maier-s/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: 0.0341
• Validation Loss: 0.0617
• Train Precision: 0.9192
• Train Recall: 0.9329
• Train F1: 0.9260
• 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, '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
0.1944	0.0844	0.8738	0.8998	0.8866	0.9750	0
0.0536	0.0630	0.9108	0.9278	0.9193	0.9816	1
0.0341	0.0617	0.9192	0.9329	0.9260	0.9827	2

Framework versions

• Transformers 4.42.4
• TensorFlow 2.17.0
• Datasets 2.21.0
• Tokenizers 0.19.1