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IntelDAOS5ALBERT_Unbalance
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: 0.3214
- Train Accuracy: 0.8780
- Validation Loss: 0.4591
- Validation Accuracy: 0.7958
- Epoch: 5
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': 0.001, 'clipnorm': 1.0, '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': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|---|---|---|---|---|
| 0.3804 | 0.8740 | 0.5133 | 0.8438 | 0 |
| 0.3851 | 0.8740 | 0.4805 | 0.8438 | 1 |
| 0.3791 | 0.8740 | 0.4239 | 0.8438 | 2 |
| 0.3758 | 0.8740 | 0.4267 | 0.8438 | 3 |
| 0.3588 | 0.8740 | 0.4247 | 0.8438 | 4 |
| 0.3214 | 0.8780 | 0.4591 | 0.7958 | 5 |
Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
