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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
