Sidziesama/Legal_NER_Support_Model_distilledbert

This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.0582
• Validation Loss: 0.0980
• Train Precision: 0.7952
• Train Recall: 0.8552
• Train F1: 0.8241
• Train Accuracy: 0.9716
• Epoch: 4

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': 3435, '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
0.4207	0.1608	0.6623	0.7498	0.7034	0.9557	0
0.1304	0.1118	0.7580	0.8116	0.7839	0.9668	1
0.0891	0.1012	0.7698	0.8525	0.8090	0.9701	2
0.0699	0.0976	0.7933	0.8507	0.8210	0.9713	3
0.0582	0.0980	0.7952	0.8552	0.8241	0.9716	4

Framework versions

• Transformers 4.39.3
• TensorFlow 2.15.0
• Datasets 2.18.0
• Tokenizers 0.15.2