t5-small-fine-tuned_model_2
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6840
- Rouge1: 27.9407
- Rouge2: 21.2221
- Rougel: 26.6074
- Rougelsum: 27.1976
- Gen Len: 19.0
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: 0.02
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 55
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 0.7692 | 10 | 3.5053 | 8.1509 | 0.0 | 8.1741 | 8.4813 | 19.0 |
| No log | 1.5385 | 20 | 2.5820 | 13.0567 | 0.083 | 13.0988 | 13.3345 | 19.0 |
| No log | 2.3077 | 30 | 2.3186 | 16.0127 | 1.8324 | 16.0821 | 16.0127 | 19.0 |
| No log | 3.0769 | 40 | 1.9916 | 16.0889 | 0.0 | 16.0713 | 16.1796 | 19.0 |
| No log | 3.8462 | 50 | 1.9778 | 20.3459 | 5.6938 | 20.2576 | 20.3429 | 19.0 |
| No log | 4.6154 | 60 | 1.7593 | 26.5421 | 11.8534 | 21.734 | 21.9902 | 19.0 |
| No log | 5.3846 | 70 | 1.6659 | 28.5447 | 21.2829 | 25.4746 | 25.6562 | 19.0 |
| No log | 6.1538 | 80 | 1.5705 | 28.5447 | 20.1071 | 26.4433 | 27.249 | 19.0 |
| No log | 6.9231 | 90 | 1.4828 | 21.5765 | 10.5401 | 20.3284 | 20.4294 | 19.0 |
| No log | 7.6923 | 100 | 1.5481 | 25.6706 | 19.9548 | 23.8958 | 24.5591 | 19.0 |
| No log | 8.4615 | 110 | 1.3113 | 28.4758 | 15.8255 | 25.9681 | 26.2583 | 19.0 |
| No log | 9.2308 | 120 | 1.3679 | 30.4635 | 21.805 | 29.0806 | 29.3168 | 19.0 |
| No log | 10.0 | 130 | 1.3058 | 33.5286 | 25.8263 | 32.2465 | 32.7425 | 19.0 |
| No log | 10.7692 | 140 | 1.2688 | 24.8181 | 19.118 | 24.5614 | 25.1229 | 19.0 |
| No log | 11.5385 | 150 | 1.2822 | 30.4635 | 21.805 | 29.0806 | 29.3168 | 19.0 |
| No log | 12.3077 | 160 | 1.1995 | 31.7376 | 21.7636 | 31.7376 | 32.6177 | 19.0 |
| No log | 13.0769 | 170 | 1.1703 | 31.7376 | 26.151 | 30.3779 | 31.3066 | 19.0 |
| No log | 13.8462 | 180 | 1.2166 | 28.9345 | 22.4858 | 29.0024 | 29.5613 | 19.0 |
| No log | 14.6154 | 190 | 1.1418 | 33.5286 | 28.3248 | 32.2465 | 32.7425 | 19.0 |
| No log | 15.3846 | 200 | 1.2054 | 27.8007 | 21.0803 | 26.4436 | 27.0704 | 19.0 |
| No log | 16.1538 | 210 | 1.1424 | 27.199 | 19.9548 | 25.9145 | 26.1883 | 19.0 |
| No log | 16.9231 | 220 | 1.0981 | 28.1764 | 21.4646 | 28.1764 | 28.5716 | 19.0 |
| No log | 17.6923 | 230 | 1.0723 | 30.9564 | 23.5664 | 28.3825 | 28.7377 | 19.0 |
| No log | 18.4615 | 240 | 1.1689 | 29.2957 | 23.6463 | 29.2599 | 29.687 | 19.0 |
| No log | 19.2308 | 250 | 1.0526 | 25.955 | 21.0803 | 25.955 | 26.3995 | 19.0 |
| No log | 20.0 | 260 | 1.0602 | 27.199 | 19.9548 | 25.9145 | 26.1883 | 19.0 |
| No log | 20.7692 | 270 | 1.0928 | 28.9345 | 22.4858 | 27.5281 | 28.0461 | 19.0 |
| No log | 21.5385 | 280 | 1.0386 | 30.1723 | 26.5116 | 29.0031 | 29.8913 | 19.0 |
| No log | 22.3077 | 290 | 1.0837 | 29.4234 | 25.2504 | 28.4788 | 29.2811 | 19.0 |
| No log | 23.0769 | 300 | 1.1175 | 30.1723 | 26.5116 | 30.7741 | 31.0498 | 19.0 |
| No log | 23.8462 | 310 | 1.0160 | 28.3905 | 21.3235 | 28.4674 | 28.6007 | 19.0 |
| No log | 24.6154 | 320 | 1.0721 | 27.8007 | 21.0803 | 26.4436 | 27.0704 | 19.0 |
| No log | 25.3846 | 330 | 1.0059 | 27.199 | 19.9548 | 25.9145 | 26.1883 | 19.0 |
| No log | 26.1538 | 340 | 0.9716 | 24.5772 | 20.3029 | 24.9898 | 25.2349 | 19.0 |
