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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Training a GPT-2 language model

This section will train a GPT-2 model on a custom dataset that we will encode. We will then interact with our customized model. We will be using the same kant.txt dataset as in Chapter 3, Pretraining a RoBERTa Model from Scratch.

This section refers to the code of Training_OpenAI_GPT_2.ipynb, which is in this chapter's directory of the book on GitHub.

It is important to note that we are running a low-level GPT- 2 model and not a one-line call to obtain a result. We are also avoiding pre-packaged versions. We are getting our hands dirty to understand the architecture of a GPT-2 from scratch. You might get some deprecation messages. However, the effort is worthwhile.

We will open the notebook and run it cell by cell.

Step 1: Prerequisites

The files referred to in this section are available in the chapter directory of the GitHub repository of this book:

  • Activate the GPU in the notebook's runtime...