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)
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In this chapter, we found that question-answering isn't as easy as it seems. Implementing a transformer model only takes a few minutes. Getting it to work can take a few hours or several months!

We first asked the default transformer in the Hugging Face pipeline to answer some simple questions. DistilBERT, the default transformer, answered the simple questions quite well. However, we chose easy questions. In real life, users ask all kinds of questions. The transformer can get confused and produce erroneous outputs.

We then had the choice of continuing to ask random questions and get random answers, or we could begin to design the blueprint of a question generator, which is a more productive solution.

We started by using NER to find useful content. We designed a function that could automatically create questions based on NER output. The quality was promising but required more work.

We tried an ELECTRA model that did not produce the results...