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raisvaza/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: 0.0352
- Validation Loss: 0.0607
- Train Precision: 0.9246
- Train Recall: 0.9330
- Train F1: 0.9288
- Train Accuracy: 0.9832
- 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 |
|---|---|---|---|---|---|---|
| 0.1955 | 0.0720 | 0.8998 | 0.9157 | 0.9077 | 0.9792 | 0 |
| 0.0557 | 0.0620 | 0.9200 | 0.9271 | 0.9235 | 0.9822 | 1 |
| 0.0352 | 0.0607 | 0.9246 | 0.9330 | 0.9288 | 0.9832 | 2 |
Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.7.0
- Tokenizers 0.13.2
