Book Image

Mastering Transformers

By : Savaş Yıldırım, Meysam Asgari- Chenaghlu
Book Image

Mastering Transformers

By: Savaş Yıldırım, Meysam Asgari- Chenaghlu

Overview of this book

Transformer-based language models have dominated natural language processing (NLP) studies and have now become a new paradigm. With this book, you'll learn how to build various transformer-based NLP applications using the Python Transformers library. The book gives you an introduction to Transformers by showing you how to write your first hello-world program. You'll then learn how a tokenizer works and how to train your own tokenizer. As you advance, you'll explore the architecture of autoencoding models, such as BERT, and autoregressive models, such as GPT. You'll see how to train and fine-tune models for a variety of natural language understanding (NLU) and natural language generation (NLG) problems, including text classification, token classification, and text representation. This book also helps you to learn efficient models for challenging problems, such as long-context NLP tasks with limited computational capacity. You'll also work with multilingual and cross-lingual problems, optimize models by monitoring their performance, and discover how to deconstruct these models for interpretability and explainability. Finally, you'll be able to deploy your transformer models in a production environment. By the end of this NLP book, you'll have learned how to use Transformers to solve advanced NLP problems using advanced models.
Table of Contents (16 chapters)
1
Section 1: Introduction – Recent Developments in the Field, Installations, and Hello World Applications
4
Section 2: Transformer Models – From Autoencoding to Autoregressive Models
10
Section 3: Advanced Topics

Summary

In this chapter, we discussed how to fine-tune a pre-trained model for any text classification downstream task. We fine-tuned the models using sentiment analysis, multi-class classification, and sentence-pair classification – more specifically, sentence-pair regression. We worked with a well-known IMDb dataset and our own custom dataset to train the models. While we took advantage of the Trainer class to cope with much of the complexity of the processes for training and fine-tuning, we learned how to train from scratch with native libraries to understand forward propagation and backpropagation with the transformers library. To summarize, we discussed and conducted fine-tuning single-sentence classification with Trainer, sentiment classification with native PyTorch without Trainer, single-sentence multi-class classification, and fine-tuning sentence-pair regression.

In the next chapter, we will learn how to fine-tune a pre-trained model to any token classification...