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1_5e-3_1_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: 0.9257
- Accuracy: 0.7330
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 |
---|---|---|---|---|
1.0669 | 1.0 | 590 | 0.6986 | 0.6217 |
0.9963 | 2.0 | 1180 | 1.8702 | 0.3792 |
1.0427 | 3.0 | 1770 | 2.2910 | 0.3798 |
0.8982 | 4.0 | 2360 | 0.7642 | 0.4159 |
0.9871 | 5.0 | 2950 | 0.9999 | 0.6217 |
0.8853 | 6.0 | 3540 | 0.6842 | 0.5278 |
0.8006 | 7.0 | 4130 | 1.4763 | 0.3878 |
0.7667 | 8.0 | 4720 | 0.8226 | 0.6239 |
0.7472 | 9.0 | 5310 | 0.7288 | 0.6364 |
0.7638 | 10.0 | 5900 | 0.5834 | 0.6636 |
0.7755 | 11.0 | 6490 | 1.6914 | 0.4269 |
0.6952 | 12.0 | 7080 | 0.9552 | 0.6324 |
0.7343 | 13.0 | 7670 | 0.5715 | 0.6835 |
0.6358 | 14.0 | 8260 | 1.0425 | 0.6284 |
0.6214 | 15.0 | 8850 | 0.6728 | 0.6807 |
0.7714 | 16.0 | 9440 | 0.5675 | 0.6991 |
0.6478 | 17.0 | 10030 | 0.6009 | 0.6976 |
0.6253 | 18.0 | 10620 | 0.5959 | 0.6942 |
0.5884 | 19.0 | 11210 | 0.6113 | 0.6896 |
0.6143 | 20.0 | 11800 | 0.5812 | 0.7165 |
0.5621 | 21.0 | 12390 | 0.5986 | 0.7125 |
0.561 | 22.0 | 12980 | 0.9897 | 0.5994 |
0.5203 | 23.0 | 13570 | 0.8431 | 0.6606 |
0.5278 | 24.0 | 14160 | 1.2396 | 0.5673 |
0.5013 | 25.0 | 14750 | 0.6779 | 0.6850 |
0.5121 | 26.0 | 15340 | 0.8150 | 0.6459 |
0.4987 | 27.0 | 15930 | 0.6473 | 0.7208 |
0.4915 | 28.0 | 16520 | 0.6165 | 0.6997 |
0.4362 | 29.0 | 17110 | 0.7189 | 0.6587 |
0.4401 | 30.0 | 17700 | 0.6948 | 0.7211 |
0.4488 | 31.0 | 18290 | 0.9311 | 0.6924 |
0.4593 | 32.0 | 18880 | 0.6527 | 0.7297 |
0.4209 | 33.0 | 19470 | 1.0135 | 0.6437 |
0.3953 | 34.0 | 20060 | 0.8262 | 0.7162 |
0.3813 | 35.0 | 20650 | 0.8390 | 0.6911 |
0.3916 | 36.0 | 21240 | 0.7626 | 0.7 |
0.3736 | 37.0 | 21830 | 0.6349 | 0.7199 |
0.3558 | 38.0 | 22420 | 0.6932 | 0.7284 |
0.378 | 39.0 | 23010 | 0.9384 | 0.6706 |
0.3104 | 40.0 | 23600 | 0.8561 | 0.7269 |
0.3366 | 41.0 | 24190 | 0.7296 | 0.7110 |
0.3089 | 42.0 | 24780 | 0.7695 | 0.7183 |
0.3099 | 43.0 | 25370 | 0.9426 | 0.6933 |
0.3225 | 44.0 | 25960 | 0.8238 | 0.7330 |
0.2853 | 45.0 | 26550 | 0.7910 | 0.7346 |
0.3031 | 46.0 | 27140 | 1.0613 | 0.6713 |
0.2865 | 47.0 | 27730 | 0.8105 | 0.7263 |
0.2736 | 48.0 | 28320 | 0.9241 | 0.7119 |
