Thamer/roberta-fine-tuned
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.3386
- Train Binary Accuracy: 0.8828
- Validation Loss: 0.5065
- Validation Binary Accuracy: 0.8114
- Train Accuracy: 0.4392
- Epoch: 4
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-05, 'decay_steps': 8416, '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 | Train Binary Accuracy | Validation Loss | Validation Binary Accuracy | Train Accuracy | Epoch |
|---|---|---|---|---|---|
| 0.3433 | 0.8777 | 0.5065 | 0.8114 | 0.4392 | 0 |
| 0.3349 | 0.8815 | 0.5065 | 0.8114 | 0.4392 | 1 |
| 0.3376 | 0.8812 | 0.5065 | 0.8114 | 0.4392 | 2 |
| 0.3332 | 0.8816 | 0.5065 | 0.8114 | 0.4392 | 3 |
| 0.3386 | 0.8828 | 0.5065 | 0.8114 | 0.4392 | 4 |
Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.3
- Tokenizers 0.13.3