A trainable knowledge graph embedding model encoding the unified evidence hypergraph for Case 2025-137857. This model encodes a unified hypergraph linking financial transactions, email communications, legal evidence, and entity relationships into a single trainable knowledge representation. The model can be fine-tuned on link prediction tasks: - model.safetensors — Model weights - config.json — Model configuration - graphdata.safetensors — Encoded graph tensors (nodes, edges) - tokenizer.json — Character-level tokenizer for node labels - nodeidmapping.json — Node ID string to integer index mapping - modelsummary.json — Compact statistics summary Generated by the Unicosys intelligence…
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35M parameters
transformers
This repository hosts pretrained EdenGNN checkpoints and reference configuration files associated with the paper Efficient equivariant framework for universal charge density prediction. EdenGNN (Equivariant Density Graph Neural Network) predicts charge densities from atomic structures and can be coupled with downstream DFT workflows for electronic-structure calculations. The config.yaml files stored in this repository are for reference only. They are not plug-and-play configuration files and must be modified based on the user's own environment before use. In particular, users should update paths, software-specific templates, dataset locations, checkpoint paths, output directories, and…
Open weights
mit
