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SIA86/bert-cased-text-classification
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0719
- Train Accuracy: 0.9772
- Validation Loss: 0.8075
- Validation Accuracy: 0.8485
- Epoch: 19
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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 2320, 'end_learning_rate': 0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|---|---|---|---|---|
| 2.8423 | 0.2313 | 2.5340 | 0.3593 | 0 |
| 2.4502 | 0.3181 | 2.3051 | 0.3333 | 1 |
| 2.2064 | 0.3648 | 1.9143 | 0.4416 | 2 |
| 1.6431 | 0.5494 | 1.5876 | 0.5411 | 3 |
| 1.1282 | 0.6960 | 1.4404 | 0.6190 | 4 |
| 0.8128 | 0.7861 | 1.0982 | 0.7143 | 5 |
| 0.6016 | 0.8534 | 1.0513 | 0.7532 | 6 |
| 0.4495 | 0.8947 | 0.9108 | 0.7879 | 7 |
| 0.2991 | 0.9414 | 0.8437 | 0.8182 | 8 |
| 0.2068 | 0.9609 | 0.7936 | 0.8182 | 9 |
| 0.1594 | 0.9729 | 0.8264 | 0.8182 | 10 |
| 0.1364 | 0.9707 | 0.7984 | 0.8312 | 11 |
| 0.1217 | 0.9707 | 0.7948 | 0.8268 | 12 |
| 0.1053 | 0.9729 | 0.7847 | 0.8398 | 13 |
| 0.0968 | 0.9729 | 0.7850 | 0.8398 | 14 |
| 0.0879 | 0.9739 | 0.7976 | 0.8442 | 15 |
| 0.0821 | 0.9718 | 0.8005 | 0.8442 | 16 |
| 0.0770 | 0.9750 | 0.7967 | 0.8485 | 17 |
| 0.0772 | 0.9772 | 0.8043 | 0.8485 | 18 |
| 0.0719 | 0.9772 | 0.8075 | 0.8485 | 19 |
Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.1
- Tokenizers 0.13.3
