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

Benchmarking for speed and memory

Just comparing the classification performance of large models on a specific task or a benchmark turns out to be no longer sufficient. We must now take care of the computational cost of a particular model for a given environment (Random-Access Memory (RAM), CPU, GPU) in terms of memory usage and speed. The computational cost of training and deploying to production for inference are two main values to be measured. Two classes of the Transformer library, PyTorchBenchmark and TensorFlowBenchmark, make it possible to benchmark models for both TensorFlow and PyTorch.

Before we start our experiment, we need to check our GPU capabilities with the following execution:

>>> import torch
>>> print(f"The GPU total memory is {torch.cuda.get_device_properties(0).total_memory /(1024**3)} GB")
The GPU total memory is 2.94921875 GB

The output is obtained from NVIDIA GeForce GTX 1050 (3 Gigabytes (GB)). We need more powerful resources...