extract_long_text_unbalanced_smaller

This model is a fine-tuned version of weny22/sum_model_t5_saved on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 2.4822
  • Rouge1: 0.1997
  • Rouge2: 0.0696
  • Rougel: 0.1604
  • Rougelsum: 0.1602
  • Gen Len: 18.9893

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: 0.002
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
No log 1.0 72 2.6068 0.1714 0.0484 0.1369 0.1367 18.988
No log 2.0 144 2.3827 0.1801 0.0547 0.1414 0.1412 18.994
No log 3.0 216 2.2953 0.1858 0.0568 0.1457 0.1456 19.0
No log 4.0 288 2.2509 0.188 0.0599 0.1479 0.1478 18.9953
No log 5.0 360 2.2338 0.1834 0.057 0.1449 0.1447 18.9967
No log 6.0 432 2.2428 0.1871 0.0608 0.1483 0.1482 18.9953
3.0458 7.0 504 2.2195 0.1926 0.0626 0.1538 0.1537 18.9867
3.0458 8.0 576 2.2549 0.1932 0.0619 0.1521 0.152 18.9967
3.0458 9.0 648 2.2675 0.1955 0.0642 0.156 0.1558 18.9607
3.0458 10.0 720 2.2858 0.1981 0.0665 0.1573 0.1572 18.9807
3.0458 11.0 792 2.2980 0.1942 0.0653 0.1557 0.1554 18.972
3.0458 12.0 864 2.3413 0.1999 0.0682 0.1597 0.1595 18.9807
3.0458 13.0 936 2.3324 0.1987 0.0676 0.1585 0.1585 18.9733
1.907 14.0 1008 2.3481 0.2002 0.0688 0.1599 0.1597 18.9913
1.907 15.0 1080 2.4027 0.2023 0.0704 0.1617 0.1617 18.9887
1.907 16.0 1152 2.4132 0.2032 0.0728 0.1634 0.1634 18.9833
1.907 17.0 1224 2.4393 0.1988 0.0682 0.1586 0.1584 18.9853
1.907 18.0 1296 2.4435 0.1991 0.0698 0.1594 0.1591 18.9867
1.907 19.0 1368 2.4703 0.2014 0.0703 0.1608 0.1608 18.9873
1.907 20.0 1440 2.4822 0.1997 0.0696 0.1604 0.1602 18.9893

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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