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bart-large-cnn-small-billsum-3epochs

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

  • Loss: 1.7523
  • Rouge1: 0.5409
  • Rouge2: 0.3112
  • Rougel: 0.3929
  • Rougelsum: 0.4633

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: 2.5764683748161164e-05
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 16
  • num_epochs: 3
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum
2.7132 0.32 8 2.2000 0.4619 0.2328 0.3201 0.3939
2.236 0.64 16 1.9705 0.499 0.2768 0.3651 0.4216
2.1109 0.96 24 1.8845 0.5214 0.2974 0.3844 0.4417
1.7663 1.28 32 1.8211 0.5226 0.2935 0.3718 0.4479
1.7838 1.6 40 1.7981 0.5338 0.3001 0.383 0.4466
1.5229 1.92 48 1.7625 0.5299 0.3012 0.3839 0.4494
1.5221 2.24 56 1.7532 0.5384 0.3117 0.3939 0.4637
1.2879 2.56 64 1.7560 0.5338 0.3075 0.3865 0.4584
1.4046 2.88 72 1.7523 0.5409 0.3112 0.3929 0.4633

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.0+cu117
  • Datasets 2.7.1
  • Tokenizers 0.13.2
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Dataset used to train bheshaj/bart-large-cnn-small-billsum-3epochs

Evaluation results