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This model is a fine-tuned version of on the city_learn dataset.

Model description

state_mean = np.array([6.52472527e+00, 4.00000000e+00, 1.25000000e+01, 1.68241415e+01, 1.68242216e+01, 1.68249313e+01, 1.68268315e+01, 7.29934753e+01, 7.29969093e+01, 7.29977106e+01, 7.29979396e+01, 2.08098329e+02, 2.08098329e+02, 2.07998283e+02, 2.08040522e+02, 2.01204785e+02, 2.01204785e+02, 2.00978709e+02, 2.01073375e+02, 1.56447270e-01, 1.06496225e+00, 6.98845768e-01, 2.90539899e-01, 4.02466726e-01, 2.73094091e-01, 2.73094091e-01, 2.73094091e-01, 2.73094091e-01]) state_std = np.array([3.45249551e+00, 2.00000100e+00, 6.92218755e+00, 3.55839049e+00, 3.55843321e+00, 3.55972060e+00, 3.56299330e+00, 1.64936264e+01, 1.64957718e+01, 1.64978640e+01, 1.65000009e+01, 2.92600647e+02, 2.92600647e+02, 2.92543689e+02, 2.92592247e+02, 2.96262436e+02, 2.96262436e+02, 2.96151575e+02, 2.96175911e+02, 3.53418023e-02, 8.88195655e-01, 1.01691038e+00, 3.23315111e-01, 9.21189104e-01, 1.17759695e-01, 1.17759695e-01, 1.17759695e-01, 1.17759695e-01])

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 64
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 500

Training results

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1+cu116
  • Datasets 2.10.0
  • Tokenizers 0.13.2
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