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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Standard NLP tasks with specific vocabulary

This section focuses on Case 3: Rare words and Case 4: Replacing rare words from the Word2Vec tokenization section of this chapter.

We will use Training_OpenAI_GPT_2_CH08.ipynb, a renamed version of the notebook we used to train a dataset in Chapter 6, Text Generation with OpenAI GPT-2 and GPT-3 Models.

Two changes were made to the notebook:

  • dset, the dataset, was renamed mdset and contains medical content
  • A Python function was added to control the text that was tokenized using byte-level BPE

We will not describe Training_OpenAI_GPT_2_CH08.ipynb in detail. If necessary, take some time to go back through Chapter 6, Text Generation with OpenAI GPT-2 and GPT-3 Models. Make sure you upload the necessary files before beginning, as explained in Chapter 6. The files are on GitHub in the gpt-2-train_files directory of Chapter08. Although we are using the same notebook as in Chapter 6, note that the dataset, dset...