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Cube/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: 0.0646
- Train Precision: 0.9217
- Train Recall: 0.9295
- Train F1: 0.9256
- Train Accuracy: 0.9827
- 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 |
|---|---|---|---|---|---|---|
| 0.1996 | 0.0735 | 0.8930 | 0.9179 | 0.9053 | 0.9784 | 0 |
| 0.0545 | 0.0666 | 0.9137 | 0.9292 | 0.9214 | 0.9817 | 1 |
| 0.0339 | 0.0646 | 0.9217 | 0.9295 | 0.9256 | 0.9827 | 2 |
Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
