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 went through three additional essential aspects of the original Transformer.

We started by defining machine translation. Human translation sets a very high baseline for machines to reach. We saw that English-French and English-German translations imply quite an amount of problems to solve. The Transformer tackled these problems and set state-of-the-art BLEU records to beat.

We then preprocessed a WMT French-English dataset from the European Parliament that required cleaning. We had to transform the datasets into lines and clean the data up. Once that was done, we reduced the dataset's size by suppressing words that occurred below a frequency threshold.

Machine translation NLP models require identical evaluation methods. Training a model on a WMT dataset requires BLEU evaluations. We saw that geometric assessments are a good basis to score translations but even modified BLEU has its limits. We thus added a smoothing technique to enhance...