uraskargi/bert-base-cased-fine-tuned-4

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

• Train Loss: 0.1922
• Train Accuracy: 0.9310
• Validation Loss: 0.5247
• Validation Accuracy: 0.8303
• Train Matthews Correlation: 0.5830
• 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 9.858432402113778e-06, 'decay_steps': 665, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Train Matthews Correlation	Epoch
0.6040	0.7007	0.5308	0.7191	0.2443	0
0.4246	0.8114	0.4163	0.8188	0.5525	1
0.2897	0.8848	0.5054	0.8121	0.5343	2
0.2224	0.9146	0.4868	0.8274	0.5754	3
0.1922	0.9310	0.5247	0.8303	0.5830	4

Framework versions

• Transformers 4.28.1
• TensorFlow 2.12.0
• Datasets 2.12.0
• Tokenizers 0.13.3