Applications for Natural Language Processing (NLP) have exploded in the past decade. With the proliferation of AI assistants, and organizations infusing their businesses with more interactive human/machine experiences, understanding how NLP techniques can be used to manipulate, analyze, and generate text-based data is essential. Modern techniques can be used to capture the nuance, context, and sophistication of language, just as humans do. And when designed correctly, developers can use these techniques to build powerful NLP applications that provide natural and seamless human-computer interactions within Chat Bots, AI Voice Agents, and many more.

Deep learning models have gained widespread popularity for NLP because of their ability to accurately generalize over a range of contexts and languages. Transformer-based models, such as Bidirectional Encoder Representations from Transformers (BERT), have revolutionized progress in NLP by offering accuracy comparable to human baselines on benchmarks like SQuAD for question-answer, entity recognition, intent recognition, sentiment analysis, and more. NVIDIA provides software and hardware that helps you quickly build state-of-the-art NLP models. You can speed-up the training process up to 4.5x with mixed-precision, and easily scale performance to multi-GPU across multiple server nodes without compromising accuracy.

In this workshop, you’ll learn how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents. You will also learn how to leverage Transformer-based models for named-entity recognition (NER) tasks and how to analyze various model features, constraints, and characteristics to determine which model is best suited for a particular use case based on metrics, domain specificity, and available resources.

By participating in this workshop, you’ll be able to:

Understand how text embeddings have rapidly evolved in NLP tasks such as Word2Vec, recurrent neural network (RNN)-based embeddings, and Transformers

See how Transformer architecture features, especially self-attention, are used to create language models without RNNs

Use self-supervision to improve the Transformer architecture in BERT, Megatron, and other variants for superior NLP results

Leverage pre-trained, modern NLP models to solve multiple tasks such as text classification, NER, and question answering

Manage inference challenges and deploy refined models for live applications

Prerequisites:

Experience with Python coding and use of library functions and parameters

Fundamental understanding of a deep learning framework such as TensorFlow, PyTorch, or Keras.

Basic understanding of neural networks.

Tools, libraries, and frameworks: CuDF, CuPy, TensorFlow 2, and NVIDIA Triton™ Inference Server