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 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 chatbots, AI voice agents, and 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 NLP by offering accuracy comparable to human baselines on benchmarks like SQuAD for question-answer, entity recognition, intent recognition, sentiment analysis, and more. 

In this workshop, you’ll learn how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents. You’ll 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.

Learning Objectives

By participating in this workshop, you’ll:

• 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

Suggested materials to satisfy prerequisites: Python Tutorial, Overview of Deep Learning Frameworks, PyTorch Tutorial, Deep Learning in a Nutshell, Deep Learning Demystified

Technologies:

PyTorch, pandas, NVIDIA NeMo™, NVIDIA Triton™ Inference Server

Assessment Type: 

Skills-based coding assessments evaluate students’ ability to build an NLP task, including a neural module pipeline and training.

Multiple-choice questions evaluate students’ understanding of the NLP concepts presented in the class

Certificate:

Upon successful completion of the assessment, participants will receive an NVIDIA DLI certificate to recognize their subject matter competency and support professional career growth.

Hardware Requirements:

Desktop or laptop computer capable of running the latest version of Chrome or Firefox. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated server in the cloud.

Workshop Outline

Introduction

• Meet the instructor.
• Create an account at courses.nvidia.com/join

Introduction to Transformers

Explore how the Transformer architecture works in detail:

• Build the Transformer architecture in PyTorch.
• Calculate the self-attention matrix.
• Translate English to German with a pre-trained Transformer model.

Self-Supervision, BERT, and Beyond

Learn how to apply self-supervised Transformer-based models to concrete NLP tasks using NVIDIA NeMo:

• Build a text classification project to classify abstracts.
• Build a NER project to identify disease names in text.
• Improve project accuracy with domain-specific models.

Inference and Deployment for NLP

Learn how to deploy an NLP project for live inference on NVIDIA Triton:

• Prepare the model for deployment.
• Optimize the model with NVIDIA® TensorRT™.
• Deploy the model and test it.

Final Review

• Review key learnings and answer questions.
• Complete the assessment and earn a certificate.
• Take the workshop survey.
• Learn how to set up your own environment and discuss additional resources and training.