gustavokpc/IC_quarto

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: 0.1061
• Train Accuracy: 0.9634
• Train F1 M: 0.5432
• Train Precision M: 0.3978
• Train Recall M: 0.9159
• Validation Loss: 0.2101
• Validation Accuracy: 0.9235
• Validation F1 M: 0.5596
• Validation Precision M: 0.4070
• Validation Recall M: 0.9389
• 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': 2e-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
0.3469	0.8494	0.4233	0.3470	0.6216	0.2535	0.8945	0.5613	0.4145	0.9125	0
0.1742	0.9335	0.5237	0.3895	0.8572	0.2315	0.9017	0.5765	0.4256	0.9353	1
0.1061	0.9634	0.5432	0.3978	0.9159	0.2101	0.9235	0.5596	0.4070	0.9389	2

Framework versions

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