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DistibertNER
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: 0.0368
- Validation Loss: 0.0173
- Train Precision: 0.9941
- Train Recall: 0.9971
- Train F1: 0.9956
- Train Accuracy: 0.9972
- 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 9, '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, 'weight_decay_rate': 1e-08}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|---|---|---|---|---|---|---|
| 0.0700 | 0.0422 | 0.9941 | 0.9912 | 0.9926 | 0.9945 | 0 |
| 0.0860 | 0.0423 | 0.9971 | 0.9941 | 0.9956 | 0.9972 | 1 |
| 0.0694 | 0.0354 | 0.9971 | 0.9941 | 0.9956 | 0.9972 | 2 |
| 0.0615 | 0.0287 | 0.9941 | 0.9912 | 0.9926 | 0.9945 | 3 |
| 0.0462 | 0.0244 | 0.9941 | 0.9912 | 0.9926 | 0.9945 | 4 |
| 0.0462 | 0.0208 | 0.9941 | 0.9971 | 0.9956 | 0.9972 | 5 |
| 0.0497 | 0.0188 | 0.9941 | 0.9971 | 0.9956 | 0.9972 | 6 |
| 0.0339 | 0.0178 | 0.9941 | 0.9971 | 0.9956 | 0.9972 | 7 |
| 0.0386 | 0.0173 | 0.9941 | 0.9971 | 0.9956 | 0.9972 | 8 |
| 0.0368 | 0.0173 | 0.9941 | 0.9971 | 0.9956 | 0.9972 | 9 |
Framework versions
- Transformers 4.27.4
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3
