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HWJin/SMU-NLP-assignment2-finetuned-best
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: 0.9936
- Validation Loss: 0.9867
- Epoch: 13
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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 990, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 10, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 1.6490 | 1.2199 | 0 |
| 1.2679 | 1.1622 | 1 |
| 1.1796 | 1.0931 | 2 |
| 1.1200 | 1.0274 | 3 |
| 1.0841 | 1.0739 | 4 |
| 1.0567 | 1.0317 | 5 |
| 1.0164 | 0.9895 | 6 |
| 0.9819 | 1.0365 | 7 |
| 0.9960 | 0.9857 | 8 |
| 1.0143 | 0.9954 | 9 |
| 1.0156 | 1.0173 | 10 |
| 0.9915 | 1.0391 | 11 |
| 1.0246 | 1.0288 | 12 |
| 0.9936 | 0.9867 | 13 |
Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
