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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Chapter 3, Pretraining a RoBERTa Model from Scratch

  1. RoBERTa uses a byte-level byte-pair encoding tokenizer. (True/False)


  2. A trained Hugging Face tokenizer produces merges.txt and vocab.json. (True/False)


  3. RoBERTa does not use token type IDs. (True/False)


  4. DistilBERT has 6 layers and 12 heads. (True/False)


  5. A transformer model with 80 million parameters is enormous. (True/False)

    False. 80 million parameters is a small model.

  6. We cannot train a tokenizer. (True/False)

    False. A tokenizer can be trained.

  7. A BERT-like model has 6 decoder layers. (True/False)

    False. BERT contains 6 encoder layers, not decoder layers.

  8. Masked language modeling predicts a word contained in a mask token in a sentence. (True/False)


  9. A BERT-like model has no self-attention sub-layers. (True/False)

    False. BERT has self-attention layers.

  10. Data collators are helpful for...