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2_2e-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: 0.5541
- Accuracy: 0.7003
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: 0.002
- 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 |
---|---|---|---|---|
0.8034 | 1.0 | 590 | 0.6537 | 0.6217 |
0.8338 | 2.0 | 1180 | 0.7014 | 0.6217 |
0.8142 | 3.0 | 1770 | 0.6716 | 0.5596 |
0.7701 | 4.0 | 2360 | 0.6599 | 0.6217 |
0.7412 | 5.0 | 2950 | 0.7053 | 0.6217 |
0.7414 | 6.0 | 3540 | 0.6539 | 0.6217 |
0.7411 | 7.0 | 4130 | 0.9828 | 0.3817 |
0.7237 | 8.0 | 4720 | 0.6571 | 0.6061 |
0.7339 | 9.0 | 5310 | 0.6448 | 0.6232 |
0.7005 | 10.0 | 5900 | 0.6632 | 0.6223 |
0.7171 | 11.0 | 6490 | 0.6442 | 0.6220 |
0.7084 | 12.0 | 7080 | 0.7522 | 0.4477 |
0.6985 | 13.0 | 7670 | 0.6253 | 0.6336 |
0.7044 | 14.0 | 8260 | 0.7021 | 0.6217 |
0.6752 | 15.0 | 8850 | 0.6321 | 0.6183 |
0.6817 | 16.0 | 9440 | 0.6388 | 0.6073 |
0.6715 | 17.0 | 10030 | 0.6276 | 0.6358 |
0.6591 | 18.0 | 10620 | 0.6297 | 0.6474 |
0.6681 | 19.0 | 11210 | 0.6139 | 0.6407 |
0.6595 | 20.0 | 11800 | 0.6048 | 0.6541 |
0.6463 | 21.0 | 12390 | 0.6135 | 0.6541 |
0.6391 | 22.0 | 12980 | 0.6181 | 0.6437 |
0.6407 | 23.0 | 13570 | 0.6047 | 0.6615 |
0.6226 | 24.0 | 14160 | 0.6077 | 0.6615 |
0.6271 | 25.0 | 14750 | 0.6129 | 0.6642 |
0.6288 | 26.0 | 15340 | 0.6329 | 0.6343 |
0.6254 | 27.0 | 15930 | 0.5903 | 0.6728 |
0.6085 | 28.0 | 16520 | 0.5946 | 0.6743 |
0.6107 | 29.0 | 17110 | 0.5848 | 0.6737 |
0.5917 | 30.0 | 17700 | 0.6179 | 0.6725 |
0.5997 | 31.0 | 18290 | 0.5991 | 0.6618 |
0.5877 | 32.0 | 18880 | 0.6386 | 0.6709 |
0.5894 | 33.0 | 19470 | 0.5830 | 0.6771 |
0.5804 | 34.0 | 20060 | 0.5765 | 0.6856 |
0.5751 | 35.0 | 20650 | 0.5944 | 0.6615 |
0.5825 | 36.0 | 21240 | 0.5702 | 0.6890 |
0.5824 | 37.0 | 21830 | 0.5807 | 0.6774 |
0.5671 | 38.0 | 22420 | 0.5671 | 0.6838 |
0.573 | 39.0 | 23010 | 0.5678 | 0.6862 |
0.5615 | 40.0 | 23600 | 0.5685 | 0.6893 |
0.5658 | 41.0 | 24190 | 0.5820 | 0.6792 |
0.5669 | 42.0 | 24780 | 0.5692 | 0.6902 |
0.5663 | 43.0 | 25370 | 0.5665 | 0.6881 |
0.5533 | 44.0 | 25960 | 0.5599 | 0.6920 |
0.5552 | 45.0 | 26550 | 0.5637 | 0.6905 |
0.5515 | 46.0 | 27140 | 0.5616 | 0.6893 |
0.5593 | 47.0 | 27730 | 0.5650 | 0.6887 |
0.5487 | 48.0 | 28320 | 0.5620 | 0.6948 |
0.5563 | 49.0 | 28910 | 0.5631 | 0.6911 |
0.5486 | 50.0 | 29500 | 0.5604 | 0.6972 |
0.5464 | 51.0 | 30090 | 0.5590 | 0.6939 |
0.5469 | 52.0 | 30680 | 0.5561 | 0.6969 |
0.5458 | 53.0 | 31270 | 0.5573 | 0.7 |
0.5425 | 54.0 | 31860 | 0.5558 | 0.6976 |
0.5412 | 55.0 | 32450 | 0.5552 | 0.6991 |
0.5434 | 56.0 | 33040 | 0.5564 | 0.6979 |
0.5363 | 57.0 | 33630 | 0.5536 | 0.6982 |
0.5404 | 58.0 | 34220 | 0.5556 | 0.6982 |
0.5378 | 59.0 | 34810 | 0.5542 | 0.6991 |
0.5431 | 60.0 | 35400 | 0.5541 | 0.7003 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3