--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: my_awesome_billsum_model_68 results: [] --- # my_awesome_billsum_model_68 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: 0.2186 - Rouge1: 0.9718 - Rouge2: 0.8861 - Rougel: 0.9312 - Rougelsum: 0.9298 - Gen Len: 5.0625 ## 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 | 2.0043 | 0.3937 | 0.2702 | 0.3788 | 0.3776 | 17.75 | | No log | 2.0 | 24 | 1.4138 | 0.4258 | 0.2978 | 0.4009 | 0.3998 | 16.8333 | | No log | 3.0 | 36 | 0.8103 | 0.5858 | 0.4637 | 0.5658 | 0.5614 | 12.7083 | | No log | 4.0 | 48 | 0.5156 | 0.9539 | 0.8354 | 0.8948 | 0.8934 | 4.8542 | | No log | 5.0 | 60 | 0.4552 | 0.9539 | 0.8354 | 0.8948 | 0.8934 | 4.8542 | | No log | 6.0 | 72 | 0.4053 | 0.965 | 0.8514 | 0.9092 | 0.9055 | 4.8958 | | No log | 7.0 | 84 | 0.3565 | 0.968 | 0.8653 | 0.9144 | 0.9144 | 4.9167 | | No log | 8.0 | 96 | 0.3263 | 0.968 | 0.8653 | 0.9144 | 0.9144 | 4.9167 | | No log | 9.0 | 108 | 0.2998 | 0.968 | 0.8653 | 0.9144 | 0.9144 | 4.9167 | | No log | 10.0 | 120 | 0.2807 | 0.967 | 0.8788 | 0.9273 | 0.9253 | 4.8958 | | No log | 11.0 | 132 | 0.2694 | 0.967 | 0.8788 | 0.9273 | 0.9253 | 4.8958 | | No log | 12.0 | 144 | 0.2622 | 0.967 | 0.8795 | 0.9273 | 0.9253 | 4.9375 | | No log | 13.0 | 156 | 0.2490 | 0.967 | 0.8795 | 0.9273 | 0.9253 | 4.9375 | | No log | 14.0 | 168 | 0.2427 | 0.967 | 0.8795 | 0.9273 | 0.9253 | 4.9375 | | No log | 15.0 | 180 | 0.2385 | 0.967 | 0.8795 | 0.9273 | 0.9253 | 4.9375 | | No log | 16.0 | 192 | 0.2350 | 0.967 | 0.8795 | 0.9273 | 0.9253 | 4.9375 | | No log | 17.0 | 204 | 0.2284 | 0.967 | 0.8795 | 0.9273 | 0.9253 | 4.9375 | | No log | 18.0 | 216 | 0.2212 | 0.967 | 0.8795 | 0.9273 | 0.9253 | 4.9375 | | No log | 19.0 | 228 | 0.2173 | 0.97 | 0.892 | 0.936 | 0.9343 | 4.9583 | | No log | 20.0 | 240 | 0.2177 | 0.97 | 0.892 | 0.936 | 0.9343 | 4.9583 | | No log | 21.0 | 252 | 0.2161 | 0.97 | 0.892 | 0.936 | 0.9343 | 4.9583 | | No log | 22.0 | 264 | 0.2111 | 0.97 | 0.892 | 0.936 | 0.9343 | 4.9583 | | No log | 23.0 | 276 | 0.2072 | 0.967 | 0.8799 | 0.9273 | 0.9271 | 4.9792 | | No log | 24.0 | 288 | 0.2066 | 0.97 | 0.892 | 0.936 | 0.9343 | 4.9583 | | No log | 25.0 | 300 | 0.2068 | 0.973 | 0.9146 | 0.9464 | 0.9435 | 4.9792 | | No log | 26.0 | 312 | 0.2080 | 0.97 | 0.892 | 0.936 | 0.9343 | 4.9583 | | No log | 27.0 | 324 | 0.2078 | 0.97 | 0.892 | 0.936 | 0.9343 | 4.9583 | | No log | 28.0 | 336 | 0.1976 | 0.973 | 0.8993 | 0.9346 | 0.9328 | 4.9792 | | No log | 29.0 | 348 | 0.1921 | 0.973 | 0.8993 | 0.9346 | 0.9328 | 4.9792 | | No log | 30.0 | 360 | 0.1896 | 0.973 | 0.8993 | 0.9346 | 0.9328 | 4.9792 | | No log | 31.0 | 372 | 0.1906 | 0.9686 | 0.8792 | 0.9223 | 0.9204 | 5.0 | | No log | 32.0 | 384 | 0.1942 | 0.973 | 0.8993 | 0.9346 | 0.9328 | 4.9792 | | No log | 33.0 | 396 | 0.1976 | 0.97 | 0.8868 | 0.926 | 0.9253 | 5.0 | | No log | 34.0 | 408 | 0.2006 | 0.97 | 0.9021 | 0.9363 | 0.9353 | 5.0 | | No log | 35.0 | 420 | 0.1983 | 0.97 | 0.9021 | 0.9363 | 0.9353 | 5.0 | | No log | 36.0 | 432 | 0.2010 | 0.967 | 0.8799 | 0.9273 | 0.9271 | 4.9792 | | No log | 37.0 | 444 | 0.2014 | 0.97 | 0.9021 | 0.9363 | 0.9353 | 5.0 | | No log | 38.0 | 456 | 0.2027 | 0.97 | 0.9021 | 0.9363 | 0.9353 | 5.0 | | No log | 39.0 | 468 | 0.2059 | 0.97 | 0.9021 | 0.9363 | 0.9353 | 5.0 | | No log | 40.0 | 480 | 0.2035 | 0.97 | 0.9021 | 0.9363 | 0.9353 | 5.0 | | No log | 41.0 | 492 | 0.1989 | 0.97 | 0.8937 | 0.9363 | 0.9353 | 5.0 | | 0.4765 | 42.0 | 504 | 0.1969 | 0.973 | 0.892 | 0.9346 | 0.933 | 5.0208 | | 0.4765 | 43.0 | 516 | 0.1958 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 44.0 | 528 | 0.1937 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 45.0 | 540 | 0.1922 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 46.0 | 552 | 0.1940 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 47.0 | 564 | 0.1944 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 48.0 | 576 | 0.1943 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 49.0 | 588 | 0.1985 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 50.0 | 600 | 0.2034 