--- language: - pt license: mit tags: - generated_from_keras_callback datasets: - assin2 metrics: - accuracy - f1 pipeline_tag: text-classification base_model: neuralmind/bert-base-portuguese-cased model-index: - name: pmfsl/bertimbau-base-finetuned-rte results: - task: type: text-classification name: Natural Lenguage Inference dataset: name: ASSIN2 type: assin2 metrics: - type: accuracy value: 0.877859477124183 - type: f1 value: 0.8860083873427372 --- # pmfsl/bertimbau-base-finetuned-rte This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0326 - Validation Loss: 0.1834 - Test Loss: 0.5695 - Train Accuracy: 0.9531 - Train F1: 0.9534 - Test Accuracy: 0.8778 - Test F1: 0.8860 - Epoch: 4 ## 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': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 505, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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 F1 | Epoch | |:----------:|:---------------:|:--------------:|:--------:|:-----:| | 0.3846 | 0.2204 | 0.9152 | 0.9191 | 0 | | 0.1981 | 0.1577 | 0.9442 | 0.9455 | 1 | | 0.1026 | 0.1348 | 0.9509 | 0.9511 | 2 | | 0.0593 | 0.1492 | 0.9531 | 0.9542 | 3 | | 0.0326 | 0.1834 | 0.9531 | 0.9534 | 4 | ### Framework versions - Transformers 4.27.4 - TensorFlow 2.12.0 - Datasets 2.11.0 - Tokenizers 0.13.2