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drzo/unicosys-hypergraph

sourceHugging Facemitupdated 36m agoView on Hugging Face
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Model Card

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

ComponentDetails
Node Embedding128-dim structural + 256-dim text
Hidden Dimension256
Text Encoder2-layer Transformer, 4 heads
Graph Attention2-layer GAT, 4 heads
Link Predictor2-layer MLP with margin ranking loss
Total Parameters34,762,497

Knowledge Graph Statistics

MetricCount
Total Nodes198,019
Total Edges13,415
Cross-Links3,664
Entities16
Emails197,993
Financial Documents0
Timeline Events10
LEX Schemes0
Legal Filings0

Subsystems

SubsystemNodes
Core (Entities)16
Fincosys (Financial)0
Comcosys (Communications)197,993
RevStream1 (Evidence)0
Ad-Res-J7 (Legal)10

Training

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

python
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.