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---
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model_24
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_billsum_model_24
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: 0.1106
- Rouge1: 0.997
- Rouge2: 0.9736
- Rougel: 0.9807
- Rougelsum: 0.9807
- Gen Len: 5.0
## 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 | 0.1051 | 0.9929 | 0.9646 | 0.9765 | 0.978 | 4.9792 |
| No log | 2.0 | 24 | 0.1272 | 0.9869 | 0.9319 | 0.9586 | 0.96 | 4.9792 |
| No log | 3.0 | 36 | 0.1472 | 0.9892 | 0.9458 | 0.9669 | 0.9684 | 5.0417 |
| No log | 4.0 | 48 | 0.1401 | 0.9892 | 0.9458 | 0.9669 | 0.9684 | 5.0417 |
| No log | 5.0 | 60 | 0.1206 | 0.9922 | 0.9655 | 0.9758 | 0.9773 | 5.0625 |
| No log | 6.0 | 72 | 0.1185 | 0.9922 | 0.9655 | 0.9758 | 0.9773 | 5.0625 |
| No log | 7.0 | 84 | 0.1177 | 0.9922 | 0.9655 | 0.9758 | 0.9773 | 5.0625 |
| No log | 8.0 | 96 | 0.1223 | 0.9922 | 0.9655 | 0.9758 | 0.9773 | 5.0625 |
| No log | 9.0 | 108 | 0.1253 | 0.9922 | 0.9655 | 0.9758 | 0.9773 | 5.0625 |
| No log | 10.0 | 120 | 0.1257 | 0.9892 | 0.9458 | 0.9669 | 0.9684 | 5.0417 |
| No log | 11.0 | 132 | 0.1289 | 0.9899 | 0.9444 | 0.9676 | 0.969 | 4.9583 |
| No log | 12.0 | 144 | 0.1164 | 0.9899 | 0.9444 | 0.9676 | 0.969 | 4.9583 |
| No log | 13.0 | 156 | 0.1188 | 0.9911 | 0.9521 | 0.9688 | 0.969 | 5.0 |
| No log | 14.0 | 168 | 0.1235 | 0.9929 | 0.9646 | 0.9765 | 0.978 | 4.9792 |
| No log | 15.0 | 180 | 0.1323 | 0.9899 | 0.9444 | 0.9676 | 0.969 | 4.9583 |
| No log | 16.0 | 192 | 0.1341 | 0.9899 | 0.9444 | 0.9676 | 0.969 | 4.9583 |
| No log | 17.0 | 204 | 0.1331 | 0.9899 | 0.9444 | 0.9676 | 0.969 | 4.9583 |
| No log | 18.0 | 216 | 0.1169 | 0.9929 | 0.9646 | 0.9765 | 0.978 | 4.9792 |
| No log | 19.0 | 228 | 0.1169 | 0.9929 | 0.9646 | 0.9765 | 0.978 | 4.9792 |
| No log | 20.0 | 240 | 0.1162 | 0.9929 | 0.9646 | 0.9765 | 0.978 | 4.9792 |
| No log | 21.0 | 252 | 0.1200 | 0.9929 | 0.9646 | 0.9765 | 0.978 | 4.9792 |
| No log | 22.0 | 264 | 0.1176 | 0.9947 | 0.9661 | 0.9792 | 0.9797 | 4.9792 |
| No log | 23.0 | 276 | 0.1110 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| No log | 24.0 | 288 | 0.1146 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| No log | 25.0 | 300 | 0.1101 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| No log | 26.0 | 312 | 0.1064 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| No log | 27.0 | 324 | 0.1059 | 0.994 | 0.9625 | 0.9717 | 0.9717 | 5.0208 |
| No log | 28.0 | 336 | 0.1064 | 0.994 | 0.9625 | 0.9717 | 0.9717 | 5.0208 |
| No log | 29.0 | 348 | 0.1047 | 0.994 | 0.9625 | 0.9717 | 0.9717 | 5.0208 |
| No log | 30.0 | 360 | 0.1005 | 0.994 | 0.9625 | 0.9717 | 0.9717 | 5.0208 |
| No log | 31.0 | 372 | 0.0986 | 0.994 | 0.9625 | 0.9717 | 0.9717 | 5.0208 |
| No log | 32.0 | 384 | 0.0981 | 0.994 | 0.9625 | 0.9717 | 0.9717 | 5.0208 |
| No log | 33.0 | 396 | 0.0989 | 0.994 | 0.9625 | 0.9717 | 0.9717 | 5.0208 |
| No log | 34.0 | 408 | 0.1026 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| No log | 35.0 | 420 | 0.1036 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| No log | 36.0 | 432 | 0.1033 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| No log | 37.0 | 444 | 0.0995 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| No log | 38.0 | 456 | 0.0977 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| No log | 39.0 | 468 | 0.0949 | 0.994 | 0.9625 | 0.9717 | 0.9717 | 5.0208 |
| No log | 40.0 | 480 | 0.0926 | 0.9911 | 0.9521 | 0.9688 | 0.969 | 5.0 |
| No log | 41.0 | 492 | 0.0893 | 0.9911 | 0.9521 | 0.9688 | 0.969 | 5.0 |
| 0.0105 | 42.0 | 504 | 0.0871 | 0.9911 | 0.9521 | 0.9688 | 0.969 | 5.0 |
| 0.0105 | 43.0 | 516 | 0.0863 | 0.9911 | 0.9521 | 0.9688 | 0.969 | 5.0 |
| 0.0105 | 44.0 | 528 | 0.0915 | 0.9911 | 0.9521 | 0.9688 | 0.969 | 5.0 |
| 0.0105 | 45.0 | 540 | 0.0937 | 0.994 | 0.9625 | 0.9717 | 0.9717 | 5.0208 |
| 0.0105 | 46.0 | 552 | 0.0950 | 0.994 | 0.9625 | 0.9717 | 0.9717 | 5.0208 |
| 0.0105 | 47.0 | 564 | 0.0955 | 0.994 | 0.9625 | 0.9717 | 0.9717 | 5.0208 |
| 0.0105 | 48.0 | 576 | 0.0956 | 0.994 | 0.9625 | 0.9717 | 0.9717 | 5.0208 |
