Abstract: 

Advances in Natural Language Processing (NLP) over the last year have been changing the way we work with text-based data. We have seen the release of Google’s BERT, OpenAI’s GPT-2, and Hugging Face’s Transformers library that has helped many use these kinds of models. But it can still be challenging for many users to get started and apply them to their own datasets.
To address this challenge, Novetta has open sourced AdaptNLP, an intuitive
framework that lowers the barrier to entry to use these advanced capabilities. This
high-level framework enables users to use fine-tune pre-trained language models for text classification, question answering, entity extraction, and part-of-speech
tagging. This tutorial will provide quick and easy access to a variety of
embedding schemes for downstream use. The ability to
stand each of these tasks up as a service for easy integration into existing workflows and
applications becomes fast and straightforward.

Background Knowledge
- Python;
- Basic NLP concepts.

Bio: 

Brian Sacash is a Machine Learning Engineer in Novetta's Machine Learning Center of Excellence. He helps various organizations discover the best ways to extract value from data. His interests are in the areas of Natural Language Processing, Machine Learning, Big Data, and Statistical Methods. Brian holds a Master of Science in Quantitative Analysis from the University of Cincinnati and a Bachelor of Science in Physics from Ohio Northern University.