1_1e-2_10_0.1
This model is a fine-tuned version of bert-large-uncased on the super_glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.9213
- Accuracy: 0.7489
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: 0.01
- train_batch_size: 16
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100.0
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
1.8284 | 1.0 | 590 | 2.0796 | 0.6220 |
1.4411 | 2.0 | 1180 | 1.1449 | 0.6220 |
1.3365 | 3.0 | 1770 | 1.0330 | 0.6217 |
1.305 | 4.0 | 2360 | 0.9705 | 0.6349 |
1.1782 | 5.0 | 2950 | 0.9411 | 0.6339 |
1.1021 | 6.0 | 3540 | 1.4542 | 0.6223 |
1.091 | 7.0 | 4130 | 1.3703 | 0.4969 |
0.9725 | 8.0 | 4720 | 1.4839 | 0.6425 |
0.9313 | 9.0 | 5310 | 0.7887 | 0.7009 |
0.8889 | 10.0 | 5900 | 0.8354 | 0.7052 |
0.8457 | 11.0 | 6490 | 0.8120 | 0.6807 |
0.7264 | 12.0 | 7080 | 0.9915 | 0.6190 |
0.7354 | 13.0 | 7670 | 0.7554 | 0.7205 |
0.686 | 14.0 | 8260 | 0.8069 | 0.7183 |
0.6549 | 15.0 | 8850 | 0.7395 | 0.7379 |
0.6278 | 16.0 | 9440 | 0.7282 | 0.7275 |
0.5753 | 17.0 | 10030 | 0.9035 | 0.6795 |
0.5773 | 18.0 | 10620 | 0.8699 | 0.6887 |
0.5437 | 19.0 | 11210 | 0.7501 | 0.7226 |
0.5266 | 20.0 | 11800 | 0.9360 | 0.7336 |
0.509 | 21.0 | 12390 | 0.8204 | 0.7199 |
0.497 | 22.0 | 12980 | 0.7944 | 0.7343 |
0.4379 | 23.0 | 13570 | 0.8074 | 0.7147 |
0.4276 | 24.0 | 14160 | 0.8147 | 0.7306 |
0.4132 | 25.0 | 14750 | 0.8578 | 0.7373 |
0.3944 | 26.0 | 15340 | 0.9502 | 0.7015 |
0.3845 | 27.0 | 15930 | 0.8962 | 0.7021 |
0.3754 | 28.0 | 16520 | 0.8571 | 0.7275 |
0.3478 | 29.0 | 17110 | 0.8433 | 0.7373 |
0.3561 | 30.0 | 17700 | 0.8819 | 0.7327 |
0.3301 | 31.0 | 18290 | 0.8623 | 0.7382 |
0.3217 | 32.0 | 18880 | 0.9132 | 0.7419 |
0.3182 | 33.0 | 19470 | 0.9184 | 0.7281 |
0.2892 | 34.0 | 20060 | 0.8482 | 0.7358 |
0.2915 | 35.0 | 20650 | 0.8988 | 0.7474 |
0.2816 | 36.0 | 21240 | 0.8834 | 0.7446 |
0.2763 | 37.0 | 21830 | 0.9208 | 0.7251 |
0.2679 | 38.0 | 22420 | 0.8656 | 0.7379 |
0.2785 | 39.0 | 23010 | 0.9177 | 0.7315 |
0.2551 | 40.0 | 23600 | 0.9989 | 0.7508 |
0.2491 | 41.0 | 24190 | 0.9483 | 0.7505 |
0.2482 | 42.0 | 24780 | 0.8921 | 0.7391 |
0.2577 | 43.0 | 25370 | 0.9175 | 0.7459 |
0.24 | 44.0 | 25960 | 0.9345 | 0.7453 |
0.2368 | 45.0 | 26550 | 0.9161 | 0.7428 |
0.2261 | 46.0 | 27140 | 0.8859 | 0.7315 |
0.2317 | 47.0 | 27730 | 0.8984 | 0.7437 |
