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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Matching datasets and tokenizers

Downloading benchmarks datasets to train transformers has many advantages. The data has been prepared, and every research lab uses the same references. Also, the performance of a transformer model can be compared to another model with the same data.

However, more needs to be done to improve the performance of transformers. Furthermore, implementing a transformer model in production requires careful planning and defining best practices.

In this section, we will define some best practices to avoid critical stumbling blocks.

Then we will go through a few examples in Python using cosine similarity to measure the limits of tokenization and encoding datasets.

Let's start with best practices.

Best practices

Raffel et al. (2019) defined a standard text-2-text T5 transformer model. They also went further. They began destroying the myth of using raw data without preprocessing it first. Preprocessing data reduces training time. Common...