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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Text completion with GPT-2

This section will clone the OpenAI GPT-2 repository, download the 345M parameter GPT-2 transformer model, and interact with it. We will enter context sentences and analyze the text generated by the transformer. The goal is to see how it creates new content.

This section is divided into 9 steps. Open OpenAI_GPT_2.ipynb in Google Colaboratory. The notebook is in the chapter of the GitHub repository of this book. You will notice that the notebook is also divided into the same 9 steps and cells as this section.

Run the notebook cell by cell. The process is tedious, but the result produced by the cloned OpenAI GPT-2 repository is gratifying.

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...