Knowledge Graph Risk Engine Baseline Model
Model Description
This repository contains a small, transparent prototype model for
Risk teams need relationship-level explanations instead of opaque entity scores.
The model combines per-label token weights with IDF-weighted evidence
retrieval. It was generated for reproducible architecture demonstrations and
does not call a hosted LLM.
Evaluation
- Held-out synthetic examples: 4
- Accuracy: 1
- Intended metrics: relation_accuracy, path_coverage, entity_resolution_precision
Intended Use
- Architecture prototyping
- CI and evaluation examples
- Local baseline comparisons
- Educational experimentation
Hugging Face Task Coverage
token-classificationfeature-extractionquestion-answeringsentence-similarity
Limitations and Risks
All entities are fictional. Real identity or financial data requires governance, consent, and bias review.
The dataset is synthetic and small. Do not use this model for consequential
decisions without representative data, expert review, and production-grade
evaluation.
Reproducibility
The linked GitHub repository includes train.py, the exact dataset split,
evaluation code, and the model JSON format.
