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

Faster Transformer model serving using TFX

TFX provides a faster and more efficient way to serve deep learning-based models. But it has some important key points you must understand before you use it. The model must be a saved model type from TensorFlow so that it can be used by TFX Docker or the CLI. Let's take a look:

  1. You can perform TFX model serving by using a saved model format from TensorFlow. For more information about TensorFlow saved models, you can read the official documentation at https://www.tensorflow.org/guide/saved_model. To make a saved model from Transformers, you can simply use the following code:
    from transformers import TFBertForSequenceClassification
    model = \ TFBertForSequenceClassification.from_pretrained("nateraw/bert-base-uncased-imdb", from_pt=True)
    model.save_pretrained("tfx_model", saved_model=True)
  2. Before we understand how to use it to serve Transformers, it is required to pull the Docker image for TFX:
    $ docker pull...