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Regression_bert_10
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: 0.0535
- Train Mae: 0.2673
- Train Mse: 0.1031
- Train R2-score: 0.6896
- Validation Loss: 0.1142
- Validation Mae: 0.3549
- Validation Mse: 0.1957
- Validation R2-score: 0.9230
- Epoch: 9
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 |
|---|---|---|---|---|---|---|---|---|
| 0.2988 | 0.4759 | 0.3361 | 0.6079 | 0.1967 | 0.3939 | 0.2542 | 0.9026 | 0 |
| 0.1715 | 0.4010 | 0.2357 | 0.6812 | 0.1680 | 0.4014 | 0.2478 | 0.9049 | 1 |
| 0.0903 | 0.3374 | 0.1532 | 0.8384 | 0.1354 | 0.3432 | 0.1971 | 0.9210 | 2 |
| 0.0636 | 0.3139 | 0.1272 | 0.4117 | 0.1538 | 0.4066 | 0.2304 | 0.9034 | 3 |
| 0.0746 | 0.3142 | 0.1294 | 0.9220 | 0.1184 | 0.3589 | 0.2015 | 0.9224 | 4 |
| 0.0604 | 0.2837 | 0.1119 | 0.9439 | 0.1268 | 0.3450 | 0.1994 | 0.9209 | 5 |
| 0.0556 | 0.2660 | 0.1049 | 0.6002 | 0.1193 | 0.3037 | 0.1704 | 0.9265 | 6 |
| 0.0541 | 0.2581 | 0.1007 | 0.8081 | 0.1125 | 0.3350 | 0.1743 | 0.9229 | 7 |
| 0.0532 | 0.2679 | 0.1044 | 0.8917 | 0.1109 | 0.3131 | 0.1757 | 0.9311 | 8 |
| 0.0535 | 0.2673 | 0.1031 | 0.6896 | 0.1142 | 0.3549 | 0.1957 | 0.9230 | 9 |
Framework versions
- Transformers 4.27.4
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.2
