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

Chapter 6: Fine-Tuning Language Models for Token Classification

In this chapter, we will learn about fine-tuning language models for token classification. Tasks such as Named Entity Recognition (NER), Part-of-Speech (POS) tagging, and Question Answering (QA) are explored in this chapter. We will learn how a specific language model can be fine-tuned on such tasks. We will focus on BERT more than other language models. You will learn how to apply POS, NER, and QA using BERT. You will get familiar with the theoretical details of these tasks such as their respective datasets and how to perform them. After finishing this chapter, you will be able to perform any token classification using Transformers.

In this chapter, we will fine-tune BERT for the following tasks: fine-tuning BERT for token classification problems such as NER and POS, fine-tuning a language model for an NER problem, and thinking of the QA problem as a start/stop token classification.

The following topics will be...