| 0.0105 | 49.0 | 588 | 0.0968 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 50.0 | 600 | 0.0986 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 51.0 | 612 | 0.1001 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 52.0 | 624 | 0.0995 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 53.0 | 636 | 0.0983 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 54.0 | 648 | 0.0995 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 55.0 | 660 | 0.1024 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 56.0 | 672 | 0.1040 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 57.0 | 684 | 0.1052 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 58.0 | 696 | 0.1055 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 59.0 | 708 | 0.1061 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 60.0 | 720 | 0.1053 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 61.0 | 732 | 0.1078 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 62.0 | 744 | 0.1087 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 63.0 | 756 | 0.1074 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 64.0 | 768 | 0.1039 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 65.0 | 780 | 0.1022 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 66.0 | 792 | 0.1017 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 67.0 | 804 | 0.1026 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 68.0 | 816 | 0.1050 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 69.0 | 828 | 0.1060 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 70.0 | 840 | 0.1069 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 71.0 | 852 | 0.1070 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 72.0 | 864 | 0.1048 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 73.0 | 876 | 0.1041 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 74.0 | 888 | 0.1039 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 75.0 | 900 | 0.1042 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 76.0 | 912 | 0.1056 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 77.0 | 924 | 0.1057 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 78.0 | 936 | 0.1058 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 79.0 | 948 | 0.1062 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 80.0 | 960 | 0.1072 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 81.0 | 972 | 0.1070 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 82.0 | 984 | 0.1068 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0105 | 83.0 | 996 | 0.1064 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0053 | 84.0 | 1008 | 0.1078 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0053 | 85.0 | 1020 | 0.1077 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0053 | 86.0 | 1032 | 0.1086 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0053 | 87.0 | 1044 | 0.1087 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0053 | 88.0 | 1056 | 0.1088 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0053 | 89.0 | 1068 | 0.1081 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0053 | 90.0 | 1080 | 0.1081 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0053 | 91.0 | 1092 | 0.1085 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0053 | 92.0 | 1104 | 0.1089 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0053 | 93.0 | 1116 | 0.1093 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0053 | 94.0 | 1128 | 0.1098 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0053 | 95.0 | 1140 | 0.1102 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0053 | 96.0 | 1152 | 0.1106 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0053 | 97.0 | 1164 | 0.1108 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0053 | 98.0 | 1176 | 0.1109 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0053 | 99.0 | 1188 | 0.1107 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
| 0.0053 | 100.0 | 1200 | 0.1106 | 0.997 | 0.9736 | 0.9807 | 0.9807 | 5.0 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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