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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Method 2: SRL first

The transformer could not find who was driving to go to Las Vegas and thought it was the Nat King Cole instead of Jo and Maria.

What went wrong? Can we see what the transformers think and obtain an explanation? To find out, let's go back to semantic role modeling. If necessary, take a few minutes to review Chapter 9, Semantic Role Labeling with BERT-Based Transformers.

Let's run the same sequence on AllenNLP,, in the Semantic Role Labeling section to obtain a visual representation of the verb "drove" in our sequence:

Figure 10.2: EER Semantic Role Labeling (SRL)

We can see the problem. The argument of the verb "driving" is "they." There is no relationship established between "they" and "Jo" and "Maria." It seems that the inference could be made.

Transformer models keep evolving. The output might vary; however, the...