ratish/DBERT_CleanDesc_Collision_v2.1.3

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: 0.4790
• Validation Loss: 1.3887
• Train Accuracy: 0.6154
• Epoch: 9

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': 3050, '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
1.6006	1.7368	0.3077	0
1.4578	1.7548	0.3077	1
1.3664	1.6852	0.4103	2
1.1923	1.5441	0.4615	3
1.0295	1.4601	0.5385	4
0.8769	1.4388	0.5897	5
0.7829	1.4165	0.5897	6
0.6690	1.4263	0.5897	7
0.5765	1.3252	0.6154	8
0.4790	1.3887	0.6154	9

Framework versions

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