ac-01-distilbert-finetuned
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: 0.0135
- Validation Loss: 0.5022
- Train Recall: 0.8636
- Epoch: 8
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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 Recall | Epoch |
|---|---|---|---|
| 0.5302 | 0.2915 | 0.9167 | 0 |
| 0.2481 | 0.2611 | 0.8788 | 1 |
| 0.1306 | 0.2634 | 0.9167 | 2 |
| 0.0701 | 0.3197 | 0.8636 | 3 |
| 0.0352 | 0.3511 | 0.8864 | 4 |
| 0.0199 | 0.3923 | 0.9091 | 5 |
| 0.0168 | 0.4305 | 0.8864 | 6 |
| 0.0140 | 0.4629 | 0.8788 | 7 |
| 0.0135 | 0.5022 | 0.8636 | 8 |
Framework versions
- Transformers 4.31.0
- TensorFlow 2.13.0
- Datasets 2.14.4
- Tokenizers 0.13.3