<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
1_9e-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: 0.8873
- Accuracy: 0.7443
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 |
---|---|---|---|---|
1.2619 | 1.0 | 590 | 0.6470 | 0.6217 |
1.0747 | 2.0 | 1180 | 0.6993 | 0.4211 |
0.8969 | 3.0 | 1770 | 0.6604 | 0.5719 |
0.8368 | 4.0 | 2360 | 0.7051 | 0.5043 |
0.8124 | 5.0 | 2950 | 0.7117 | 0.6294 |
0.7078 | 6.0 | 3540 | 0.6893 | 0.6557 |
0.6885 | 7.0 | 4130 | 1.0081 | 0.4541 |
0.648 | 8.0 | 4720 | 0.5951 | 0.6951 |
0.6353 | 9.0 | 5310 | 0.6077 | 0.6624 |
0.6037 | 10.0 | 5900 | 0.5867 | 0.6920 |
0.5823 | 11.0 | 6490 | 0.5554 | 0.7024 |
0.5648 | 12.0 | 7080 | 0.5959 | 0.6602 |
0.5628 | 13.0 | 7670 | 0.5532 | 0.6966 |
0.5323 | 14.0 | 8260 | 0.5416 | 0.7107 |
0.5218 | 15.0 | 8850 | 0.5633 | 0.6969 |
0.505 | 16.0 | 9440 | 0.5292 | 0.7110 |
0.4968 | 17.0 | 10030 | 0.5375 | 0.7235 |
0.4821 | 18.0 | 10620 | 0.6966 | 0.6667 |
0.4692 | 19.0 | 11210 | 0.5588 | 0.7254 |
0.4651 | 20.0 | 11800 | 0.5620 | 0.7177 |
0.4215 | 21.0 | 12390 | 0.5768 | 0.7306 |
0.4361 | 22.0 | 12980 | 0.5720 | 0.7278 |
0.4138 | 23.0 | 13570 | 0.6098 | 0.7321 |
0.3883 | 24.0 | 14160 | 0.5691 | 0.7315 |
0.3852 | 25.0 | 14750 | 0.5940 | 0.7315 |
0.3691 | 26.0 | 15340 | 0.7810 | 0.6657 |
0.3689 | 27.0 | 15930 | 0.6396 | 0.7220 |
0.3413 | 28.0 | 16520 | 0.6304 | 0.7385 |
0.3333 | 29.0 | 17110 | 0.6135 | 0.7343 |
0.3259 | 30.0 | 17700 | 0.6418 | 0.7242 |
0.3049 | 31.0 | 18290 | 0.6385 | 0.7327 |
0.3203 | 32.0 | 18880 | 0.7961 | 0.7275 |
0.2978 | 33.0 | 19470 | 0.6375 | 0.7260 |
0.2831 | 34.0 | 20060 | 0.7307 | 0.7116 |
0.2782 | 35.0 | 20650 | 0.7057 | 0.7422 |
0.2668 | 36.0 | 21240 | 0.6802 | 0.7391 |
0.2673 | 37.0 | 21830 | 0.7305 | 0.7260 |
0.2478 | 38.0 | 22420 | 0.7019 | 0.7367 |
0.2481 | 39.0 | 23010 | 0.7238 | 0.7465 |
0.2406 | 40.0 | 23600 | 0.8325 | 0.7300 |
0.2344 | 41.0 | 24190 | 0.8143 | 0.7367 |
0.2151 | 42.0 | 24780 | 0.8423 | 0.7413 |
0.226 | 43.0 | 25370 | 0.7901 | 0.7343 |
0.2141 | 44.0 | 25960 | 0.8760 | 0.7355 |
0.2062 | 45.0 | 26550 | 0.8387 | 0.7416 |
0.192 | 46.0 | 27140 | 0.7825 | 0.7413 |
0.2045 | 47.0 | 27730 | 0.8157 | 0.7211 |
0.1922 | 48.0 | 28320 | 0.8735 | 0.7190 |
