ratish/DBERT_Fault_v1.4

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: 0.0165
• Validation Loss: 1.1489
• Train Accuracy: 0.7436
• Epoch: 13

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': 2e-05, 'decay_steps': 2128, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Validation Loss	Train Accuracy	Epoch
0.6677	0.7030	0.5128	0
0.6287	0.6204	0.7436	1
0.4746	0.4927	0.7949	2
0.3647	0.5168	0.7692	3
0.2682	0.5776	0.7436	4
0.2184	0.4834	0.8205	5
0.1997	0.5296	0.7692	6
0.1188	0.6967	0.7949	7
0.0945	0.6440	0.8205	8
0.0539	0.6911	0.7949	9
0.0271	0.8044	0.7949	10
0.0242	0.7906	0.7949	11
0.0264	0.8078	0.8462	12
0.0165	1.1489	0.7436	13

Framework versions

• Transformers 4.28.1
• TensorFlow 2.12.0
• Datasets 2.12.0
• Tokenizers 0.13.3