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my_awesome_billsum_model_70

Text2Text Generation
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t5
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my_awesome_billsum_model_70

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: 0.2720
  • Rouge1: 0.9718
  • Rouge2: 0.8861
  • Rougel: 0.9312
  • Rougelsum: 0.9298
  • Gen Len: 5.0625

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 0.2204 0.9718 0.8861 0.9312 0.9298 5.0625
No log 2.0 24 0.2198 0.9718 0.8861 0.9312 0.9298 5.0625
No log 3.0 36 0.2171 0.9718 0.8861 0.9312 0.9298 5.0625
No log 4.0 48 0.2171 0.9718 0.8861 0.9312 0.9298 5.0625
No log 5.0 60 0.2202 0.9718 0.8861 0.9312 0.9298 5.0625
No log 6.0 72 0.2240 0.9718 0.8861 0.9312 0.9298 5.0625
No log 7.0 84 0.2256 0.9718 0.8861 0.9312 0.9298 5.0625
No log 8.0 96 0.2194 0.9718 0.8861 0.9312 0.9298 5.0625
No log 9.0 108 0.2187 0.9718 0.8861 0.9312 0.9298 5.0625
No log 10.0 120 0.2168 0.975 0.9038 0.9399 0.9381 5.0417
No log 11.0 132 0.2171 0.9718 0.8861 0.9312 0.9298 5.0625
No log 12.0 144 0.2187 0.9718 0.8861 0.9312 0.9298 5.0625
No log 13.0 156 0.2261 0.9718 0.8861 0.9312 0.9298 5.0625
No log 14.0 168 0.2277 0.9718 0.8861 0.9312 0.9298 5.0625
No log 15.0 180 0.2269 0.9718 0.8861 0.9312 0.9298 5.0625
No log 16.0 192 0.2309 0.9718 0.8861 0.9312 0.9298 5.0625
No log 17.0 204 0.2321 0.976 0.8915 0.9359 0.9351 5.125
No log 18.0 216 0.2273 0.976 0.8915 0.9359 0.9351 5.125
No log 19.0 228 0.2230 0.979 0.9109 0.9443 0.9428 5.1042
No log 20.0 240 0.2208 0.979 0.9109 0.9443 0.9428 5.1042
No log 21.0 252 0.2174 0.975 0.9038 0.9399 0.9381 5.0417
No log 22.0 264 0.2158 0.975 0.9038 0.9399 0.9381 5.0417
No log 23.0 276 0.2197 0.9718 0.8861 0.9312 0.9298 5.0625
No log 24.0 288 0.2168 0.9718 0.8861 0.9312 0.9298 5.0625
No log 25.0 300 0.2211 0.975 0.9038 0.9399 0.9381 5.0417
No log 26.0 312 0.2261 0.9718 0.8861 0.9312 0.9298 5.0625
No log 27.0 324 0.2238 0.9718 0.8861 0.9312 0.9298 5.0625
No log 28.0 336 0.2252 0.9718 0.8861 0.9312 0.9298 5.0625
No log 29.0 348 0.2311 0.9718 0.8861 0.9312 0.9298 5.0625
No log 30.0 360 0.2372 0.9718 0.8861 0.9312 0.9298 5.0625
No log 31.0 372 0.2368 0.9718 0.8861 0.9312 0.9298 5.0625
No log 32.0 384 0.2358 0.9718 0.8861 0.9312 0.9298 5.0625
No log 33.0 396 0.2330 0.9718 0.8861 0.9312 0.9298 5.0625
No log 34.0 408 0.2289 0.9759 0.9062 0.9428 0.942 5.0417
No log 35.0 420 0.2317 0.9759 0.9062 0.9428 0.942 5.0417
No log 36.0 432 0.2367 0.9759 0.9062 0.9428 0.942 5.0417
No log 37.0 444 0.2455 0.9759 0.9062 0.9428 0.942 5.0417
No log 38.0 456 0.2478 0.9759 0.9062 0.9428 0.942 5.0417
No log 39.0 468 0.2459 0.9789 0.9257 0.9518 0.9506 5.0208
No log 40.0 480 0.2448 0.9759 0.9062 0.9428 0.942 5.0417
No log 41.0 492 0.2451 0.9759 0.9062 0.9428 0.942 5.0417
0.0486 42.0 504 0.2493 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 43.0 516 0.2479 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 44.0 528 0.2458 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 45.0 540 0.2458 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 46.0 552 0.2475 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 47.0 564 0.2479 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 48.0 576 0.2499 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 49.0 588 0.2546 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 50.0 600 0.2579 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 51.0 612 0.2580 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 52.0 624 0.2586 0.9759 0.9062 0.9428 0.942 5.0417
