ratish/DBERT_CleanDesc_COLLISION_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: 0.1992
• Validation Loss: 1.6291
• Train Accuracy: 0.6154
• 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': 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
1.6309	1.7295	0.3077	0
1.4522	1.7291	0.3077	1
1.3637	1.6656	0.3590	2
1.2159	1.5797	0.4103	3
1.0494	1.4799	0.4872	4
0.8847	1.4288	0.5385	5
0.7629	1.4239	0.5128	6
0.6739	1.4484	0.5128	7
0.5598	1.4533	0.6154	8
0.4606	1.4160	0.6154	9
0.3736	1.4206	0.5897	10
0.3065	1.5229	0.5897	11
0.2580	1.6168	0.5641	12
0.2342	1.5924	0.6410	13
0.1992	1.6291	0.6154	14

Framework versions

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