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mmiteva/qa_model_test
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.4469
- Train End Logits Accuracy: 0.8470
- Train Start Logits Accuracy: 0.8386
- Validation Loss: 1.0938
- Validation End Logits Accuracy: 0.7318
- Validation Start Logits Accuracy: 0.7255
- 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': 108280, '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 |
|---|---|---|---|---|---|---|
| 1.4847 | 0.5934 | 0.5787 | 1.1159 | 0.6724 | 0.6590 | 0 |
| 0.9507 | 0.7042 | 0.6909 | 1.0094 | 0.6973 | 0.6875 | 1 |
| 0.7253 | 0.7637 | 0.7515 | 0.9841 | 0.7182 | 0.7124 | 2 |
| 0.5678 | 0.8090 | 0.7986 | 1.0107 | 0.7260 | 0.7194 | 3 |
| 0.4469 | 0.8470 | 0.8386 | 1.0938 | 0.7318 | 0.7255 | 4 |
Framework versions
- Transformers 4.20.1
- TensorFlow 2.9.2
- Datasets 2.1.0
- Tokenizers 0.12.1
