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2_5e-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: 0.6847
- Accuracy: 0.7226
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: 60.0
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
1.2209 | 1.0 | 590 | 0.9564 | 0.6162 |
1.1661 | 2.0 | 1180 | 0.9456 | 0.5817 |
1.1103 | 3.0 | 1770 | 0.9574 | 0.6214 |
1.0789 | 4.0 | 2360 | 0.9671 | 0.6217 |
1.0422 | 5.0 | 2950 | 1.0276 | 0.4997 |
0.9949 | 6.0 | 3540 | 0.8934 | 0.6312 |
0.99 | 7.0 | 4130 | 1.5786 | 0.4119 |
0.9632 | 8.0 | 4720 | 1.2903 | 0.6232 |
0.9329 | 9.0 | 5310 | 0.8528 | 0.6352 |
0.9157 | 10.0 | 5900 | 0.8400 | 0.6557 |
0.9187 | 11.0 | 6490 | 0.9022 | 0.6404 |
0.8408 | 12.0 | 7080 | 0.8227 | 0.6679 |
0.8295 | 13.0 | 7670 | 1.4711 | 0.5606 |
0.9554 | 14.0 | 8260 | 0.8134 | 0.6884 |
0.7759 | 15.0 | 8850 | 0.7988 | 0.6774 |
0.7568 | 16.0 | 9440 | 0.9273 | 0.6031 |
0.7197 | 17.0 | 10030 | 0.7468 | 0.6966 |
0.739 | 18.0 | 10620 | 0.7418 | 0.6976 |
0.725 | 19.0 | 11210 | 0.7303 | 0.7043 |
0.7215 | 20.0 | 11800 | 0.7322 | 0.7024 |
0.7028 | 21.0 | 12390 | 0.7489 | 0.7073 |
0.6929 | 22.0 | 12980 | 0.7376 | 0.7125 |
0.6907 | 23.0 | 13570 | 0.7165 | 0.7122 |
0.6862 | 24.0 | 14160 | 0.7102 | 0.7101 |
0.6583 | 25.0 | 14750 | 0.7060 | 0.7193 |
0.6713 | 26.0 | 15340 | 0.7305 | 0.6905 |
0.6625 | 27.0 | 15930 | 0.7407 | 0.6914 |
0.6516 | 28.0 | 16520 | 0.7057 | 0.7232 |
0.6465 | 29.0 | 17110 | 0.7047 | 0.7135 |
0.6389 | 30.0 | 17700 | 0.7340 | 0.7272 |
0.6333 | 31.0 | 18290 | 0.7067 | 0.7055 |
0.6212 | 32.0 | 18880 | 0.7071 | 0.7235 |
0.6179 | 33.0 | 19470 | 0.6851 | 0.7202 |
0.5935 | 34.0 | 20060 | 0.6888 | 0.7187 |
0.5851 | 35.0 | 20650 | 0.7105 | 0.6985 |
0.5921 | 36.0 | 21240 | 0.6810 | 0.7284 |
0.5838 | 37.0 | 21830 | 0.6814 | 0.7315 |
0.5746 | 38.0 | 22420 | 0.6984 | 0.7086 |
0.5744 | 39.0 | 23010 | 0.6864 | 0.7214 |
0.5628 | 40.0 | 23600 | 0.6842 | 0.7260 |
0.5694 | 41.0 | 24190 | 0.7091 | 0.7083 |
0.5595 | 42.0 | 24780 | 0.6805 | 0.7214 |
0.5552 | 43.0 | 25370 | 0.6899 | 0.7321 |
0.5553 | 44.0 | 25960 | 0.7324 | 0.7021 |
0.5439 | 45.0 | 26550 | 0.6960 | 0.7122 |
0.5328 | 46.0 | 27140 | 0.6965 | 0.7131 |
0.5367 | 47.0 | 27730 | 0.6844 | 0.7257 |
0.5377 | 48.0 | 28320 | 0.6752 | 0.7275 |
0.5364 | 49.0 | 28910 | 0.6861 | 0.7165 |
0.5224 | 50.0 | 29500 | 0.6903 | 0.7153 |
0.5239 | 51.0 | 30090 | 0.6895 | 0.7202 |
0.5259 | 52.0 | 30680 | 0.6885 | 0.7162 |
0.5235 | 53.0 | 31270 | 0.6772 | 0.7281 |
0.5227 | 54.0 | 31860 | 0.7113 | 0.7141 |
0.5176 | 55.0 | 32450 | 0.6802 | 0.7266 |
0.5116 | 56.0 | 33040 | 0.6807 | 0.7284 |
0.5029 | 57.0 | 33630 | 0.6786 | 0.7239 |
0.5068 | 58.0 | 34220 | 0.6862 | 0.7226 |
0.498 | 59.0 | 34810 | 0.6838 | 0.7251 |
0.5037 | 60.0 | 35400 | 0.6847 | 0.7226 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3