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JairParra/my_awesome_wnut_model
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: 0.1247
- Validation Loss: 0.2703
- Train Precision: 0.5781
- Train Recall: 0.3983
- Train F1: 0.4717
- Train Accuracy: 0.9441
- 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 |
|---|---|---|---|---|---|---|
| 0.3233 | 0.3269 | 0.4469 | 0.1711 | 0.2474 | 0.9320 | 0 |
| 0.1619 | 0.2789 | 0.5210 | 0.3110 | 0.3895 | 0.9390 | 1 |
| 0.1247 | 0.2703 | 0.5781 | 0.3983 | 0.4717 | 0.9441 | 2 |
Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
