distilbertbaseuncasedz

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: 0.5368
• Train End Logits Accuracy: 0.8401
• Train Start Logits Accuracy: 0.8078
• Validation Loss: 1.2427
• Validation End Logits Accuracy: 0.7050
• Validation Start Logits Accuracy: 0.6725
• Epoch: 3

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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 29508, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train End Logits Accuracy	Train Start Logits Accuracy	Validation Loss	Validation End Logits Accuracy	Validation Start Logits Accuracy	Epoch
1.3338	0.6448	0.6045	1.1322	0.6906	0.6563	0
0.9044	0.7466	0.7090	1.0996	0.7032	0.6720	1
0.6756	0.8042	0.7680	1.1416	0.7047	0.6718	2
0.5368	0.8401	0.8078	1.2427	0.7050	0.6725	3

Framework versions

• Transformers 4.20.1
• TensorFlow 2.6.4
• Datasets 2.1.0
• Tokenizers 0.12.1