--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: my_awesome_billsum_model_78 results: [] --- # my_awesome_billsum_model_78 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: 0.5080 - Rouge1: 0.9792 - Rouge2: 0.8868 - Rougel: 0.9405 - Rougelsum: 0.94 - Gen Len: 4.9792 ## 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 | 0.4089 | 0.9821 | 0.9104 | 0.9484 | 0.9484 | 4.9583 | | No log | 2.0 | 24 | 0.4068 | 0.9821 | 0.9104 | 0.9484 | 0.9484 | 4.9583 | | No log | 3.0 | 36 | 0.4284 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | No log | 4.0 | 48 | 0.4548 | 0.9792 | 0.8903 | 0.9395 | 0.9405 | 5.0208 | | No log | 5.0 | 60 | 0.4590 | 0.9792 | 0.8903 | 0.9395 | 0.9405 | 5.0208 | | No log | 6.0 | 72 | 0.4543 | 0.9792 | 0.8903 | 0.9395 | 0.9405 | 5.0208 | | No log | 7.0 | 84 | 0.4863 | 0.9752 | 0.8708 | 0.9311 | 0.9311 | 5.0417 | | No log | 8.0 | 96 | 0.4935 | 0.9732 | 0.8569 | 0.9221 | 0.9216 | 5.0208 | | No log | 9.0 | 108 | 0.4931 | 0.9762 | 0.8691 | 0.9311 | 0.9311 | 5.0 | | No log | 10.0 | 120 | 0.4817 | 0.9762 | 0.8691 | 0.9311 | 0.9311 | 5.0 | | No log | 11.0 | 132 | 0.4741 | 0.9762 | 0.8691 | 0.9311 | 0.9311 | 5.0 | | No log | 12.0 | 144 | 0.4732 | 0.9762 | 0.8691 | 0.9311 | 0.9311 | 5.0 | | No log | 13.0 | 156 | 0.4742 | 0.9762 | 0.8691 | 0.9311 | 0.9311 | 5.0 | | No log | 14.0 | 168 | 0.4736 | 0.9792 | 0.8903 | 0.9395 | 0.9405 | 5.0208 | | No log | 15.0 | 180 | 0.4680 | 0.9792 | 0.8903 | 0.9395 | 0.9405 | 5.0208 | | No log | 16.0 | 192 | 0.4534 | 0.9821 | 0.9007 | 0.9479 | 0.9494 | 5.0 | | No log | 17.0 | 204 | 0.4412 | 0.9821 | 0.9007 | 0.9479 | 0.9494 | 5.0 | | No log | 18.0 | 216 | 0.4341 | 0.9821 | 0.9007 | 0.9479 | 0.9494 | 5.0 | | No log | 19.0 | 228 | 0.4317 | 0.9821 | 0.9007 | 0.9479 | 0.9494 | 5.0 | | No log | 20.0 | 240 | 0.4315 | 0.9821 | 0.9007 | 0.9479 | 0.9494 | 5.0 | | No log | 21.0 | 252 | 0.4313 | 0.9792 | 0.8903 | 0.9395 | 0.9405 | 5.0208 | | No log | 22.0 | 264 | 0.4277 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | No log | 23.0 | 276 | 0.4376 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | No log | 24.0 | 288 | 0.4432 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | No log | 25.0 | 300 | 0.4450 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | No log | 26.0 | 312 | 0.4468 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | No log | 27.0 | 324 | 0.4415 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | No log | 28.0 | 336 | 0.4560 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | No log | 29.0 | 348 | 0.4713 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | No log | 30.0 | 360 | 0.4732 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | No log | 31.0 | 372 | 0.4726 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | No log | 32.0 | 384 | 0.4682 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | No log | 33.0 | 396 | 0.4647 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | No log | 34.0 | 408 | 0.4644 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | No log | 35.0 | 420 | 0.4657 | 0.9821 | 0.9007 | 0.9479 | 0.9494 | 5.0 | | No log | 36.0 | 432 | 0.4643 | 0.9821 | 0.9007 | 0.9479 | 0.9494 | 5.0 | | No log | 37.0 | 444 | 0.4572 | 0.9821 | 0.9007 | 0.9479 | 0.9494 | 5.0 | | No log | 38.0 | 456 | 0.4447 | 0.9821 | 0.9007 | 0.9479 | 0.9494 | 5.0 | | No log | 39.0 | 468 | 0.4437 | 0.9821 | 0.9007 | 0.9479 | 0.9494 | 5.0 | | No log | 40.0 | 480 | 0.4684 | 0.9821 | 0.9007 | 0.9479 | 0.9494 | 5.0 | | No log | 41.0 | 492 | 0.4722 | 0.9821 | 0.9007 | 0.9479 | 0.9494 | 5.0 | | 0.0088 | 42.0 | 504 | 0.4716 | 0.9821 | 0.9007 | 0.9479 | 0.9494 | 5.0 | | 0.0088 | 43.0 | 516 | 0.4803 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 44.0 | 528 | 0.4854 | 0.9762 | 0.8691 | 0.9311 | 0.9311 | 5.0 | | 0.0088 | 45.0 | 540 | 0.4830 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 46.0 | 552 | 0.4819 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 47.0 | 564 | 0.4812 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 48.0 | 576 | 0.4806 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 