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bert_large_xsum_samsum2

This model is a fine-tuned version of alexdg19/bert_large_xsum_samsum on the samsum dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1949
  • Rouge1: 0.6112
  • Rouge2: 0.3855
  • Rougel: 0.5301
  • Rougelsum: 0.5296
  • Gen Len: 30.5427

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: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • 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
No log 1.0 41 0.9966 0.6323 0.416 0.5587 0.5598 26.9573
No log 2.0 82 1.0976 0.6279 0.413 0.5569 0.5583 27.8171
No log 3.0 123 1.1576 0.6236 0.4141 0.553 0.5537 29.5183
No log 4.0 164 1.1998 0.6148 0.3948 0.5402 0.541 30.5061
No log 5.0 205 1.1949 0.6112 0.3855 0.5301 0.5296 30.5427

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1
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Dataset used to train alexdg19/bert_large_xsum_samsum2

Evaluation results