0.0486 53.0 636 0.2579 0.9759 0.9062 0.9428 0.942 5.0417
0.0486 54.0 648 0.2591 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 55.0 660 0.2594 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 56.0 672 0.2589 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 57.0 684 0.2583 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 58.0 696 0.2596 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 59.0 708 0.2595 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 60.0 720 0.2596 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 61.0 732 0.2624 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 62.0 744 0.2630 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 63.0 756 0.2613 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 64.0 768 0.2629 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 65.0 780 0.2662 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 66.0 792 0.2688 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 67.0 804 0.2663 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 68.0 816 0.2664 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 69.0 828 0.2657 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 70.0 840 0.2678 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 71.0 852 0.2699 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 72.0 864 0.2710 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 73.0 876 0.2718 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 74.0 888 0.2711 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 75.0 900 0.2727 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 76.0 912 0.2736 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 77.0 924 0.2722 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 78.0 936 0.2720 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 79.0 948 0.2749 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 80.0 960 0.2758 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 81.0 972 0.2756 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 82.0 984 0.2758 0.9718 0.8861 0.9312 0.9298 5.0625
0.0486 83.0 996 0.2767 0.9718 0.8861 0.9312 0.9298 5.0625
0.0237 84.0 1008 0.2747 0.9718 0.8861 0.9312 0.9298 5.0625
0.0237 85.0 1020 0.2735 0.9718 0.8861 0.9312 0.9298 5.0625
0.0237 86.0 1032 0.2734 0.9718 0.8861 0.9312 0.9298 5.0625
0.0237 87.0 1044 0.2737 0.9718 0.8861 0.9312 0.9298 5.0625
0.0237 88.0 1056 0.2729 0.9718 0.8861 0.9312 0.9298 5.0625
0.0237 89.0 1068 0.2727 0.9718 0.8861 0.9312 0.9298 5.0625
0.0237 90.0 1080 0.2719 0.9718 0.8861 0.9312 0.9298 5.0625
0.0237 91.0 1092 0.2716 0.9718 0.8861 0.9312 0.9298 5.0625
0.0237 92.0 1104 0.2714 0.9718 0.8861 0.9312 0.9298 5.0625
0.0237 93.0 1116 0.2715 0.9718 0.8861 0.9312 0.9298 5.0625
0.0237 94.0 1128 0.2718 0.9718 0.8861 0.9312 0.9298 5.0625
0.0237 95.0 1140 0.2720 0.9718 0.8861 0.9312 0.9298 5.0625
0.0237 96.0 1152 0.2722 0.9718 0.8861 0.9312 0.9298 5.0625
0.0237 97.0 1164 0.2722 0.9718 0.8861 0.9312 0.9298 5.0625
0.0237 98.0 1176 0.2723 0.9718 0.8861 0.9312 0.9298 5.0625
0.0237 99.0 1188 0.2720 0.9718 0.8861 0.9312 0.9298 5.0625
0.0237 100.0 1200 0.2720 0.9718 0.8861 0.9312 0.9298 5.0625

Framework versions

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