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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Translations with Trax

Google Brain developed Tensor2Tensor (T2T) to make deep learning development easier. T2T is an extension of TensorFlow and contains a library of deep learning models that contains many Transformer examples.

Though T2T was a good start, Google Brain produced Trax, an end-to-end deep learning library. Trax contains a transformer model that can be applied to translations. The Google Brain team presently maintains Trax.

In this section, we will focus on the minimum functions to initialize the English-German problem described by Vaswani et al. (2017) to illustrate the Transformer's performance.

We will be using preprocessed English and German datasets to show that the Transformer architecture is language-agnostic.

Open Trax_Translation.ipynb.

We will begin by installing the modules we need.

Installing Trax

Google Brain has made Trax easy to install and run. We will import the basics along with Trax, which can be installed in one line...