<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. -->
nandysoham/Gregorian_calendar-theme-finetuned-overfinetuned
This model is a fine-tuned version of nandysoham/distilbert-base-uncased-finetuned-squad on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.1838
- Train End Logits Accuracy: 0.9500
- Train Start Logits Accuracy: 0.9688
- Validation Loss: 2.0017
- Validation End Logits Accuracy: 0.5238
- Validation Start Logits Accuracy: 0.4762
- Epoch: 8
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': 100, '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 |
|---|---|---|---|---|---|---|
| 2.2861 | 0.3688 | 0.4062 | 1.6038 | 0.5952 | 0.5714 | 0 |
| 1.2774 | 0.5938 | 0.5938 | 1.4240 | 0.5952 | 0.5714 | 1 |
| 0.8752 | 0.7000 | 0.7375 | 1.4402 | 0.5952 | 0.5476 | 2 |
| 0.5245 | 0.8250 | 0.8438 | 1.5027 | 0.6429 | 0.5952 | 3 |
| 0.4132 | 0.8313 | 0.8938 | 1.6252 | 0.5714 | 0.5 | 4 |
| 0.3140 | 0.9000 | 0.9062 | 1.7524 | 0.5476 | 0.4762 | 5 |
| 0.2534 | 0.9688 | 0.9312 | 1.8646 | 0.5238 | 0.4762 | 6 |
| 0.1999 | 0.9500 | 0.9563 | 1.9513 | 0.5238 | 0.4762 | 7 |
| 0.1838 | 0.9500 | 0.9688 | 2.0017 | 0.5238 | 0.4762 | 8 |
Framework versions
- Transformers 4.25.1
- TensorFlow 2.9.2
- Datasets 2.8.0
- Tokenizers 0.13.2
