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ATS-Bart-Large-CNN

This model is a fine-tuned version of facebook/bart-large-cnn on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1044
  • Rouge1: 0.6648
  • Rouge2: 0.4542
  • Rougel: 0.5743
  • Rougelsum: 0.5743
  • Gen Len: 79.5693

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 8
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
No log 1.0 274 0.8020 0.6276 0.3994 0.5208 0.5212 77.3942
0.7545 2.0 548 0.8005 0.6469 0.4278 0.5488 0.5488 79.8577
0.7545 3.0 822 0.8290 0.6533 0.4371 0.56 0.56 78.7865
0.3296 4.0 1096 0.8996 0.6581 0.4439 0.5636 0.5637 78.8522
0.3296 5.0 1370 0.9740 0.6602 0.4486 0.5644 0.5645 78.8869
0.1575 6.0 1644 1.0374 0.6617 0.4493 0.5689 0.5687 78.7974
0.1575 7.0 1918 1.0972 0.6613 0.4513 0.5706 0.5706 79.9069
0.0868 8.0 2192 1.1044 0.6648 0.4542 0.5743 0.5743 79.5693

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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