gustavokpc/IC_quinto

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

• Train Loss: 0.1646
• Train Accuracy: 0.9419
• Train F1 M: 0.5524
• Train Precision M: 0.4019
• Train Recall M: 0.9429
• Validation Loss: 0.2503
• Validation Accuracy: 0.9070
• Validation F1 M: 0.5680
• Validation Precision M: 0.4108
• Validation Recall M: 0.9671
• 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 2274, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train Accuracy	Train F1 M	Train Precision M	Train Recall M	Validation Loss	Validation Accuracy	Validation F1 M	Validation Precision M	Validation Recall M	Epoch
0.4076	0.8160	0.5002	0.3900	0.7694	0.2792	0.8859	0.5648	0.4123	0.9419	0
0.2272	0.9143	0.5487	0.4020	0.9253	0.2778	0.8925	0.5752	0.4181	0.9630	1
0.1646	0.9419	0.5524	0.4019	0.9429	0.2503	0.9070	0.5680	0.4108	0.9671	2

Framework versions

• Transformers 4.34.1
• TensorFlow 2.14.0
• Datasets 2.14.5
• Tokenizers 0.14.1