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

Introduction to sentence embeddings

Pre-trained BERT models do not produce efficient and independent sentence embeddings as they always need to be fine-tuned in an end-to-end supervised setting. This is because we can think of a pre-trained BERT model as an indivisible whole and semantics is spread across all layers, not just the final layer. Without fine-tuning, it may be ineffective to use its internal representations independently. It is also hard to handle unsupervised tasks such as clustering, topic modeling, information retrieval, or semantic search. Because we have to evaluate many sentence pairs during clustering tasks, for instance, this causes massive computational overhead.

Luckily, many modifications have been made to the original BERT model, such as Sentence-BERT (SBERT), to derive semantically meaningful and independent sentence embeddings. We will talk about these approaches in a moment. In the NLP literature, many neural sentence embedding methods have been proposed...