letingliu/my_awesome_model3

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: 0.5057
• Validation Loss: 0.4900
• Train Accuracy: 0.9245
• Epoch: 18

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': 30, '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
0.6850	0.6546	0.7170	0
0.6449	0.6103	0.7547	1
0.5989	0.5549	0.8774	2
0.5506	0.5088	0.9151	3
0.5059	0.4900	0.9245	4
0.4885	0.4900	0.9245	5
0.4939	0.4900	0.9245	6
0.4969	0.4900	0.9245	7
0.4993	0.4900	0.9245	8
0.4951	0.4900	0.9245	9
0.5035	0.4900	0.9245	10
0.5064	0.4900	0.9245	11
0.5022	0.4900	0.9245	12
0.5111	0.4900	0.9245	13
0.5057	0.4900	0.9245	14
0.4979	0.4900	0.9245	15
0.5110	0.4900	0.9245	16
0.5080	0.4900	0.9245	17
0.5057	0.4900	0.9245	18

Framework versions

• Transformers 4.26.1
• TensorFlow 2.11.0
• Datasets 2.10.0
• Tokenizers 0.13.2