| --- |
| license: mit |
| library_name: custom |
| pipeline_tag: feature-extraction |
| datasets: |
| - RKB109/knowledge-graph-risk-engine-20260719-dataset |
| tags: |
| - synthetic-data |
| - transparent-baseline |
| - knowledge-graphs |
| - token-classification |
| - feature-extraction |
| - question-answering |
| - sentence-similarity |
| metrics: |
| - accuracy |
| --- |
| |
| # 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-classification` |
| - `feature-extraction` |
| - `question-answering` |
| - `sentence-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. |
|
|
