Upload load_winner.py with huggingface_hub

load_winner.py

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+import sys
+from pathlib import Path
+from huggingface_hub import hf_hub_download
+
+def load_winner_model(kge_path: str = r"kge", device: str = "cpu"):
+    """
+    Load the CoDEx-S ComplEx winner model from Hugging Face.
+
+    Args:
+        kge_path : absolute path to your local codex/kge directory
+        device   : "cpu" or "cuda"
+
+    Returns:
+        winner_model : KgeModel ready for inference
+    """
+    sys.path.insert(0, kge_path)
+    from kge.model import KgeModel
+    from kge.util.io import load_checkpoint
+
+    print("Downloading winner_model from Hugging Face...")
+    path = hf_hub_download(
+        repo_id="aaryaupadhya20/codex-s-complex-winner",
+        filename="winner_model.pt"
+    )
+
+    print("Loading checkpoint...")
+    checkpoint   = load_checkpoint(path, device=device)
+    winner_model = KgeModel.create_from(checkpoint)
+    winner_model.eval()
+    print("winner_model loaded and ready!")
+    return winner_model
+
+
+if __name__ == "__main__":
+    import torch
+
+    model = load_winner_model()
+
+    # Score a test triple (integer indices from CoDEx-S)
+    s = torch.tensor([0])
+    p = torch.tensor([1])
+    o = torch.tensor([2])
+
+    score = model.score_spo(s, p, o, direction="o")
+    print(f"Test triple score: {score.item():.4f}")