RafaelMayer/copec-whatsapp-13-roberta

This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.6498
• Validation Loss: 0.6487
• Train Accuracy: 0.7647
• Train Precision: [0. 0.76470588]
• Train Precision W: 0.5848
• Train Recall: [0. 1.]
• Train Recall W: 0.7647
• Train F1: [0. 0.86666667]
• Train F1 W: 0.6627
• 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': -460, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 500, '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
0.6829	0.6850	0.7647	[0. 0.76470588]	0.5848	[0. 1.]	0.7647	[0. 0.86666667]	0.6627	1
0.6834	0.6838	0.7647	[0. 0.76470588]	0.5848	[0. 1.]	0.7647	[0. 0.86666667]	0.6627	2
0.6808	0.6818	0.7647	[0. 0.76470588]	0.5848	[0. 1.]	0.7647	[0. 0.86666667]	0.6627	3
0.6787	0.6790	0.7647	[0. 0.76470588]	0.5848	[0. 1.]	0.7647	[0. 0.86666667]	0.6627	4
0.6743	0.6753	0.7647	[0. 0.76470588]	0.5848	[0. 1.]	0.7647	[0. 0.86666667]	0.6627	5
0.6684	0.6706	0.7647	[0. 0.76470588]	0.5848	[0. 1.]	0.7647	[0. 0.86666667]	0.6627	6
0.6643	0.6647	0.7647	[0. 0.76470588]	0.5848	[0. 1.]	0.7647	[0. 0.86666667]	0.6627	7
0.6522	0.6576	0.7647	[0. 0.76470588]	0.5848	[0. 1.]	0.7647	[0. 0.86666667]	0.6627	8
0.6498	0.6487	0.7647	[0. 0.76470588]	0.5848	[0. 1.]	0.7647	[0. 0.86666667]	0.6627	9

Framework versions

• Transformers 4.31.0
• TensorFlow 2.12.0
• Datasets 2.14.4
• Tokenizers 0.13.3