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t5-small-thaisum-title-mt5tokenizer
This model is a fine-tuned version of Nopphakorn/t5-small-thaisum-title-mt5tokenizer on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.4541
- Rouge1: 0.0552
- Rouge2: 0.0069
- Rougel: 0.0547
- Rougelsum: 0.0546
- Gen Len: 18.9956
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: 5e-05
- 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: cosine
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 4.7436 | 1.0 | 765 | 4.8643 | 0.0029 | 0.0 | 0.0029 | 0.0029 | 18.9897 |
| 4.7069 | 2.0 | 1530 | 4.8482 | 0.0114 | 0.0015 | 0.0111 | 0.0115 | 18.9941 |
| 4.6588 | 3.0 | 2295 | 4.8272 | 0.0054 | 0.0 | 0.0055 | 0.0055 | 18.9883 |
| 4.6664 | 4.0 | 3060 | 4.8044 | 0.0116 | 0.0 | 0.0113 | 0.0116 | 18.9853 |
| 4.6283 | 5.0 | 3825 | 4.7903 | 0.0094 | 0.0 | 0.0095 | 0.0093 | 18.9868 |
| 4.6145 | 6.0 | 4590 | 4.7669 | 0.009 | 0.0 | 0.0088 | 0.0088 | 18.9941 |
| 4.5759 | 7.0 | 5355 | 4.7432 | 0.008 | 0.0 | 0.0079 | 0.0079 | 18.9883 |
| 4.5419 | 8.0 | 6120 | 4.7275 | 0.012 | 0.0015 | 0.012 | 0.012 | 18.9941 |
| 4.5486 | 9.0 | 6885 | 4.7043 | 0.0098 | 0.0015 | 0.0099 | 0.0097 | 18.9941 |
| 4.5095 | 10.0 | 7650 | 4.6773 | 0.0085 | 0.0 | 0.0088 | 0.0086 | 18.9941 |
| 4.4682 | 11.0 | 8415 | 4.6561 | 0.0115 | 0.0 | 0.0112 | 0.0114 | 18.9927 |
| 4.4783 | 12.0 | 9180 | 4.6326 | 0.0063 | 0.0 | 0.0063 | 0.0061 | 18.9868 |
| 4.4825 | 13.0 | 9945 | 4.6069 | 0.0111 | 0.0 | 0.0109 | 0.0111 | 18.9838 |
| 4.4455 | 14.0 | 10710 | 4.5836 | 0.0086 | 0.0 | 0.0084 | 0.0085 | 18.9941 |
| 4.4328 | 15.0 | 11475 | 4.5587 | 0.0089 | 0.0 | 0.0091 | 0.0088 | 18.9912 |
| 4.3982 | 16.0 | 12240 | 4.5294 | 0.0111 | 0.0 | 0.0112 | 0.0112 | 18.9868 |
| 4.3463 | 17.0 | 13005 | 4.5069 | 0.0135 | 0.0 | 0.0137 | 0.0137 | 18.9853 |
| 4.3379 | 18.0 | 13770 | 4.4717 | 0.011 | 0.0007 | 0.0109 | 0.011 | 18.9897 |
| 4.3303 | 19.0 | 14535 | 4.4460 | 0.0117 | 0.0007 | 0.0119 | 0.0118 | 18.9853 |
| 4.2983 | 20.0 | 15300 | 4.4110 | 0.0107 | 0.0 | 0.0109 | 0.0108 | 18.9868 |
| 4.265 | 21.0 | 16065 | 4.3800 | 0.0095 | 0.0 | 0.0096 | 0.0095 | 18.9941 |
