bert-large-uncased-finetuned-lowR100-5-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: 1.9006
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 |
---|---|---|---|
6.5116 | 1.0 | 1 | 6.5297 |
6.6949 | 2.0 | 2 | 6.9289 |
6.0946 | 3.0 | 3 | 7.6464 |
5.8742 | 4.0 | 4 | 4.8191 |
5.4365 | 5.0 | 5 | 6.1273 |
5.171 | 6.0 | 6 | 4.5528 |
4.4944 | 7.0 | 7 | 4.8541 |
4.1146 | 8.0 | 8 | 3.4321 |
3.4689 | 9.0 | 9 | 2.4818 |
3.6228 | 10.0 | 10 | 2.4444 |
3.147 | 11.0 | 11 | 1.0668 |
2.969 | 12.0 | 12 | 3.5394 |
2.9788 | 13.0 | 13 | 3.1681 |
2.9108 | 14.0 | 14 | 1.6325 |
2.9377 | 15.0 | 15 | 2.0480 |
2.6179 | 16.0 | 16 | 2.6157 |
2.8978 | 17.0 | 17 | 3.3663 |
2.6496 | 18.0 | 18 | 2.6341 |
2.592 | 19.0 | 19 | 2.6462 |
2.5212 | 20.0 | 20 | 2.2172 |
2.402 | 21.0 | 21 | 3.3419 |
2.3146 | 22.0 | 22 | 1.8095 |
2.5215 | 23.0 | 23 | 2.7622 |
2.1736 | 24.0 | 24 | 3.9402 |
2.4366 | 25.0 | 25 | 2.3742 |
2.1603 | 26.0 | 26 | 2.4520 |
2.21 | 27.0 | 27 | 3.8185 |
2.1954 | 28.0 | 28 | 4.0015 |
2.6556 | 29.0 | 29 | 2.4132 |
2.3936 | 30.0 | 30 | 3.8690 |
2.2442 | 31.0 | 31 | 3.7408 |
2.2486 | 32.0 | 32 | 2.5657 |
2.5066 | 33.0 | 33 | 3.6632 |
2.0527 | 34.0 | 34 | 2.9892 |
2.6207 | 35.0 | 35 | 3.5594 |
2.296 | 36.0 | 36 | 2.3785 |
2.4068 | 37.0 | 37 | 3.6126 |
2.257 | 38.0 | 38 | 1.0477 |
2.0597 | 39.0 | 39 | 1.5386 |
2.1702 | 40.0 | 40 | 2.4686 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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