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silviacamplani/distilbert-base-uncased-finetuned-ner-wnut
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: 0.1241
- Validation Loss: 0.3433
- Train Precision: 0.5677
- Train Recall: 0.3660
- Train F1: 0.4451
- Train Accuracy: 0.9215
- 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: {'inner_optimizer': {'class_name': 'Adam', 'config': {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|---|---|---|---|---|---|---|
| 0.3454 | 0.4475 | 0.0 | 0.0 | 0.0 | 0.8961 | 0 |
| 0.1637 | 0.3637 | 0.6297 | 0.2990 | 0.4055 | 0.9154 | 1 |
| 0.1241 | 0.3433 | 0.5677 | 0.3660 | 0.4451 | 0.9215 | 2 |
Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
