File size: 11,499 Bytes
a5df7c6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 |
---
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: []
---
<!-- 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_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
|