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 language models and tokenizers

In this section, we will look at using the Transformer library with language models, along with their related tokenizers. In order to use any specified language model, we first need to import it. We will start with the BERT model provided by Google and use its pretrained version, as follows:

>>> from Transformer import BERTTokenizer
>>> tokenizer = \
BERTTokenizer.from_pretrained('BERT-base-uncased')

The first line of the preceding code snippet imports the BERT tokenizer, and the second line downloads a pretrained tokenizer for the BERT base version. Note that the uncased version is trained with uncased letters, so it does not matter whether the letters appear in upper- or lowercase. To test and see the output, you must run the following line of code:

>>> text = "Using Transformer is easy!"
>>> tokenizer(text)

This will be the output:

{'input_ids': [101, 2478...