--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: my_awesome_billsum_model_28 results: [] --- # my_awesome_billsum_model_28 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: 0.3463 - 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 | 0.3001 | 0.9821 | 0.9347 | 0.9494 | 0.9511 | 5.2708 | | No log | 2.0 | 24 | 0.3040 | 0.979 | 0.8986 | 0.9355 | 0.9368 | 5.25 | | No log | 3.0 | 36 | 0.3007 | 0.9814 | 0.9208 | 0.9479 | 0.9487 | 5.2292 | | No log | 4.0 | 48 | 0.3041 | 0.9814 | 0.9208 | 0.9479 | 0.9487 | 5.2292 | | No log | 5.0 | 60 | 0.3050 | 0.9814 | 0.9208 | 0.9479 | 0.9487 | 5.2292 | | No log | 6.0 | 72 | 0.3048 | 0.9814 | 0.9208 | 0.9479 | 0.9487 | 5.2292 | | No log | 7.0 | 84 | 0.2996 | 0.9814 | 0.9208 | 0.9479 | 0.9487 | 5.2292 | | No log | 8.0 | 96 | 0.2991 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 9.0 | 108 | 0.3005 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 10.0 | 120 | 0.2967 | 0.9866 | 0.9486 | 0.9628 | 0.9628 | 5.2292 | | No log | 11.0 | 132 | 0.2947 | 0.9866 | 0.9486 | 0.9628 | 0.9628 | 5.2292 | | No log | 12.0 | 144 | 0.2935 | 0.9866 | 0.9486 | 0.9628 | 0.9628 | 5.2292 | | No log | 13.0 | 156 | 0.2947 | 0.9866 | 0.9486 | 0.9628 | 0.9628 | 5.2292 | | No log | 14.0 | 168 | 0.2950 | 0.9866 | 0.9486 | 0.9628 | 0.9628 | 5.2292 | | No log | 15.0 | 180 | 0.2873 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 16.0 | 192 | 0.2813 | 0.9866 | 0.9486 | 0.9628 | 0.9628 | 5.2292 | | No log | 17.0 | 204 | 0.2861 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 18.0 | 216 | 0.2947 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 19.0 | 228 | 0.3042 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 20.0 | 240 | 0.3125 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 21.0 | 252 | 0.3223 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 22.0 | 264 | 0.3225 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 23.0 | 276 | 0.3132 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 24.0 | 288 | 0.3082 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 25.0 | 300 | 0.3109 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 26.0 | 312 | 0.3193 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 27.0 | 324 | 0.3314 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 28.0 | 336 | 0.3288 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 29.0 | 348 | 0.3214 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 30.0 | 360 | 0.3261 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 31.0 | 372 | 0.3247 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 32.0 | 384 | 0.3286 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 33.0 | 396 | 0.3209 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 34.0 | 408 | 0.3167 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 35.0 | 420 | 0.3226 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 36.0 | 432 | 0.3304 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 37.0 | 444 | 0.3320 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 38.0 | 456 | 0.3258 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | No log | 39.0 | 468 | 0.3298 | 0.9844 | 0.9278 | 0.9472 | 0.9479 | 5.25 | | No log | 40.0 | 480 | 0.3278 | 0.9844 | 0.9278 | 0.9472 | 0.9479 | 5.25 | | No log | 41.0 | 492 | 0.3314 | 0.9844 | 0.9278 | 0.9472 | 0.9479 | 5.25 | | 0.0342 | 42.0 | 504 | 0.3370 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 43.0 | 516 | 0.3360 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 44.0 | 528 | 0.3416 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 45.0 | 540 | 0.3348 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 46.0 | 552 | 0.3350 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 47.0 | 564 | 0.3394 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 48.0 | 576 | 0.3381 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 49.0 | 588 | 0.3427 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 50.0 | 600 | 0.3385 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 51.0 | 612 | 0.3376 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 52.0 | 624 | 0.3377 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 53.0 | 636 | 0.3372 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 54.0 | 648 | 0.3492 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 55.0 | 660 | 0.3564 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 56.0 | 672 | 0.3556 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 57.0 | 684 | 0.3441 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 58.0 | 696 | 0.3406 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 59.0 | 708 | 0.3341 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 60.0 | 720 | 0.3333 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 61.0 | 732 | 0.3367 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 62.0 | 744 | 0.3379 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 63.0 | 756 | 0.3366 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 64.0 | 768 | 0.3376 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 65.0 | 780 | 0.3384 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 66.0 | 792 | 0.3444 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 67.0 | 804 | 0.3422 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 68.0 | 816 | 0.3444 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 69.0 | 828 | 0.3407 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 70.0 | 840 | 0.3380 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 71.0 | 852 | 0.3376 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 72.0 | 864 | 0.3442 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 73.0 | 876 | 0.3493 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 74.0 | 888 | 0.3550 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 75.0 | 900 | 0.3600 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 76.0 | 912 | 0.3592 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 77.0 | 924 | 0.3571 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 78.0 | 936 | 0.3584 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 79.0 | 948 | 0.3601 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 80.0 | 960 | 0.3585 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 81.0 | 972 | 0.3552 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 82.0 | 984 | 0.3561 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0342 | 83.0 | 996 | 0.3555 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0138 | 84.0 | 1008 | 0.3533 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0138 | 85.0 | 1020 | 0.3491 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0138 | 86.0 | 1032 | 0.3482 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0138 | 87.0 | 1044 | 0.3477 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0138 | 88.0 | 1056 | 0.3475 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0138 | 89.0 | 1068 | 0.3482 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0138 | 90.0 | 1080 | 0.3479 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0138 | 91.0 | 1092 | 0.3475 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0138 | 92.0 | 1104 | 0.3467 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0138 | 93.0 | 1116 | 0.3464 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0138 | 94.0 | 1128 | 0.3456 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0138 | 95.0 | 1140 | 0.3452 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0138 | 96.0 | 1152 | 0.3446 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0138 | 97.0 | 1164 | 0.3455 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0138 | 98.0 | 1176 | 0.3460 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0138 | 99.0 | 1188 | 0.3465 | 0.9844 | 0.9417 | 0.9576 | 0.9576 | 5.25 | | 0.0138 | 100.0 | 1200 | 0.3463 | 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