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