--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: my_awesome_billsum_model_40 results: [] --- # my_awesome_billsum_model_40 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: 0.1082 - Rouge1: 0.9787 - Rouge2: 0.8875 - Rougel: 0.9329 - Rougelsum: 0.9315 - Gen Len: 5.2708 ## 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 | 1.8920 | 0.4209 | 0.2805 | 0.384 | 0.3833 | 17.2292 | | No log | 2.0 | 24 | 1.3065 | 0.4547 | 0.3125 | 0.4113 | 0.41 | 16.0 | | No log | 3.0 | 36 | 0.8117 | 0.6973 | 0.546 | 0.6397 | 0.6374 | 10.3125 | | No log | 4.0 | 48 | 0.6088 | 0.9492 | 0.7941 | 0.867 | 0.8609 | 5.1458 | | No log | 5.0 | 60 | 0.5672 | 0.9513 | 0.797 | 0.8689 | 0.8631 | 5.125 | | No log | 6.0 | 72 | 0.5178 | 0.9537 | 0.8052 | 0.8814 | 0.878 | 5.1458 | | No log | 7.0 | 84 | 0.4737 | 0.9669 | 0.8387 | 0.9018 | 0.8988 | 5.1458 | | No log | 8.0 | 96 | 0.4479 | 0.9709 | 0.8452 | 0.8972 | 0.8948 | 5.1667 | | No log | 9.0 | 108 | 0.4178 | 0.9739 | 0.8595 | 0.9048 | 0.9038 | 5.1875 | | No log | 10.0 | 120 | 0.3904 | 0.9739 | 0.8595 | 0.9048 | 0.9038 | 5.1875 | | No log | 11.0 | 132 | 0.3681 | 0.9739 | 0.8595 | 0.9048 | 0.9038 | 5.1875 | | No log | 12.0 | 144 | 0.3463 | 0.9769 | 0.8601 | 0.9066 | 0.9056 | 5.2083 | | No log | 13.0 | 156 | 0.3295 | 0.9669 | 0.8253 | 0.887 | 0.8832 | 5.2917 | | No log | 14.0 | 168 | 0.3124 | 0.9648 | 0.8236 | 0.8917 | 0.8885 | 5.3125 | | No log | 15.0 | 180 | 0.3007 | 0.9648 | 0.8236 | 0.8917 | 0.8885 | 5.3125 | | No log | 16.0 | 192 | 0.2976 | 0.9692 | 0.8346 | 0.8947 | 0.8908 | 5.2708 | | No log | 17.0 | 204 | 0.2963 | 0.9671 | 0.833 | 0.8986 | 0.8952 | 5.2917 | | No log | 18.0 | 216 | 0.2911 | 0.9671 | 0.833 | 0.8986 | 0.8952 | 5.2917 | | No log | 19.0 | 228 | 0.2853 | 0.9717 | 0.8469 | 0.9028 | 0.9002 | 5.2917 | | No log | 20.0 | 240 | 0.2782 | 0.9717 | 0.8469 | 0.9028 | 0.9002 | 5.2917 | | No log | 21.0 | 252 | 0.2802 | 0.97 | 0.8462 | 0.9066 | 0.9043 | 5.3125 | | No log | 22.0 | 264 | 0.2746 | 0.97 | 0.8462 | 0.9066 | 0.9043 | 5.3125 | | No log | 23.0 | 276 | 0.2615 | 0.97 | 0.8462 | 0.9066 | 0.9043 | 5.3125 | | No log | 24.0 | 288 | 0.2504 | 0.97 | 0.8462 | 0.9066 | 0.9043 | 5.3125 | | No log | 25.0 | 300 | 0.2398 | 0.9656 | 0.8254 | 0.8946 | 0.8916 | 5.3333 | | No log | 26.0 | 312 | 0.2301 | 0.9656 | 0.8254 | 0.8946 | 0.8916 | 5.3333 | | No log | 27.0 | 324 | 0.2173 | 0.9656 | 0.8254 | 0.8946 | 0.8916 | 5.3333 | | No log | 28.0 | 336 | 0.2109 | 0.9632 | 0.8237 | 0.8931 | 0.8899 | 5.3542 | | No log | 29.0 | 348 | 0.2028 | 0.9632 | 0.8237 | 0.8931 | 0.8899 | 5.3542 | | No log | 30.0 | 360 | 0.2016 | 0.9632 | 0.8237 | 0.8931 | 0.8899 | 5.3542 | | No log | 31.0 | 372 | 0.1994 | 0.9632 | 0.8237 | 0.8931 | 0.8899 | 5.3542 | | No log | 32.0 | 384 | 0.1986 | 0.9632 | 0.8237 | 0.8931 | 0.8899 | 5.3542 | | No log | 33.0 | 396 | 0.1987 | 0.9632 | 0.8237 | 0.8931 | 0.8899 | 5.3542 | | No log | 34.0 | 408 | 0.1965 | 0.9632 | 0.8237 | 0.8931 | 0.8899 | 5.3542 | | No log | 35.0 | 420 | 0.1853 | 0.9632 | 0.8237 | 0.8931 | 0.8899 | 5.3542 | | No log | 36.0 | 432 | 0.1841 | 0.9657 | 0.8368 | 0.9013 | 0.8982 | 5.3333 | | No log | 37.0 | 444 | 0.1792 | 0.9657 | 0.8368 | 0.9013 | 0.8982 | 5.3333 | | No log | 38.0 | 456 | 0.1778 | 0.9681 | 0.8379 | 0.8979 | 0.8954 | 5.3125 | | No log | 39.0 | 468 | 0.1758 | 0.9657 | 0.8368 | 0.9013 | 0.8982 | 5.3333 | | No log | 40.0 | 480 | 0.1778 | 0.9657 | 0.8368 | 0.9013 | 0.8982 | 5.3333 | | No log | 41.0 | 492 | 0.1689 | 0.9638 | 0.8399 | 0.9064 | 0.904 | 5.3542 | | 0.4636 | 42.0 | 504 | 0.1665 | 0.9638 | 0.8399 | 0.9064 | 0.904 | 5.3542 | | 0.4636 | 43.0 | 516 | 0.1629 | 0.9657 | 0.8368 | 0.9013 | 0.8982 | 5.3333 | | 0.4636 | 44.0 | 528 | 0.1616 | 0.9657 | 0.8472 | 0.9145 | 0.9109 | 5.3333 | | 0.4636 | 45.0 | 540 | 0.1603 | 0.9681 | 0.8492 | 0.9112 | 0.9079 | 5.3125 | | 0.4636 | 46.0 | 552 | 0.1592 | 0.9681 | 0.8492 | 0.9112 | 0.9079 | 5.3125 | | 0.4636 | 47.0 | 564 | 0.1547 | 0.9681 | 0.8492 | 0.9112 | 0.9079 | 5.3125 | | 0.4636 | 48.0 | 576 | 0.1500 | 0.9681 | 0.8492 | 0.9112 | 0.9079 | 5.3125 | | 0.4636 | 49.0 | 588 | 0.1405 | 0.9681 | 0.8379 | 0.8979 | 0.8954 | 5.3125 | | 0.4636 | 50.0 | 600 | 0.1316 | 0.9681 | 0.8379 | 0.8979 | 0.8954 | 5.3125 | | 0.4636 | 51.0 | 612 | 0.1338 | 0.9681 | 0.8492 | 0.9112 | 0.9079 | 5.3125 | | 0.4636 | 52.0 | 624 | 0.1351 | 0.9681 | 0.8492 | 0.9112 | 0.9079 | 5.3125 | | 0.4636 | 53.0 | 636 | 0.1376 | 0.9681 | 0.8492 | 0.9112 | 0.9079 | 5.3125 | | 0.4636 | 54.0 | 648 | 0.1349 | 0.9681 | 0.8492 | 0.9112 | 0.9079 | 5.3125 | | 0.4636 | 55.0 | 660 | 0.1349 | 0.9681 | 0.8492 | 0.9112 | 0.9079 | 5.3125 | | 0.4636 | 56.0 | 672 | 0.1319 | 0.9681 | 0.8492 | 0.9112 | 0.9079 | 5.3125 | | 0.4636 | 57.0 | 684 | 0.1264 | 0.9681 | 0.8492 | 0.9112 | 0.9079 | 5.3125 | | 0.4636 | 58.0 | 696 | 0.1223 | 0.9739 | 0.875 | 0.9282 | 0.926 | 5.2708 | | 0.4636 | 59.0 | 708 | 0.1215 | 0.9739 | 0.875 | 0.9282 | 0.926 | 5.2708 | | 0.4636 | 60.0 | 720 | 0.1233 | 0.9739 | 0.875 | 0.9282 | 0.926 | 5.2708 | | 0.4636 | 61.0 | 732 | 0.1225 | 0.9739 | 0.875 | 0.9282 | 0.926 | 5.2708 | | 0.4636 | 62.0 | 744 | 0.1201 | 0.9739 | 0.875 | 0.9282 | 0.926 | 5.2708 | | 0.4636 | 63.0 | 756 | 0.1217 | 0.9739 | 0.875 | 0.9282 | 0.926 | 5.2708 | | 0.4636 | 64.0 | 768 | 0.1220 | 0.9739 | 0.875 | 0.9282 | 0.926 | 5.2708 | | 0.4636 | 65.0 | 780 | 0.1227 | 0.9739 | 0.875 | 0.9282 | 0.926 | 5.2708 | | 0.4636 | 66.0 | 792 | 0.1215 | 0.9739 | 0.875 | 0.9282 | 0.926 | 5.2708 | | 0.4636 | 67.0 | 804 | 0.1192 | 0.9787 | 0.8875 | 0.9329 | 0.9315 | 5.2708 | | 0.4636 | 68.0 | 816 | 0.1171 | 0.9787 | 0.8875 | 0.9329 | 0.9315 | 5.2708 | | 0.4636 | 69.0 | 828 | 0.1146 | 0.9821 | 0.8958 | 0.9424 | 0.9408 | 5.2917 | | 0.4636 | 70.0 | 840 | 0.1129 | 0.9821 | 0.8958 | 0.9424 | 0.9408 | 5.2917 | | 0.4636 | 71.0 | 852 | 0.1120 | 0.9821 | 0.8958 | 0.9424 | 0.9408 | 5.2917 | | 0.4636 | 72.0 | 864 | 0.1098 | 0.9816 | 0.9101 | 0.9459 | 0.9455 | 5.2917 | | 0.4636 | 73.0 | 876 | 0.1091 | 0.9722 | 0.8833 | 0.9304 | 0.9289 | 5.3125 | | 0.4636 | 74.0 | 888 | 0.1086 | 0.9757 | 0.8976 | 0.9329 | 0.9325 | 5.3333 | | 0.4636 | 75.0 | 900 | 0.1076 | 0.9816 | 0.9101 | 0.9459 | 0.9455 | 5.2917 | | 0.4636 | 76.0 | 912 | 0.1080 | 0.9783 | 0.8958 | 0.9433 | 0.9419 | 5.2708 | | 0.4636 | 77.0 | 924 | 0.1095 | 0.9821 | 0.8958 | 0.9424 | 0.9408 | 5.2917 | | 0.4636 | 78.0 | 936 | 0.1112 | 0.9821 | 0.8958 | 0.9424 | 0.9408 | 5.2917 | | 0.4636 | 79.0 | 948 | 0.1109 | 0.9821 | 0.8958 | 0.9424 | 0.9408 | 5.2917 | | 0.4636 | 80.0 | 960 | 0.1101 | 0.9821 | 0.8958 | 0.9424 | 0.9408 | 5.2917 | | 0.4636 | 81.0 | 972 | 0.1111 | 0.9821 | 0.8958 | 0.9424 | 0.9408 | 5.2917 | | 0.4636 | 82.0 | 984 | 0.1102 | 0.9821 | 0.8958 | 0.9424 | 0.9408 | 5.2917 | | 0.4636 | 83.0 | 996 | 0.1083 | 0.9821 | 0.911 | 0.9474 | 0.9464 | 5.2917 | | 0.1189 | 84.0 | 1008 | 0.1084 | 0.9821 | 0.911 | 0.9474 | 0.9464 | 5.2917 | | 0.1189 | 85.0 | 1020 | 0.1085 | 0.9851 | 0.9244 | 0.9502 | 0.9498 | 5.3125 | | 0.1189 | 86.0 | 1032 | 0.1085 | 0.9816 | 0.9244 | 0.9508 | 0.9508 | 5.2917 | | 0.1189 | 87.0 | 1044 | 0.1087 | 0.9816 | 0.9244 | 0.9508 | 0.9508 | 5.2917 | | 0.1189 | 88.0 | 1056 | 0.1076 | 0.9816 | 0.9244 | 0.9508 | 0.9508 | 5.2917 | | 0.1189 | 89.0 | 1068 | 0.1085 | 0.9788 | 0.9018 | 0.9364 | 0.9359 | 5.2708 | | 0.1189 | 90.0 | 1080 | 0.1081 | 0.9823 | 0.9018 | 0.9359 | 0.9349 | 5.2917 | | 0.1189 | 91.0 | 1092 | 0.1075 | 0.9788 | 0.9018 | 0.9364 | 0.9359 | 5.2708 | | 0.1189 | 92.0 | 1104 | 0.1084 | 0.9823 | 0.9018 | 0.9359 | 0.9349 | 5.2917 | | 0.1189 | 93.0 | 1116 | 0.1086 | 0.9823 | 0.9018 | 0.9359 | 0.9349 | 5.2917 | | 0.1189 | 94.0 | 1128 | 0.1084 | 0.9787 | 0.8875 | 0.9329 | 0.9315 | 5.2708 | | 0.1189 | 95.0 | 1140 | 0.1088 | 0.9787 | 0.8875 | 0.9329 | 0.9315 | 5.2708 | | 0.1189 | 96.0 | 1152 | 0.1086 | 0.9787 | 0.8875 | 0.9329 | 0.9315 | 5.2708 | | 0.1189 | 97.0 | 1164 | 0.1085 | 0.9787 | 0.8875 | 0.9329 | 0.9315 | 5.2708 | | 0.1189 | 98.0 | 1176 | 0.1083 | 0.9787 | 0.8875 | 0.9329 | 0.9315 | 5.2708 | | 0.1189 | 99.0 | 1188 | 0.1082 | 0.9787 | 0.8875 | 0.9329 | 0.9315 | 5.2708 | | 0.1189 | 100.0 | 1200 | 0.1082 | 0.9787 | 0.8875 | 0.9329 | 0.9315 | 5.2708 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1