my_awesome_billsum_model_62
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: 0.7970
- Rouge1: 0.9571
- Rouge2: 0.8259
- Rougel: 0.8928
- Rougelsum: 0.8902
- Gen Len: 5.0208
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 | 2.4286 | 0.3894 | 0.2336 | 0.3514 | 0.3508 | 17.8542 |
No log | 2.0 | 24 | 1.8139 | 0.4266 | 0.2737 | 0.389 | 0.3886 | 16.4167 |
No log | 3.0 | 36 | 1.2636 | 0.6493 | 0.4505 | 0.568 | 0.5646 | 11.1042 |
No log | 4.0 | 48 | 1.0763 | 0.9258 | 0.7101 | 0.8078 | 0.8059 | 4.9792 |
No log | 5.0 | 60 | 1.0843 | 0.935 | 0.7341 | 0.8244 | 0.8199 | 5.0833 |
No log | 6.0 | 72 | 1.0524 | 0.9404 | 0.7398 | 0.8318 | 0.8271 | 4.7917 |
No log | 7.0 | 84 | 0.9935 | 0.9404 | 0.7398 | 0.8318 | 0.8271 | 4.7917 |
No log | 8.0 | 96 | 0.9337 | 0.9461 | 0.7441 | 0.8277 | 0.827 | 4.875 |
No log | 9.0 | 108 | 0.9054 | 0.9491 | 0.7772 | 0.8475 | 0.8461 | 4.8958 |
No log | 10.0 | 120 | 0.8916 | 0.9491 | 0.7772 | 0.8475 | 0.8461 | 4.8958 |
No log | 11.0 | 132 | 0.8979 | 0.9514 | 0.7797 | 0.8496 | 0.8483 | 4.9375 |
No log | 12.0 | 144 | 0.8762 | 0.9514 | 0.7797 | 0.8496 | 0.8483 | 4.9375 |
No log | 13.0 | 156 | 0.8374 | 0.9514 | 0.7797 | 0.8496 | 0.8483 | 4.9375 |
No log | 14.0 | 168 | 0.8129 | 0.9496 | 0.7903 | 0.8673 | 0.8652 | 5.0 |
No log | 15.0 | 180 | 0.7959 | 0.9496 | 0.7903 | 0.8673 | 0.8652 | 5.0 |
No log | 16.0 | 192 | 0.7882 | 0.9496 | 0.7903 | 0.8673 | 0.8652 | 5.0 |
No log | 17.0 | 204 | 0.7801 | 0.9516 | 0.791 | 0.8642 | 0.8611 | 4.9792 |
No log | 18.0 | 216 | 0.7644 | 0.9516 | 0.791 | 0.8642 | 0.8611 | 4.9792 |
No log | 19.0 | 228 | 0.7450 | 0.9496 | 0.7903 | 0.8673 | 0.8652 | 5.0 |
No log | 20.0 | 240 | 0.7485 | 0.9474 | 0.7847 | 0.8589 | 0.8566 | 4.9583 |
No log | 21.0 | 252 | 0.7483 | 0.9498 | 0.7857 | 0.8551 | 0.8537 | 4.9375 |
No log | 22.0 | 264 | 0.7495 | 0.9452 | 0.7942 | 0.8701 | 0.8681 | 4.9792 |
No log | 23.0 | 276 | 0.7544 | 0.9476 | 0.7955 | 0.866 | 0.8646 | 4.9583 |
No log | 24.0 | 288 | 0.7588 | 0.9498 | 0.7971 | 0.8623 | 0.8598 | 4.9375 |
No log | 25.0 | 300 | 0.7542 | 0.9523 | 0.8027 | 0.87 | 0.8689 | 4.9792 |
No log | 26.0 | 312 | 0.7427 | 0.9523 | 0.7919 | 0.8629 | 0.8615 | 4.9792 |
No log | 27.0 | 324 | 0.7295 | 0.9463 | 0.7886 | 0.8647 | 0.8631 | 5.0208 |
No log | 28.0 | 336 | 0.7257 | 0.9463 | 0.7886 | 0.8647 | 0.8631 | 5.0208 |
No log | 29.0 | 348 | 0.7276 | 0.9498 | 0.8014 | 0.8738 | 0.8727 | 5.0417 |
No log | 30.0 | 360 | 0.7367 | 0.9498 | 0.8014 | 0.8738 | 0.8727 | 5.0417 |
No log | 31.0 | 372 | 0.7455 | 0.9549 | 0.8155 | 0.8804 | 0.8771 | 5.0 |
No log | 32.0 | 384 | 0.7482 | 0.9549 | 0.8155 | 0.8804 | 0.8771 | 5.0 |
No log | 33.0 | 396 | 0.7448 | 0.9522 | 0.8028 | 0.8698 | 0.8691 | 5.0208 |
No log | 34.0 | 408 | 0.7516 | 0.9491 | 0.7899 | 0.8609 | 0.8601 | 5.0 |
No log | 35.0 | 420 | 0.7536 | 0.9491 | 0.7899 | 0.8609 | 0.8601 | 5.0 |
No log | 36.0 | 432 | 0.7522 | 0.9522 | 0.8028 | 0.8698 | 0.8691 | 5.0208 |
No log | 37.0 | 444 | 0.7485 | 0.9522 | 0.8028 | 0.8698 | 0.8691 | 5.0208 |
No log | 38.0 | 456 | 0.7476 | 0.9522 | 0.7956 | 0.8698 | 0.8691 | 5.0208 |
No log | 39.0 | 468 | 0.7528 | 0.9522 | 0.7956 | 0.8698 | 0.8691 | 5.0208 |
No log | 40.0 | 480 | 0.7573 | 0.9522 | 0.7956 | 0.8698 | 0.8691 | 5.0208 |
No log | 41.0 | 492 | 0.7593 | 0.9542 | 0.8037 | 0.8773 | 0.8764 | 5.0 |
0.4192 | 42.0 | 504 | 0.7629 | 0.9542 | 0.8037 | 0.8773 | 0.8764 | 5.0 |
0.4192 | 43.0 | 516 | 0.7512 | 0.9542 | 0.8037 | 0.8773 | 0.8764 | 5.0 |
0.4192 | 44.0 | 528 | 0.7405 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 45.0 | 540 | 0.7307 | 0.955 | 0.8251 | 0.8969 | 0.894 | 5.0417 |
0.4192 | 46.0 | 552 | 0.7344 | 0.9542 | 0.8037 | 0.8773 | 0.8764 | 5.0 |
0.4192 | 47.0 | 564 | 0.7373 | 0.9542 | 0.8037 | 0.8773 | 0.8764 | 5.0 |
0.4192 | 48.0 | 576 | 0.7474 | 0.9542 | 0.8037 | 0.8773 | 0.8764 | 5.0 |
0.4192 | 49.0 | 588 | 0.7551 | 0.9542 | 0.8037 | 0.8773 | 0.8764 | 5.0 |
0.4192 | 50.0 | 600 | 0.7698 | 0.9542 | 0.8037 | 0.8773 | 0.8764 | 5.0 |
