# Autoencoder to encode features/categories of data

My question is regarding the use of autoencoders (in PyTorch). I have a tabular dataset with a categorical feature that has 10 different categories. Names of these categories are quite different - some names consist of one word, some of two or three words. But all in all I have 10 unique category names. What I'm trying to do is to create an autoencoder which will encode names of these categories - for example, if I have a category named Medium size class, I want to see if it is possible to train autoencoder to encode this name as something like mdmsc or something like that. The use of it would be to found out which data points are hard to encode or not typical or something like that. I tried to adapt autoencoder architectures from various tutorials online however nothing seems to work for me or I simply do not know how to use them as they are all about images. Maybe someone has any idea how this type of autoencoder might be accomplished if it is at all possible?