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2_1e-2_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: 0.8066
- Accuracy: 0.7550
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 |
---|---|---|---|---|
4.8797 | 1.0 | 590 | 3.6447 | 0.6217 |
4.0161 | 2.0 | 1180 | 3.3996 | 0.6291 |
3.6981 | 3.0 | 1770 | 2.6674 | 0.6297 |
3.2078 | 4.0 | 2360 | 2.9843 | 0.5676 |
2.8064 | 5.0 | 2950 | 2.1131 | 0.6804 |
2.4341 | 6.0 | 3540 | 3.3843 | 0.6673 |
2.403 | 7.0 | 4130 | 1.8655 | 0.7043 |
2.3212 | 8.0 | 4720 | 1.8492 | 0.7055 |
2.2831 | 9.0 | 5310 | 1.5678 | 0.7024 |
2.1715 | 10.0 | 5900 | 1.6676 | 0.7193 |
2.0967 | 11.0 | 6490 | 1.5610 | 0.7174 |
1.9909 | 12.0 | 7080 | 1.3225 | 0.7122 |
1.9391 | 13.0 | 7670 | 1.3815 | 0.7180 |
1.852 | 14.0 | 8260 | 1.4632 | 0.7260 |
1.8568 | 15.0 | 8850 | 1.3623 | 0.7101 |
1.776 | 16.0 | 9440 | 1.3193 | 0.7015 |
1.6984 | 17.0 | 10030 | 1.3270 | 0.7208 |
1.6811 | 18.0 | 10620 | 1.3129 | 0.7055 |
1.6857 | 19.0 | 11210 | 1.3154 | 0.7382 |
1.6594 | 20.0 | 11800 | 1.2337 | 0.7352 |
1.5595 | 21.0 | 12390 | 1.2297 | 0.7404 |
1.6112 | 22.0 | 12980 | 1.1512 | 0.7450 |
1.5746 | 23.0 | 13570 | 1.1148 | 0.7208 |
1.5216 | 24.0 | 14160 | 1.1788 | 0.7373 |
1.5245 | 25.0 | 14750 | 1.0049 | 0.7361 |
1.4803 | 26.0 | 15340 | 1.5312 | 0.6890 |
1.5122 | 27.0 | 15930 | 1.0611 | 0.7187 |
1.4459 | 28.0 | 16520 | 1.5559 | 0.7431 |
1.4638 | 29.0 | 17110 | 1.3813 | 0.7450 |
1.3627 | 30.0 | 17700 | 1.0913 | 0.7456 |
1.3834 | 31.0 | 18290 | 1.1301 | 0.7113 |
1.3657 | 32.0 | 18880 | 1.2116 | 0.7560 |
1.373 | 33.0 | 19470 | 1.0198 | 0.7339 |
1.3113 | 34.0 | 20060 | 1.1041 | 0.7563 |
1.3327 | 35.0 | 20650 | 0.9885 | 0.7446 |
1.3544 | 36.0 | 21240 | 1.2174 | 0.7508 |
1.3198 | 37.0 | 21830 | 1.0094 | 0.7498 |
1.3 | 38.0 | 22420 | 0.9895 | 0.7306 |
1.2688 | 39.0 | 23010 | 1.0118 | 0.7471 |
1.3101 | 40.0 | 23600 | 1.1384 | 0.7517 |
1.2849 | 41.0 | 24190 | 1.1154 | 0.7520 |
1.2455 | 42.0 | 24780 | 0.9685 | 0.7431 |
1.2155 | 43.0 | 25370 | 1.0038 | 0.7498 |
1.2078 | 44.0 | 25960 | 0.9498 | 0.7382 |
1.2362 | 45.0 | 26550 | 0.9510 | 0.7413 |
1.2271 | 46.0 | 27140 | 0.9461 | 0.7514 |
1.2351 | 47.0 | 27730 | 0.9943 | 0.7272 |
1.2383 | 48.0 | 28320 | 0.9020 | 0.7422 |
