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

Working with AR language models

The Transformer architecture was originally intended to be effective for Seq2Seq tasks such as MT or summarization, but it has since been used in diverse NLP problems ranging from token classification to coreference resolution. Subsequent works began to use separately and more creatively the left and right parts of the architecture. The objective, also known as denoising objective, is to fully recover the original input from the corrupted one in a bidirectional fashion, as shown on the left side of Figure 4.1, which you will see shortly. As seen in the Bidirectional Encoder Representations from Transformers (BERT) architecture, which is a notable example of AE models, they can incorporate the context of both sides of a word. However, the first issue is that the corrupting [MASK] symbols that are used during the pre-training phase are absent from the data during the fine-tuning phase, leading to a pre-training-fine-tuning discrepancy. Secondly, the BERT...