output

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

Model description

state_mean = np.array( [6.51944444e+00, 3.98379630e+00, 1.25000000e+01, 1.67850000e+01, 1.67849190e+01, 1.67851968e+01, 1.67854977e+01, 7.28990741e+01, 7.29056713e+01, 7.29093750e+01, 7.29134259e+01, 2.07319097e+02, 2.07319097e+02, 2.07185417e+02, 2.07236111e+02, 2.01118634e+02, 2.01118634e+02, 2.00806481e+02, 2.00887616e+02, 1.56366486e-01, 1.05916886e+00, 6.96371636e-01, 2.91179937e-01, 3.99157702e-01, 2.73105321e-01, 2.73105321e-01, 2.73105321e-01, 2.73105321e-01])

state_std = np.array( [3.47125753e+00, 2.00155513e+00, 6.92218755e+00, 3.55389420e+00, 3.55381195e+00, 3.55403913e+00, 3.55461251e+00, 1.65420140e+01, 1.65465337e+01, 1.65478974e+01, 1.65489647e+01, 2.91883900e+02, 2.91883900e+02, 2.91755278e+02, 2.91833913e+02, 2.96415007e+02, 2.96415007e+02, 2.96260649e+02, 2.96305327e+02, 3.53750260e-02, 8.83521126e-01, 1.01549677e+00, 3.23319869e-01, 9.20646312e-01, 1.17879328e-01, 1.17879328e-01, 1.17879328e-01, 1.17879328e-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: 360

Training results

Framework versions

• Transformers 4.26.1
• Pytorch 1.13.1+cu116
• Datasets 2.9.0
• Tokenizers 0.13.2