Cube/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: 0.0646
• Train Precision: 0.9217
• Train Recall: 0.9295
• Train F1: 0.9256
• Train Accuracy: 0.9827
• 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
0.1996	0.0735	0.8930	0.9179	0.9053	0.9784	0
0.0545	0.0666	0.9137	0.9292	0.9214	0.9817	1
0.0339	0.0646	0.9217	0.9295	0.9256	0.9827	2

Framework versions

• Transformers 4.19.2
• TensorFlow 2.8.2
• Datasets 2.2.2
• Tokenizers 0.12.1