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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Before we go

This chapter focused more on applying transformers to a problem than finding a silver bullet transformer model, which does not exist.

You have two main options to solve an NLP problem: find new transformer models or create reliable, durable methods to implement transformer models.

Looking for the silver bullet

Looking for a silver bullet transformer model can be time-consuming or rewarding, depending on how much time and money you want to spend on continually changing models.

For example, a new approach to transformers can be found through disentanglement. Disentanglement in AI allows you to separate the features of a representation to make the training process more flexible. Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen designed DeBERTa, a disentangled version of a transformer, and described the model in an interesting article:

DeBERTa: Decoding-enhanced BERT with Disentangled Attention,

The two...