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: 

Andrew Chang is an Applied Machine Learning Researcher in Novetta’s Machine Learning (ML) Center of Excellence. Andrew is a graduate from Carnegie Mellon University who has a focus on researching state of the art machine learning models and rapid prototyping ML technologies and solutions across the scope of customer problems. He has an interest in open source projects and research in natural language processing, geometric deep learning, reinforcement learning, and computer vision. Andrew is the author and creator of NovettaNLP.