The main idea of this exercise is to examine how PyTorch work with generating new samples using recurrent models, handling of sequnces with variable lengths, data preparation and loading, accurate calculation of loss for padded time-steps, etc. This project is quite simple and heavily inspired by one of the examples in PyTorch tutorials. After the model is trained, we can generate new names by giving the model a first letter (or a random one), race, gender and sample letters successively until we hit the stop character or reach some maximum word length. Similarly to the language models we are going to train RNN to predict the next letter in a name given the previous letter, race and gender. Continuing, once we are set-up with data, we'll create a character-based RNN network, LSTM to be more precise (it's all about that memory lane, am I right?). Sorry, Aarakocra and others for not making the cut! From genders, we'll only have Male and Female option. From races, we have Humans, Elves, Dwarfs, Half-orcs, Halflings, Tieflings and Dragonborns. Each name is going to be associated with the corresponding race and gender. These will be obtained from D&D 5e Player's Handbook and Xanathar's Guide to Everything. Well, firstly, we are going to get as much fantasy names possible. Instead of having database dictating our beloved future character name, we are putting our faith in (trustworthy) hands of a neural network. we are not doing any of that fancy stuff here.
While there is a lot of online name generators that enables you to customize your character name by looking at your class, origin, birthplace, etc. What is the hardest decision you have to make when creating a new character? Exactly! Choosing a name. Greetings fellow D&D players! I have one question for you.
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