Applied Explainability for Natural Language Processing Models
Question & Answering Problem with the Albert Model Introduction This blog will illustrate different explainability methods, focusing on natural language processing (NLP). You will learn how Applied Explainability techniques increase NLP models’ transparency, making them more credible and faster to develop. Task Overview Model I use the ALBERT, introduced in ALBERT: A Lite BERT for […]
Population Analysis with Applied Explainability
If you have ever worked on deploying a machine learning model, you know how challenging it is to ensure a smooth transition from your controlled environment to the real world. You need to consider many factors to achieve a successful implementation. One aspect that can be easily overlooked is the misinterpretation of samples in the […]