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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  1. News labeled as fake news is always fake. (True/False)
  2. News that everybody agrees with is always accurate. (True/False)
  3. Transformers can be used to run sentiment analysis on Tweets. (True/False)
  4. Key entities can be extracted from Facebook messages with a DistilBERT model running NER. (True/False)
  5. Key verbs can be identified from YouTube chats with BERT-based models running SRL. (True/False)
  6. Emotional reactions are a natural first response to fake news. (True/False)
  7. A rational approach to fake news can help clarify one's position. (True/False)
  8. Connecting transformers to reliable websites can help somebody understand why some news is fake. (True/False)
  9. Transformers can make summaries of reliable websites to help us understand some of the topics labeled as fake news. (True/False)
  10. You can change the world if you use AI for the good of us all. (True/False)