Link source and reproduction repository

MODEL_CARD.md

@@ -34,7 +34,8 @@ with `transformers.AutoModel`. Use the bundled `inference.py`; no remote code or
 The model follows [BinProv: Binary Code Provenance Identification without
 Disassembly](https://doi.org/10.1145/3545948.3545956), RAID 2022. This checkpoint
 comes from a clean reproduction run of the repository's best single-model
-recipe.
+recipe. Source code and full reproduction scripts are available at
+[Viewer-HX/BinProv](https://github.com/Viewer-HX/BinProv).
 Evaluation (this export): sequence accuracy **84.2255%**. Binary-level accuracy **93.8830%**. A fresh training run's numbers vary by ~1-2 points with seed.
 
 ## Direct use

README.md

@@ -34,7 +34,8 @@ with `transformers.AutoModel`. Use the bundled `inference.py`; no remote code or
 The model follows [BinProv: Binary Code Provenance Identification without
 Disassembly](https://doi.org/10.1145/3545948.3545956), RAID 2022. This checkpoint
 comes from a clean reproduction run of the repository's best single-model
-recipe.
+recipe. Source code and full reproduction scripts are available at
+[Viewer-HX/BinProv](https://github.com/Viewer-HX/BinProv).
 Evaluation (this export): sequence accuracy **84.2255%**. Binary-level accuracy **93.8830%**. A fresh training run's numbers vary by ~1-2 points with seed.
 
 ## Direct use