ratish/DBERT_CleanDesc_Collision_v2.1.4

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: 0.3438
• Validation Loss: 1.4467
• Train Accuracy: 0.5897
• Epoch: 11

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': 4575, '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
1.6148	1.7151	0.3077	0
1.4783	1.7263	0.3077	1
1.3926	1.6779	0.4103	2
1.2462	1.5778	0.4359	3
1.0592	1.5154	0.4359	4
0.8814	1.5370	0.4615	5
0.7554	1.4250	0.5385	6
0.6303	1.4385	0.5641	7
0.5458	1.3870	0.4872	8
0.4808	1.3459	0.5385	9
0.4098	1.5049	0.5385	10
0.3438	1.4467	0.5897	11

Framework versions

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