gacoan_reviewer / README.md
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Training in progress epoch 24
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metadata
license: mit
base_model: indobenchmark/indobert-large-p1
tags:
  - generated_from_keras_callback
model-index:
  - name: aditnnda/gacoan_reviewer
    results: []

aditnnda/gacoan_reviewer

This model is a fine-tuned version of indobenchmark/indobert-large-p1 on an unknown dataset. It achieves the following results on the evaluation set:

  • Train Loss: 0.0001
  • Validation Loss: 0.4435
  • Train Accuracy: 0.9386
  • Epoch: 24

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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3550, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 Epoch
0.2553 0.1732 0.9331 0
0.0938 0.1571 0.9400 1
0.0310 0.2345 0.9386 2
0.0138 0.3288 0.9358 3
0.0140 0.3345 0.9177 4
0.0033 0.3502 0.9386 5
0.0118 0.3387 0.9344 6
0.0269 0.4487 0.9024 7
0.0188 0.3228 0.9331 8
0.0017 0.3581 0.9372 9
0.0020 0.4125 0.9233 10
0.0021 0.4143 0.9247 11
0.0011 0.4353 0.9303 12
0.0002 0.4285 0.9344 13
0.0005 0.4350 0.9344 14
0.0002 0.4340 0.9344 15
0.0002 0.4026 0.9400 16
0.0001 0.4123 0.9414 17
0.0001 0.4228 0.9414 18
0.0001 0.4294 0.9386 19
0.0001 0.4385 0.9386 20
0.0001 0.4411 0.9386 21
0.0001 0.4423 0.9386 22
0.0001 0.4431 0.9386 23
0.0001 0.4435 0.9386 24

Framework versions

  • Transformers 4.35.2
  • TensorFlow 2.15.0
  • Datasets 2.16.0
  • Tokenizers 0.15.0