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my_awesome_billsum_model_64

Text2Text Generation
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Safetensors
t5
Generated from Trainer
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text-generation-inference
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my_awesome_billsum_model_64

This model is a fine-tuned version of google-t5/t5-small on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9763
  • Rouge1: 0.9612
  • Rouge2: 0.844
  • Rougel: 0.9033
  • Rougelsum: 0.9017
  • Gen Len: 5.0833

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: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
No log 1.0 12 0.8485 0.9571 0.8119 0.8882 0.8859 5.0208
No log 2.0 24 0.8935 0.9571 0.8119 0.8882 0.8859 5.0208
No log 3.0 36 0.8809 0.9604 0.8177 0.887 0.884 5.0417
No log 4.0 48 0.8664 0.9604 0.8177 0.887 0.884 5.0417
No log 5.0 60 0.8449 0.9571 0.8259 0.8928 0.8902 5.0208
No log 6.0 72 0.8350 0.9604 0.8324 0.8912 0.8885 5.0417
No log 7.0 84 0.8348 0.9604 0.8324 0.8912 0.8885 5.0417
No log 8.0 96 0.8322 0.9604 0.8324 0.8912 0.8885 5.0417
No log 9.0 108 0.8269 0.9604 0.8324 0.8912 0.8885 5.0417
No log 10.0 120 0.8218 0.958 0.8311 0.8953 0.8925 5.0625
No log 11.0 132 0.8252 0.9604 0.8324 0.8912 0.8885 5.0417
No log 12.0 144 0.8302 0.9604 0.8324 0.8912 0.8885 5.0417
No log 13.0 156 0.8310 0.9604 0.8324 0.8912 0.8885 5.0417
No log 14.0 168 0.8299 0.9633 0.852 0.9008 0.8974 5.0208
No log 15.0 180 0.8360 0.9604 0.8324 0.8912 0.8885 5.0417
No log 16.0 192 0.8435 0.9633 0.8453 0.8997 0.8974 5.0625
No log 17.0 204 0.8570 0.9603 0.8397 0.901 0.8987 5.0417
No log 18.0 216 0.8725 0.9571 0.8259 0.8928 0.8902 5.0208
No log 19.0 228 0.8580 0.9633 0.8453 0.8997 0.8974 5.0625
No log 20.0 240 0.8545 0.9612 0.844 0.9033 0.9017 5.0833
No log 21.0 252 0.8630 0.9612 0.844 0.9033 0.9017 5.0833
No log 22.0 264 0.8652 0.9612 0.844 0.9033 0.9017 5.0833
No log 23.0 276 0.8782 0.9633 0.8453 0.8997 0.8974 5.0625
No log 24.0 288 0.8781 0.9633 0.8453 0.8997 0.8974 5.0625
No log 25.0 300 0.8863 0.9604 0.8324 0.8912 0.8885 5.0417
No log 26.0 312 0.8921 0.9633 0.8453 0.8997 0.8974 5.0625
No log 27.0 324 0.8998 0.9633 0.8453 0.8997 0.8974 5.0625
No log 28.0 336 0.8914 0.9612 0.844 0.9033 0.9017 5.0833
No log 29.0 348 0.8952 0.9612 0.844 0.9033 0.9017 5.0833
No log 30.0 360 0.9034 0.9612 0.844 0.9033 0.9017 5.0833
No log 31.0 372 0.9191 0.9633 0.8453 0.8997 0.8974 5.0625
No log 32.0 384 0.9315 0.9633 0.8453 0.8997 0.8974 5.0625
No log 33.0 396 0.9278 0.9633 0.8453 0.8997 0.8974 5.0625
No log 34.0 408 0.9266 0.9603 0.8397 0.901 0.8987 5.0417
No log 35.0 420 0.9362 0.9603 0.8397 0.901 0.8987 5.0417
No log 36.0 432 0.9378 0.9603 0.8397 0.901 0.8987 5.0417
No log 37.0 444 0.9359 0.9603 0.8397 0.901 0.8987 5.0417
No log 38.0 456 0.9397 0.9625 0.8409 0.8967 0.8942 5.0208
No log 39.0 468 0.9427 0.9625 0.8409 0.8967 0.8942 5.0208
No log 40.0 480 0.9438 0.9625 0.8409 0.8967 0.8942 5.0208
No log 41.0 492 0.9530 0.9625 0.8409 0.8967 0.8942 5.0208
0.0391 42.0 504 0.9583 0.9625 0.8409 0.8967 0.8942 5.0208
0.0391 43.0 516 0.9597 0.9625 0.8409 0.8967 0.8942 5.0208
0.0391 44.0 528 0.9534 0.9603 0.8397 0.901 0.8987 5.0417
