contextualAug_evaluation
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 1.0810
- Validation Loss: 1.6858
- Train Precision: 0.3411
- Train Recall: 0.1705
- Train F1: 0.2273
- Train Accuracy: 0.6749
- 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': 120, '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 |
---|---|---|---|---|---|---|
2.4440 | 2.3476 | 0.0 | 0.0 | 0.0 | 0.6084 | 0 |
1.2442 | 1.7924 | 0.43 | 0.0156 | 0.0302 | 0.6140 | 1 |
1.0810 | 1.6858 | 0.3411 | 0.1705 | 0.2273 | 0.6749 | 2 |
Framework versions
- Transformers 4.41.2
- TensorFlow 2.16.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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