Graph Machine Learning
Transformers
Safetensors
English
unicosys_hypergraph
knowledge-graph
hypergraph
legal-evidence
graph-neural-network
unicosys
Instructions to use drzo/unicosys-hypergraph with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use drzo/unicosys-hypergraph with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("drzo/unicosys-hypergraph", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| 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 | 300,830 | | |
| | Total Edges | 14,800 | | |
| | Cross-Links | 3,624 | | |
| | Entities | 16 | | |
| | Emails | 199,204 | | |
| | Financial Documents | 12,103 | | |
| | Timeline Events | 59,955 | | |
| | LEX Schemes | 13 | | |
| | Legal Filings | 5 | | |
| ### Subsystems | |
| | Subsystem | Nodes | | |
| |---|---| | |
| | Core (Entities) | 16 | | |
| | Fincosys (Financial) | 101,429 | | |
| | Comcosys (Communications) | 199,204 | | |
| | RevStream1 (Evidence) | 150 | | |
| | Ad-Res-J7 (Legal) | 31 | | |
| ## 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` β Model weights | |
| - `config.json` β Model configuration | |
| - `graph_data.safetensors` β Encoded graph tensors (nodes, edges) | |
| - `tokenizer.json` β Character-level tokenizer for node labels | |
| - `node_id_mapping.json` β Node ID string to integer index mapping | |
| - `model_summary.json` β Compact statistics summary | |
| ## Source | |
| Generated by the [Unicosys](https://github.com/hyperholmes/unicosys) intelligence pipeline. | |