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ko-news-summarization

This model is a fine-tuned version of psyche/KoT5-summarization on an unknown dataset. It achieves the following results on the evaluation set:

  • Train Loss: 0.9267
  • Validation Loss: 1.2897
  • Train Rougel: tf.Tensor(0.39195082, shape=(), dtype=float32)
  • Epoch: 6

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', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
  • training_precision: float32

Training results

Train Loss Validation Loss Train Rougel Epoch
1.2735 1.2266 tf.Tensor(0.38460648, shape=(), dtype=float32) 0
1.2050 1.2290 tf.Tensor(0.38383868, shape=(), dtype=float32) 1
1.1428 1.2320 tf.Tensor(0.38434482, shape=(), dtype=float32) 2
1.0866 1.2497 tf.Tensor(0.38514885, shape=(), dtype=float32) 3
1.0311 1.2612 tf.Tensor(0.3883608, shape=(), dtype=float32) 4
0.9740 1.2713 tf.Tensor(0.39306718, shape=(), dtype=float32) 5
0.9267 1.2897 tf.Tensor(0.39195082, shape=(), dtype=float32) 6

Framework versions

  • Transformers 4.25.1
  • TensorFlow 2.9.2
  • Datasets 2.8.0
  • Tokenizers 0.13.2
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