1_8e-3_5_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: 0.9466
- Accuracy: 0.7446
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: 0.008
- 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 |
---|---|---|---|---|
1.6159 | 1.0 | 590 | 1.3915 | 0.6214 |
1.4042 | 2.0 | 1180 | 1.2318 | 0.3810 |
1.2048 | 3.0 | 1770 | 0.9197 | 0.5642 |
1.2385 | 4.0 | 2360 | 0.9595 | 0.6220 |
1.1978 | 5.0 | 2950 | 1.2082 | 0.6220 |
1.1014 | 6.0 | 3540 | 1.3630 | 0.4590 |
1.0282 | 7.0 | 4130 | 1.1057 | 0.5538 |
0.9517 | 8.0 | 4720 | 0.9745 | 0.6789 |
0.9333 | 9.0 | 5310 | 0.7981 | 0.7040 |
0.8832 | 10.0 | 5900 | 0.7960 | 0.6979 |
0.8637 | 11.0 | 6490 | 0.7471 | 0.6920 |
0.8329 | 12.0 | 7080 | 0.7465 | 0.7104 |
0.7866 | 13.0 | 7670 | 0.7123 | 0.7034 |
0.7031 | 14.0 | 8260 | 0.8286 | 0.7089 |
0.6925 | 15.0 | 8850 | 0.7817 | 0.7061 |
0.6896 | 16.0 | 9440 | 0.7579 | 0.6963 |
0.6103 | 17.0 | 10030 | 0.8758 | 0.6563 |
0.6307 | 18.0 | 10620 | 1.1495 | 0.6211 |
0.5815 | 19.0 | 11210 | 0.7249 | 0.7315 |
0.554 | 20.0 | 11800 | 1.1488 | 0.6862 |
0.5376 | 21.0 | 12390 | 0.8074 | 0.7303 |
0.4969 | 22.0 | 12980 | 0.8280 | 0.6969 |
0.4813 | 23.0 | 13570 | 0.7972 | 0.7235 |
0.457 | 24.0 | 14160 | 0.8829 | 0.6807 |
0.4489 | 25.0 | 14750 | 0.7627 | 0.7303 |
0.4306 | 26.0 | 15340 | 0.9458 | 0.6945 |
0.4171 | 27.0 | 15930 | 1.0878 | 0.6823 |
0.4069 | 28.0 | 16520 | 0.8638 | 0.7125 |
0.3713 | 29.0 | 17110 | 0.9637 | 0.7306 |
0.3471 | 30.0 | 17700 | 0.8357 | 0.7205 |
0.341 | 31.0 | 18290 | 0.8430 | 0.7355 |
0.3677 | 32.0 | 18880 | 0.8911 | 0.7199 |
0.329 | 33.0 | 19470 | 1.0170 | 0.7 |
0.3019 | 34.0 | 20060 | 0.8981 | 0.7214 |
0.2912 | 35.0 | 20650 | 0.8809 | 0.7306 |
0.2962 | 36.0 | 21240 | 0.9446 | 0.7327 |
0.3018 | 37.0 | 21830 | 0.9218 | 0.7254 |
0.2793 | 38.0 | 22420 | 0.8054 | 0.7327 |
0.2786 | 39.0 | 23010 | 0.9709 | 0.7180 |
0.2608 | 40.0 | 23600 | 1.0428 | 0.7407 |
0.2705 | 41.0 | 24190 | 1.2935 | 0.7266 |
0.2551 | 42.0 | 24780 | 0.8896 | 0.7294 |
0.2383 | 43.0 | 25370 | 0.9849 | 0.7361 |
0.2306 | 44.0 | 25960 | 0.9547 | 0.7278 |
0.23 | 45.0 | 26550 | 0.9607 | 0.7373 |
0.2192 | 46.0 | 27140 | 0.9475 | 0.7248 |
0.2276 | 47.0 | 27730 | 0.9442 | 0.7333 |
0.2129 | 48.0 | 28320 | 0.9928 | 0.7294 |
0.2245 | 49.0 | 28910 | 0.9539 | 0.7324 |
