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CS685-text-summarizer-2

This model is a fine-tuned version of facebook/bart-base on the billsum dataset. It achieves the following results on the evaluation set:

  • Loss: 1.7516
  • Rouge1: 17.4066
  • Rouge2: 14.022
  • Rougel: 16.9378
  • Rougelsum: 17.0519

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: 5.6e-05
  • train_batch_size: 6
  • eval_batch_size: 6
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum
2.3529 1.0 1052 1.9277 17.1288 13.5932 16.6346 16.7728
1.9686 2.0 2104 1.8297 17.2756 13.7685 16.7924 16.9242
1.789 3.0 3156 1.7903 17.4219 14.0205 16.9082 17.0564
1.6619 4.0 4208 1.7632 17.5055 14.1186 16.996 17.1265
1.5819 5.0 5260 1.7516 17.4066 14.022 16.9378 17.0519

Framework versions

  • Transformers 4.28.0
  • Pytorch 2.0.0+cu118
  • Datasets 2.12.0
  • Tokenizers 0.13.3
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Dataset used to train cs608/billsum-model-2

Evaluation results