bart-large-cnn-finetuned-scientific-articles

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

  • Loss: 2.6416
  • Rouge1: 34.2136
  • Rouge2: 11.6215
  • Rougel: 20.2516
  • Rougelsum: 30.6019

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: 9
  • eval_batch_size: 9
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum
3.3695 1.0 56 2.8464 32.133 10.3816 18.7538 29.311
2.7639 2.0 112 2.6667 31.5794 10.8708 19.2408 28.6171
2.517 3.0 168 2.6220 33.1806 11.2477 19.7199 30.1012
2.2989 4.0 224 2.6031 32.7604 10.9356 19.4766 29.6503
2.0883 5.0 280 2.6416 34.2136 11.6215 20.2516 30.6019

Framework versions

  • Transformers 4.41.1
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1
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Dataset used to train GabsAki/bart-large-cnn-finetuned-scientific-articles

Evaluation results