0.2229 | 50.0 | 29500 | 0.9369 | 0.7245 |
0.2036 | 51.0 | 30090 | 1.0106 | 0.7239 |
0.206 | 52.0 | 30680 | 0.9619 | 0.7410 |
0.2056 | 53.0 | 31270 | 0.9298 | 0.7376 |
0.2007 | 54.0 | 31860 | 0.9451 | 0.7333 |
0.1953 | 55.0 | 32450 | 0.9762 | 0.7223 |
0.1992 | 56.0 | 33040 | 0.9447 | 0.7416 |
0.1806 | 57.0 | 33630 | 0.9956 | 0.7440 |
0.1859 | 58.0 | 34220 | 1.0206 | 0.7391 |
0.191 | 59.0 | 34810 | 0.9121 | 0.7385 |
0.1729 | 60.0 | 35400 | 0.9958 | 0.7278 |
0.1773 | 61.0 | 35990 | 0.9859 | 0.7428 |
0.1738 | 62.0 | 36580 | 0.9922 | 0.7398 |
0.1709 | 63.0 | 37170 | 0.9094 | 0.7419 |
0.1734 | 64.0 | 37760 | 0.9329 | 0.7431 |
0.1698 | 65.0 | 38350 | 0.9349 | 0.7391 |
0.1614 | 66.0 | 38940 | 1.0098 | 0.7327 |
0.1609 | 67.0 | 39530 | 0.9705 | 0.7269 |
0.1606 | 68.0 | 40120 | 0.9001 | 0.7425 |
0.1564 | 69.0 | 40710 | 0.9798 | 0.7407 |
0.1588 | 70.0 | 41300 | 0.9898 | 0.7382 |
0.1585 | 71.0 | 41890 | 0.9410 | 0.7410 |
0.1554 | 72.0 | 42480 | 0.9762 | 0.7404 |
0.1471 | 73.0 | 43070 | 0.9262 | 0.7401 |
0.1474 | 74.0 | 43660 | 0.8916 | 0.7410 |
0.1504 | 75.0 | 44250 | 0.9635 | 0.7385 |
0.1482 | 76.0 | 44840 | 0.9420 | 0.7413 |
0.1538 | 77.0 | 45430 | 0.9594 | 0.7413 |
0.1426 | 78.0 | 46020 | 0.9633 | 0.7440 |
0.1419 | 79.0 | 46610 | 0.9489 | 0.7437 |
0.1452 | 80.0 | 47200 | 0.9420 | 0.7398 |
0.1437 | 81.0 | 47790 | 0.9826 | 0.7410 |
0.1489 | 82.0 | 48380 | 0.9691 | 0.7453 |
0.1386 | 83.0 | 48970 | 0.9704 | 0.7398 |
0.1341 | 84.0 | 49560 | 0.8968 | 0.7398 |
0.1345 | 85.0 | 50150 | 0.9537 | 0.7367 |
0.1314 | 86.0 | 50740 | 0.9844 | 0.7453 |
0.1291 | 87.0 | 51330 | 0.9527 | 0.7379 |
0.1286 | 88.0 | 51920 | 0.9672 | 0.7419 |
0.1261 | 89.0 | 52510 | 0.9531 | 0.7379 |
0.1274 | 90.0 | 53100 | 0.9543 | 0.7419 |
0.1227 | 91.0 | 53690 | 0.9765 | 0.7422 |
0.1276 | 92.0 | 54280 | 0.9331 | 0.7388 |
0.1222 | 93.0 | 54870 | 0.9318 | 0.7425 |
0.1287 | 94.0 | 55460 | 0.9397 | 0.7437 |
0.1207 | 95.0 | 56050 | 0.9776 | 0.7410 |
0.1213 | 96.0 | 56640 | 0.9470 | 0.7462 |
0.1243 | 97.0 | 57230 | 0.9408 | 0.7428 |
0.1197 | 98.0 | 57820 | 0.9454 | 0.7450 |
0.1251 | 99.0 | 58410 | 0.9556 | 0.7428 |
0.1173 | 100.0 | 59000 | 0.9466 | 0.7446 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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