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dissertation-tgn
bambezius/dissertation-tgn
dissertation-tgn is a machine learning model from bambezius. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
JAX checkpoint for the Tgn temporal graph model with separate intention and outcome heads.
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.joblib15.4 MB · 100%
At a glance
- Access
- Public
- Created
- Apr 30, 2026
- Updated
- Apr 30, 2026
- SHA
- 8cdfa20e
Model card
From the Hugging Face model README
tgn
JAX checkpoint for the Tgn temporal graph model with separate intention and outcome heads.
Checkpoint
- Model class:
Tgn - Framework:
jax - Run timestamp:
20260430_135010 - Parameters:
model_params.joblib - Hyperparameters:
config.json - Metrics:
metrics.json
Validation Metrics
{
"val/loss": 1.1337319612503052,
"val/intention_top1_accuracy": 0.6611314415931702,
"val/intention_top2_accuracy": 0.8483576774597168,
"val/intention_top3_accuracy": 0.9177007675170898,
"val/intention_mrr": 0.7940667867660522,
"val/outcome_top1_accuracy": 0.5961679220199585,
"val/outcome_top2_accuracy": 0.7636861801147461,
"val/outcome_top3_accuracy": 0.8319343328475952,
"val/outcome_mrr": 0.7304578423500061
}
Loading
import json
from pathlib import Path
import joblib
checkpoint_path = Path("path/to/checkpoint")
with open(checkpoint_path / "config.json") as f:
config = json.load(f)
params = joblib.load(checkpoint_path / "model_params.joblib")
- Created
- Apr 30, 2026
- Updated
- Apr 30, 2026