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20230826022757
This model is a fine-tuned version of bert-large-cased on the super_glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.5491
- Accuracy: 0.74
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: 0.01
- 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: 80.0
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 1.0 | 25 | 0.6588 | 0.44 |
No log | 2.0 | 50 | 0.6258 | 0.63 |
No log | 3.0 | 75 | 0.6839 | 0.66 |
No log | 4.0 | 100 | 0.6238 | 0.63 |
No log | 5.0 | 125 | 0.5878 | 0.64 |
No log | 6.0 | 150 | 0.5895 | 0.61 |
No log | 7.0 | 175 | 0.5951 | 0.63 |
No log | 8.0 | 200 | 0.6701 | 0.62 |
No log | 9.0 | 225 | 0.5858 | 0.62 |
No log | 10.0 | 250 | 0.6603 | 0.64 |
No log | 11.0 | 275 | 0.5708 | 0.65 |
No log | 12.0 | 300 | 0.5657 | 0.63 |
No log | 13.0 | 325 | 0.5691 | 0.68 |
No log | 14.0 | 350 | 0.5820 | 0.67 |
No log | 15.0 | 375 | 0.5245 | 0.7 |
No log | 16.0 | 400 | 0.6291 | 0.7 |
No log | 17.0 | 425 | 0.6177 | 0.7 |
No log | 18.0 | 450 | 0.7375 | 0.7 |
No log | 19.0 | 475 | 0.6500 | 0.68 |
0.6647 | 20.0 | 500 | 0.6727 | 0.71 |
0.6647 | 21.0 | 525 | 0.7042 | 0.72 |
0.6647 | 22.0 | 550 | 0.7448 | 0.71 |
0.6647 | 23.0 | 575 | 0.6157 | 0.72 |
0.6647 | 24.0 | 600 | 0.7661 | 0.72 |
0.6647 | 25.0 | 625 | 0.6832 | 0.72 |
0.6647 | 26.0 | 650 | 0.6971 | 0.72 |
0.6647 | 27.0 | 675 | 0.6274 | 0.72 |
0.6647 | 28.0 | 700 | 0.6846 | 0.73 |
0.6647 | 29.0 | 725 | 0.6319 | 0.73 |
0.6647 | 30.0 | 750 | 0.7387 | 0.74 |
0.6647 | 31.0 | 775 | 0.6482 | 0.74 |
0.6647 | 32.0 | 800 | 0.6043 | 0.73 |
0.6647 | 33.0 | 825 | 0.6589 | 0.72 |
0.6647 | 34.0 | 850 | 0.7023 | 0.74 |
0.6647 | 35.0 | 875 | 0.6197 | 0.74 |
0.6647 | 36.0 | 900 | 0.6325 | 0.75 |
0.6647 | 37.0 | 925 | 0.6264 | 0.75 |
0.6647 | 38.0 | 950 | 0.6198 | 0.73 |
0.6647 | 39.0 | 975 | 0.6239 | 0.74 |
0.2917 | 40.0 | 1000 | 0.6072 | 0.74 |
0.2917 | 41.0 | 1025 | 0.6354 | 0.74 |
0.2917 | 42.0 | 1050 | 0.5724 | 0.74 |
0.2917 | 43.0 | 1075 | 0.5799 | 0.74 |
0.2917 | 44.0 | 1100 | 0.5863 | 0.75 |
0.2917 | 45.0 | 1125 | 0.6033 | 0.74 |
0.2917 | 46.0 | 1150 | 0.6735 | 0.73 |
0.2917 | 47.0 | 1175 | 0.6068 | 0.73 |
0.2917 | 48.0 | 1200 | 0.6064 | 0.73 |
0.2917 | 49.0 | 1225 | 0.6205 | 0.74 |
0.2917 | 50.0 | 1250 | 0.5605 | 0.74 |
0.2917 | 51.0 | 1275 | 0.6015 | 0.75 |
0.2917 | 52.0 | 1300 | 0.5771 | 0.75 |
0.2917 | 53.0 | 1325 | 0.5400 | 0.75 |
0.2917 | 54.0 | 1350 | 0.5911 | 0.76 |
0.2917 | 55.0 | 1375 | 0.5665 | 0.76 |
0.2917 | 56.0 | 1400 | 0.5658 | 0.75 |
0.2917 | 57.0 | 1425 | 0.5775 | 0.75 |
0.2917 | 58.0 | 1450 | 0.5690 | 0.74 |
0.2917 | 59.0 | 1475 | 0.5689 | 0.75 |
0.2234 | 60.0 | 1500 | 0.5793 | 0.74 |
0.2234 | 61.0 | 1525 | 0.5490 | 0.75 |
0.2234 | 62.0 | 1550 | 0.5899 | 0.75 |
0.2234 | 63.0 | 1575 | 0.5612 | 0.75 |
0.2234 | 64.0 | 1600 | 0.5451 | 0.75 |
0.2234 | 65.0 | 1625 | 0.5690 | 0.74 |
0.2234 | 66.0 | 1650 | 0.5391 | 0.74 |
0.2234 | 67.0 | 1675 | 0.5607 | 0.74 |
0.2234 | 68.0 | 1700 | 0.5451 | 0.74 |
0.2234 | 69.0 | 1725 | 0.5675 | 0.74 |
0.2234 | 70.0 | 1750 | 0.5486 | 0.74 |
0.2234 | 71.0 | 1775 | 0.5502 | 0.74 |
0.2234 | 72.0 | 1800 | 0.5445 | 0.74 |
0.2234 | 73.0 | 1825 | 0.5577 | 0.74 |
0.2234 | 74.0 | 1850 | 0.5533 | 0.74 |
0.2234 | 75.0 | 1875 | 0.5534 | 0.74 |
0.2234 | 76.0 | 1900 | 0.5549 | 0.74 |
0.2234 | 77.0 | 1925 | 0.5495 | 0.74 |
0.2234 | 78.0 | 1950 | 0.5492 | 0.74 |
0.2234 | 79.0 | 1975 | 0.5488 | 0.74 |
0.2032 | 80.0 | 2000 | 0.5491 | 0.74 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3