gustavokpc/IC_setimo

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

• Train Loss: 0.1339
• Train Accuracy: 0.9523
• Train F1 M: 0.5559
• Train Precision M: 0.4041
• Train Recall M: 0.9513
• Validation Loss: 0.2110
• Validation Accuracy: 0.9222
• Validation F1 M: 0.5681
• Validation Precision M: 0.4137
• Validation Recall M: 0.9574
• 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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2274, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, '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
0.4176	0.8080	0.4543	0.3667	0.6857	0.2600	0.8991	0.5567	0.4108	0.9084	0
0.2122	0.9203	0.5400	0.3991	0.8908	0.2049	0.9215	0.5529	0.4068	0.9089	1
0.1339	0.9523	0.5559	0.4041	0.9513	0.2110	0.9222	0.5681	0.4137	0.9574	2

Framework versions

• Transformers 4.34.1
• TensorFlow 2.10.0
• Datasets 2.14.5
• Tokenizers 0.14.1