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

Semantic similarity experiment with FLAIR

In this experiment, we will qualitatively evaluate the sentence representation models thanks to the flair library, which really simplifies obtaining the document embeddings for us.

We will perform experiments while taking on the following approaches:

  • Document average pool embeddings
  • RNN-based embeddings
  • BERT embeddings
  • SBERT embeddings

We need to install these libraries before we can start the experiments:

!pip install sentence-transformers
!pip install dataset
!pip install flair

For qualitative evaluation, we define a list of similar sentence pairs and a list of dissimilar sentence pairs (five pairs for each). What we expect from the embeddings models is that they should measure a high score and a low score, respectively.

The sentence pairs are extracted from the SBS Benchmark dataset, which we are already familiar with from the sentence-pair regression part of Chapter 6, Fine-Tuning Language Models...