# 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")
```