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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The Stanford Sentiment Treebank (SST)

Socher et al. (2013) designed semantic word spaces over long phrases. They defined principles of compositionality applied to long sequences. The principle of compositionality means that an NLP model must examine the constituent expressions of a complex sentence and the rules that combine them to understand the meaning of a sequence.

Let's take a sample from the SST to grasp the meaning of the principle of compositionality.

This section and chapter are self-contained, so you can choose to perform the actions described or read the chapter and view the screenshots provided.

Go to the interactive sentiment treebank:

You can make the selections you wish. Graphs of sentiment trees will appear on the page. Click on an image to obtain a sentiment tree:

Figure 11.1: Graphs of sentiment trees

For this example, I clicked on graph number 6, which...