Mafoya1er/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: 0.0692
• Validation Loss: 0.3969
• Train Precision: 0.8119
• Train Recall: 0.8141
• Train F1: 0.8130
• Train Accuracy: 0.9206
• Epoch: 9

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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5890, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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
0.9080	0.5831	0.6437	0.6212	0.6323	0.8573	0
0.3883	0.4573	0.7478	0.7245	0.7360	0.8925	1
0.2596	0.4092	0.7628	0.7696	0.7662	0.9037	2
0.1926	0.3848	0.7919	0.7838	0.7878	0.9121	3
0.1480	0.3850	0.7919	0.7963	0.7941	0.9135	4
0.1188	0.3860	0.8066	0.8006	0.8036	0.9172	5
0.0979	0.3905	0.8060	0.8085	0.8073	0.9186	6
0.0836	0.3911	0.8053	0.8120	0.8086	0.9190	7
0.0737	0.3996	0.8205	0.8113	0.8159	0.9215	8
0.0692	0.3969	0.8119	0.8141	0.8130	0.9206	9

Framework versions

• Transformers 4.35.2
• TensorFlow 2.15.0
• Datasets 2.15.0
• Tokenizers 0.15.0