0.1967 | 49.0 | 28910 | 0.7669 | 0.7416 |
0.1814 | 50.0 | 29500 | 0.7925 | 0.7401 |
0.1814 | 51.0 | 30090 | 0.8249 | 0.7367 |
0.1721 | 52.0 | 30680 | 0.8772 | 0.7352 |
0.1607 | 53.0 | 31270 | 0.8614 | 0.7355 |
0.162 | 54.0 | 31860 | 0.8165 | 0.7376 |
0.1745 | 55.0 | 32450 | 0.8330 | 0.7287 |
0.1644 | 56.0 | 33040 | 0.8343 | 0.7370 |
0.1478 | 57.0 | 33630 | 0.8965 | 0.7318 |
0.1571 | 58.0 | 34220 | 0.9214 | 0.7232 |
0.1506 | 59.0 | 34810 | 0.9052 | 0.7401 |
0.1469 | 60.0 | 35400 | 0.8536 | 0.7428 |
0.1472 | 61.0 | 35990 | 0.8885 | 0.7309 |
0.1408 | 62.0 | 36580 | 0.8733 | 0.7413 |
0.1356 | 63.0 | 37170 | 0.9329 | 0.7214 |
0.1445 | 64.0 | 37760 | 0.8954 | 0.7480 |
0.1398 | 65.0 | 38350 | 0.8575 | 0.7391 |
0.1389 | 66.0 | 38940 | 0.8679 | 0.7422 |
0.1278 | 67.0 | 39530 | 0.9074 | 0.7446 |
0.1337 | 68.0 | 40120 | 0.8901 | 0.7346 |
0.123 | 69.0 | 40710 | 0.9254 | 0.7453 |
0.1362 | 70.0 | 41300 | 0.8586 | 0.7388 |
0.1214 | 71.0 | 41890 | 0.9126 | 0.7321 |
0.1245 | 72.0 | 42480 | 0.8943 | 0.7394 |
0.1142 | 73.0 | 43070 | 0.9241 | 0.7349 |
0.1227 | 74.0 | 43660 | 0.9128 | 0.7391 |
0.1121 | 75.0 | 44250 | 0.8904 | 0.7373 |
0.1172 | 76.0 | 44840 | 0.9219 | 0.7404 |
0.1122 | 77.0 | 45430 | 0.9410 | 0.7486 |
0.1047 | 78.0 | 46020 | 0.8903 | 0.7379 |
0.1088 | 79.0 | 46610 | 0.9508 | 0.7330 |
0.1076 | 80.0 | 47200 | 0.8921 | 0.7416 |
0.0986 | 81.0 | 47790 | 0.8941 | 0.7327 |
0.1037 | 82.0 | 48380 | 0.9029 | 0.7343 |
0.0983 | 83.0 | 48970 | 0.8863 | 0.7370 |
0.104 | 84.0 | 49560 | 0.8850 | 0.7361 |
0.0996 | 85.0 | 50150 | 0.9146 | 0.7453 |
0.0994 | 86.0 | 50740 | 0.8958 | 0.7355 |
0.0905 | 87.0 | 51330 | 0.8989 | 0.7474 |
0.0953 | 88.0 | 51920 | 0.9067 | 0.7422 |
0.0952 | 89.0 | 52510 | 0.9108 | 0.7410 |
0.0947 | 90.0 | 53100 | 0.9015 | 0.7382 |
0.09 | 91.0 | 53690 | 0.8984 | 0.7431 |
0.0936 | 92.0 | 54280 | 0.8893 | 0.7339 |
0.0908 | 93.0 | 54870 | 0.8919 | 0.7367 |
0.0872 | 94.0 | 55460 | 0.9024 | 0.7450 |
0.0847 | 95.0 | 56050 | 0.9029 | 0.7364 |
0.0901 | 96.0 | 56640 | 0.9023 | 0.7385 |
0.085 | 97.0 | 57230 | 0.8978 | 0.7370 |
0.0852 | 98.0 | 57820 | 0.8812 | 0.7413 |
0.0887 | 99.0 | 58410 | 0.8885 | 0.7385 |
0.0855 | 100.0 | 59000 | 0.8873 | 0.7443 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3