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mmiteva/qa_model-customs_optimized
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: 0.2417
- Train End Logits Accuracy: 0.9192
- Train Start Logits Accuracy: 0.9158
- Validation Loss: 0.6317
- Validation End Logits Accuracy: 0.8375
- Validation Start Logits Accuracy: 0.8303
- Epoch: 3
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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 50860, '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}
- training_precision: float32
Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|---|---|---|---|---|---|---|
| 1.1671 | 0.6791 | 0.6668 | 0.6925 | 0.7903 | 0.7716 | 0 |
| 0.5541 | 0.8257 | 0.8179 | 0.5747 | 0.8230 | 0.8090 | 1 |
| 0.3527 | 0.8856 | 0.8786 | 0.5971 | 0.8352 | 0.8203 | 2 |
| 0.2417 | 0.9192 | 0.9158 | 0.6317 | 0.8375 | 0.8303 | 3 |
Framework versions
- Transformers 4.25.1
- TensorFlow 2.10.1
- Datasets 2.7.1
- Tokenizers 0.12.1
