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hubert2BertMusic200

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.4016
  • Rouge1: 29.7694
  • Rouge2: 6.8881
  • Rougel: 20.8382
  • Rougelsum: 20.7475
  • Gen Len: 54.68

Model description

More information needed

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: 1e-06
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
1.5312 1.0 1361 1.4350 29.7078 7.6167 21.0154 20.9599 57.96
1.479 2.0 2722 1.4311 29.5523 7.5127 20.8746 20.7455 55.77
1.4774 3.0 4083 1.4296 29.4948 7.41 20.6514 20.531 55.12
1.4663 4.0 5444 1.4239 28.8275 6.8898 20.1906 20.1528 54.0
1.5195 5.0 6805 1.4219 31.4265 7.8481 21.8264 21.7471 53.33
1.5115 6.0 8166 1.4177 29.8254 6.8757 21.0269 20.9813 53.45
1.5424 7.0 9527 1.4154 29.9847 7.0621 20.9479 20.9081 54.73
1.5635 8.0 10888 1.4131 29.8807 7.1097 20.9508 20.902 53.83
1.6138 9.0 12249 1.4113 29.1418 6.725 20.4919 20.404 55.46
1.676 10.0 13610 1.4094 29.4633 6.7466 20.5997 20.5457 54.8
1.6447 11.0 14971 1.4087 30.1765 6.7892 20.9128 20.8485 54.75
1.6683 12.0 16332 1.4074 29.7832 6.7904 20.6381 20.5824 54.86
1.6927 13.0 17693 1.4056 29.5094 6.6848 20.5682 20.4713 53.62
1.6567 14.0 19054 1.4040 29.674 6.7272 20.7709 20.7017 52.78
1.672 15.0 20415 1.4032 29.447 6.6842 20.707 20.6147 53.81
1.6468 16.0 21776 1.4032 30.1311 7.1838 21.0813 21.0099 54.62
1.6661 17.0 23137 1.4022 30.1715 7.1566 21.0716 20.9984 54.95
1.668 18.0 24498 1.4020 30.1766 7.1429 21.1461 21.0611 54.56
1.6448 19.0 25859 1.4016 29.5354 6.7505 20.5517 20.4719 54.34
1.6647 20.0 27220 1.4016 29.7694 6.8881 20.8382 20.7475 54.68

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.2
  • Tokenizers 0.13.3
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