1_9e-3_10_0.5
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: 0.8950
- Accuracy: 0.7544
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: 0.009
- 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 |
---|---|---|---|---|
3.4984 | 1.0 | 590 | 2.4126 | 0.4138 |
2.7588 | 2.0 | 1180 | 3.3455 | 0.6217 |
2.6474 | 3.0 | 1770 | 2.9962 | 0.6232 |
2.5401 | 4.0 | 2360 | 2.3051 | 0.6171 |
2.4721 | 5.0 | 2950 | 1.9595 | 0.5939 |
2.0867 | 6.0 | 3540 | 1.8172 | 0.6780 |
1.9063 | 7.0 | 4130 | 1.6558 | 0.6281 |
1.7809 | 8.0 | 4720 | 1.4817 | 0.7009 |
1.6707 | 9.0 | 5310 | 1.3547 | 0.7012 |
1.5201 | 10.0 | 5900 | 1.7296 | 0.6813 |
1.4583 | 11.0 | 6490 | 1.4437 | 0.7107 |
1.2224 | 12.0 | 7080 | 1.0975 | 0.7168 |
1.202 | 13.0 | 7670 | 1.1965 | 0.6960 |
1.1209 | 14.0 | 8260 | 1.0158 | 0.7287 |
1.093 | 15.0 | 8850 | 1.0843 | 0.7413 |
1.0345 | 16.0 | 9440 | 1.0067 | 0.7410 |
0.9682 | 17.0 | 10030 | 1.0831 | 0.7382 |
0.9253 | 18.0 | 10620 | 1.0804 | 0.6985 |
0.8916 | 19.0 | 11210 | 0.9652 | 0.7321 |
0.869 | 20.0 | 11800 | 1.0800 | 0.7388 |
0.8274 | 21.0 | 12390 | 0.9542 | 0.7477 |
0.8245 | 22.0 | 12980 | 0.9244 | 0.7379 |
0.8061 | 23.0 | 13570 | 0.9591 | 0.7388 |
0.7804 | 24.0 | 14160 | 0.9471 | 0.7248 |
0.7538 | 25.0 | 14750 | 1.0707 | 0.7440 |
0.7006 | 26.0 | 15340 | 1.1137 | 0.7046 |
0.7006 | 27.0 | 15930 | 1.0523 | 0.7199 |
0.6916 | 28.0 | 16520 | 1.1686 | 0.7358 |
0.6904 | 29.0 | 17110 | 0.9189 | 0.7306 |
0.6631 | 30.0 | 17700 | 0.9745 | 0.7235 |
0.6323 | 31.0 | 18290 | 1.0716 | 0.7422 |
0.632 | 32.0 | 18880 | 1.0115 | 0.7465 |
0.6287 | 33.0 | 19470 | 0.9677 | 0.7217 |
0.6044 | 34.0 | 20060 | 0.9363 | 0.7419 |
0.5925 | 35.0 | 20650 | 0.9818 | 0.7297 |
0.5741 | 36.0 | 21240 | 1.3325 | 0.7361 |
0.5764 | 37.0 | 21830 | 0.9738 | 0.7471 |
0.5714 | 38.0 | 22420 | 0.9287 | 0.7480 |
0.5845 | 39.0 | 23010 | 1.2000 | 0.7450 |
0.5831 | 40.0 | 23600 | 0.9481 | 0.7462 |
0.5602 | 41.0 | 24190 | 1.0474 | 0.7440 |
0.5576 | 42.0 | 24780 | 0.9117 | 0.7434 |
0.5419 | 43.0 | 25370 | 0.9980 | 0.7495 |
0.5274 | 44.0 | 25960 | 0.9491 | 0.7462 |
0.5363 | 45.0 | 26550 | 0.9289 | 0.7468 |
0.5117 | 46.0 | 27140 | 0.9171 | 0.7498 |
0.5228 | 47.0 | 27730 | 0.9486 | 0.7535 |
0.4971 | 48.0 | 28320 | 0.9034 | 0.7498 |
0.5325 | 49.0 | 28910 | 0.9143 | 0.7495 |
