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1_5e-3_10_0.9
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: 0.9486
- Accuracy: 0.7520
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: 0.005
- 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 |
|---|---|---|---|---|
| 4.2553 | 1.0 | 590 | 3.4885 | 0.6217 |
| 3.8771 | 2.0 | 1180 | 5.2589 | 0.4156 |
| 3.8841 | 3.0 | 1770 | 3.1457 | 0.6217 |
| 3.4978 | 4.0 | 2360 | 3.6630 | 0.5073 |
| 3.4514 | 5.0 | 2950 | 2.8535 | 0.6538 |
| 2.8512 | 6.0 | 3540 | 4.5431 | 0.6401 |
| 2.8629 | 7.0 | 4130 | 2.9999 | 0.5774 |
| 2.7803 | 8.0 | 4720 | 4.0455 | 0.6440 |
| 2.3648 | 9.0 | 5310 | 3.4814 | 0.6618 |
| 2.3135 | 10.0 | 5900 | 1.8693 | 0.6985 |
| 2.2615 | 11.0 | 6490 | 1.7206 | 0.7095 |
| 1.938 | 12.0 | 7080 | 2.2772 | 0.6664 |
| 1.9168 | 13.0 | 7670 | 1.5057 | 0.7012 |
| 1.7411 | 14.0 | 8260 | 1.4510 | 0.7239 |
| 1.7184 | 15.0 | 8850 | 1.3241 | 0.7211 |
| 1.5774 | 16.0 | 9440 | 1.8563 | 0.7153 |
| 1.5229 | 17.0 | 10030 | 1.3243 | 0.7226 |
| 1.4652 | 18.0 | 10620 | 1.3866 | 0.7333 |
| 1.4321 | 19.0 | 11210 | 1.2208 | 0.7294 |
| 1.4205 | 20.0 | 11800 | 1.4391 | 0.7080 |
| 1.3537 | 21.0 | 12390 | 1.2900 | 0.7382 |
| 1.3302 | 22.0 | 12980 | 1.2322 | 0.7398 |
| 1.2616 | 23.0 | 13570 | 1.2189 | 0.7391 |
| 1.2586 | 24.0 | 14160 | 1.1687 | 0.7410 |
| 1.2259 | 25.0 | 14750 | 1.1797 | 0.7336 |
| 1.1804 | 26.0 | 15340 | 1.0929 | 0.7394 |
| 1.1907 | 27.0 | 15930 | 1.2820 | 0.7168 |
| 1.2066 | 28.0 | 16520 | 1.2464 | 0.7422 |
| 1.1128 | 29.0 | 17110 | 1.1798 | 0.7180 |
| 1.0889 | 30.0 | 17700 | 1.1373 | 0.7474 |
| 1.0637 | 31.0 | 18290 | 1.0453 | 0.7382 |
| 1.058 | 32.0 | 18880 | 1.1689 | 0.7446 |
| 1.0553 | 33.0 | 19470 | 1.0705 | 0.7321 |
| 1.0404 | 34.0 | 20060 | 1.0731 | 0.7425 |
| 1.014 | 35.0 | 20650 | 1.0481 | 0.7459 |
| 1.0166 | 36.0 | 21240 | 1.0434 | 0.7508 |
| 0.9983 | 37.0 | 21830 | 1.1358 | 0.7471 |
| 1.0144 | 38.0 | 22420 | 1.0030 | 0.7425 |
| 1.0236 | 39.0 | 23010 | 1.2874 | 0.7437 |
| 0.9749 | 40.0 | 23600 | 1.3199 | 0.7370 |
| 0.9592 | 41.0 | 24190 | 1.0072 | 0.7352 |
| 0.9467 | 42.0 | 24780 | 1.0282 | 0.7422 |
| 0.921 | 43.0 | 25370 | 1.3284 | 0.7446 |
| 0.9328 | 44.0 | 25960 | 0.9873 | 0.7364 |
| 0.9192 | 45.0 | 26550 | 1.3185 | 0.7425 |
| 0.8882 | 46.0 | 27140 | 0.9961 | 0.7453 |
| 0.8986 | 47.0 | 27730 | 0.9880 | 0.7373 |
| 0.8635 | 48.0 | 28320 | 1.0019 | 0.7480 |
