Natural Language Processing With Transformers In Python

Description :

Transformer models are the de-facto standard in modern NLP. They have proven themselves as the most expressive, powerful models for language by a large margin, beating all major language-based benchmarks time and time again.

In this course, we learn all you need to know to get started with building cutting-edge performance NLP applications using transformer models like Google AI’s BERT, or Facebook AI’s DPR.

We cover several key NLP frameworks including:

HuggingFace’s Transformers
TensorFlow 2
PyTorch
spaCy
NLTK
Flair

And learn how to apply transformers to some of the most popular NLP use-cases:

Language classification/sentiment analysis
Named entity recognition (NER)
Question and Answering
Similarity/comparative learning

Throughout each of these use-cases we work through a variety of examples to ensure that what, how, and why transformers are so important. Alongside these sections we also work through two full-size NLP projects, one for sentiment analysis of financial Reddit data, and another covering a fully-fledged open domain question-answering application.

All of this is supported by several other sections that encourage us to learn how to better design, implement, and measure the performance of our models, such as:

History of NLP and where transformers come from
Common preprocessing techniques for NLP
The theory behind transformers
How to fine-tune transformers

We cover all this and more, I look forward to seeing you in the course!