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Regression_bert_1
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: 0.1363
- Train Mae: 0.2288
- Train Mse: 0.0928
- Train R2-score: 0.8065
- Validation Loss: 0.0994
- Validation Mae: 0.2297
- Validation Mse: 0.0947
- Validation R2-score: 0.8577
- 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 |
|---|---|---|---|---|---|---|---|---|
| 0.4890 | 0.3421 | 0.1734 | 0.1995 | 0.1833 | 0.3662 | 0.1820 | 0.8574 | 0 |
| 0.1771 | 0.3049 | 0.1526 | 0.7380 | 0.1715 | 0.3553 | 0.1701 | 0.8642 | 1 |
| 0.1563 | 0.2820 | 0.1337 | 0.7278 | 0.1823 | 0.3662 | 0.1810 | 0.8577 | 2 |
| 0.1578 | 0.2739 | 0.1288 | 0.7954 | 0.1689 | 0.3535 | 0.1674 | 0.8641 | 3 |
| 0.2301 | 0.3034 | 0.1383 | 0.7186 | 0.1158 | 0.3132 | 0.1136 | 0.8866 | 4 |
| 0.2067 | 0.2768 | 0.1203 | 0.6568 | 0.1462 | 0.3375 | 0.1445 | 0.8772 | 5 |
| 0.1631 | 0.2726 | 0.1200 | 0.7418 | 0.1646 | 0.3473 | 0.1632 | 0.8659 | 6 |
| 0.1689 | 0.2843 | 0.1269 | 0.4797 | 0.0993 | 0.2797 | 0.0972 | 0.8961 | 7 |
| 0.1756 | 0.2115 | 0.0726 | 0.8657 | 0.1047 | 0.2537 | 0.1027 | 0.8923 | 8 |
| 0.1363 | 0.2288 | 0.0928 | 0.8065 | 0.0994 | 0.2297 | 0.0947 | 0.8577 | 9 |
Framework versions
- Transformers 4.27.3
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
