--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: my_awesome_billsum_model_30 results: [] --- # my_awesome_billsum_model_30 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4150 - Rouge1: 0.9844 - Rouge2: 0.9417 - Rougel: 0.9576 - Rougelsum: 0.9576 - Gen Len: 5.25 ## 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 | 0.3628 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 2.0 | 24 | 0.3725 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 3.0 | 36 | 0.3888 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 4.0 | 48 | 0.4046 | 0.9779 | 0.9378 | 0.9561 | 0.9561 | 5.2083 | | No log | 5.0 | 60 | 0.4100 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 6.0 | 72 | 0.3963 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 7.0 | 84 | 0.3786 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 8.0 | 96 | 0.3765 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 9.0 | 108 | 0.3928 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 10.0 | 120 | 0.3881 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 11.0 | 132 | 0.3780 | 0.9866 | 0.9486 | 0.9628 | 0.9628 | 5.2292 | | No log | 12.0 | 144 | 0.3859 | 0.9866 | 0.9486 | 0.9628 | 0.9628 | 5.2292 | | No log | 13.0 | 156 | 0.3843 | 0.9866 | 0.9486 | 0.9628 | 0.9628 | 5.2292 | | No log | 14.0 | 168 | 0.3782 | 0.9866 | 0.9486 | 0.9628 | 0.9628 | 5.2292 | | No log | 15.0 | 180 | 0.3802 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 16.0 | 192 | 0.3542 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 17.0 | 204 | 0.3478 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 18.0 | 216 | 0.3549 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 19.0 | 228 | 0.3581 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 20.0 | 240 | 0.3675 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 21.0 | 252 | 0.3728 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 22.0 | 264 | 0.3606 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 23.0 | 276 | 0.3327 | 0.9866 | 0.9486 | 0.9628 | 0.9628 | 5.2292 | | No log | 24.0 | 288 | 0.3361 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 25.0 | 300 | 0.3485 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 26.0 | 312 | 0.3550 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 27.0 | 324 | 0.3590 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 28.0 | 336 | 0.3670 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 29.0 | 348 | 0.3715 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 30.0 | 360 | 0.3780 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 31.0 | 372 | 0.3968 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 32.0 | 384 | 0.4152 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 33.0 | 396 | 0.4171 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 34.0 | 408 | 0.4122 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 35.0 | 420 | 0.4035 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 36.0 | 432 | 0.3880 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 37.0 | 444 | 0.3796 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 38.0 | 456 | 0.3713 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 39.0 | 468 | 0.3801 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 40.0 | 480 | 0.3973 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 41.0 | 492 | 0.3983 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 42.0 | 504 | 0.4107 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 43.0 | 516 | 0.4200 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 44.0 | 528 | 0.4209 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 45.0 | 540 | 0.4172 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 46.0 | 552 | 0.4136 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 47.0 | 564 | 0.4100 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 48.0 | 576 | 0.3916 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 49.0 | 588 | 0.3910 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 50.0 | 600 | 0.3989 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 51.0 | 612 | 0.4052 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 52.0 | 624 | 0.4111 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 53.0 | 636 | 0.4099 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 54.0 | 648 | 0.4135 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 55.0 | 660 | 0.4160 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 56.0 | 672 | 0.4088 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 57.0 | 684 | 0.3945 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 58.0 | 696 | 0.3872 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 59.0 | 708 | 0.3690 | 0.9866 | 0.9486 | 0.9628 | 0.9628 | 5.2292 | | 0.0033 | 60.0 | 720 | 0.3610 | 0.9866 | 0.9486 | 0.9628 | 0.9628 | 5.2292 | | 0.0033 | 61.0 | 732 | 0.3652 | 0.9866 | 0.9486 | 0.9628 | 0.9628 | 5.2292 | | 0.0033 | 62.0 | 744 | 0.3710 | 0.9866 | 0.9486 | 0.9628 | 0.9628 | 5.2292 | | 0.0033 | 63.0 | 756 | 0.3731 | 0.9866 | 0.9486 | 0.9628 | 0.9628 | 5.2292 | | 0.0033 | 64.0 | 768 | 0.3884 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 65.0 | 780 | 0.3859 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 66.0 | 792 | 0.3844 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 67.0 | 804 | 0.3839 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 68.0 | 816 | 0.3891 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 69.0 | 828 | 0.3926 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 70.0 | 840 | 0.3991 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 71.0 | 852 | 0.4008 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 72.0 | 864 | 0.4135 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 73.0 | 876 | 0.4268 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 74.0 | 888 | 0.4344 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 75.0 | 900 | 0.4383 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 76.0 | 912 | 0.4366 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 77.0 | 924 | 0.4270 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 78.0 | 936 | 0.4260 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 79.0 | 948 | 0.4327 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 80.0 | 960 | 0.4291 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 81.0 | 972 | 0.4221 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 82.0 | 984 | 0.4191 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0033 | 83.0 | 996 | 0.4193 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0025 | 84.0 | 1008 | 0.4208 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0025 | 85.0 | 1020 | 0.4211 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0025 | 86.0 | 1032 | 0.4207 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0025 | 87.0 | 1044 | 0.4190 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0025 | 88.0 | 1056 | 0.4182 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0025 | 89.0 | 1068 | 0.4178 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0025 | 90.0 | 1080 | 0.4173 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0025 | 91.0 | 1092 | 0.4149 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0025 | 92.0 | 1104 | 0.4130 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0025 | 93.0 | 1116 | 0.4123 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0025 | 94.0 | 1128 | 0.4127 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0025 | 95.0 | 1140 | 0.4119 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0025 | 96.0 | 1152 | 0.4122 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0025 | 97.0 | 1164 | 0.4135 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0025 | 98.0 | 1176 | 0.4148 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0025 | 99.0 | 1188 | 0.4152 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0025 | 100.0 | 1200 | 0.4150 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1