<!-- 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. -->
francheutsia/bert-base-uncased-finetuned-ner
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0229
- Validation Loss: 0.0465
- Train Precision: 0.8265
- Train Recall: 0.8702
- Train F1: 0.8478
- Train Accuracy: 0.9850
- Epoch: 2
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': 2e-05, 'decay_steps': 1602, '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}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|---|---|---|---|---|---|---|
| 0.1034 | 0.0641 | 0.6823 | 0.8230 | 0.7461 | 0.9751 | 0 |
| 0.0419 | 0.0433 | 0.8160 | 0.8499 | 0.8326 | 0.9836 | 1 |
| 0.0229 | 0.0465 | 0.8265 | 0.8702 | 0.8478 | 0.9850 | 2 |
Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3
