Building Transformer Based NLP Applications

Prerequisites

Participants should have:

• Experience with Python coding, including working with library functions and parameters
• A fundamental understanding of a deep learning framework such as PyTorch, TensorFlow, or Keras
• A basic understanding of neural networks and core deep learning concepts

Suggested preparation materials may include introductory Python tutorials, overviews of deep learning frameworks, PyTorch fundamentals, and high-level deep learning concepts.

Target audience

This course is designed for:

• Developers and engineers building NLP or AI-powered applications
• Data scientists seeking to deepen their understanding of Transformer architectures
• Technical professionals deploying deep learning models into production environments
• Organisations looking to strengthen applied capability in AI inference and model deployment

Building Transformer Based NLP Applications Course Content

Introduction and course setup

• Meet the instructor and review course objectives
• Set up access to the training environment
• Overview of workshop structure, tools, and assessment approach

Introduction to Transformers
Explore how the Transformer architecture works in detail:

• Core components of the Transformer model
• Multi-head self-attention and positional encoding
• Calculating and interpreting the self-attention matrix
• Building the Transformer architecture in PyTorch
• Using a pretrained Transformer model to translate English to German
• Discussion of task-agnostic modelling approaches

Self-supervision, BERT, and beyond
Learn how self-supervised Transformer-based models are applied to concrete NLP tasks:

• Understanding self-supervision and masked language modelling
• How BERT and other LLM variants improve upon the base Transformer architecture
• Introduction to NVIDIA NeMo for NLP workflows
• Building a text classification project to classify abstracts
• Developing a named-entity recognition project to identify disease names in text
• Improving model accuracy using domain-specific pretrained models
• Evaluating performance metrics and refining model outputs

Inference and deployment for NLP
Deploy and optimise NLP applications for live inference:

• Preparing trained models for deployment
• Exporting models to ONNX format
• Optimising models using NVIDIA TensorRT for accelerated inference
• Deploying models to an NVIDIA Triton Inference Server
• Testing, validating, and monitoring deployed NLP services
• Managing inference challenges in production environments

Final review and assessment

• Consolidation of key learnings across architecture, training, and deployment
• Completion of skills-based coding assessments
• Multiple-choice knowledge checks covering NLP concepts and model design
• Guidance on setting up a local development environment
• Exploration of additional resources and next steps

Exams and assessments

Assessment consists of:

• Skills-based coding assessments that evaluate the ability to build an NLP task, including creating a neural module pipeline and training a model
• Multiple-choice questions assessing understanding of NLP concepts and Transformer-based architectures

Upon successful completion, participants will receive an NVIDIA DLI certificate recognising subject matter competency and supporting professional career growth.

Hands-on learning

This workshop is highly practical and application-focused:

• Guided implementation of a Transformer architecture in PyTorch
• Development of real NLP applications, including text classification and named-entity recognition
• Model optimisation using ONNX and NVIDIA TensorRT
• Live deployment to an NVIDIA Triton Inference Server
• Dedicated access to a fully configured, GPU-accelerated cloud environment for the duration of the workshop

Hardware and delivery information

Participants require a desktop or laptop capable of running the latest version of Chrome or Firefox. Each learner is provided with dedicated access to a GPU-accelerated server in the cloud.

The course is delivered in English and Simplified Chinese.

Duration: 8 hours