0.218 | 48.0 | 28320 | 0.8986 | 0.7465 |
0.224 | 49.0 | 28910 | 0.8665 | 0.7431 |
0.2064 | 50.0 | 29500 | 0.8869 | 0.7492 |
0.2163 | 51.0 | 30090 | 0.8786 | 0.7394 |
0.2145 | 52.0 | 30680 | 0.9545 | 0.7446 |
0.1998 | 53.0 | 31270 | 0.8586 | 0.7462 |
0.2008 | 54.0 | 31860 | 0.9008 | 0.7446 |
0.1978 | 55.0 | 32450 | 0.9236 | 0.7471 |
0.2025 | 56.0 | 33040 | 0.8906 | 0.7474 |
0.1903 | 57.0 | 33630 | 0.9517 | 0.7459 |
0.1846 | 58.0 | 34220 | 0.9696 | 0.7529 |
0.1819 | 59.0 | 34810 | 0.9163 | 0.7419 |
0.1883 | 60.0 | 35400 | 0.9419 | 0.7373 |
0.1851 | 61.0 | 35990 | 0.9657 | 0.7419 |
0.1805 | 62.0 | 36580 | 0.9279 | 0.7413 |
0.1866 | 63.0 | 37170 | 0.8996 | 0.7495 |
0.1752 | 64.0 | 37760 | 0.9427 | 0.7554 |
0.1703 | 65.0 | 38350 | 0.9364 | 0.7379 |
0.1702 | 66.0 | 38940 | 0.9546 | 0.7502 |
0.1688 | 67.0 | 39530 | 0.9265 | 0.7498 |
0.1724 | 68.0 | 40120 | 0.9043 | 0.7446 |
0.1635 | 69.0 | 40710 | 0.9426 | 0.7465 |
0.1652 | 70.0 | 41300 | 0.9702 | 0.7471 |
0.1643 | 71.0 | 41890 | 0.9191 | 0.7379 |
0.1684 | 72.0 | 42480 | 0.9362 | 0.7526 |
0.1575 | 73.0 | 43070 | 0.9399 | 0.7511 |
0.1585 | 74.0 | 43660 | 0.9585 | 0.7483 |
0.1551 | 75.0 | 44250 | 0.9481 | 0.7532 |
0.1587 | 76.0 | 44840 | 0.9233 | 0.7483 |
0.1499 | 77.0 | 45430 | 0.9115 | 0.7508 |
0.1541 | 78.0 | 46020 | 0.9531 | 0.7535 |
0.1505 | 79.0 | 46610 | 0.9306 | 0.7456 |
0.1521 | 80.0 | 47200 | 0.9185 | 0.7535 |
0.1448 | 81.0 | 47790 | 0.9228 | 0.7459 |
0.1475 | 82.0 | 48380 | 0.9214 | 0.7446 |
0.1491 | 83.0 | 48970 | 0.9355 | 0.7465 |
0.1433 | 84.0 | 49560 | 0.9403 | 0.7523 |
0.1416 | 85.0 | 50150 | 0.9270 | 0.7492 |
0.1391 | 86.0 | 50740 | 0.9208 | 0.7517 |
0.1391 | 87.0 | 51330 | 0.9134 | 0.7517 |
0.1415 | 88.0 | 51920 | 0.9198 | 0.7486 |
0.1343 | 89.0 | 52510 | 0.9380 | 0.7483 |
0.128 | 90.0 | 53100 | 0.9429 | 0.7505 |
0.1328 | 91.0 | 53690 | 0.9211 | 0.7529 |
0.1311 | 92.0 | 54280 | 0.9180 | 0.7431 |
0.1383 | 93.0 | 54870 | 0.9522 | 0.7535 |
0.133 | 94.0 | 55460 | 0.9047 | 0.7486 |
0.1331 | 95.0 | 56050 | 0.9339 | 0.7526 |
0.1304 | 96.0 | 56640 | 0.9177 | 0.7480 |
0.1293 | 97.0 | 57230 | 0.9194 | 0.7471 |
0.128 | 98.0 | 57820 | 0.9213 | 0.7492 |
0.1268 | 99.0 | 58410 | 0.9260 | 0.7492 |
0.1297 | 100.0 | 59000 | 0.9213 | 0.7489 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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