ML-pokedex – experiments trying to train some models directly on RDF(onnxruntime.ai)

Four small experiments with language models trained on RDF
instead of English, over a single Pokémon knowledge graph. Each runs entirely in your
browser. Each draws what the model says, not what the page knows.

My first go at models that read and write the Semantic Web, published as a
curiosity. The honest verdict is on the about page: a neural net
loses to gzip as a database, but the rules it learns are the interesting part.

Pick real Pokémon. The model recalls their stored facts (typing, abilities, egg group)
and draws the constellation, sprites and all. It memorised the whole graph.

Name a Pokémon that never existed. The model can't look it up, so it invents one: a
coherent typing, weaknesses, moves, a habitat, all made up on the spot.

Choose a typing. The model derives its weaknesses from a type chart it was never handed,
and the page grades it live against the real chart. It aces the typings it saw and
stumbles on the ones held back.

Scrub through training, epoch by epoch, and watch RDF structure appear in layers: first
syntax, then vocabulary, then schema, and reasoning last of all. The RDF tinyshakespeare.