Add model card

README.md

@@ -1,10 +1,28 @@
+
 ---
+license: apache-2.0
 tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
+  - graph-neural-network
+  - rgcn
+  - edge-attention
+  - binary-classification
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: [More Information Needed]
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+# BFS_EA_RGCN Model
+
+This is a Graph Neural Network model combining Residual Graph Convolutional Networks (RGCN) and Edge Attention mechanisms, designed for binary classification of graphs (vulnerable vs. non-vulnerable).
+
+- **Architecture**: Classifier with res_GCN (9 layers, 11 hidden units) and Edge_Attention (2 heads, 100 feature length)
+- **Task**: Binary classification of software graphs
+- **Dataset**: SG_Final_Train and SG_Final_Test (vulnerable and non-vulnerable graphs)
+- **Input**: Graph node features (100-dim), edge features (100-dim), adjacency matrix
+- **Output**: Binary label (0: non-vulnerable, 1: vulnerable)
+- **Checkpoint**: Loaded from /kaggle/input/egat-peculiar-model/checkpoint_epoch_4.pt
+- **Performance**: 
+  - Test Accuracy: ~0.5959
+  - Precision: ~0.6345
+  - Recall: ~0.4623
+  - F1 Score: ~0.5349
+  - AUC: ~0.6222 (from exp-0 in notebook)
+
+For more details, refer to the original training notebook or dataset documentation.