model: graph_conv-dot_product_h128_l3_edge_prediction | (graph_conv-dot_product_h128_l3) | WandB: s6xbw29d

README.md

@@ -39,7 +39,7 @@ optionally incorporating relation types for relation-aware prediction.
   - Type: `dot_product`
   - Relation-Aware: ✗
 
-**Training Date**: 2025-11-29
+**Training Date**: 2025-12-04
 
 For detailed experiment and training settings see this repository's `config.json` file.
 
@@ -47,15 +47,15 @@ For detailed experiment and training settings see this repository's `config.json
 
 | Metric | Value |
 |--------|-------|
-| Validation AUC | 0.7990 |
-| Test AUC | 0.8003 |
-| Validation AP | 0.7972 |
-| Test AP | 0.7988 |
+| Validation AUC | 0.7957 |
+| Test AUC | 0.7964 |
+| Validation AP | 0.7938 |
+| Test AP | 0.7947 |
 
 
 ## Links
 
-- 📊 [W&B Run](https://wandb.ai/napistu/napistu-experiments/runs/3nw21cyg)
+- 📊 [W&B Run](https://wandb.ai/napistu/napistu-experiments/runs/s6xbw29d)
 - 💻 [GitHub Repository](https://github.com/napistu/Napistu-Torch)
 - 📖 [Read the Docs](https://napistu-torch.readthedocs.io/en/latest)
 - 📚 [Napistu Wiki](https://github.com/napistu/napistu/wiki)
@@ -70,7 +70,7 @@ To reproduce the environment used for training, run the following commands:
 pip install torch==2.8.0
 pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/2.8.0+cpu.html
 pip install 'napistu==0.8.2'
-pip install 'napistu-torch[pyg,lightning]==0.2.12.dev1'
+pip install 'napistu-torch[pyg,lightning]==0.2.13'
 ```
 
 ### 2. Setup Data Store