49.0 | 588 | 0.4762 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 50.0 | 600 | 0.4737 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 51.0 | 612 | 0.4735 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 52.0 | 624 | 0.4738 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 53.0 | 636 | 0.4736 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 54.0 | 648 | 0.4738 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 55.0 | 660 | 0.4776 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 56.0 | 672 | 0.4866 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 57.0 | 684 | 0.4926 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 58.0 | 696 | 0.4938 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 59.0 | 708 | 0.4902 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 60.0 | 720 | 0.4962 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 61.0 | 732 | 0.5033 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 62.0 | 744 | 0.5043 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 63.0 | 756 | 0.5025 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 64.0 | 768 | 0.5176 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 65.0 | 780 | 0.5708 | 0.9762 | 0.8691 | 0.9311 | 0.9311 | 5.0 | | 0.0088 | 66.0 | 792 | 0.5707 | 0.9762 | 0.8691 | 0.9311 | 0.9311 | 5.0 | | 0.0088 | 67.0 | 804 | 0.5278 | 0.9762 | 0.8691 | 0.9311 | 0.9311 | 5.0 | | 0.0088 | 68.0 | 816 | 0.5179 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 69.0 | 828 | 0.5164 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 70.0 | 840 | 0.5504 | 0.9762 | 0.8691 | 0.9311 | 0.9311 | 5.0 | | 0.0088 | 71.0 | 852 | 0.5584 | 0.9762 | 0.8691 | 0.9311 | 0.9311 | 5.0 | | 0.0088 | 72.0 | 864 | 0.5281 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 73.0 | 876 | 0.5198 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 74.0 | 888 | 0.5176 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 75.0 | 900 | 0.5103 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 76.0 | 912 | 0.5068 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 77.0 | 924 | 0.5030 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 78.0 | 936 | 0.5025 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 79.0 | 948 | 0.4968 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 80.0 | 960 | 0.5113 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 81.0 | 972 | 0.5083 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 82.0 | 984 | 0.5031 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0088 | 83.0 | 996 | 0.5066 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0059 | 84.0 | 1008 | 0.5177 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0059 | 85.0 | 1020 | 0.5192 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0059 | 86.0 | 1032 | 0.5104 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0059 | 87.0 | 1044 | 0.5085 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0059 | 88.0 | 1056 | 0.5130 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0059 | 89.0 | 1068 | 0.5116 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0059 | 90.0 | 1080 | 0.5081 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0059 | 91.0 | 1092 | 0.5074 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0059 | 92.0 | 1104 | 0.5090 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0059 | 93.0 | 1116 | 0.5097 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0059 | 94.0 | 1128 | 0.5123 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0059 | 95.0 | 1140 | 0.5118 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0059 | 96.0 | 1152 | 0.5089 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0059 | 97.0 | 1164 | 0.5080 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0059 | 98.0 | 1176 | 0.5079 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0059 | 99.0 | 1188 | 0.5076 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | | 0.0059 | 100.0 | 1200 | 0.5080 | 0.9792 | 0.8868 | 0.9405 | 0.94 | 4.9792 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1