qa-persian-bert-fa-base-uncased
This model is a fine-tuned version of makhataei/qa-persian-bert-fa-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 5.3355
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: 1.5625e-09
- train_batch_size: 14
- eval_batch_size: 14
- 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 |
|---|---|---|---|
| 5.5993 | 1.0 | 9 | 5.3355 |
| 5.6986 | 2.0 | 18 | 5.3355 |
| 5.6845 | 3.0 | 27 | 5.3355 |
| 5.6475 | 4.0 | 36 | 5.3355 |
| 5.7219 | 5.0 | 45 | 5.3355 |
| 5.6464 | 6.0 | 54 | 5.3355 |
| 5.5938 | 7.0 | 63 | 5.3355 |
| 5.577 | 8.0 | 72 | 5.3355 |
| 5.5841 | 9.0 | 81 | 5.3355 |
| 5.5863 | 10.0 | 90 | 5.3355 |
| 5.5771 | 11.0 | 99 | 5.3355 |
| 5.6131 | 12.0 | 108 | 5.3355 |
| 5.6692 | 13.0 | 117 | 5.3355 |
| 5.7031 | 14.0 | 126 | 5.3355 |
| 5.6995 | 15.0 | 135 | 5.3355 |
| 5.6724 | 16.0 | 144 | 5.3355 |
| 5.7379 | 17.0 | 153 | 5.3355 |
| 5.6687 | 18.0 | 162 | 5.3355 |
| 5.7009 | 19.0 | 171 | 5.3355 |
| 5.6232 | 20.0 | 180 | 5.3355 |
| 5.6514 | 21.0 | 189 | 5.3355 |
| 5.6814 | 22.0 | 198 | 5.3355 |
| 5.6305 | 23.0 | 207 | 5.3355 |
| 5.7508 | 24.0 | 216 | 5.3355 |
| 5.6747 | 25.0 | 225 | 5.3355 |
| 5.6642 | 26.0 | 234 | 5.3355 |
| 5.6913 | 27.0 | 243 | 5.3355 |
| 5.673 | 28.0 | 252 | 5.3355 |
| 5.6222 | 29.0 | 261 | 5.3355 |
| 5.6194 | 30.0 | 270 | 5.3355 |
| 5.6944 | 31.0 | 279 | 5.3355 |
| 5.6899 | 32.0 | 288 | 5.3355 |
| 5.6165 | 33.0 | 297 | 5.3355 |
| 5.6643 | 34.0 | 306 | 5.3355 |
| 5.7039 | 35.0 | 315 | 5.3355 |
| 5.6704 | 36.0 | 324 | 5.3355 |
| 5.694 | 37.0 | 333 | 5.3355 |
| 5.6058 | 38.0 | 342 | 5.3355 |
| 5.5774 | 39.0 | 351 | 5.3355 |
| 5.5987 | 40.0 | 360 | 5.3355 |
| 5.6706 | 41.0 | 369 | 5.3355 |
| 5.644 | 42.0 | 378 | 5.3355 |
| 5.6432 | 43.0 | 387 | 5.3355 |
| 5.6055 | 44.0 | 396 | 5.3355 |
| 5.6086 | 45.0 | 405 | 5.3355 |
| 5.738 | 46.0 | 414 | 5.3355 |
| 5.6526 | 47.0 | 423 | 5.3355 |
| 5.6566 | 48.0 | 432 | 5.3355 |
| 5.6381 | 49.0 | 441 | 5.3355 |
| 5.7056 | 50.0 | 450 | 5.3355 |
| 5.6693 | 51.0 | 459 | 5.3355 |
| 5.6042 | 52.0 | 468 | 5.3355 |
| 5.6551 | 53.0 | 477 | 5.3355 |
| 5.5851 | 54.0 | 486 | 5.3355 |
| 5.6209 | 55.0 | 495 | 5.3355 |
| 5.6143 | 56.0 | 504 | 5.3355 |
| 5.6426 | 57.0 | 513 | 5.3355 |
| 5.589 | 58.0 | 522 | 5.3355 |
| 5.6143 | 59.0 | 531 | 5.3355 |
| 5.6736 | 60.0 | 540 | 5.3355 |
| 5.6754 | 61.0 | 549 | 5.3355 |
| 5.6884 | 62.0 | 558 | 5.3355 |
| 5.677 | 63.0 | 567 | 5.3355 |
| 5.6157 | 64.0 | 576 | 5.3355 |
| 5.618 | 65.0 | 585 | 5.3355 |
| 5.678 | 66.0 | 594 | 5.3355 |
| 5.6859 | 67.0 | 603 | 5.3355 |
| 5.6751 | 68.0 | 612 | 5.3355 |
| 5.5911 | 69.0 | 621 | 5.3355 |
| 5.66 | 70.0 | 630 | 5.3355 |
| 5.7322 | 71.0 | 639 | 5.3355 |
| 5.6169 | 72.0 | 648 | 5.3355 |
| 5.6718 | 73.0 | 657 | 5.3355 |
| 5.6933 | 74.0 | 666 | 5.3355 |
| 5.5852 | 75.0 | 675 | 5.3355 |
| 5.5871 | 76.0 | 684 | 5.3355 |
| 5.6518 | 77.0 | 693 | 5.3355 |
| 5.6022 | 78.0 | 702 | 5.3355 |
| 5.6427 | 79.0 | 711 | 5.3355 |
| 5.639 | 80.0 | 720 | 5.3355 |
| 5.6559 | 81.0 | 729 | 5.3355 |
| 5.6959 | 82.0 | 738 | 5.3355 |
| 5.6081 | 83.0 | 747 | 5.3355 |
| 5.6185 | 84.0 | 756 | 5.3355 |
| 5.638 | 85.0 | 765 | 5.3355 |
| 5.6206 | 86.0 | 774 | 5.3355 |
| 5.7414 | 87.0 | 783 | 5.3355 |
| 5.7041 | 88.0 | 792 | 5.3355 |
| 5.6389 | 89.0 | 801 | 5.3355 |
| 5.6339 | 90.0 | 810 | 5.3355 |
| 5.6446 | 91.0 | 819 | 5.3355 |
| 5.6303 | 92.0 | 828 | 5.3355 |
| 5.6814 | 93.0 | 837 | 5.3355 |
| 5.6435 | 94.0 | 846 | 5.3355 |
| 5.6822 | 95.0 | 855 | 5.3355 |
| 5.6318 | 96.0 | 864 | 5.3355 |
| 5.6404 | 97.0 | 873 | 5.3355 |
| 5.6277 | 98.0 | 882 | 5.3355 |
| 5.639 | 99.0 | 891 | 5.3355 |
| 5.6655 | 100.0 | 900 | 5.3355 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
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