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Librarian Bot: Add base_model information to model (#1)
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metadata
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 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