Thamer/albert-fine-tuned
This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.6843
- Train Binary Accuracy: 0.5640
- Validation Loss: 0.6990
- Validation Binary Accuracy: 0.5092
- Train Accuracy: 0.6032
- Epoch: 2
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': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0002, 'decay_steps': 3156, '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 |
|---|---|---|---|---|---|
| 0.6987 | 0.5410 | 0.6446 | 0.6835 | 0.5333 | 0 |
| 0.6976 | 0.5642 | 0.6981 | 0.5092 | 0.4908 | 1 |
| 0.6843 | 0.5640 | 0.6990 | 0.5092 | 0.6032 | 2 |
Framework versions
- Transformers 4.31.0
- TensorFlow 2.11.0
- Datasets 2.13.1
- Tokenizers 0.13.3