extract_long_text_unbalanced_smaller_5

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: 2.2242
  • Rouge1: 0.2008
  • Rouge2: 0.0688
  • Rougel: 0.1593
  • Rougelsum: 0.1594
  • Gen Len: 18.9847

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: 0.001
  • 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 2.3970 0.1842 0.0572 0.1461 0.1458 18.98
No log 2.0 144 2.2826 0.1923 0.0623 0.1516 0.1515 19.0
No log 3.0 216 2.2308 0.1945 0.0634 0.1529 0.1527 18.9953
No log 4.0 288 2.1962 0.1944 0.0636 0.1528 0.1527 18.9967
No log 5.0 360 2.1940 0.1948 0.0633 0.1529 0.1528 18.9953
No log 6.0 432 2.1734 0.1882 0.0628 0.1492 0.1491 18.99
3.0387 7.0 504 2.1584 0.1964 0.0663 0.156 0.1559 18.992
3.0387 8.0 576 2.1588 0.197 0.068 0.1563 0.1562 18.9847
3.0387 9.0 648 2.1852 0.1967 0.0669 0.156 0.1559 18.9793
3.0387 10.0 720 2.1859 0.201 0.0685 0.159 0.1587 18.982
3.0387 11.0 792 2.1760 0.1936 0.0643 0.1534 0.1531 18.9953
3.0387 12.0 864 2.2081 0.1978 0.0672 0.1566 0.1564 18.9753
3.0387 13.0 936 2.2030 0.1991 0.068 0.1584 0.158 18.9833
2.204 14.0 1008 2.2029 0.1981 0.0686 0.1578 0.1578 18.9867
2.204 15.0 1080 2.2076 0.2016 0.0694 0.1595 0.1592 18.9773
2.204 16.0 1152 2.2172 0.203 0.0716 0.1617 0.1617 18.9893
2.204 17.0 1224 2.2136 0.2018 0.0697 0.1604 0.1603 18.9827
2.204 18.0 1296 2.2147 0.2016 0.0695 0.1601 0.1599 18.988
2.204 19.0 1368 2.2224 0.2007 0.0687 0.1592 0.1592 18.9847
2.204 20.0 1440 2.2242 0.2008 0.0688 0.1593 0.1594 18.9847

Framework versions

  • Transformers 4.39.1
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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