0.4951 | 50.0 | 29500 | 0.9264 | 0.7373 |
0.5123 | 51.0 | 30090 | 0.9220 | 0.7471 |
0.52 | 52.0 | 30680 | 0.9128 | 0.7502 |
0.4884 | 53.0 | 31270 | 0.9343 | 0.7443 |
0.4878 | 54.0 | 31860 | 1.0356 | 0.7422 |
0.4967 | 55.0 | 32450 | 0.9414 | 0.7486 |
0.4823 | 56.0 | 33040 | 0.9257 | 0.7480 |
0.4789 | 57.0 | 33630 | 0.9966 | 0.7511 |
0.463 | 58.0 | 34220 | 0.9716 | 0.7495 |
0.4662 | 59.0 | 34810 | 0.9143 | 0.7425 |
0.4663 | 60.0 | 35400 | 0.9606 | 0.7526 |
0.4525 | 61.0 | 35990 | 0.9436 | 0.7541 |
0.4683 | 62.0 | 36580 | 0.9041 | 0.7480 |
0.4435 | 63.0 | 37170 | 0.9220 | 0.7443 |
0.4537 | 64.0 | 37760 | 0.9282 | 0.7394 |
0.4582 | 65.0 | 38350 | 0.9121 | 0.7398 |
0.4486 | 66.0 | 38940 | 0.9371 | 0.7477 |
0.4437 | 67.0 | 39530 | 0.9325 | 0.7431 |
0.4309 | 68.0 | 40120 | 0.9278 | 0.7492 |
0.4314 | 69.0 | 40710 | 1.0054 | 0.7489 |
0.4468 | 70.0 | 41300 | 0.9489 | 0.7532 |
0.4403 | 71.0 | 41890 | 0.9136 | 0.7446 |
0.4311 | 72.0 | 42480 | 0.9262 | 0.7489 |
0.4157 | 73.0 | 43070 | 0.9242 | 0.7511 |
0.4257 | 74.0 | 43660 | 0.9193 | 0.7508 |
0.4384 | 75.0 | 44250 | 1.0083 | 0.7566 |
0.4348 | 76.0 | 44840 | 0.9168 | 0.7382 |
0.4248 | 77.0 | 45430 | 0.9467 | 0.7550 |
0.4337 | 78.0 | 46020 | 0.9040 | 0.7477 |
0.4222 | 79.0 | 46610 | 0.9154 | 0.7526 |
0.4155 | 80.0 | 47200 | 0.9295 | 0.7508 |
0.4005 | 81.0 | 47790 | 0.9250 | 0.7505 |
0.4189 | 82.0 | 48380 | 1.0215 | 0.7560 |
0.4098 | 83.0 | 48970 | 0.9297 | 0.7557 |
0.4001 | 84.0 | 49560 | 0.9180 | 0.7547 |
0.408 | 85.0 | 50150 | 0.9020 | 0.7526 |
0.3984 | 86.0 | 50740 | 0.9035 | 0.7532 |
0.4118 | 87.0 | 51330 | 0.9474 | 0.7532 |
0.4011 | 88.0 | 51920 | 0.9016 | 0.7471 |
0.3934 | 89.0 | 52510 | 0.9447 | 0.7557 |
0.3867 | 90.0 | 53100 | 0.8979 | 0.7517 |
0.3999 | 91.0 | 53690 | 0.9156 | 0.7541 |
0.4011 | 92.0 | 54280 | 0.9033 | 0.7508 |
0.3826 | 93.0 | 54870 | 0.9343 | 0.7526 |
0.3912 | 94.0 | 55460 | 0.9071 | 0.7532 |
0.3803 | 95.0 | 56050 | 0.9248 | 0.7572 |
0.3842 | 96.0 | 56640 | 0.8987 | 0.7535 |
0.3846 | 97.0 | 57230 | 0.9031 | 0.7550 |
0.3743 | 98.0 | 57820 | 0.8933 | 0.7544 |
0.3782 | 99.0 | 58410 | 0.8965 | 0.7532 |
0.3939 | 100.0 | 59000 | 0.8950 | 0.7544 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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