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robbery_dataset_tf_finetuned_20230213_delitos_validados
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: 0.1837
- Train Sparse Categorical Accuracy: 0.9465
- Validation Loss: 0.3629
- Validation Sparse Categorical Accuracy: 0.8995
- 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', 'weight_decay': None, 'clipnorm': None, '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-06, '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 |
|---|---|---|---|---|
| 1.2571 | 0.6335 | 0.6850 | 0.7797 | 0 |
| 0.5901 | 0.8318 | 0.4557 | 0.8733 | 1 |
| 0.4303 | 0.8821 | 0.3878 | 0.8888 | 2 |
| 0.3557 | 0.9000 | 0.3471 | 0.9030 | 3 |
| 0.3096 | 0.9122 | 0.3476 | 0.9000 | 4 |
| 0.2750 | 0.9212 | 0.3380 | 0.9005 | 5 |
| 0.2474 | 0.9295 | 0.3238 | 0.9053 | 6 |
| 0.2210 | 0.9355 | 0.3464 | 0.9022 | 7 |
| 0.2016 | 0.9416 | 0.3296 | 0.9053 | 8 |
| 0.1837 | 0.9465 | 0.3629 | 0.8995 | 9 |
Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2
