RafaelMayer/bert-copec-1

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

• Train Loss: 0.1258
• Validation Loss: 0.4666
• Train Accuracy: 0.7647
• Train Precision: [0. 0.8125]
• Train Precision W: 0.6691
• Train Recall: [0. 0.92857143]
• Train Recall W: 0.7647
• Train F1: [0. 0.86666667]
• Train F1 W: 0.7137
• Epoch: 9

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': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 35, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 5, 'power': 1.0, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Validation Loss	Train Accuracy	Train Precision	Train Precision W	Train Recall	Train Recall W	Train F1	Train F1 W	Epoch
0.5926	0.4830	0.8235	[0. 0.82352941]	0.6782	[0. 1.]	0.8235	[0. 0.90322581]	0.7438	1
0.3224	0.5166	0.8235	[0. 0.82352941]	0.6782	[0. 1.]	0.8235	[0. 0.90322581]	0.7438	2
0.2419	0.6137	0.8235	[0. 0.82352941]	0.6782	[0. 1.]	0.8235	[0. 0.90322581]	0.7438	3
0.2583	0.5984	0.8235	[0. 0.82352941]	0.6782	[0. 1.]	0.8235	[0. 0.90322581]	0.7438	4
0.2308	0.5345	0.8235	[0. 0.82352941]	0.6782	[0. 1.]	0.8235	[0. 0.90322581]	0.7438	5
0.2178	0.4710	0.8235	[0. 0.82352941]	0.6782	[0. 1.]	0.8235	[0. 0.90322581]	0.7438	6
0.1861	0.4562	0.8235	[0. 0.82352941]	0.6782	[0. 1.]	0.8235	[0. 0.90322581]	0.7438	7
0.1456	0.4568	0.7647	[0. 0.8125]	0.6691	[0. 0.92857143]	0.7647	[0. 0.86666667]	0.7137	8
0.1258	0.4666	0.7647	[0. 0.8125]	0.6691	[0. 0.92857143]	0.7647	[0. 0.86666667]	0.7137	9

Framework versions

• Transformers 4.32.1
• TensorFlow 2.12.0
• Datasets 2.14.4
• Tokenizers 0.13.3