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lsbuitrago/wnut_test_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: 0.1136
- Validation Loss: 0.2519
- Train Precision: 0.6410
- Train Recall: 0.4486
- Train F1: 0.5278
- Train Accuracy: 0.9491
- 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': 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, '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 | 0.3078 | 0.5333 | 0.1914 | 0.2817 | 0.9334 | 0 |
| 0.1534 | 0.2625 | 0.6388 | 0.4019 | 0.4934 | 0.9455 | 1 |
| 0.1136 | 0.2519 | 0.6410 | 0.4486 | 0.5278 | 0.9491 | 2 |
Framework versions
- Transformers 4.26.1
- TensorFlow 2.10.0
- Datasets 2.10.1
- Tokenizers 0.13.2
