1_5e-3_1_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: 0.5068
- Accuracy: 0.7388
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 |
---|---|---|---|---|
0.8736 | 1.0 | 590 | 1.0074 | 0.6217 |
0.8968 | 2.0 | 1180 | 1.0334 | 0.6217 |
0.8293 | 3.0 | 1770 | 0.6363 | 0.4920 |
0.7568 | 4.0 | 2360 | 0.6064 | 0.6232 |
0.66 | 5.0 | 2950 | 0.6124 | 0.6223 |
0.6953 | 6.0 | 3540 | 0.5216 | 0.6550 |
0.6411 | 7.0 | 4130 | 0.5622 | 0.6012 |
0.5966 | 8.0 | 4720 | 0.4958 | 0.6584 |
0.5765 | 9.0 | 5310 | 0.8209 | 0.6300 |
0.6133 | 10.0 | 5900 | 0.4712 | 0.6826 |
0.605 | 11.0 | 6490 | 0.4679 | 0.7034 |
0.5325 | 12.0 | 7080 | 0.7704 | 0.6443 |
0.5728 | 13.0 | 7670 | 0.5719 | 0.6024 |
0.5194 | 14.0 | 8260 | 0.8197 | 0.6535 |
0.501 | 15.0 | 8850 | 0.4650 | 0.6758 |
0.5197 | 16.0 | 9440 | 0.4482 | 0.6908 |
0.4824 | 17.0 | 10030 | 0.5545 | 0.6208 |
0.4937 | 18.0 | 10620 | 0.8156 | 0.5514 |
0.4855 | 19.0 | 11210 | 0.4380 | 0.7061 |
0.4705 | 20.0 | 11800 | 0.4712 | 0.7055 |
0.4481 | 21.0 | 12390 | 0.4595 | 0.7098 |
0.4624 | 22.0 | 12980 | 0.5374 | 0.6532 |
0.4222 | 23.0 | 13570 | 0.4828 | 0.6731 |
0.4293 | 24.0 | 14160 | 0.4509 | 0.7147 |
0.4082 | 25.0 | 14750 | 0.4616 | 0.7018 |
0.392 | 26.0 | 15340 | 0.4615 | 0.7061 |
0.4079 | 27.0 | 15930 | 0.4404 | 0.7278 |
0.3798 | 28.0 | 16520 | 0.5590 | 0.6691 |
0.4075 | 29.0 | 17110 | 0.5303 | 0.7122 |
0.3755 | 30.0 | 17700 | 0.4535 | 0.7312 |
0.3686 | 31.0 | 18290 | 0.5050 | 0.6771 |
0.3553 | 32.0 | 18880 | 0.4831 | 0.7269 |
0.3576 | 33.0 | 19470 | 0.4556 | 0.7177 |
0.343 | 34.0 | 20060 | 0.4762 | 0.7269 |
0.3275 | 35.0 | 20650 | 0.4346 | 0.7275 |
0.327 | 36.0 | 21240 | 0.4859 | 0.7269 |
0.3328 | 37.0 | 21830 | 0.4580 | 0.7080 |
0.3228 | 38.0 | 22420 | 0.4488 | 0.7266 |
0.3103 | 39.0 | 23010 | 0.4543 | 0.7379 |
0.2946 | 40.0 | 23600 | 0.4612 | 0.7379 |
0.3044 | 41.0 | 24190 | 0.5015 | 0.7352 |
0.3008 | 42.0 | 24780 | 0.4525 | 0.7281 |
0.2823 | 43.0 | 25370 | 0.5095 | 0.7278 |
0.2779 | 44.0 | 25960 | 0.4926 | 0.7095 |
0.2763 | 45.0 | 26550 | 0.4621 | 0.7343 |
0.2726 | 46.0 | 27140 | 0.4941 | 0.7343 |
0.2714 | 47.0 | 27730 | 0.4843 | 0.7187 |
0.2637 | 48.0 | 28320 | 0.5355 | 0.7336 |
0.2699 | 49.0 | 28910 | 0.4733 | 0.7355 |
0.2579 | 50.0 | 29500 | 0.4887 | 0.7187 |