0.4192 | 51.0 | 612 | 0.7650 | 0.9542 | 0.8037 | 0.8773 | 0.8764 | 5.0 |
0.4192 | 52.0 | 624 | 0.7509 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 53.0 | 636 | 0.7529 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 54.0 | 648 | 0.7593 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 55.0 | 660 | 0.7594 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 56.0 | 672 | 0.7623 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 57.0 | 684 | 0.7701 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 58.0 | 696 | 0.7710 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 59.0 | 708 | 0.7684 | 0.959 | 0.8279 | 0.8891 | 0.8867 | 5.0 |
0.4192 | 60.0 | 720 | 0.7661 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 61.0 | 732 | 0.7649 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 62.0 | 744 | 0.7722 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 63.0 | 756 | 0.7689 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 64.0 | 768 | 0.7618 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 65.0 | 780 | 0.7609 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 66.0 | 792 | 0.7674 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 67.0 | 804 | 0.7722 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 68.0 | 816 | 0.7726 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 69.0 | 828 | 0.7724 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 70.0 | 840 | 0.7750 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 71.0 | 852 | 0.7745 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 72.0 | 864 | 0.7756 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 73.0 | 876 | 0.7798 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 74.0 | 888 | 0.7895 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 75.0 | 900 | 0.7929 | 0.959 | 0.8279 | 0.8891 | 0.8867 | 5.0 |
0.4192 | 76.0 | 912 | 0.7903 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 77.0 | 924 | 0.7869 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 78.0 | 936 | 0.7883 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 79.0 | 948 | 0.7888 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 80.0 | 960 | 0.7918 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 81.0 | 972 | 0.7921 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 82.0 | 984 | 0.7921 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.4192 | 83.0 | 996 | 0.7945 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.0933 | 84.0 | 1008 | 0.7962 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.0933 | 85.0 | 1020 | 0.7955 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.0933 | 86.0 | 1032 | 0.7977 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.0933 | 87.0 | 1044 | 0.7991 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.0933 | 88.0 | 1056 | 0.7986 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.0933 | 89.0 | 1068 | 0.7989 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.0933 | 90.0 | 1080 | 0.7995 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.0933 | 91.0 | 1092 | 0.8005 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.0933 | 92.0 | 1104 | 0.7990 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.0933 | 93.0 | 1116 | 0.7980 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.0933 | 94.0 | 1128 | 0.7978 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.0933 | 95.0 | 1140 | 0.7972 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.0933 | 96.0 | 1152 | 0.7966 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.0933 | 97.0 | 1164 | 0.7961 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.0933 | 98.0 | 1176 | 0.7966 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.0933 | 99.0 | 1188 | 0.7972 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
0.0933 | 100.0 | 1200 | 0.7970 | 0.9571 | 0.8259 | 0.8928 | 0.8902 | 5.0208 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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