How to Use GPT-J for (Almost) Any NLP Task

In a previous blog post we had a look at how we can set up our very own GPT-J Playground using Streamlit, Hugging Face, and Amazon SageMaker. With this playground we can now start experimenting with the model and generate some text, which is a lot of fun. But eventually we want the model to actually perform NLP tasks like translation, classification, and many more. In this blog post we will have a look how we can achieve that using different parameters and particular prompts for the GPT-J model. This blog post will build on this previous blog post and this Github repo and it is assumed that you have already built your own GPT-J playground.