aaryaupadhya20/codex-s-complex-winner

CoDEx-S ComplEx — Winner Model

Knowledge graph link prediction on CoDEx-S using ComplEx embeddings, trained with the LibKGE framework. Reproduces and slightly improves results from the CoDEx paper (EMNLP 2020).

Results (Validation Set — Filtered with Test)

Metric	This Model	Paper
MRR	0.474	0.465
Hits@1	0.377	0.372
Hits@3	0.522	0.504
Hits@10	0.664	0.646

Training stopped early at epoch 345 via early stopping.

Dataset — CoDEx-S

	Count
Entities	2,034
Relations	42
Train triples	32,888
Valid triples	1,827
Test triples	1,828

Hyperparameters

Parameter	Value
Embedding dim	512
Optimizer	Adam
Learning rate	0.000339
Batch size	1024
Max epochs	400
Training type	1vsAll
Loss	KL divergence
LR scheduler	ReduceLROnPlateau
Entity dropout	0.079
Relation dropout	0.056

Load in Your App

import sys
sys.path.insert(0, r"C:/path/to/codex/kge")

from huggingface_hub import hf_hub_download
from kge.model import KgeModel
from kge.util.io import load_checkpoint
import torch

# Download from Hugging Face
path = hf_hub_download(
    repo_id="aaryaupadhya20/codex-s-complex-winner",
    filename="winner_model.pt"
)

# Load model
checkpoint   = load_checkpoint(path, device="cpu")
winner_model = KgeModel.create_from(checkpoint)
winner_model.eval()

print("winner_model ready!")

# Score a triple using entity/relation integer indices
s = torch.tensor([0])   # head entity index
p = torch.tensor([1])   # relation index
o = torch.tensor([2])   # tail entity index

score = winner_model.score_spo(s, p, o, direction="o")
print("Score:", score.item())

Citation

@inproceedings{safavi-koutra-2020-codex,
    title     = "CoDEx: A Comprehensive Knowledge Graph Completion Benchmark",
    author    = "Safavi, Tara and Koutra, Danai",
    booktitle = "Proceedings of EMNLP 2020",
    year      = "2020",
    url       = "https://arxiv.org/pdf/2009.07810.pdf"
}