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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Transformer performances versus Human Baselines

Transformers, like humans, can be fine-tuned to perform downstream tasks by inheriting the properties of a pretrained model. The pretrained model provides its architecture and language representations through its parameters.

A pretrained model trains on key tasks to enable it to acquire a general knowledge of the language. A fine-tuned model trains on downstream tasks. Not every transformer model uses the same tasks for pretraining. Potentially, tasks can all be pretrained or fine-tuned tasks.

Every NLP model needs to be evaluated with a standard method.

In this section, we will first go through some of the key measurement methods. Then, we will go through some of the main benchmark tasks and datasets.

Let's start by going through some of the key metric methods.

Evaluating models with metrics

It is impossible to compare one transformer model to another transformer model (or any other NLP model) without a...