| No log | 26.9231 | 350 | 0.9880 | 30.1723 | 26.5116 | 29.0031 | 29.8913 | 19.0 |
| No log | 27.6923 | 360 | 0.9736 | 23.762 | 20.9502 | 23.762 | 24.4233 | 19.0 |
| No log | 28.4615 | 370 | 1.0070 | 25.2666 | 19.4028 | 24.6344 | 25.3836 | 19.0 |
| No log | 29.2308 | 380 | 0.9300 | 33.4444 | 25.7492 | 31.9997 | 32.6753 | 19.0 |
| No log | 30.0 | 390 | 0.9159 | 27.1927 | 25.4742 | 27.3467 | 28.3371 | 19.0 |
| No log | 30.7692 | 400 | 0.9200 | 24.3606 | 19.6902 | 24.4156 | 24.6809 | 19.0 |
| No log | 31.5385 | 410 | 0.9571 | 27.8007 | 21.0803 | 26.4436 | 27.0704 | 19.0 |
| No log | 32.3077 | 420 | 0.9171 | 30.6846 | 24.2042 | 29.6777 | 30.4199 | 19.0 |
| No log | 33.0769 | 430 | 0.9265 | 27.8596 | 21.1306 | 27.8517 | 28.4864 | 19.0 |
| No log | 33.8462 | 440 | 0.8980 | 30.1723 | 26.5116 | 29.0031 | 29.8913 | 19.0 |
| No log | 34.6154 | 450 | 0.8724 | 29.6346 | 20.0025 | 28.1468 | 28.8559 | 19.0 |
| No log | 35.3846 | 460 | 0.8870 | 30.1723 | 26.5116 | 30.7741 | 31.0498 | 19.0 |
| No log | 36.1538 | 470 | 0.8401 | 27.8007 | 21.0803 | 26.4436 | 27.0704 | 19.0 |
| No log | 36.9231 | 480 | 0.8336 | 30.1723 | 26.5116 | 29.0031 | 29.8913 | 19.0 |
| No log | 37.6923 | 490 | 0.8397 | 30.1723 | 26.5116 | 29.0031 | 29.8913 | 19.0 |
| 1.3001 | 38.4615 | 500 | 0.8521 | 34.2264 | 27.0678 | 31.7585 | 32.3653 | 19.0 |
| 1.3001 | 39.2308 | 510 | 0.8345 | 33.2444 | 26.9698 | 30.6883 | 31.271 | 19.0 |
| 1.3001 | 40.0 | 520 | 0.8426 | 30.1723 | 26.5116 | 29.0031 | 29.8913 | 19.0 |
| 1.3001 | 40.7692 | 530 | 0.8046 | 30.1723 | 26.5116 | 30.7741 | 31.0498 | 19.0 |
| 1.3001 | 41.5385 | 540 | 0.7954 | 30.2048 | 26.6356 | 29.1189 | 29.9736 | 19.0 |
| 1.3001 | 42.3077 | 550 | 0.8215 | 30.1723 | 26.5116 | 29.0031 | 29.8913 | 19.0 |
| 1.3001 | 43.0769 | 560 | 0.7793 | 27.8007 | 21.0803 | 26.4436 | 27.0704 | 19.0 |
| 1.3001 | 43.8462 | 570 | 0.7746 | 30.2043 | 26.6352 | 29.1185 | 29.9732 | 19.0 |
| 1.3001 | 44.6154 | 580 | 0.7967 | 30.1723 | 26.5116 | 29.0031 | 29.8913 | 19.0 |
| 1.3001 | 45.3846 | 590 | 0.7529 | 30.1723 | 26.5116 | 29.0031 | 29.8913 | 19.0 |
| 1.3001 | 46.1538 | 600 | 0.7502 | 27.9407 | 21.2221 | 26.6074 | 27.1976 | 19.0 |
| 1.3001 | 46.9231 | 610 | 0.7496 | 30.2043 | 26.6352 | 29.1185 | 29.9732 | 19.0 |
| 1.3001 | 47.6923 | 620 | 0.7198 | 27.8596 | 21.2135 | 26.5263 | 27.1531 | 19.0 |
| 1.3001 | 48.4615 | 630 | 0.7107 | 31.4706 | 22.8709 | 28.3819 | 29.143 | 19.0 |
| 1.3001 | 49.2308 | 640 | 0.7140 | 27.9407 | 21.2221 | 26.6074 | 27.1976 | 19.0 |
| 1.3001 | 50.0 | 650 | 0.7091 | 27.8596 | 21.2135 | 26.5263 | 27.1531 | 19.0 |
| 1.3001 | 50.7692 | 660 | 0.7070 | 27.8596 | 21.2135 | 26.5263 | 27.1531 | 19.0 |
| 1.3001 | 51.5385 | 670 | 0.6957 | 24.5996 | 20.5362 | 24.9898 | 25.4809 | 19.0 |
| 1.3001 | 52.3077 | 680 | 0.6883 | 24.5772 | 20.3029 | 24.9898 | 25.2349 | 19.0 |
| 1.3001 | 53.0769 | 690 | 0.6866 | 27.8007 | 21.0803 | 26.4436 | 27.0704 | 19.0 |
| 1.3001 | 53.8462 | 700 | 0.6857 | 30.2043 | 26.6352 | 29.1185 | 29.9732 | 19.0 |
| 1.3001 | 54.6154 | 710 | 0.6840 | 27.9407 | 21.2221 | 26.6074 | 27.1976 | 19.0 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1