This model was trained to identify procedural similarity in C++ code (POJ-104 dataset).
Unlike semantic models that focus on intent ("what it does"), this model focuses on structure ("how it does it").
Architecture
Backbone: Frozen CodeBERT (microsoft/codebert-base) for node features.
Head: 2-layer GIN (Graph Isomorphism Network) for structural aggregation.
Objective: Triplet Margin Loss (Margin=0.8) to separate structurally distinct implementations.
Usage
This is a custom PyTorch model. You must define the CFGEncoder class structure exactly as used in training to load these weights.
