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namwoo/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: 0.0339
- Validation Loss: 0.0623
- Train Precision: 0.9239
- Train Recall: 0.9335
- Train F1: 0.9287
- Train Accuracy: 0.9829
- 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 |
|---|---|---|---|---|---|---|
| 0.1982 | 0.0715 | 0.9040 | 0.9218 | 0.9128 | 0.9799 | 0 |
| 0.0537 | 0.0618 | 0.9202 | 0.9305 | 0.9254 | 0.9827 | 1 |
| 0.0339 | 0.0623 | 0.9239 | 0.9335 | 0.9287 | 0.9829 | 2 |
Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
