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Introduction of dissertation-tgn-sym-dynamic-pc-cnn

Model Details of dissertation-tgn-sym-dynamic-pc-cnn

tgn-sym-dynamic-pc-cnn

JAX checkpoint for the TgnSymDynamicPcCnn D2-invariant temporal graph model with pitch-control CNN fusion and separate intention and outcome heads.

Checkpoint

Model class: TgnSymDynamicPcCnn
Framework: jax
Run timestamp: 20260430_160136
Parameters: model_params.joblib
Hyperparameters: config.json
Metrics: metrics.json

Validation Metrics

{
  "val/loss": 0.9380925893783569,
  "val/intention_top1_accuracy": 0.7312043905258179,
  "val/intention_top2_accuracy": 0.8857664465904236,
  "val/intention_top3_accuracy": 0.9348540306091309,
  "val/intention_mrr": 0.8379348516464233,
  "val/outcome_top1_accuracy": 0.6596715450286865,
  "val/outcome_top2_accuracy": 0.8060219287872314,
  "val/outcome_top3_accuracy": 0.8671533465385437,
  "val/outcome_mrr": 0.7761701941490173
}

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")

Runs of bambezius dissertation-tgn-sym-dynamic-pc-cnn on huggingface.co

Total runs

24-hour runs

-1

3-day runs

-4

7-day runs

-2

30-day runs

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