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dissertation-gnn-sym
bambezius/dissertation-gnn-sym
dissertation-gnn-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 GatV2Sym D2-invariant shared-trunk graph model with separate intention and outcome heads.
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.joblib3.4 MB · 100%
At a glance
- Access
- Public
- Created
- Apr 30, 2026
- Updated
- Apr 30, 2026
- SHA
- f9152ee6
Model card
From the Hugging Face model README
gnn-sym
JAX checkpoint for the GatV2Sym D2-invariant shared-trunk graph model with separate intention and outcome heads.
Checkpoint
- Model class:
GatV2Sym - Framework:
jax - Run timestamp:
20260430_113740 - Parameters:
model_params.joblib - Hyperparameters:
config.json - Metrics:
metrics.json
Validation Metrics
{
"val/loss": 1.0811628103256226,
"val/intention_top1_accuracy": 0.6713362336158752,
"val/intention_top2_accuracy": 0.8514277935028076,
"val/intention_top3_accuracy": 0.9220097064971924,
"val/intention_mrr": 0.8004188537597656,
"val/outcome_top1_accuracy": 0.6073545217514038,
"val/outcome_top2_accuracy": 0.7707435488700867,
"val/outcome_top3_accuracy": 0.8465786576271057,
"val/outcome_mrr": 0.7399893403053284
}
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