zmansoor/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: 0.1157
- Validation Loss: 0.2606
- Train Precision: 0.4901
- Train Recall: 0.4139
- Train F1: 0.4488
- Train Accuracy: 0.9447
- 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 |
---|---|---|---|---|---|---|
0.3338 | 0.3048 | 0.4689 | 0.1986 | 0.2790 | 0.9333 | 0 |
0.1540 | 0.2713 | 0.4875 | 0.3720 | 0.4220 | 0.9425 | 1 |
0.1157 | 0.2606 | 0.4901 | 0.4139 | 0.4488 | 0.9447 | 2 |
Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.16.1
- Tokenizers 0.15.0
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