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t5-small-science-papers

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

  • Loss: 3.6405
  • Rouge1: 12.3568
  • Rouge2: 2.4449
  • Rougel: 10.2371
  • Rougelsum: 11.4209
  • Gen Len: 19.0

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: 16
  • eval_batch_size: 16
  • seed: 42
  • 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
4.4735 1.0 12690 4.3727 9.9604 1.7641 8.6213 9.2779 19.0
4.0104 2.0 25380 3.9384 11.4001 2.1474 9.6516 10.6602 19.0
3.8237 3.0 38070 3.7580 11.1806 2.1229 9.3881 10.3853 19.0
3.7382 4.0 50760 3.6738 11.9298 2.3222 9.9077 11.045 19.0
3.6994 5.0 63450 3.6405 12.3568 2.4449 10.2371 11.4209 19.0

Framework versions

  • Transformers 4.24.0
  • Pytorch 1.12.1+cu113
  • Datasets 2.6.1
  • Tokenizers 0.13.1
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Dataset used to train Dagar/t5-small-science-papers

Evaluation results