Entity Resolution is a fundamental data cleaning and integration problem that has received considerable attention in the past few decades. While rule-based methods have been used in many practical scenarios and are often easy to understand, machine-learning-based methods provide the best accuracy. However, the state-of-the-art classifiers are very opaque. There has been some work towards understanding and debugging the early stages of the entity resolution pipeline, e.g. blocking and generating features (similarity scores). However, there are no such efforts for explaining the model or its predictions. In this demo, we propose ExplainER, a tool to understand and explain entity resolution classifiers with different granularity levels of explanations. Using several benchmark datasets, we will demonstrate how ExplainER can handle different scenarios for a variety of classifiers.