<!-- 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_10_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: 1.0354
- Accuracy: 0.7401
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 |
---|---|---|---|---|
1.6659 | 1.0 | 590 | 1.0802 | 0.3801 |
1.744 | 2.0 | 1180 | 1.0364 | 0.5086 |
1.4165 | 3.0 | 1770 | 1.0525 | 0.4324 |
1.3808 | 4.0 | 2360 | 1.2296 | 0.6217 |
1.307 | 5.0 | 2950 | 3.0278 | 0.3835 |
1.228 | 6.0 | 3540 | 1.1153 | 0.6489 |
1.2785 | 7.0 | 4130 | 2.8946 | 0.4211 |
1.1321 | 8.0 | 4720 | 0.9307 | 0.6416 |
1.0781 | 9.0 | 5310 | 0.8861 | 0.6914 |
1.0489 | 10.0 | 5900 | 2.3977 | 0.6220 |
0.9691 | 11.0 | 6490 | 0.8622 | 0.6609 |
1.012 | 12.0 | 7080 | 0.7911 | 0.7031 |
0.9394 | 13.0 | 7670 | 0.7907 | 0.7086 |
0.9733 | 14.0 | 8260 | 1.6734 | 0.4859 |
0.8923 | 15.0 | 8850 | 1.1847 | 0.5654 |
0.8492 | 16.0 | 9440 | 0.9835 | 0.7116 |
0.8235 | 17.0 | 10030 | 1.1283 | 0.6428 |
0.7418 | 18.0 | 10620 | 0.9441 | 0.6832 |
0.8598 | 19.0 | 11210 | 0.7886 | 0.7190 |
0.7646 | 20.0 | 11800 | 0.7994 | 0.7211 |
0.6827 | 21.0 | 12390 | 0.8823 | 0.7122 |
0.6563 | 22.0 | 12980 | 1.1212 | 0.6364 |
0.6387 | 23.0 | 13570 | 0.8303 | 0.7113 |
0.6676 | 24.0 | 14160 | 1.3662 | 0.6251 |
0.598 | 25.0 | 14750 | 1.0796 | 0.6474 |
0.5547 | 26.0 | 15340 | 0.9681 | 0.6835 |
0.5539 | 27.0 | 15930 | 0.8656 | 0.7055 |
0.542 | 28.0 | 16520 | 1.0407 | 0.6688 |
0.519 | 29.0 | 17110 | 1.0368 | 0.7223 |
0.5087 | 30.0 | 17700 | 1.4459 | 0.7110 |
0.5462 | 31.0 | 18290 | 0.8618 | 0.7324 |
0.4592 | 32.0 | 18880 | 1.0897 | 0.7168 |
0.4374 | 33.0 | 19470 | 0.9626 | 0.7107 |
0.4665 | 34.0 | 20060 | 0.9022 | 0.7379 |
0.4086 | 35.0 | 20650 | 0.8794 | 0.7339 |
0.4042 | 36.0 | 21240 | 1.2955 | 0.7153 |
0.4267 | 37.0 | 21830 | 1.0492 | 0.7275 |
0.3928 | 38.0 | 22420 | 0.8772 | 0.7306 |
0.3777 | 39.0 | 23010 | 0.9378 | 0.7193 |
0.3693 | 40.0 | 23600 | 1.3226 | 0.6832 |
0.3782 | 41.0 | 24190 | 1.3153 | 0.7284 |
0.3429 | 42.0 | 24780 | 0.9722 | 0.7171 |
0.3359 | 43.0 | 25370 | 1.0545 | 0.7321 |
0.3431 | 44.0 | 25960 | 0.9919 | 0.7321 |
0.326 | 45.0 | 26550 | 0.8933 | 0.7202 |
0.3004 | 46.0 | 27140 | 1.0468 | 0.7361 |
0.3233 | 47.0 | 27730 | 1.0189 | 0.7318 |
0.3045 | 48.0 | 28320 | 1.3587 | 0.6740 |
