ratish/DBERT_CleanDesc_Mode_v10

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: 0.0060
• Validation Loss: 0.2047
• Train Accuracy: 0.925
• Epoch: 14

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': 4620, '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
0.6863	0.6733	0.475	0
0.5131	0.3023	0.95	1
0.2499	0.1768	0.95	2
0.1463	0.1702	0.925	3
0.1212	0.1646	0.9	4
0.0897	0.1807	0.925	5
0.0689	0.1947	0.925	6
0.0544	0.1885	0.925	7
0.0476	0.1888	0.925	8
0.0325	0.2012	0.925	9
0.0229	0.1717	0.925	10
0.0113	0.2052	0.95	11
0.0087	0.1650	0.925	12
0.0063	0.1987	0.95	13
0.0060	0.2047	0.925	14

Framework versions

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