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bert-large-uncased-finetuned-lowR100-4-uncased-DA-20
This model is a fine-tuned version of bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.2624
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: 2e-05
- train_batch_size: 30
- eval_batch_size: 30
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40.0
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
No log | 1.0 | 1 | 5.4063 |
5.9654 | 2.0 | 2 | 6.1913 |
5.9654 | 3.0 | 3 | 5.8608 |
5.5864 | 4.0 | 4 | 4.4613 |
5.5864 | 5.0 | 5 | 5.0257 |
5.1093 | 6.0 | 6 | 4.7405 |
5.1093 | 7.0 | 7 | 4.7516 |
4.3965 | 8.0 | 8 | 3.1646 |
4.3965 | 9.0 | 9 | 3.7378 |
3.7825 | 10.0 | 10 | 2.0073 |
3.7825 | 11.0 | 11 | 5.5725 |
3.009 | 12.0 | 12 | 3.0077 |
3.009 | 13.0 | 13 | 2.6288 |
2.7427 | 14.0 | 14 | 2.8630 |
2.7427 | 15.0 | 15 | 2.3270 |
2.4122 | 16.0 | 16 | 3.3092 |
2.4122 | 17.0 | 17 | 2.7499 |
2.3707 | 18.0 | 18 | 3.2892 |
2.3707 | 19.0 | 19 | 2.9385 |
2.6243 | 20.0 | 20 | 1.5626 |
2.6243 | 21.0 | 21 | 1.0104 |
2.1606 | 22.0 | 22 | 3.3464 |
2.1606 | 23.0 | 23 | 3.9334 |
2.1419 | 24.0 | 24 | 1.7512 |
2.1419 | 25.0 | 25 | 2.5855 |
2.2265 | 26.0 | 26 | 2.2896 |
2.2265 | 27.0 | 27 | 2.3118 |
2.2539 | 28.0 | 28 | 2.9997 |
2.2539 | 29.0 | 29 | 3.0682 |
2.3866 | 30.0 | 30 | 3.3435 |
2.3866 | 31.0 | 31 | 3.5848 |
2.1788 | 32.0 | 32 | 2.4658 |
2.1788 | 33.0 | 33 | 2.4060 |
2.0932 | 34.0 | 34 | 2.8497 |
2.0932 | 35.0 | 35 | 3.8500 |
1.9799 | 36.0 | 36 | 1.9458 |
1.9799 | 37.0 | 37 | 2.4557 |
2.0164 | 38.0 | 38 | 3.3370 |
2.0164 | 39.0 | 39 | 3.6850 |
2.1732 | 40.0 | 40 | 1.7986 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2