1.1625 | 49.0 | 28910 | 0.9276 | 0.7385 |
1.1711 | 50.0 | 29500 | 0.9250 | 0.7352 |
1.1454 | 51.0 | 30090 | 0.9967 | 0.7483 |
1.1319 | 52.0 | 30680 | 0.9347 | 0.7309 |
1.1622 | 53.0 | 31270 | 0.9274 | 0.7456 |
1.1189 | 54.0 | 31860 | 1.0497 | 0.7483 |
1.1265 | 55.0 | 32450 | 0.9079 | 0.7462 |
1.0948 | 56.0 | 33040 | 0.9022 | 0.7477 |
1.0921 | 57.0 | 33630 | 0.8855 | 0.7385 |
1.0819 | 58.0 | 34220 | 0.8766 | 0.7327 |
1.0894 | 59.0 | 34810 | 0.8820 | 0.7462 |
1.0512 | 60.0 | 35400 | 0.8711 | 0.7428 |
1.075 | 61.0 | 35990 | 0.8970 | 0.7336 |
1.0505 | 62.0 | 36580 | 0.8912 | 0.7401 |
1.0612 | 63.0 | 37170 | 0.8774 | 0.7428 |
1.0458 | 64.0 | 37760 | 0.8675 | 0.7532 |
1.043 | 65.0 | 38350 | 1.0193 | 0.7554 |
1.1037 | 66.0 | 38940 | 0.8751 | 0.7367 |
1.0246 | 67.0 | 39530 | 0.8489 | 0.7514 |
1.0428 | 68.0 | 40120 | 0.8590 | 0.7373 |
1.0486 | 69.0 | 40710 | 0.8615 | 0.7514 |
1.0103 | 70.0 | 41300 | 0.9673 | 0.7596 |
1.0363 | 71.0 | 41890 | 0.8328 | 0.7440 |
1.0077 | 72.0 | 42480 | 0.8548 | 0.7489 |
1.0046 | 73.0 | 43070 | 0.9124 | 0.7407 |
0.9814 | 74.0 | 43660 | 0.8423 | 0.7508 |
0.9962 | 75.0 | 44250 | 1.0146 | 0.7532 |
0.9867 | 76.0 | 44840 | 0.8612 | 0.7517 |
0.9623 | 77.0 | 45430 | 0.8438 | 0.7563 |
0.9448 | 78.0 | 46020 | 0.8514 | 0.7505 |
0.961 | 79.0 | 46610 | 0.9149 | 0.7566 |
0.9521 | 80.0 | 47200 | 0.8576 | 0.7560 |
0.9835 | 81.0 | 47790 | 0.8314 | 0.7498 |
0.9777 | 82.0 | 48380 | 0.8524 | 0.7572 |
0.9259 | 83.0 | 48970 | 0.8440 | 0.7529 |
0.9246 | 84.0 | 49560 | 0.8429 | 0.7557 |
0.9222 | 85.0 | 50150 | 0.8880 | 0.7563 |
0.9152 | 86.0 | 50740 | 0.8348 | 0.7587 |
0.9218 | 87.0 | 51330 | 0.8254 | 0.7538 |
0.9379 | 88.0 | 51920 | 0.8099 | 0.7514 |
0.9387 | 89.0 | 52510 | 0.8407 | 0.7575 |
0.9154 | 90.0 | 53100 | 0.8735 | 0.7575 |
0.9331 | 91.0 | 53690 | 0.8920 | 0.7593 |
0.892 | 92.0 | 54280 | 0.8117 | 0.7566 |
0.9002 | 93.0 | 54870 | 0.8450 | 0.7569 |
0.9134 | 94.0 | 55460 | 0.7989 | 0.7569 |
0.8965 | 95.0 | 56050 | 0.8088 | 0.7541 |
0.8834 | 96.0 | 56640 | 0.8058 | 0.7529 |
0.9075 | 97.0 | 57230 | 0.8254 | 0.7557 |
0.8821 | 98.0 | 57820 | 0.8172 | 0.7547 |
0.9119 | 99.0 | 58410 | 0.8069 | 0.7550 |
0.9082 | 100.0 | 59000 | 0.8066 | 0.7550 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3