mke10/distilbert-base-uncased-finetuned-ner

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: 0.0339
• Validation Loss: 0.0603
• Train Precision: 0.9245
• Train Recall: 0.9356
• Train F1: 0.9300
• Train Accuracy: 0.9834
• 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': 2631, '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
0.1985	0.0727	0.8930	0.9177	0.9052	0.9786	0
0.0535	0.0605	0.9255	0.9323	0.9289	0.9832	1
0.0339	0.0603	0.9245	0.9356	0.9300	0.9834	2

Framework versions

• Transformers 4.32.1
• TensorFlow 2.12.0
• Datasets 2.14.4
• Tokenizers 0.13.3