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mattladewig/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: 0.0342
- Validation Loss: 0.0614
- Train Precision: 0.9248
- Train Recall: 0.9365
- Train F1: 0.9306
- Train Accuracy: 0.9833
- 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': 2631, '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 |
|---|---|---|---|---|---|---|
| 0.1951 | 0.0694 | 0.9087 | 0.9181 | 0.9134 | 0.9799 | 0 |
| 0.0530 | 0.0621 | 0.9246 | 0.9301 | 0.9273 | 0.9823 | 1 |
| 0.0342 | 0.0614 | 0.9248 | 0.9365 | 0.9306 | 0.9833 | 2 |
Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.0
- Tokenizers 0.13.3
