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 measured the impact of the tokenization and subsequent data encoding process on transformer models. A transformer model can only attend to tokens from the embedding and positional encoding sub-layers of a stack. It does not matter if the model is an encoder-decoder, encoder-only, or decoder-only model. It does not matter if the dataset seems good enough to train.

If the tokenization process fails, even partly, the transformer model we are running will miss critical tokens.

We first saw that for standard language tasks, raw datasets might be enough to train a transformer.

However, we discovered that even if a pretrained tokenizer has gone through a billion words, it only creates a dictionary with a small portion of the vocabulary it comes across. Like us, a tokenizer captures the essence of the language it is learning and only "remembers" the most important words if these words are also frequently used. This approach works well for...