gustavokpc/IC_terceiro

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: 0.1004
• Train Accuracy: 0.9641
• Train F1 M: 0.5542
• Train Precision M: 0.4036
• Train Recall M: 0.9415
• Validation Loss: 0.2133
• Validation Accuracy: 0.9222
• Validation F1 M: 0.5610
• Validation Precision M: 0.4087
• Validation Recall M: 0.9424
• 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': 3e-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
0.3546	0.8475	0.5020	0.4252	0.7323	0.3033	0.8747	0.5815	0.4266	0.9558	0
0.1757	0.9344	0.5389	0.3987	0.8912	0.2040	0.9261	0.5665	0.4137	0.9427	1
0.1004	0.9641	0.5542	0.4036	0.9415	0.2133	0.9222	0.5610	0.4087	0.9424	2

Framework versions

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