0.0391 45.0 540 0.9508 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 46.0 552 0.9519 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 47.0 564 0.9433 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 48.0 576 0.9401 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 49.0 588 0.9506 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 50.0 600 0.9630 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 51.0 612 0.9651 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 52.0 624 0.9641 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 53.0 636 0.9592 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 54.0 648 0.9584 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 55.0 660 0.9574 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 56.0 672 0.9594 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 57.0 684 0.9616 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 58.0 696 0.9607 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 59.0 708 0.9563 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 60.0 720 0.9615 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 61.0 732 0.9628 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 62.0 744 0.9678 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 63.0 756 0.9699 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 64.0 768 0.9694 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 65.0 780 0.9663 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 66.0 792 0.9755 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 67.0 804 0.9824 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 68.0 816 0.9811 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 69.0 828 0.9752 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 70.0 840 0.9725 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 71.0 852 0.9733 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 72.0 864 0.9741 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 73.0 876 0.9743 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 74.0 888 0.9746 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 75.0 900 0.9726 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 76.0 912 0.9732 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 77.0 924 0.9741 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 78.0 936 0.9759 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 79.0 948 0.9796 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 80.0 960 0.9808 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 81.0 972 0.9815 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 82.0 984 0.9797 0.9612 0.844 0.9033 0.9017 5.0833
0.0391 83.0 996 0.9789 0.9612 0.844 0.9033 0.9017 5.0833
0.0214 84.0 1008 0.9786 0.9612 0.844 0.9033 0.9017 5.0833
0.0214 85.0 1020 0.9810 0.9612 0.844 0.9033 0.9017 5.0833
0.0214 86.0 1032 0.9822 0.9612 0.844 0.9033 0.9017 5.0833
0.0214 87.0 1044 0.9831 0.9612 0.844 0.9033 0.9017 5.0833
0.0214 88.0 1056 0.9818 0.9612 0.844 0.9033 0.9017 5.0833
0.0214 89.0 1068 0.9814 0.9612 0.844 0.9033 0.9017 5.0833
0.0214 90.0 1080 0.9806 0.9612 0.844 0.9033 0.9017 5.0833
0.0214 91.0 1092 0.9805 0.9612 0.844 0.9033 0.9017 5.0833
0.0214 92.0 1104 0.9796 0.9612 0.844 0.9033 0.9017 5.0833
0.0214 93.0 1116 0.9786 0.9612 0.844 0.9033 0.9017 5.0833
0.0214 94.0 1128 0.9785 0.9612 0.844 0.9033 0.9017 5.0833
0.0214 95.0 1140 0.9793 0.9612 0.844 0.9033 0.9017 5.0833
0.0214 96.0 1152 0.9773 0.9612 0.844 0.9033 0.9017 5.0833
0.0214 97.0 1164 0.9767 0.9612 0.844 0.9033 0.9017 5.0833
0.0214 98.0 1176 0.9762 0.9612 0.844 0.9033 0.9017 5.0833
0.0214 99.0 1188 0.9765 0.9612 0.844 0.9033 0.9017 5.0833
0.0214 100.0 1200 0.9763 0.9612 0.844 0.9033 0.9017 5.0833

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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Evaluation results

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