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mmiteva/qa_model-customs
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: 0.3517
- Train End Logits Accuracy: 0.8772
- Train Start Logits Accuracy: 0.8735
- Validation Loss: 0.8793
- Validation End Logits Accuracy: 0.7642
- Validation Start Logits Accuracy: 0.7586
- Epoch: 4
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': 32050, '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 |
|---|---|---|---|---|---|---|
| 1.3795 | 0.6168 | 0.6015 | 0.9590 | 0.7074 | 0.6950 | 0 |
| 0.8193 | 0.7377 | 0.7260 | 0.8504 | 0.7313 | 0.7260 | 1 |
| 0.5982 | 0.8004 | 0.7932 | 0.8225 | 0.7505 | 0.7440 | 2 |
| 0.4467 | 0.8462 | 0.8405 | 0.8469 | 0.7633 | 0.7584 | 3 |
| 0.3517 | 0.8772 | 0.8735 | 0.8793 | 0.7642 | 0.7586 | 4 |
Framework versions
- Transformers 4.25.1
- TensorFlow 2.10.1
- Datasets 2.7.1
- Tokenizers 0.12.1
