|
Download README.md from bambezius/dissertation-tgn-sym-dynamic: direct link, hf CLI and curl.
- Browser
- Download file 1.02 kB
-
https://huggingface.co/bambezius/dissertation-tgn-sym-dynamic/resolve/main/README.md
- Command line
-
hf download hf://bambezius/dissertation-tgn-sym-dynamic/README.md
-
curl -L -o README.md https://huggingface.co/bambezius/dissertation-tgn-sym-dynamic/resolve/main/README.md
1.02 kB
tgn-sym-dynamic
JAX checkpoint for the TgnSymDynamic football pass prediction model.
Checkpoint
- Model class:
TgnSymDynamic - Framework:
jax - Run timestamp:
20260430_084243 - Parameters:
model_params.joblib - Hyperparameters:
config.json - Metrics:
metrics.json
Validation Metrics
{
"val/loss": 0.928921639919281,
"val/intention_top1_accuracy": 0.7339416146278381,
"val/intention_top2_accuracy": 0.8846715688705444,
"val/intention_top3_accuracy": 0.9397810697555542,
"val/intention_mrr": 0.8394966125488281,
"val/outcome_top1_accuracy": 0.6613138914108276,
"val/outcome_top2_accuracy": 0.8071168065071106,
"val/outcome_top3_accuracy": 0.8708029389381409,
"val/outcome_mrr": 0.777962327003479
}
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")