# Classification: Entity Embeddings

In this guide, we will implement entity embeddings in two ways via PyTorch: (1) via nn.Embedding(), and (2) via transformers. We will also show how to load data in a more efficient manner through a custom PyTorch data set class. This style of data management is slightly more complicated to initialize, but is the precise way we want to load our data when dealing: (1) big data, or (2) a memory-conservative environment. Entity embeddings refers to the idea of transforming categorical variables into continuous embeddings to avoid one-hot encoding and sparse matrices.