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 discovered the new era of transformer models training 100,000,000,000+ parameters on supercomputers. OpenAI's GPT models are taking NLU beyond the reach of most NLP development teams.

We first examined transformer models from a project management perspective to see if transformers can be designed to use only one GPU, for example, and remain accessible to all. We saw that by optimizing a transformer model's architecture (Reformer) and training methods such as PET, we could reduce the model's size, requiring less machine power.

We then explored the design of GPT models, which are all built on the decoder stack of the original Transformer. The masked attention sub-layer continues the philosophy of left-to-right training. However, the sheer power of the calculations and the subsequent self-attention sub-layer makes it extremely efficient.

We then implemented a 345M parameter GPT-2 model with TensorFlow. The goal was to interact...