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metadata
license: mit
base_model: indolem/indobert-base-uncased
tags:
  - generated_from_keras_callback
model-index:
  - name: damand2061/innermore-x-indobert-base-uncased
    results: []

damand2061/innermore-x-indobert-base-uncased

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

  • Train Loss: 0.0053
  • Validation Loss: 0.1740
  • Validation Precision: 0.7319
  • Validation Recall: 0.7644
  • Validation F1: 0.7478
  • Validation Accuracy: 0.9582
  • Epoch: 14

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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0002, 'decay_steps': 420, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
  • training_precision: float32

Training results

Train Loss Validation Loss Validation Precision Validation Recall Validation F1 Validation Accuracy Epoch
0.7318 0.4161 0.1453 0.1156 0.1287 0.8751 0
0.3556 0.2296 0.5610 0.5111 0.5349 0.9324 1
0.2050 0.1668 0.6972 0.6756 0.6862 0.9521 2
0.1289 0.1603 0.6807 0.72 0.6998 0.9531 3
0.0875 0.1874 0.7281 0.7022 0.7149 0.9521 4
0.0754 0.1931 0.6653 0.7156 0.6895 0.9479 5
0.0416 0.1637 0.6935 0.7644 0.7273 0.9554 6
0.0238 0.1413 0.7598 0.7733 0.7665 0.9638 7
0.0152 0.1494 0.7479 0.8044 0.7752 0.9634 8
0.0152 0.1946 0.7061 0.7156 0.7108 0.9531 9
0.0128 0.1815 0.7241 0.7467 0.7352 0.9554 10
0.0072 0.1766 0.7210 0.7467 0.7336 0.9568 11
0.0080 0.1860 0.6987 0.7422 0.7198 0.9531 12
0.0089 0.1826 0.7227 0.7644 0.7430 0.9563 13
0.0053 0.1740 0.7319 0.7644 0.7478 0.9582 14

Framework versions

  • Transformers 4.38.2
  • TensorFlow 2.15.0
  • Tokenizers 0.15.2