Challenges of using GPT-3
Despite its impressive capabilities, GPT-3 also presents some challenges. Due to its large size, it requires substantial computational resources to train. It can sometimes generate incorrect or nonsensical responses, and it can reflect biases present in the training data. It also struggles with tasks that require a deep understanding of the world or common sense reasoning beyond what can be learned from text.
Reviewing our use case – ML/DL system design for NLP classification in a Jupyter Notebook
In this section, we are going to work on a real-world problem and see how we can use an NLP pipeline to solve it. The code for this part is shared as a Google Colab notebook at Ch6_Text_Classification_DL.ipynb.
The business objective
In this scenario, we are in the healthcare sector. Our objective is to develop a general medical knowledge engine that is very up to date with recent findings in the world of healthcare.
