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AraT5-base-title-generation-finetune-ar-xlsum

This model is a fine-tuned version of UBC-NLP/AraT5-base-title-generation on the xlsum dataset. It achieves the following results on the evaluation set:

  • Loss: 4.2837
  • Rouge-1: 32.46
  • Rouge-2: 15.15
  • Rouge-l: 28.38
  • Gen Len: 18.48
  • Bertscore: 74.24

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: 0.0005
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 250
  • num_epochs: 10
  • label_smoothing_factor: 0.1

Training results

Training Loss Epoch Step Validation Loss Rouge-1 Rouge-2 Rouge-l Gen Len Bertscore
5.815 1.0 293 4.7437 27.05 10.49 23.56 18.03 72.56
5.0818 2.0 586 4.5004 28.92 11.97 25.09 18.61 73.08
4.7855 3.0 879 4.3910 29.66 12.57 25.79 18.58 73.3
4.588 4.0 1172 4.3469 30.22 13.05 26.36 18.59 73.61
4.4388 5.0 1465 4.3226 30.88 13.81 27.01 18.65 73.78
4.3162 6.0 1758 4.2990 30.9 13.6 26.92 18.68 73.78
4.2178 7.0 2051 4.2869 31.35 14.01 27.41 18.57 73.96
4.1387 8.0 2344 4.2794 31.28 13.98 27.34 18.6 73.87
4.0787 9.0 2637 4.2806 31.45 14.17 27.46 18.66 73.97
4.0371 10.0 2930 4.2837 31.55 14.19 27.52 18.65 74.0

Framework versions

  • Transformers 4.20.0
  • Pytorch 1.11.0+cu113
  • Datasets 2.3.2
  • Tokenizers 0.12.1
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