README.md

metadata

license: mit
tags:
  - knowledge-graph
  - hypergraph
  - legal-evidence
  - graph-neural-network
  - unicosys
language:
  - en
library_name: transformers
pipeline_tag: graph-ml

Unicosys Hypergraph Knowledge Model

A trainable knowledge graph embedding model encoding the unified evidence hypergraph for Case 2025-137857.

Model Description

This model encodes a unified hypergraph linking financial transactions, email communications, legal evidence, and entity relationships into a single trainable knowledge representation.

Architecture

Component	Details
Node Embedding	128-dim structural + 256-dim text
Hidden Dimension	256
Text Encoder	2-layer Transformer, 4 heads
Graph Attention	2-layer GAT, 4 heads
Link Predictor	2-layer MLP with margin ranking loss
Total Parameters	36,023,937

Knowledge Graph Statistics

Metric	Count
Total Nodes	299,724
Total Edges	14,815
Cross-Links	3,976
Entities	16
Emails	199,553
Financial Documents	12,096
Timeline Events	59,789
LEX Schemes	13
Legal Filings	7

Subsystems

Subsystem	Nodes
Core (Entities)	16
Fincosys (Financial)	99,976
Comcosys (Communications)	199,553
RevStream1 (Evidence)	146
Ad-Res-J7 (Legal)	33

Training

The model can be fine-tuned on link prediction tasks:

from model.unicosys_model import UnicosysHypergraphModel, UnicosysConfig

model = UnicosysHypergraphModel.from_pretrained("hyperholmes/unicosys-hypergraph")
# ... prepare training data ...
# model.forward(node_ids, node_type_ids, subsystem_ids, edge_index, edge_type_ids,
#               pos_edge_index=pos, neg_edge_index=neg, labels=labels)

Files

• model.safetensors — Model weights
• config.json — Model configuration
• graph_data.safetensors — Encoded graph tensors (nodes, edges)
• tokenizer.json — Character-level tokenizer for node labels
• node_id_mapping.json — Node ID string to integer index mapping
• model_summary.json — Compact statistics summary

Source

Generated by the Unicosys intelligence pipeline.