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dissertation-tgn-sym
bambezius/dissertation-tgn-sym
dissertation-tgn-sym 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 TgnSym D2-invariant temporal graph model with separate intention and outcome heads.
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.joblib14.6 MB · 100%
At a glance
- Access
- Public
- Created
- Apr 30, 2026
- Updated
- Apr 30, 2026
- SHA
- 88588e1c
Model card
From the Hugging Face model README
tgn-sym
JAX checkpoint for the TgnSym D2-invariant temporal graph model with separate intention and outcome heads.
Checkpoint
- Model class:
TgnSym - Framework:
jax - Run timestamp:
20260430_130357 - Parameters:
model_params.joblib - Hyperparameters:
config.json - Metrics:
metrics.json
Validation Metrics
{
"val/loss": 1.0408462285995483,
"val/intention_top1_accuracy": 0.6916058659553528,
"val/intention_top2_accuracy": 0.8633211851119995,
"val/intention_top3_accuracy": 0.9248175621032715,
"val/intention_mrr": 0.8131301403045654,
"val/outcome_top1_accuracy": 0.6186131834983826,
"val/outcome_top2_accuracy": 0.782299280166626,
"val/outcome_top3_accuracy": 0.8479927182197571,
"val/outcome_mrr": 0.7483379244804382
}
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