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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
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