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SHS/tokenization-practice
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: 0.1203
- Validation Loss: 0.2537
- Train Precision: 0.5971
- Train Recall: 0.4450
- Train F1: 0.5099
- Train Accuracy: 0.9475
- 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|---|---|---|---|---|---|---|
| 0.3513 | 0.3180 | 0.3947 | 0.0718 | 0.1215 | 0.9260 | 0 |
| 0.1624 | 0.2624 | 0.5321 | 0.3971 | 0.4548 | 0.9438 | 1 |
| 0.1203 | 0.2537 | 0.5971 | 0.4450 | 0.5099 | 0.9475 | 2 |
Framework versions
- Transformers 4.26.0
- TensorFlow 2.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2
