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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Training a tokenizer and pretraining a transformer

In this chapter, we will train a transformer model named KantaiBERT using the building blocks provided by Hugging Face for BERT-like models. We covered the theory of the building blocks of the model we will be using in Chapter 2, Fine-Tuning BERT Models.

We will describe KantaiBERT, building on the knowledge we acquired in the previous chapters.

KantaiBERT is a Robustly Optimized BERT Pretraining Approach (RoBERTa)-like model based on the architecture of BERT.

The initial BERT models were undertrained. RoBERTa increases the performance of pretraining transformers for downstream tasks. RoBERTa has improved the mechanics of the pretraining process. For example, it does not use WordPiece tokenization but goes down to byte-level Byte Pair Encoding (BPE).

In this chapter, KantaiBERT, like BERT, will be trained using masked language modeling.

KantaiBERT will be trained as a small model with 6 layers, 12 heads, and 84...