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vedantjumle/indo-ml-final-test-xlnet-1

This model is a fine-tuned version of xlnet-large-cased on an unknown dataset. It achieves the following results on the evaluation set:

  • Train Loss: 0.7254
  • Validation Loss: 0.6440
  • Train Accuracy: 0.88
  • 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 6000, '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
5.1411 5.0487 0.01 0
5.0561 4.8924 0.0267 1
4.8362 4.4063 0.2033 2
4.2361 3.4961 0.45 3
3.3575 2.5506 0.6167 4
2.5070 1.8625 0.74 5
1.8507 1.3431 0.8067 6
1.3367 0.9980 0.84 7
0.9787 0.7747 0.86 8
0.7254 0.6440 0.88 9

Framework versions

  • Transformers 4.34.0
  • TensorFlow 2.13.0
  • Datasets 2.14.5
  • Tokenizers 0.14.1
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