Book Image

Transformers for Natural Language Processing

By : Denis Rothman
Book Image

Transformers for Natural Language Processing

By: Denis Rothman

Overview of this book

The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.
Table of Contents (16 chapters)
Other Books You May Enjoy

Difficult samples

In this section, we will run samples that contain problems that the BERT-based transformer will first solve. We will end with an intractable sample.

Let's start with a complex sample that the BERT-based transformer can analyze.

Sample 4

Sample 4 takes us into more tricky SRL territory. The sample separates "Alice" from the verb "liked," creating a long-term dependency that has to jump over "whose husband went jogging every Sunday."

The sentence is:

"Alice, whose husband went jogging every Sunday, liked to go to a dancing class in the meantime."

A human can isolate "Alice" and find the predicate:

"Alice, whose husband went jogging every Sunday,liked to go to a dancing class in the meantime."

Can the BERT model find the predicate like us?

Let's find out by first running the code in SRL.ipynb:

!echo '{"sentence": "Alice, whose husband went...