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RavGau/rav_nlp_qa
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: 0.1488
- Train End Logits Accuracy: 0.9536
- Train Start Logits Accuracy: 0.9542
- Validation Loss: 1.8833
- Validation End Logits Accuracy: 0.6524
- Validation Start Logits Accuracy: 0.6465
- Epoch: 9
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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5060, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, '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 |
|---|---|---|---|---|---|---|
| 2.7167 | 0.3467 | 0.3386 | 1.4679 | 0.6185 | 0.5949 | 0 |
| 1.3089 | 0.6476 | 0.6325 | 1.3092 | 0.6495 | 0.6264 | 1 |
| 0.8910 | 0.7514 | 0.7385 | 1.3037 | 0.6568 | 0.6357 | 2 |
| 0.6336 | 0.8166 | 0.8137 | 1.3668 | 0.6632 | 0.6352 | 3 |
| 0.4474 | 0.8582 | 0.8617 | 1.5254 | 0.6603 | 0.6465 | 4 |
| 0.3308 | 0.8907 | 0.9014 | 1.6029 | 0.6514 | 0.6386 | 5 |
| 0.2596 | 0.9144 | 0.9223 | 1.6924 | 0.6524 | 0.6426 | 6 |
| 0.2055 | 0.9358 | 0.9374 | 1.7831 | 0.6490 | 0.6480 | 7 |
| 0.1719 | 0.9436 | 0.9442 | 1.8572 | 0.6534 | 0.6445 | 8 |
| 0.1488 | 0.9536 | 0.9542 | 1.8833 | 0.6524 | 0.6465 | 9 |
Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