| 4.2507 | 22.0 | 16830 | 4.3528 | 0.0105 | 0.0 | 0.0106 | 0.0104 | 18.9941 |
| 4.2184 | 23.0 | 17595 | 4.3246 | 0.0125 | 0.0007 | 0.0128 | 0.0125 | 18.9941 |
| 4.1746 | 24.0 | 18360 | 4.3013 | 0.0116 | 0.0 | 0.0118 | 0.0115 | 19.0 |
| 4.1744 | 25.0 | 19125 | 4.2724 | 0.0127 | 0.0 | 0.0128 | 0.0127 | 18.9956 |
| 4.1123 | 26.0 | 19890 | 4.2408 | 0.0127 | 0.0 | 0.0128 | 0.0128 | 19.0 |
| 4.131 | 27.0 | 20655 | 4.2045 | 0.0118 | 0.0 | 0.0121 | 0.0118 | 18.9897 |
| 4.0725 | 28.0 | 21420 | 4.1815 | 0.0112 | 0.0 | 0.0113 | 0.011 | 19.0 |
| 4.0439 | 29.0 | 22185 | 4.1452 | 0.0154 | 0.0024 | 0.0154 | 0.0152 | 19.0 |
| 4.0814 | 30.0 | 22950 | 4.1151 | 0.0142 | 0.0007 | 0.0143 | 0.014 | 19.0 |
| 4.009 | 31.0 | 23715 | 4.0963 | 0.015 | 0.0007 | 0.015 | 0.015 | 18.9985 |
| 4.0326 | 32.0 | 24480 | 4.0679 | 0.016 | 0.0 | 0.016 | 0.0161 | 19.0 |
| 3.948 | 33.0 | 25245 | 4.0460 | 0.0137 | 0.0015 | 0.0133 | 0.0132 | 18.9971 |
| 3.9401 | 34.0 | 26010 | 4.0070 | 0.0162 | 0.0024 | 0.0161 | 0.016 | 18.9985 |
| 3.9111 | 35.0 | 26775 | 3.9874 | 0.0209 | 0.0042 | 0.0209 | 0.021 | 18.9985 |
| 3.899 | 36.0 | 27540 | 3.9650 | 0.0185 | 0.0 | 0.0186 | 0.0186 | 18.9985 |
| 3.8821 | 37.0 | 28305 | 3.9457 | 0.0236 | 0.0029 | 0.024 | 0.024 | 19.0 |
| 3.8687 | 38.0 | 29070 | 3.9105 | 0.0241 | 0.0034 | 0.0239 | 0.0238 | 19.0 |
| 3.8076 | 39.0 | 29835 | 3.9029 | 0.0199 | 0.002 | 0.0199 | 0.0201 | 19.0 |
| 3.8063 | 40.0 | 30600 | 3.8750 | 0.0251 | 0.0034 | 0.0244 | 0.0246 | 19.0 |
| 3.7896 | 41.0 | 31365 | 3.8448 | 0.025 | 0.0028 | 0.0248 | 0.0249 | 18.9941 |
| 3.7672 | 42.0 | 32130 | 3.8287 | 0.0336 | 0.0029 | 0.0333 | 0.0332 | 19.0 |
| 3.7919 | 43.0 | 32895 | 3.8022 | 0.0268 | 0.0039 | 0.0268 | 0.0268 | 18.9956 |
| 3.75 | 44.0 | 33660 | 3.7723 | 0.0286 | 0.0044 | 0.0286 | 0.0284 | 18.9971 |
| 3.7263 | 45.0 | 34425 | 3.7630 | 0.0308 | 0.0039 | 0.0308 | 0.0307 | 19.0 |
| 3.7053 | 46.0 | 35190 | 3.7412 | 0.0341 | 0.0037 | 0.0335 | 0.0335 | 19.0 |
| 3.7022 | 47.0 | 35955 | 3.7214 | 0.0347 | 0.0044 | 0.0335 | 0.0336 | 18.9897 |
| 3.6528 | 48.0 | 36720 | 3.7059 | 0.0318 | 0.0044 | 0.032 | 0.032 | 19.0 |
