Indobert-QA-finetuned-squad
This model is a fine-tuned version of Rifky/Indobert-QA on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 15.2477
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
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.1181 | 1.0 | 5510 | 4.8523 |
| 0.9746 | 2.0 | 11020 | 5.4560 |
| 0.8135 | 3.0 | 16530 | 5.7017 |
| 0.6964 | 4.0 | 22040 | 6.2898 |
| 0.6052 | 5.0 | 27550 | 6.0962 |
| 0.512 | 6.0 | 33060 | 6.4996 |
| 0.4303 | 7.0 | 38570 | 6.9570 |
| 0.3532 | 8.0 | 44080 | 7.4206 |
| 0.3199 | 9.0 | 49590 | 7.4004 |
| 0.4247 | 10.0 | 55100 | 6.9846 |
| 0.3641 | 11.0 | 60610 | 6.8940 |
| 0.3277 | 12.0 | 66120 | 7.0796 |
| 0.2899 | 13.0 | 71630 | 7.4511 |
| 0.2794 | 14.0 | 77140 | 7.2660 |
| 0.2496 | 15.0 | 82650 | 7.9774 |
| 0.2299 | 16.0 | 88160 | 7.6985 |
| 0.2082 | 17.0 | 93670 | 7.8321 |
| 0.1975 | 18.0 | 99180 | 8.1735 |
| 0.1784 | 19.0 | 104690 | 8.5620 |
| 0.1675 | 20.0 | 110200 | 8.7616 |
| 0.1613 | 21.0 | 115710 | 8.8350 |
| 0.1484 | 22.0 | 121220 | 8.9582 |
| 0.1482 | 23.0 | 126730 | 9.0406 |
| 0.1381 | 24.0 | 132240 | 8.9652 |
| 0.1411 | 25.0 | 137750 | 9.4613 |
| 0.1236 | 26.0 | 143260 | 9.6738 |
| 0.1216 | 27.0 | 148770 | 9.8708 |
| 0.1192 | 28.0 | 154280 | 10.3220 |
| 0.12 | 29.0 | 159790 | 10.0470 |
| 0.1041 | 30.0 | 165300 | 10.6753 |
| 0.1055 | 31.0 | 170810 | 10.2775 |
| 0.1083 | 32.0 | 176320 | 10.4515 |
| 0.0924 | 33.0 | 181830 | 10.2080 |
| 0.0959 | 34.0 | 187340 | 10.8958 |
| 0.0978 | 35.0 | 192850 | 10.8256 |
| 0.0865 | 36.0 | 198360 | 11.6631 |
| 0.0825 | 37.0 | 203870 | 11.9017 |
| 0.0807 | 38.0 | 209380 | 11.4407 |
| 0.0674 | 39.0 | 214890 | 11.5917 |
| 0.0809 | 40.0 | 220400 | 11.4535 |
| 0.0708 | 41.0 | 225910 | 12.1592 |
| 0.0778 | 42.0 | 231420 | 12.0278 |
| 0.0726 | 43.0 | 236930 | 11.7701 |
| 0.0627 | 44.0 | 242440 | 12.2976 |
| 0.0681 | 45.0 | 247950 | 12.7727 |
| 0.0672 | 46.0 | 253460 | 12.8623 |
| 0.0608 | 47.0 | 258970 | 12.9669 |
| 0.067 | 48.0 | 264480 | 13.4741 |
| 0.0625 | 49.0 | 269990 | 13.6245 |
| 0.0585 | 50.0 | 275500 | 13.4891 |
| 0.0568 | 51.0 | 281010 | 13.4374 |
| 0.0583 | 52.0 | 286520 | 12.8947 |
| 0.0467 | 53.0 | 292030 | 13.6060 |
| 0.0416 | 54.0 | 297540 | 14.3267 |
| 0.0504 | 55.0 | 303050 | 13.7715 |
| 0.0431 | 56.0 | 308560 | 13.8461 |
| 0.0412 | 57.0 | 314070 | 13.7060 |
| 0.0383 | 58.0 | 319580 | 14.3548 |
| 0.0327 | 59.0 | 325090 | 14.4535 |
| 0.0448 | 60.0 | 330600 | 14.2505 |
| 0.0409 | 61.0 | 336110 | 13.8177 |
| 0.0332 | 62.0 | 341620 | 13.0098 |
| 0.0345 | 63.0 | 347130 | 13.8678 |
| 0.0267 | 64.0 | 352640 | 14.3916 |
| 0.0302 | 65.0 | 358150 | 14.1668 |
| 0.0292 | 66.0 | 363660 | 13.6313 |
| 0.0302 | 67.0 | 369170 | 14.1120 |
| 0.0265 | 68.0 | 374680 | 15.0709 |
| 0.0276 | 69.0 | 380190 | 14.6093 |
| 0.0223 | 70.0 | 385700 | 15.0999 |
| 0.0306 | 71.0 | 391210 | 15.1224 |
| 0.0281 | 72.0 | 396720 | 15.5029 |
| 0.019 | 73.0 | 402230 | 15.3474 |
| 0.02 | 74.0 | 407740 | 14.7976 |
| 0.018 | 75.0 | 413250 | 15.3104 |
| 0.0184 | 76.0 | 418760 | 15.3137 |
| 0.0171 | 77.0 | 424270 | 14.8188 |
| 0.0164 | 78.0 | 429780 | 15.4378 |
| 0.0165 | 79.0 | 435290 | 15.1186 |
| 0.0168 | 80.0 | 440800 | 14.7998 |
| 0.0115 | 81.0 | 446310 | 14.4591 |
| 0.0138 | 82.0 | 451820 | 15.2517 |
| 0.0117 | 83.0 | 457330 | 14.7899 |
| 0.0118 | 84.0 | 462840 | 15.5304 |
| 0.0119 | 85.0 | 468350 | 14.6794 |
| 0.0134 | 86.0 | 473860 | 14.5271 |
| 0.0076 | 87.0 | 479370 | 15.7098 |
| 0.0076 | 88.0 | 484880 | 14.2286 |
| 0.01 | 89.0 | 490390 | 15.2608 |
| 0.0094 | 90.0 | 495900 | 14.9055 |
| 0.0069 | 91.0 | 501410 | 14.8540 |
| 0.0082 | 92.0 | 506920 | 15.2562 |
| 0.0068 | 93.0 | 512430 | 14.9342 |
| 0.0047 | 94.0 | 517940 | 15.3755 |
| 0.0062 | 95.0 | 523450 | 15.2753 |
| 0.0046 | 96.0 | 528960 | 15.0191 |
| 0.0057 | 97.0 | 534470 | 14.9508 |
| 0.0033 | 98.0 | 539980 | 15.4440 |
| 0.0045 | 99.0 | 545490 | 15.4171 |
| 0.0048 | 100.0 | 551000 | 15.2477 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
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