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wav2GPT2MusiSD3200

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

  • Loss: 0.8962
  • Rouge1: 28.7212
  • Rouge2: 7.4616
  • Rougel: 21.8892
  • Rougelsum: 21.8832
  • Gen Len: 46.0

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
1.0361 1.0 1361 0.9081 28.7212 7.4616 21.8892 21.8832 46.0
1.0015 2.0 2722 0.9021 28.1788 7.304 21.4695 21.4607 50.0
1.0003 3.0 4083 0.8976 27.7311 6.574 21.4759 21.4402 57.0
0.9761 4.0 5444 0.8914 27.7311 6.574 21.4759 21.4402 57.0
0.9928 5.0 6805 0.8884 27.7311 6.574 21.4759 21.4402 57.0
1.013 6.0 8166 0.8858 27.7311 6.574 21.4759 21.4402 57.0
1.0476 7.0 9527 0.8852 27.7311 6.574 21.4759 21.4402 57.0
1.0649 8.0 10888 0.8847 27.7311 6.574 21.4759 21.4402 57.0
1.1224 9.0 12249 0.8888 29.7298 7.2318 22.3598 22.3739 56.0
1.1818 10.0 13610 0.8949 29.7298 7.2318 22.3598 22.3739 56.0
1.1832 11.0 14971 0.8981 30.1982 7.2766 22.0853 22.118 59.0
1.1878 12.0 16332 0.8987 29.7298 7.2318 22.3598 22.3739 56.0
1.1833 13.0 17693 0.8983 29.7298 7.2318 22.3598 22.3739 56.0
1.1772 14.0 19054 0.8980 29.7298 7.2318 22.3598 22.3739 56.0
1.1723 15.0 20415 0.8974 28.7212 7.4616 21.8892 21.8832 46.0
1.1778 16.0 21776 0.8972 28.7212 7.4616 21.8892 21.8832 46.0
1.1707 17.0 23137 0.8968 28.7212 7.4616 21.8892 21.8832 46.0
1.1767 18.0 24498 0.8964 28.7212 7.4616 21.8892 21.8832 46.0
1.17 19.0 25859 0.8962 28.7212 7.4616 21.8892 21.8832 46.0
1.1737 20.0 27220 0.8962 28.7212 7.4616 21.8892 21.8832 46.0

Framework versions

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