ratish/DBERT_CleanDesc_MAKE_v11

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: 0.1889
• Validation Loss: 1.0498
• Train Accuracy: 0.8
• 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
2.1852	2.0907	0.375	0
1.7165	1.7453	0.525	1
1.2878	1.4632	0.55	2
0.9851	1.2769	0.575	3
0.7653	1.1689	0.675	4
0.6014	1.1163	0.65	5
0.4997	1.0490	0.7	6
0.4344	0.9967	0.7	7
0.3263	0.9887	0.75	8
0.2837	1.0332	0.775	9
0.2291	1.0496	0.775	10
0.1994	1.0560	0.775	11
0.1736	1.1081	0.775	12
0.1589	1.0679	0.8	13
0.1889	1.0498	0.8	14

Framework versions

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