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 51.0 | 612 | 0.2071 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 52.0 | 624 | 0.2113 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 53.0 | 636 | 0.2115 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 54.0 | 648 | 0.2104 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 55.0 | 660 | 0.2109 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 56.0 | 672 | 0.2114 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 57.0 | 684 | 0.2127 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 58.0 | 696 | 0.2149 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 59.0 | 708 | 0.2154 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 60.0 | 720 | 0.2187 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 61.0 | 732 | 0.2193 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 62.0 | 744 | 0.2200 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 63.0 | 756 | 0.2203 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 64.0 | 768 | 0.2192 | 0.9718 | 0.8861 | 0.9326 | 0.9296 | 5.0625 | | 0.4765 | 65.0 | 780 | 0.2185 | 0.9718 | 0.8708 | 0.9204 | 0.9193 | 5.0625 | | 0.4765 | 66.0 | 792 | 0.2189 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.4765 | 67.0 | 804 | 0.2186 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.4765 | 68.0 | 816 | 0.2181 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.4765 | 69.0 | 828 | 0.2176 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.4765 | 70.0 | 840 | 0.2193 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.4765 | 71.0 | 852 | 0.2198 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.4765 | 72.0 | 864 | 0.2202 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.4765 | 73.0 | 876 | 0.2193 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.4765 | 74.0 | 888 | 0.2191 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.4765 | 75.0 | 900 | 0.2208 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.4765 | 76.0 | 912 | 0.2206 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.4765 | 77.0 | 924 | 0.2193 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.4765 | 78.0 | 936 | 0.2183 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.4765 | 79.0 | 948 | 0.2185 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.4765 | 80.0 | 960 | 0.2176 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.4765 | 81.0 | 972 | 0.2175 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.4765 | 82.0 | 984 | 0.2181 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.4765 | 83.0 | 996 | 0.2184 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.1106 | 84.0 | 1008 | 0.2172 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.1106 | 85.0 | 1020 | 0.2177 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.1106 | 86.0 | 1032 | 0.2175 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.1106 | 87.0 | 1044 | 0.2180 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.1106 | 88.0 | 1056 | 0.2180 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.1106 | 89.0 | 1068 | 0.2181 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.1106 | 90.0 | 1080 | 0.2179 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.1106 | 91.0 | 1092 | 0.2178 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.1106 | 92.0 | 1104 | 0.2179 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.1106 | 93.0 | 1116 | 0.2175 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.1106 | 94.0 | 1128 | 0.2179 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.1106 | 95.0 | 1140 | 0.2181 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.1106 | 96.0 | 1152 | 0.2182 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.1106 | 97.0 | 1164 | 0.2184 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.1106 | 98.0 | 1176 | 0.2186 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.1106 | 99.0 | 1188 | 0.2186 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | | 0.1106 | 100.0 | 1200 | 0.2186 | 0.9718 | 0.8861 | 0.9312 | 0.9298 | 5.0625 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1