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my_awesome_billsum_model_48

Text2Text Generation
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t5
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text-generation-inference
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my_awesome_billsum_model_48

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: 0.2139
  • Rouge1: 0.9715
  • Rouge2: 0.8711
  • Rougel: 0.9127
  • Rougelsum: 0.9125
  • Gen Len: 5.3542

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 1.8158 0.4272 0.3079 0.4 0.4005 17.3125
No log 2.0 24 1.2144 0.4698 0.3444 0.4393 0.4402 15.75
No log 3.0 36 0.7083 0.8027 0.7117 0.7672 0.7688 8.6875
No log 4.0 48 0.5359 0.9573 0.8615 0.905 0.9067 5.4375
No log 5.0 60 0.4880 0.9573 0.8615 0.905 0.9067 5.4375
No log 6.0 72 0.4493 0.9635 0.8621 0.8952 0.8965 5.1875
No log 7.0 84 0.4190 0.9596 0.8438 0.8771 0.8758 5.2292
No log 8.0 96 0.4026 0.9636 0.8666 0.8941 0.8923 5.2917
No log 9.0 108 0.3907 0.9663 0.877 0.9025 0.9012 5.3125
No log 10.0 120 0.3805 0.9639 0.8681 0.895 0.8947 5.3333
No log 11.0 132 0.3761 0.9639 0.8681 0.895 0.8947 5.3333
No log 12.0 144 0.3686 0.9639 0.8681 0.895 0.8947 5.3333
No log 13.0 156 0.3611 0.9639 0.8681 0.895 0.8947 5.3333
No log 14.0 168 0.3529 0.9639 0.8681 0.895 0.8947 5.3333
No log 15.0 180 0.3467 0.9639 0.8681 0.895 0.8947 5.3333
No log 16.0 192 0.3374 0.9639 0.8681 0.895 0.8947 5.3333
No log 17.0 204 0.3272 0.9639 0.8681 0.895 0.8947 5.3333
No log 18.0 216 0.3210 0.9663 0.877 0.9025 0.9012 5.3125
No log 19.0 228 0.3186 0.9663 0.877 0.9025 0.9012 5.3125
No log 20.0 240 0.3141 0.9663 0.877 0.9025 0.9012 5.3125
No log 21.0 252 0.3092 0.9639 0.8681 0.895 0.8947 5.3333
No log 22.0 264 0.3050 0.9669 0.8753 0.9038 0.9036 5.3542
No log 23.0 276 0.3048 0.9669 0.8753 0.9038 0.9036 5.3542
No log 24.0 288 0.2992 0.9663 0.8773 0.9061 0.9068 5.3125
No log 25.0 300 0.2951 0.9639 0.8578 0.8976 0.8968 5.3333
No log 26.0 312 0.2915 0.9639 0.8578 0.8976 0.8968 5.3333
No log 27.0 324 0.2861 0.9639 0.8578 0.8976 0.8968 5.3333
No log 28.0 336 0.2855 0.9691 0.8724 0.9149 0.9136 5.3333
No log 29.0 348 0.2856 0.9691 0.8724 0.9149 0.9136 5.3333
No log 30.0 360 0.2845 0.9691 0.8724 0.9149 0.9136 5.3333
No log 31.0 372 0.2801 0.9691 0.8724 0.9149 0.9136 5.3333
No log 32.0 384 0.2753 0.9664 0.8643 0.9073 0.9065 5.3542
No log 33.0 396 0.2724 0.9664 0.8643 0.9073 0.9065 5.3542
No log 34.0 408 0.2684 0.9691 0.8724 0.9149 0.9136 5.3333
No log 35.0 420 0.2627 0.9691 0.8724 0.9149 0.9136 5.3333
No log 36.0 432 0.2569 0.9685 0.8647 0.9029 0.9027 5.3333
No log 37.0 444 0.2544 0.9685 0.8647 0.9029 0.9027 5.3333
No log 38.0 456 0.2524 0.9695 0.8633 0.9057 0.905 5.375
No log 39.0 468 0.2511 0.9695 0.8633 0.9057 0.905 5.375
No log 40.0 480 0.2506 0.9695 0.8633 0.9057 0.905 5.375
No log 41.0 492 0.2487 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 42.0 504 0.2498 0.9695 0.8633 0.9057 0.905 5.375
0.4449 43.0 516 0.2520 0.9695 0.8633 0.9057 0.905 5.375
0.4449 44.0 528 0.2505 0.9721 0.8823 0.9144 0.9151 5.3542
0.4449 45.0 540 0.2483 0.9721 0.8823 0.9144 0.9151 5.3542
0.4449 46.0 552 0.2475 0.9695 0.8633 0.9057 0.905 5.375
0.4449 47.0 564 0.2491 0.9695 0.8633 0.9057 0.905 5.375
0.4449 48.0 576 0.2524 0.9695 0.8633 0.9057 0.905 5.375
0.4449 49.0 588 0.2523 0.9695 0.8633 0.9057 0.905 5.375
