| # 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 |
|
|
| ```json |
| { |
| "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 |
|
|
| ```python |
| 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") |
| ``` |
|
|
