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Regression_bert_aug_CustomLoss_3
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.1282
- Train Mae: 0.3851
- Train Mse: 0.1862
- Train R2-score: 0.7249
- Validation Loss: 0.1246
- Validation Mae: 0.3798
- Validation Mse: 0.1857
- Validation R2-score: 0.8337
- Epoch: 14
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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Mae | Train Mse | Train R2-score | Validation Loss | Validation Mae | Validation Mse | Validation R2-score | Epoch |
|---|---|---|---|---|---|---|---|---|
| 0.1451 | 0.4188 | 0.2732 | 0.8063 | 0.0642 | 0.3529 | 0.1994 | 0.8824 | 0 |
| 0.0567 | 0.3078 | 0.1428 | 0.7511 | 0.0452 | 0.3038 | 0.1257 | 0.8820 | 1 |
| 0.0380 | 0.2662 | 0.1007 | 0.8889 | 0.0661 | 0.2984 | 0.1442 | 0.8883 | 2 |
| 0.0363 | 0.2542 | 0.0980 | 0.8034 | 0.0318 | 0.2567 | 0.0978 | 0.9117 | 3 |
| 0.0279 | 0.2257 | 0.0714 | 0.9002 | 0.0327 | 0.2305 | 0.0793 | 0.8920 | 4 |
| 0.0241 | 0.2046 | 0.0593 | 0.8695 | 0.0306 | 0.2353 | 0.0813 | 0.9330 | 5 |
| 0.0230 | 0.1960 | 0.0540 | 0.8762 | 0.0284 | 0.2160 | 0.0710 | 0.9197 | 6 |
| 0.0223 | 0.1914 | 0.0510 | 0.9366 | 0.0285 | 0.2251 | 0.0791 | 0.9282 | 7 |
| 0.0223 | 0.1923 | 0.0516 | 0.9498 | 0.0306 | 0.2042 | 0.0748 | 0.9309 | 8 |
| 0.0231 | 0.1827 | 0.0493 | 0.8516 | 0.0302 | 0.2009 | 0.0682 | 0.9198 | 9 |
| 0.0335 | 0.1794 | 0.0576 | 0.9259 | 0.0765 | 0.3684 | 0.2192 | 0.8243 | 10 |
| 0.1380 | 0.3960 | 0.2567 | 0.8748 | 0.1037 | 0.4172 | 0.2244 | 0.6992 | 11 |
| 0.1078 | 0.4071 | 0.2170 | 0.8256 | 0.1219 | 0.4020 | 0.2234 | 0.7304 | 12 |
| 0.1217 | 0.3807 | 0.2060 | 0.8084 | 0.1434 | 0.3934 | 0.2113 | 0.8317 | 13 |
| 0.1282 | 0.3851 | 0.1862 | 0.7249 | 0.1246 | 0.3798 | 0.1857 | 0.8337 | 14 |
Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
