SHS/tokenization-practice

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: 0.1203
• Validation Loss: 0.2537
• Train Precision: 0.5971
• Train Recall: 0.4450
• Train F1: 0.5099
• Train Accuracy: 0.9475
• Epoch: 2

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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, '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, 'weight_decay_rate': 0.01}
• training_precision: float32

Training results

Train Loss	Validation Loss	Train Precision	Train Recall	Train F1	Train Accuracy	Epoch
0.3513	0.3180	0.3947	0.0718	0.1215	0.9260	0
0.1624	0.2624	0.5321	0.3971	0.4548	0.9438	1
0.1203	0.2537	0.5971	0.4450	0.5099	0.9475	2

Framework versions

• Transformers 4.26.0
• TensorFlow 2.11.0
• Datasets 2.9.0
• Tokenizers 0.13.2