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ratish/DBERT_Fault_LR_v2.1
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: 0.1501
- Validation Loss: 0.6305
- Train Accuracy: 0.7179
- Epoch: 29
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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-06, 'decay_steps': 9120, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|---|---|---|---|
| 0.6963 | 0.6916 | 0.5128 | 0 |
| 0.6774 | 0.6929 | 0.5128 | 1 |
| 0.6631 | 0.7000 | 0.5128 | 2 |
| 0.6580 | 0.7070 | 0.5128 | 3 |
| 0.6409 | 0.7104 | 0.5128 | 4 |
| 0.6296 | 0.7015 | 0.5128 | 5 |
| 0.6115 | 0.6866 | 0.5128 | 6 |
| 0.5940 | 0.6573 | 0.5897 | 7 |
| 0.5616 | 0.6263 | 0.5897 | 8 |
| 0.5230 | 0.5886 | 0.6667 | 9 |
| 0.4890 | 0.5608 | 0.7179 | 10 |
| 0.4523 | 0.5386 | 0.7436 | 11 |
| 0.4307 | 0.5424 | 0.7179 | 12 |
| 0.4013 | 0.5261 | 0.7179 | 13 |
| 0.3893 | 0.4976 | 0.7436 | 14 |
| 0.3634 | 0.5459 | 0.6923 | 15 |
| 0.3337 | 0.4893 | 0.7436 | 16 |
| 0.3243 | 0.5490 | 0.7179 | 17 |
| 0.3083 | 0.5091 | 0.7179 | 18 |
| 0.2815 | 0.5457 | 0.7179 | 19 |
| 0.2654 | 0.5692 | 0.7179 | 20 |
| 0.2535 | 0.4808 | 0.7436 | 21 |
| 0.2504 | 0.5912 | 0.6923 | 22 |
| 0.2132 | 0.6228 | 0.6923 | 23 |
| 0.1962 | 0.5834 | 0.7179 | 24 |
| 0.2136 | 0.5261 | 0.7692 | 25 |
| 0.1895 | 0.6210 | 0.7179 | 26 |
| 0.1722 | 0.7140 | 0.7179 | 27 |
| 0.1580 | 0.6532 | 0.6923 | 28 |
| 0.1501 | 0.6305 | 0.7179 | 29 |
Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
