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ev_detection
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: 0.0764
- Train Accuracy: 0.9727
- Validation Loss: 0.6513
- Validation Accuracy: 0.8366
- Epoch: 4
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': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|---|---|---|---|---|
| 0.6343 | 0.6245 | 0.4200 | 0.8218 | 0 |
| 0.3990 | 0.8501 | 0.4292 | 0.8317 | 1 |
| 0.2138 | 0.9207 | 0.4555 | 0.8218 | 2 |
| 0.1133 | 0.9641 | 0.5589 | 0.8267 | 3 |
| 0.0764 | 0.9727 | 0.6513 | 0.8366 | 4 |
Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
