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francheutsia/distilbert-base-uncased-finetuned-ner
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: 0.0326
- Validation Loss: 0.0499
- Train Precision: 0.7896
- Train Recall: 0.8506
- Train F1: 0.8189
- Train Accuracy: 0.9826
- 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': 1470, '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 |
|---|---|---|---|---|---|---|
| 0.1248 | 0.0642 | 0.6858 | 0.7660 | 0.7237 | 0.9752 | 0 |
| 0.0513 | 0.0510 | 0.7684 | 0.8323 | 0.7991 | 0.9812 | 1 |
| 0.0326 | 0.0499 | 0.7896 | 0.8506 | 0.8189 | 0.9826 | 2 |
Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