| 3.6614 | 49.0 | 37485 | 3.6833 | 0.0313 | 0.0044 | 0.031 | 0.0309 | 18.9956 |
| 3.6339 | 50.0 | 38250 | 3.6691 | 0.0357 | 0.0051 | 0.0353 | 0.0352 | 18.9853 |
| 3.6153 | 51.0 | 39015 | 3.6500 | 0.0373 | 0.0044 | 0.0363 | 0.0365 | 18.9912 |
| 3.6083 | 52.0 | 39780 | 3.6360 | 0.0358 | 0.0051 | 0.0354 | 0.0354 | 18.9985 |
| 3.5857 | 53.0 | 40545 | 3.6272 | 0.0409 | 0.0044 | 0.0397 | 0.0396 | 19.0 |
| 3.5903 | 54.0 | 41310 | 3.6141 | 0.0455 | 0.0039 | 0.044 | 0.0439 | 18.9956 |
| 3.5429 | 55.0 | 42075 | 3.6044 | 0.0405 | 0.0054 | 0.0393 | 0.0394 | 18.9883 |
| 3.5526 | 56.0 | 42840 | 3.5933 | 0.0379 | 0.0049 | 0.037 | 0.0367 | 18.9883 |
| 3.5075 | 57.0 | 43605 | 3.5820 | 0.0431 | 0.0071 | 0.041 | 0.041 | 18.9985 |
| 3.5233 | 58.0 | 44370 | 3.5698 | 0.045 | 0.0064 | 0.0433 | 0.0434 | 18.9897 |
| 3.5022 | 59.0 | 45135 | 3.5680 | 0.0432 | 0.0061 | 0.0419 | 0.0416 | 18.9941 |
| 3.5258 | 60.0 | 45900 | 3.5604 | 0.047 | 0.0069 | 0.0452 | 0.0453 | 18.9956 |
| 3.4763 | 61.0 | 46665 | 3.5532 | 0.0465 | 0.0069 | 0.0452 | 0.0451 | 18.9985 |
| 3.4591 | 62.0 | 47430 | 3.5468 | 0.0429 | 0.0078 | 0.0425 | 0.0422 | 18.9971 |
| 3.471 | 63.0 | 48195 | 3.5359 | 0.0426 | 0.0078 | 0.0428 | 0.0426 | 18.9971 |
| 3.4671 | 64.0 | 48960 | 3.5348 | 0.0441 | 0.0064 | 0.0437 | 0.0436 | 18.9941 |
| 3.4588 | 65.0 | 49725 | 3.5291 | 0.0436 | 0.0064 | 0.0427 | 0.0426 | 18.9941 |
| 3.4214 | 66.0 | 50490 | 3.5168 | 0.0409 | 0.0071 | 0.0407 | 0.0408 | 18.9956 |
| 3.4531 | 67.0 | 51255 | 3.5091 | 0.0476 | 0.0082 | 0.0477 | 0.0479 | 18.9956 |
| 3.3936 | 68.0 | 52020 | 3.5016 | 0.044 | 0.0073 | 0.0441 | 0.0442 | 18.9956 |
| 3.4113 | 69.0 | 52785 | 3.5028 | 0.0473 | 0.0069 | 0.0472 | 0.0472 | 18.9956 |
| 3.4092 | 70.0 | 53550 | 3.4993 | 0.0483 | 0.0078 | 0.0488 | 0.0485 | 18.9985 |
| 3.3847 | 71.0 | 54315 | 3.4959 | 0.053 | 0.0078 | 0.0524 | 0.0524 | 18.9956 |
| 3.4099 | 72.0 | 55080 | 3.4906 | 0.0549 | 0.0069 | 0.0541 | 0.0542 | 18.9985 |
| 3.3774 | 73.0 | 55845 | 3.4821 | 0.0527 | 0.0064 | 0.052 | 0.052 | 18.9971 |
| 3.3677 | 74.0 | 56610 | 3.4790 | 0.0542 | 0.0069 | 0.0534 | 0.0534 | 18.9956 |
