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ptt5-xlsumm-temario

This model is a fine-tuned version of arthurmluz/ptt5-xlsumm-30epochs on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 2.4610
  • Rouge1: 0.0891
  • Rouge2: 0.0571
  • Rougel: 0.0781
  • Rougelsum: 0.0845
  • 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: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
No log 1.0 88 2.7016 0.0862 0.0326 0.0681 0.0805 19.0
No log 2.0 176 2.6413 0.0879 0.0389 0.0701 0.0828 19.0
2.9296 3.0 264 2.5893 0.0881 0.0438 0.0707 0.0827 19.0
2.9296 4.0 352 2.5650 0.0923 0.0479 0.0748 0.0871 19.0
2.646 5.0 440 2.5429 0.0885 0.0469 0.0732 0.0834 19.0
2.646 6.0 528 2.5247 0.088 0.0503 0.0739 0.0831 19.0
2.5072 7.0 616 2.5108 0.0891 0.0534 0.0769 0.0851 19.0
2.5072 8.0 704 2.5039 0.0884 0.0547 0.0764 0.0848 19.0
2.5072 9.0 792 2.4948 0.0864 0.0536 0.0751 0.083 19.0
2.4128 10.0 880 2.4836 0.0869 0.0546 0.076 0.0839 19.0
2.4128 11.0 968 2.4813 0.0866 0.0543 0.0764 0.0832 19.0
2.356 12.0 1056 2.4768 0.0864 0.0533 0.076 0.0828 19.0
2.356 13.0 1144 2.4728 0.0872 0.0556 0.0775 0.0838 19.0
2.2815 14.0 1232 2.4666 0.0877 0.0557 0.0774 0.0841 19.0
2.2815 15.0 1320 2.4667 0.0866 0.0552 0.0764 0.0829 19.0
2.2106 16.0 1408 2.4680 0.0869 0.0553 0.0772 0.0824 19.0
2.2106 17.0 1496 2.4647 0.0867 0.0553 0.0771 0.0828 19.0
2.2106 18.0 1584 2.4597 0.0875 0.0561 0.0777 0.0837 19.0
2.1809 19.0 1672 2.4601 0.0873 0.0557 0.0773 0.0833 19.0
2.1809 20.0 1760 2.4596 0.0873 0.0561 0.0773 0.0835 19.0
2.1541 21.0 1848 2.4592 0.0875 0.0561 0.0777 0.0837 19.0
2.1541 22.0 1936 2.4620 0.0869 0.0551 0.0768 0.0828 19.0
2.1442 23.0 2024 2.4621 0.0869 0.0551 0.0768 0.0828 19.0
2.1442 24.0 2112 2.4619 0.0868 0.0553 0.0768 0.0828 19.0
2.1071 25.0 2200 2.4613 0.0868 0.0553 0.0768 0.0828 19.0
2.1071 26.0 2288 2.4618 0.0873 0.0557 0.0768 0.0828 19.0
2.1071 27.0 2376 2.4607 0.0892 0.0575 0.0782 0.0847 19.0
2.08 28.0 2464 2.4606 0.0874 0.056 0.0769 0.083 19.0
2.08 29.0 2552 2.4616 0.0891 0.0571 0.0781 0.0845 19.0
2.1013 30.0 2640 2.4610 0.0891 0.0571 0.0781 0.0845 19.0

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.5
  • Tokenizers 0.14.1
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