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atatavana/NER2.0.4-alpha_num_dataset_
This model is a fine-tuned version of deepset/gbert-base on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.5874
- Validation Loss: 1.2684
- 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 14280, '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': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 1.7488 | 1.2389 | 0 |
| 1.1809 | 1.1271 | 1 |
| 0.9912 | 1.0946 | 2 |
| 0.8656 | 1.1282 | 3 |
| 0.7802 | 1.1623 | 4 |
| 0.7141 | 1.2208 | 5 |
| 0.6617 | 1.2170 | 6 |
| 0.6247 | 1.2547 | 7 |
| 0.5874 | 1.2684 | 8 |
Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.2.2
- Tokenizers 0.13.3