| 3.3707 | 75.0 | 57375 | 3.4747 | 0.0562 | 0.0069 | 0.0556 | 0.0557 | 18.9956 |
| 3.3953 | 76.0 | 58140 | 3.4713 | 0.0567 | 0.0069 | 0.056 | 0.056 | 18.9956 |
| 3.3767 | 77.0 | 58905 | 3.4695 | 0.0559 | 0.0069 | 0.0552 | 0.0551 | 18.9956 |
| 3.3455 | 78.0 | 59670 | 3.4668 | 0.0518 | 0.0069 | 0.0514 | 0.0508 | 18.9985 |
| 3.3749 | 79.0 | 60435 | 3.4648 | 0.055 | 0.0069 | 0.0546 | 0.0545 | 18.9941 |
| 3.3447 | 80.0 | 61200 | 3.4648 | 0.0534 | 0.0069 | 0.0529 | 0.0526 | 18.9956 |
| 3.3892 | 81.0 | 61965 | 3.4643 | 0.0572 | 0.0078 | 0.0568 | 0.0564 | 18.9985 |
| 3.3681 | 82.0 | 62730 | 3.4640 | 0.0545 | 0.0069 | 0.0542 | 0.0537 | 18.9956 |
| 3.3186 | 83.0 | 63495 | 3.4595 | 0.0574 | 0.0069 | 0.0569 | 0.0569 | 18.9956 |
| 3.3422 | 84.0 | 64260 | 3.4598 | 0.0553 | 0.0069 | 0.0549 | 0.0548 | 18.9956 |
| 3.3511 | 85.0 | 65025 | 3.4565 | 0.0563 | 0.0078 | 0.0561 | 0.0561 | 18.9956 |
| 3.3469 | 86.0 | 65790 | 3.4576 | 0.0569 | 0.0069 | 0.0562 | 0.0563 | 18.9956 |
| 3.345 | 87.0 | 66555 | 3.4579 | 0.0553 | 0.0069 | 0.0549 | 0.0548 | 18.9956 |
| 3.3611 | 88.0 | 67320 | 3.4558 | 0.0553 | 0.0069 | 0.0549 | 0.0548 | 18.9956 |
| 3.3423 | 89.0 | 68085 | 3.4559 | 0.0569 | 0.0069 | 0.0562 | 0.0563 | 18.9956 |
| 3.3575 | 90.0 | 68850 | 3.4560 | 0.0553 | 0.0069 | 0.0549 | 0.0548 | 18.9956 |
| 3.3322 | 91.0 | 69615 | 3.4560 | 0.0569 | 0.0069 | 0.0562 | 0.0563 | 18.9956 |
| 3.3303 | 92.0 | 70380 | 3.4551 | 0.0569 | 0.0069 | 0.0562 | 0.0563 | 18.9956 |
| 3.3676 | 93.0 | 71145 | 3.4542 | 0.0569 | 0.0069 | 0.0562 | 0.0563 | 18.9956 |
| 3.3219 | 94.0 | 71910 | 3.4541 | 0.0552 | 0.0069 | 0.0547 | 0.0546 | 18.9956 |
| 3.3563 | 95.0 | 72675 | 3.4540 | 0.0569 | 0.0069 | 0.0562 | 0.0563 | 18.9956 |
| 3.3616 | 96.0 | 73440 | 3.4541 | 0.0569 | 0.0069 | 0.0562 | 0.0563 | 18.9956 |
| 3.3417 | 97.0 | 74205 | 3.4543 | 0.0552 | 0.0069 | 0.0547 | 0.0546 | 18.9956 |
| 3.3683 | 98.0 | 74970 | 3.4541 | 0.0552 | 0.0069 | 0.0547 | 0.0546 | 18.9956 |
| 3.3402 | 99.0 | 75735 | 3.4541 | 0.0552 | 0.0069 | 0.0547 | 0.0546 | 18.9956 |
| 3.3413 | 100.0 | 76500 | 3.4541 | 0.0552 | 0.0069 | 0.0547 | 0.0546 | 18.9956 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