| 0.8988 | 49.0 | 28910 | 1.1254 | 0.7498 |
| 0.865 | 50.0 | 29500 | 0.9619 | 0.7468 |
| 0.8575 | 51.0 | 30090 | 1.0854 | 0.7502 |
| 0.8654 | 52.0 | 30680 | 0.9466 | 0.7462 |
| 0.8482 | 53.0 | 31270 | 1.0722 | 0.7483 |
| 0.8547 | 54.0 | 31860 | 1.1340 | 0.7492 |
| 0.8424 | 55.0 | 32450 | 1.0683 | 0.7462 |
| 0.8078 | 56.0 | 33040 | 1.0285 | 0.7495 |
| 0.8163 | 57.0 | 33630 | 0.9779 | 0.7502 |
| 0.8175 | 58.0 | 34220 | 0.9461 | 0.7505 |
| 0.816 | 59.0 | 34810 | 0.9991 | 0.7443 |
| 0.8123 | 60.0 | 35400 | 0.9554 | 0.7443 |
| 0.7827 | 61.0 | 35990 | 0.9765 | 0.7492 |
| 0.8139 | 62.0 | 36580 | 1.1876 | 0.7547 |
| 0.7938 | 63.0 | 37170 | 0.9484 | 0.7541 |
| 0.7712 | 64.0 | 37760 | 0.9400 | 0.7508 |
| 0.7834 | 65.0 | 38350 | 0.9793 | 0.7532 |
| 0.781 | 66.0 | 38940 | 0.9480 | 0.7498 |
| 0.7639 | 67.0 | 39530 | 1.1188 | 0.7593 |
| 0.7838 | 68.0 | 40120 | 1.0215 | 0.7541 |
| 0.7527 | 69.0 | 40710 | 1.0855 | 0.7529 |
| 0.7626 | 70.0 | 41300 | 1.0755 | 0.7526 |
| 0.7683 | 71.0 | 41890 | 0.9553 | 0.7566 |
| 0.7588 | 72.0 | 42480 | 0.9822 | 0.7581 |
| 0.7377 | 73.0 | 43070 | 1.0359 | 0.7557 |
| 0.731 | 74.0 | 43660 | 0.9513 | 0.7505 |
| 0.7536 | 75.0 | 44250 | 1.1317 | 0.7505 |
| 0.7449 | 76.0 | 44840 | 0.9001 | 0.7532 |
| 0.7428 | 77.0 | 45430 | 1.0150 | 0.7538 |
| 0.7271 | 78.0 | 46020 | 0.9623 | 0.7563 |
| 0.7383 | 79.0 | 46610 | 0.9535 | 0.7584 |
| 0.7186 | 80.0 | 47200 | 0.9970 | 0.7581 |
| 0.6823 | 81.0 | 47790 | 1.0485 | 0.7563 |
| 0.7259 | 82.0 | 48380 | 0.9706 | 0.7526 |
| 0.7039 | 83.0 | 48970 | 0.9543 | 0.7480 |
| 0.7259 | 84.0 | 49560 | 0.9387 | 0.7508 |
| 0.7092 | 85.0 | 50150 | 0.9828 | 0.7538 |
| 0.7259 | 86.0 | 50740 | 0.9145 | 0.7459 |
| 0.7195 | 87.0 | 51330 | 0.9313 | 0.7495 |
| 0.696 | 88.0 | 51920 | 0.9467 | 0.7492 |
| 0.6885 | 89.0 | 52510 | 0.9671 | 0.7526 |
| 0.6874 | 90.0 | 53100 | 0.9387 | 0.7511 |
| 0.6911 | 91.0 | 53690 | 1.0279 | 0.7492 |
| 0.6968 | 92.0 | 54280 | 0.9268 | 0.7511 |
| 0.6833 | 93.0 | 54870 | 0.9886 | 0.7517 |
| 0.7096 | 94.0 | 55460 | 0.9693 | 0.7532 |
| 0.6911 | 95.0 | 56050 | 0.9503 | 0.7547 |
| 0.6754 | 96.0 | 56640 | 0.9451 | 0.7544 |
| 0.6823 | 97.0 | 57230 | 0.9427 | 0.7535 |
| 0.6547 | 98.0 | 57820 | 0.9500 | 0.7526 |
| 0.6433 | 99.0 | 58410 | 0.9280 | 0.7505 |
| 0.6722 | 100.0 | 59000 | 0.9486 | 0.7520 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