0.2416 | 51.0 | 30090 | 0.4815 | 0.7211 |
0.248 | 52.0 | 30680 | 0.4938 | 0.7287 |
0.2424 | 53.0 | 31270 | 0.5618 | 0.6960 |
0.2333 | 54.0 | 31860 | 0.4903 | 0.7333 |
0.2392 | 55.0 | 32450 | 0.5097 | 0.7343 |
0.2481 | 56.0 | 33040 | 0.5276 | 0.7352 |
0.2291 | 57.0 | 33630 | 0.4934 | 0.7327 |
0.2181 | 58.0 | 34220 | 0.5084 | 0.7294 |
0.227 | 59.0 | 34810 | 0.5020 | 0.7266 |
0.2242 | 60.0 | 35400 | 0.5140 | 0.7315 |
0.2243 | 61.0 | 35990 | 0.5246 | 0.7297 |
0.2218 | 62.0 | 36580 | 0.4869 | 0.7275 |
0.2078 | 63.0 | 37170 | 0.4971 | 0.7187 |
0.2194 | 64.0 | 37760 | 0.5192 | 0.7251 |
0.2078 | 65.0 | 38350 | 0.5858 | 0.7410 |
0.2079 | 66.0 | 38940 | 0.5299 | 0.7361 |
0.2019 | 67.0 | 39530 | 0.4952 | 0.7306 |
0.2076 | 68.0 | 40120 | 0.5006 | 0.7324 |
0.2013 | 69.0 | 40710 | 0.5055 | 0.7343 |
0.2047 | 70.0 | 41300 | 0.5223 | 0.7336 |
0.2049 | 71.0 | 41890 | 0.5265 | 0.7162 |
0.1916 | 72.0 | 42480 | 0.5238 | 0.7407 |
0.1896 | 73.0 | 43070 | 0.4899 | 0.7361 |
0.19 | 74.0 | 43660 | 0.5060 | 0.7315 |
0.1918 | 75.0 | 44250 | 0.5260 | 0.7346 |
0.1877 | 76.0 | 44840 | 0.5053 | 0.7336 |
0.1952 | 77.0 | 45430 | 0.5019 | 0.7382 |
0.1851 | 78.0 | 46020 | 0.4942 | 0.7336 |
0.1862 | 79.0 | 46610 | 0.5213 | 0.7398 |
0.1833 | 80.0 | 47200 | 0.5167 | 0.7343 |
0.181 | 81.0 | 47790 | 0.5394 | 0.7358 |
0.186 | 82.0 | 48380 | 0.5684 | 0.7336 |
0.1825 | 83.0 | 48970 | 0.5106 | 0.7373 |
0.1713 | 84.0 | 49560 | 0.5482 | 0.7410 |
0.174 | 85.0 | 50150 | 0.5182 | 0.7385 |
0.1712 | 86.0 | 50740 | 0.5350 | 0.7376 |
0.1687 | 87.0 | 51330 | 0.5074 | 0.7391 |
0.172 | 88.0 | 51920 | 0.5126 | 0.7382 |
0.1702 | 89.0 | 52510 | 0.4916 | 0.7275 |
0.1695 | 90.0 | 53100 | 0.5229 | 0.7370 |
0.1705 | 91.0 | 53690 | 0.4987 | 0.7401 |
0.1703 | 92.0 | 54280 | 0.4968 | 0.7254 |
0.1696 | 93.0 | 54870 | 0.5109 | 0.7382 |
0.1651 | 94.0 | 55460 | 0.5180 | 0.7413 |
0.1623 | 95.0 | 56050 | 0.5017 | 0.7385 |
0.1659 | 96.0 | 56640 | 0.5077 | 0.7407 |
0.1592 | 97.0 | 57230 | 0.5173 | 0.7394 |
0.1608 | 98.0 | 57820 | 0.5034 | 0.7413 |
0.1599 | 99.0 | 58410 | 0.5079 | 0.7407 |
0.1638 | 100.0 | 59000 | 0.5068 | 0.7388 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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