0.2892 | 49.0 | 28910 | 0.8532 | 0.7281 |
0.2582 | 50.0 | 29500 | 0.8393 | 0.7214 |
0.2631 | 51.0 | 30090 | 1.1566 | 0.6722 |
0.2496 | 52.0 | 30680 | 0.9162 | 0.6911 |
0.2501 | 53.0 | 31270 | 0.8305 | 0.7251 |
0.2362 | 54.0 | 31860 | 1.1556 | 0.6599 |
0.2325 | 55.0 | 32450 | 1.0032 | 0.6685 |
0.2539 | 56.0 | 33040 | 0.9128 | 0.7336 |
0.2231 | 57.0 | 33630 | 0.8328 | 0.7073 |
0.2123 | 58.0 | 34220 | 0.9290 | 0.7171 |
0.2093 | 59.0 | 34810 | 0.8650 | 0.7229 |
0.2151 | 60.0 | 35400 | 0.9212 | 0.7245 |
0.2074 | 61.0 | 35990 | 0.8884 | 0.7257 |
0.2072 | 62.0 | 36580 | 0.8822 | 0.7251 |
0.1898 | 63.0 | 37170 | 0.9609 | 0.7287 |
0.1936 | 64.0 | 37760 | 0.9800 | 0.6979 |
0.197 | 65.0 | 38350 | 1.0263 | 0.7125 |
0.1856 | 66.0 | 38940 | 0.9902 | 0.7404 |
0.1751 | 67.0 | 39530 | 0.8972 | 0.7312 |
0.1791 | 68.0 | 40120 | 1.0031 | 0.7248 |
0.1693 | 69.0 | 40710 | 1.0957 | 0.7361 |
0.1783 | 70.0 | 41300 | 1.0342 | 0.7349 |
0.1801 | 71.0 | 41890 | 1.0411 | 0.7067 |
0.1768 | 72.0 | 42480 | 0.9629 | 0.7211 |
0.1595 | 73.0 | 43070 | 0.9862 | 0.7370 |
0.154 | 74.0 | 43660 | 0.9240 | 0.7333 |
0.1578 | 75.0 | 44250 | 1.1158 | 0.7336 |
0.165 | 76.0 | 44840 | 0.9100 | 0.7358 |
0.1582 | 77.0 | 45430 | 0.9886 | 0.7324 |
0.1573 | 78.0 | 46020 | 1.0058 | 0.7193 |
0.1544 | 79.0 | 46610 | 0.9316 | 0.7199 |
0.1488 | 80.0 | 47200 | 0.9493 | 0.7196 |
0.141 | 81.0 | 47790 | 0.9467 | 0.7352 |
0.1479 | 82.0 | 48380 | 0.8841 | 0.7232 |
0.1377 | 83.0 | 48970 | 0.9072 | 0.7309 |
0.1372 | 84.0 | 49560 | 0.9831 | 0.7266 |
0.1389 | 85.0 | 50150 | 0.9714 | 0.7272 |
0.136 | 86.0 | 50740 | 0.9617 | 0.7364 |
0.1383 | 87.0 | 51330 | 0.9970 | 0.7257 |
0.1324 | 88.0 | 51920 | 0.8863 | 0.7190 |
0.1262 | 89.0 | 52510 | 0.9828 | 0.7336 |
0.132 | 90.0 | 53100 | 0.9576 | 0.7333 |
0.129 | 91.0 | 53690 | 0.9326 | 0.7321 |
0.1241 | 92.0 | 54280 | 0.9571 | 0.7278 |
0.1217 | 93.0 | 54870 | 0.9131 | 0.7306 |
0.1253 | 94.0 | 55460 | 0.9053 | 0.7315 |
0.1192 | 95.0 | 56050 | 0.9126 | 0.7349 |
0.1225 | 96.0 | 56640 | 0.9336 | 0.7355 |
0.1229 | 97.0 | 57230 | 0.9702 | 0.7272 |
0.1165 | 98.0 | 57820 | 0.9494 | 0.7339 |
0.1198 | 99.0 | 58410 | 0.9183 | 0.7324 |
0.1172 | 100.0 | 59000 | 0.9257 | 0.7330 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3