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robbery_dataset_tf_finetuned_20221113
This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0506
- Train Sparse Categorical Accuracy: 0.9844
- Validation Loss: 0.4108
- Validation Sparse Categorical Accuracy: 0.9068
- Epoch: 9
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', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|---|---|---|---|---|
| 0.4908 | 0.8335 | 0.2872 | 0.9060 | 0 |
| 0.2496 | 0.9180 | 0.3137 | 0.8978 | 1 |
| 0.1947 | 0.9351 | 0.3234 | 0.9062 | 2 |
| 0.1597 | 0.9483 | 0.3092 | 0.9087 | 3 |
| 0.1304 | 0.9580 | 0.2928 | 0.9140 | 4 |
| 0.1013 | 0.9684 | 0.3450 | 0.9143 | 5 |
| 0.0785 | 0.9742 | 0.3590 | 0.9080 | 6 |
| 0.0709 | 0.9778 | 0.3711 | 0.9057 | 7 |
| 0.0541 | 0.9821 | 0.4010 | 0.9128 | 8 |
| 0.0506 | 0.9844 | 0.4108 | 0.9068 | 9 |
Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.2
