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distilbart-cnn-12-3-finetuned-pubmed

This model is a fine-tuned version of sshleifer/distilbart-cnn-12-3 on the pub_med_summarization_dataset dataset. It achieves the following results on the evaluation set:

  • Loss: 2.1743
  • Rouge1: 40.5642
  • Rouge2: 16.9812
  • Rougel: 25.3449
  • Rougelsum: 36.46
  • Gen Len: 141.95

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: 2e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
2.469 1.0 4000 2.2956 38.3713 15.2594 23.6734 34.1634 141.707
2.2527 2.0 8000 2.1994 39.5939 16.2376 24.6363 35.5106 141.831
2.0669 3.0 12000 2.1780 40.078 16.6705 25.1119 35.9605 141.8475
1.9275 4.0 16000 2.1669 40.0825 16.6169 24.9702 36.0191 141.928
1.8102 5.0 20000 2.1743 40.5642 16.9812 25.3449 36.46 141.95

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.3
  • Tokenizers 0.11.6
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Evaluation results