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t5-base-finetuned-summarization-cnn-ver2

This model is a fine-tuned version of t5-base on the cnn_dailymail dataset. It achieves the following results on the evaluation set:

  • Loss: 1.7601
  • Bertscore-mean-precision: 0.8926
  • Bertscore-mean-recall: 0.8628
  • Bertscore-mean-f1: 0.8772
  • Bertscore-median-precision: 0.8906
  • Bertscore-median-recall: 0.8600
  • Bertscore-median-f1: 0.8751

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: 5e-05
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Bertscore-mean-precision Bertscore-mean-recall Bertscore-mean-f1 Bertscore-median-precision Bertscore-median-recall Bertscore-median-f1
1.4581 1.0 5742 1.6800 0.8904 0.8615 0.8755 0.8887 0.8597 0.8737
1.2356 2.0 11484 1.7274 0.8924 0.8626 0.8771 0.8911 0.8607 0.8753
1.1073 3.0 17226 1.7601 0.8926 0.8628 0.8772 0.8906 0.8600 0.8751

Framework versions

  • Transformers 4.24.0
  • Pytorch 1.12.1+cu113
  • Datasets 2.7.1
  • Tokenizers 0.13.2
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Dataset used to train Alred/t5-base-finetuned-summarization-cnn-ver2