Book Image

Advanced Natural Language Processing with TensorFlow 2

By : Ashish Bansal, Tony Mullen
Book Image

Advanced Natural Language Processing with TensorFlow 2

By: Ashish Bansal, Tony Mullen

Overview of this book

Recently, there have been tremendous advances in NLP, and we are now moving from research labs into practical applications. This book comes with a perfect blend of both the theoretical and practical aspects of trending and complex NLP techniques. The book is focused on innovative applications in the field of NLP, language generation, and dialogue systems. It helps you apply the concepts of pre-processing text using techniques such as tokenization, parts of speech tagging, and lemmatization using popular libraries such as Stanford NLP and SpaCy. You will build Named Entity Recognition (NER) from scratch using Conditional Random Fields and Viterbi Decoding on top of RNNs. The book covers key emerging areas such as generating text for use in sentence completion and text summarization, bridging images and text by generating captions for images, and managing dialogue aspects of chatbots. You will learn how to apply transfer learning and fine-tuning using TensorFlow 2. Further, it covers practical techniques that can simplify the labelling of textual data. The book also has a working code that is adaptable to your use cases for each tech piece. By the end of the book, you will have an advanced knowledge of the tools, techniques and deep learning architecture used to solve complex NLP problems.
Table of Contents (13 chapters)
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Generative Pre-Training (GPT-2) model

OpenAI released the first version of the GPT model in June 2018. They followed up with GPT-2 in February 2019. This paper attracted much attention as full details of the large GPT-2 model were not released with the paper due to concerns of nefarious uses. The large GPT-2 model was released subsequently in November 2019. The GPT-3 model is the most recent, released in May 2020.

Figure 5.5 shows the number of parameters in the largest of each of these models:

Figure 5.5: Parameters in different GPT models

The first model used the standard Transformer decoder architecture with twelve layers, each with twelve attention heads and 768-dimensional embeddings, for a total of approximately 110 million parameters, which is very similar to the BERT model. The largest GPT-2 has over 1.5 billion parameters, and the most recently released GPT-3 model's largest variant has over 175 billion parameters!

Cost of training language...