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

Training a classification model with native PyTorch

The Trainer class is very powerful, and we have the HuggingFace team to thank for providing such a useful tool. However, in this section, we will fine-tune the pre-trained model from scratch to see what happens under the hood. Let's get started:

  1. First, let's load the model for fine-tuning. We will select DistilBERT here since it is a small, fast, and cheap version of BERT:
    from transformers import DistilBertForSequenceClassification
    model = DistilBertForSequenceClassification.from_pre-trained('distilbert-base-uncased')
  2. To fine-tune any model, we need to put it into training mode, as follows:
    model.train()
  3. Now, we must load the tokenizer:
    from transformers import DistilBertTokenizerFast
    tokenizer = DistilBertTokenizerFast.from_pre-trained('bert-base-uncased')
  4. Since the Trainer class organized the entire process for us, we did not deal with optimization and other training settings in...