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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Generating captions

First, you need to be congratulated! You made it through a whirlwind implementation of the Transformer. I am sure you must have noticed a number of common building blocks that were used in previous chapters. Since the Transformer model is complex, we left it for this chapter to look at other techniques like Bahdanau attention, custom layers, custom rate schedules, custom training using teacher forcing, and checkpointing so that we could cover a lot of ground quickly in this chapter. You should consider all these building blocks an important part of your toolkit when you try and solve an NLP problem.

Without further ado, let's try and caption some images. Again, we will use a Jupyter notebook for inference so that we can quickly try out different images. All the code for inference is in the image-captioning-inference.ipynb file.

The inference code needs to load the Subword Encoder, set up masking, instantiate a ResNet50 model to extract features...