0.4449 50.0 600 0.2496 0.9695 0.8633 0.9057 0.905 5.375
0.4449 51.0 612 0.2487 0.9695 0.8633 0.9057 0.905 5.375
0.4449 52.0 624 0.2475 0.9695 0.8633 0.9057 0.905 5.375
0.4449 53.0 636 0.2472 0.9695 0.8633 0.9057 0.905 5.375
0.4449 54.0 648 0.2426 0.9695 0.8633 0.9057 0.905 5.375
0.4449 55.0 660 0.2407 0.9695 0.8633 0.9057 0.905 5.375
0.4449 56.0 672 0.2422 0.9695 0.8633 0.9057 0.905 5.375
0.4449 57.0 684 0.2431 0.9695 0.8633 0.9057 0.905 5.375
0.4449 58.0 696 0.2388 0.9695 0.8633 0.9057 0.905 5.375
0.4449 59.0 708 0.2372 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 60.0 720 0.2340 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 61.0 732 0.2326 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 62.0 744 0.2330 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 63.0 756 0.2342 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 64.0 768 0.2328 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 65.0 780 0.2329 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 66.0 792 0.2298 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 67.0 804 0.2281 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 68.0 816 0.2272 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 69.0 828 0.2266 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 70.0 840 0.2256 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 71.0 852 0.2234 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 72.0 864 0.2219 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 73.0 876 0.2235 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 74.0 888 0.2236 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 75.0 900 0.2220 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 76.0 912 0.2201 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 77.0 924 0.2218 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 78.0 936 0.2220 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 79.0 948 0.2215 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 80.0 960 0.2219 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 81.0 972 0.2210 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 82.0 984 0.2200 0.9715 0.8711 0.9127 0.9125 5.3542
0.4449 83.0 996 0.2199 0.9715 0.8711 0.9127 0.9125 5.3542
0.1072 84.0 1008 0.2186 0.9715 0.8711 0.9127 0.9125 5.3542
0.1072 85.0 1020 0.2184 0.9715 0.8711 0.9127 0.9125 5.3542
0.1072 86.0 1032 0.2181 0.9715 0.8848 0.9179 0.9177 5.3542
0.1072 87.0 1044 0.2162 0.9715 0.8711 0.9127 0.9125 5.3542
0.1072 88.0 1056 0.2161 0.9715 0.8711 0.9127 0.9125 5.3542
0.1072 89.0 1068 0.2157 0.9715 0.8711 0.9127 0.9125 5.3542
0.1072 90.0 1080 0.2156 0.9715 0.8711 0.9127 0.9125 5.3542
0.1072 91.0 1092 0.2149 0.9715 0.8711 0.9127 0.9125 5.3542
0.1072 92.0 1104 0.2145 0.9715 0.8711 0.9127 0.9125 5.3542
0.1072 93.0 1116 0.2146 0.9715 0.8711 0.9127 0.9125 5.3542
0.1072 94.0 1128 0.2146 0.9715 0.8711 0.9127 0.9125 5.3542
0.1072 95.0 1140 0.2145 0.9715 0.8711 0.9127 0.9125 5.3542
0.1072 96.0 1152 0.2141 0.9715 0.8711 0.9127 0.9125 5.3542
0.1072 97.0 1164 0.2141 0.9715 0.8711 0.9127 0.9125 5.3542
0.1072 98.0 1176 0.2140 0.9715 0.8711 0.9127 0.9125 5.3542
0.1072 99.0 1188 0.2139 0.9715 0.8711 0.9127 0.9125 5.3542
0.1072 100.0 1200 0.2139 0.9715 0.8711 0.9127 0.9125 5.3542

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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