run it on a masked language modeling example.

Open Google Colaboratory (you need a Gmail account). Then upload KantaiBERT.ipynb, which is on GitHub in this chapter's directory.

The titles of the 15 steps of this section are similar to the titles of the cells of the notebook, which makes it easy to follow.

Let's start by loading the dataset.

Step 1: Loading the dataset

Ready-to-use datasets provide an objective way to train and compare transformers. In Chapter 4, Downstream NLP Tasks with Transformers, we will explore several datasets. However, the goal of this chapter is to understand the training process of a transformer with notebook cells that could be run in real time without having to wait for hours to obtain a result.

I chose to use the works of Immanuel Kant (1724-1804), the German philosopher, who was the epitome of the Age of Enlightenment. The...