0.3399 | 49.0 | 28910 | 1.0820 | 0.7092 |
0.2913 | 50.0 | 29500 | 1.2963 | 0.6835 |
0.2956 | 51.0 | 30090 | 0.9578 | 0.7324 |
0.2839 | 52.0 | 30680 | 1.0030 | 0.7437 |
0.2701 | 53.0 | 31270 | 1.1058 | 0.7245 |
0.2561 | 54.0 | 31860 | 1.0679 | 0.7156 |
0.2644 | 55.0 | 32450 | 1.0564 | 0.7388 |
0.2711 | 56.0 | 33040 | 1.1395 | 0.7193 |
0.2311 | 57.0 | 33630 | 1.0809 | 0.7434 |
0.2533 | 58.0 | 34220 | 1.0640 | 0.7450 |
0.2536 | 59.0 | 34810 | 1.0119 | 0.7468 |
0.2427 | 60.0 | 35400 | 1.0311 | 0.7266 |
0.2354 | 61.0 | 35990 | 1.0316 | 0.7346 |
0.223 | 62.0 | 36580 | 1.0253 | 0.7450 |
0.2257 | 63.0 | 37170 | 1.0761 | 0.7391 |
0.223 | 64.0 | 37760 | 1.0619 | 0.7388 |
0.2319 | 65.0 | 38350 | 0.9937 | 0.7443 |
0.2287 | 66.0 | 38940 | 1.1042 | 0.7413 |
0.2105 | 67.0 | 39530 | 1.0410 | 0.7404 |
0.2109 | 68.0 | 40120 | 0.9820 | 0.7343 |
0.2012 | 69.0 | 40710 | 1.0243 | 0.7456 |
0.2035 | 70.0 | 41300 | 1.0944 | 0.7434 |
0.2039 | 71.0 | 41890 | 1.0195 | 0.7346 |
0.201 | 72.0 | 42480 | 1.1017 | 0.7431 |
0.1952 | 73.0 | 43070 | 1.1423 | 0.7254 |
0.1837 | 74.0 | 43660 | 1.0600 | 0.7391 |
0.1891 | 75.0 | 44250 | 1.0447 | 0.7437 |
0.1885 | 76.0 | 44840 | 1.0443 | 0.7471 |
0.1928 | 77.0 | 45430 | 1.0006 | 0.7437 |
0.1952 | 78.0 | 46020 | 1.0411 | 0.7453 |
0.1787 | 79.0 | 46610 | 1.0275 | 0.7413 |
0.1701 | 80.0 | 47200 | 1.0867 | 0.7272 |
0.1654 | 81.0 | 47790 | 1.0261 | 0.7330 |
0.1808 | 82.0 | 48380 | 1.0537 | 0.7339 |
0.1794 | 83.0 | 48970 | 1.0808 | 0.7456 |
0.1671 | 84.0 | 49560 | 1.0418 | 0.7404 |
0.1668 | 85.0 | 50150 | 1.0140 | 0.7407 |
0.1726 | 86.0 | 50740 | 1.0860 | 0.7456 |
0.1643 | 87.0 | 51330 | 1.0581 | 0.7352 |
0.1596 | 88.0 | 51920 | 1.0603 | 0.7349 |
0.1612 | 89.0 | 52510 | 1.0412 | 0.7422 |
0.1563 | 90.0 | 53100 | 1.0482 | 0.7401 |
0.1567 | 91.0 | 53690 | 1.1036 | 0.7431 |
0.1601 | 92.0 | 54280 | 1.0126 | 0.7388 |
0.1566 | 93.0 | 54870 | 1.0497 | 0.7352 |
0.1558 | 94.0 | 55460 | 1.0246 | 0.7388 |
0.1518 | 95.0 | 56050 | 1.0406 | 0.7413 |
0.1503 | 96.0 | 56640 | 1.0261 | 0.7425 |
0.1523 | 97.0 | 57230 | 1.0411 | 0.7370 |
0.1426 | 98.0 | 57820 | 1.0398 | 0.7416 |
0.1465 | 99.0 | 58410 | 1.0459 | 0.7388 |
0.1388 | 100.0 | 59000 | 1.0354 | 0.7401 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3