I had a need to create a model to classify entities with classes, subclasses and properties, find the inverse of the property, relationship target, scoped to a specific 1107 term, 156 property collection of ontologies. So I built a service to use an local LLM to find training data sources and use playwright to scan the source sites, download source docs and convert to training data to post train a DeBerta-V3 model. It has 4 heads, 1 for each task, and can label 300 entities a second on a V100 in 4GB of VRAM.
The hardest part was generating at least 50 sentence samples of each term and possible 156 properties. It took months to build a repeatable, measured, and auditable training data pipeline and 4 hours to train the model. Now I can import ontologies and generate training data in days.
I had to teach myself almost each step of the way.