Pytorch Embedding Model Part 3

Today we are getting back to our embedding model. We finished most of the foundation yesterday, and now we want to train it, measure it, and visualize it. The core idea is simple: an embedding layer is just a two dimensional matrix, and we use token IDs to pick the right rows. I also store the dictionary inside the model so we can tokenize text on the fly and pass in full sentences. Most of the work so far has not been PyTorch code, it has been data prep and shaping tensors so everything lines up. I built a basic tokenizer from scratch that normalizes words, builds a Python dictionary of word to index, and handles padding. Now the main goal is to get training working by wiring up the loss and optimizer, but we hit a shape mismatch: the dictionary has 23 words and the labels are size 23, yet the model output is size 32. Next step is to trace where that 32 comes from, confirm the final layer outputs vocab size, and reshape the batch correctly so cross entropy can run.