Learn How to Build Transformer-Based Natural Language Processing Applications

The NVIDIA Deep Learning Institute (DLI) is offering instructor-led, hands-on training on how to use Transformer-based natural language processing models for…

NVIDIA logo
NVIDIA
•
Nefi Alarcon
•2 min read•intermediate•
--
•View Original
BERTDeep LearningNatural Language ProcessingTransformerTransformers

Overview

The article discusses the growing importance of Transformer-based models in natural language processing (NLP) and highlights a training workshop offered by the NVIDIA Deep Learning Institute. It covers the capabilities of models like BERT in various NLP tasks and the benefits of hands-on training for developers.

What You'll Learn

1

How to use Transformer-based models for text classification tasks

2

How to analyze model features and constraints for NLP applications

3

How to leverage self-supervision to improve Transformer architecture

4

How to manage inference challenges for deploying NLP models

Key Questions Answered

What are the benefits of using Transformer-based models in NLP?
Transformer-based models like BERT provide accuracy comparable to human baselines on benchmarks such as SQuAD, making them highly effective for tasks like question answering and sentiment analysis. Their ability to generalize across various contexts and languages is a significant advantage in NLP applications.
What training does the NVIDIA Deep Learning Institute offer for NLP?
The NVIDIA Deep Learning Institute offers instructor-led, hands-on training focused on using Transformer-based models for tasks such as text classification and named-entity recognition. Participants will learn to analyze model features and determine the best model for specific use cases.
When is the NVIDIA training workshop available?
The workshop will be available to the public during the GPU Technology Conference from October 5-9. Early registration is encouraged as spots are limited, with the Early Bird Rate ending on September 25.

Technologies & Tools

Model
Bert
Used as a benchmark for accuracy in various NLP tasks.
Model
Megatron
Another variant of Transformer architecture discussed for enhancing NLP results.

Key Actionable Insights

1
Participating in the NVIDIA DLI workshop will enhance your understanding of Transformer-based models and their applications in NLP.
This training is particularly beneficial for developers looking to implement advanced NLP solutions, as it covers both theoretical and practical aspects of using these models.
2
Learning about self-supervision in Transformer architectures can significantly improve your model's performance.
By understanding how to leverage self-supervision, developers can enhance the capabilities of models like BERT and Megatron, leading to better results in various NLP tasks.
3
Understanding the evolution of word embeddings is crucial for modern NLP applications.
This knowledge helps developers appreciate the advancements from traditional methods like Word2Vec to contemporary Transformer-based contextualized embeddings, which are essential for effective text processing.

Common Pitfalls

1
Failing to properly analyze model features and constraints can lead to suboptimal performance in NLP applications.
Developers often overlook the importance of understanding which model is best suited for their specific use case, which can result in wasted resources and ineffective solutions.

Related Concepts

Natural Language Processing (nlp)
Deep Learning
Machine Learning
Text Classification
Named-entity Recognition (ner)