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--- |
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license: mit |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: madatnlp/gamza-bart-for-kormath |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# madatnlp/gamza-bart-for-kormath |
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This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.1418 |
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- Validation Loss: 0.3009 |
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- Epoch: 29 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- optimizer: {'name': 'Adam', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Validation Loss | Epoch | |
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|:----------:|:---------------:|:-----:| |
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| 4.4155 | 1.9300 | 0 | |
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| 1.4995 | 1.0293 | 1 | |
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| 1.0445 | 0.8365 | 2 | |
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| 0.8775 | 0.7569 | 3 | |
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| 0.8198 | 0.7778 | 4 | |
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| 0.7619 | 0.7430 | 5 | |
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| 0.7324 | 0.7259 | 6 | |
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| 0.7234 | 0.7214 | 7 | |
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| 0.6697 | 0.6819 | 8 | |
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| 0.6599 | 0.6673 | 9 | |
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| 0.6387 | 0.6433 | 10 | |
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| 0.6227 | 0.6651 | 11 | |
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| 0.6017 | 0.6128 | 12 | |
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| 0.5820 | 0.6430 | 13 | |
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| 0.5229 | 0.5611 | 14 | |
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| 0.4617 | 0.4675 | 15 | |
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| 0.4071 | 0.4463 | 16 | |
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| 0.3495 | 0.4213 | 17 | |
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| 0.3202 | 0.4103 | 18 | |
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| 0.2875 | 0.4477 | 19 | |
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| 0.2528 | 0.3244 | 20 | |
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| 0.2331 | 0.4037 | 21 | |
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| 0.2117 | 0.3041 | 22 | |
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| 0.1943 | 0.3069 | 23 | |
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| 0.1805 | 0.3385 | 24 | |
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| 0.2267 | 0.3347 | 25 | |
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| 0.2049 | 0.2993 | 26 | |
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| 0.1800 | 0.3792 | 27 | |
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| 0.1583 | 0.2905 | 28 | |
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| 0.1418 | 0.3009 | 29 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- TensorFlow 2.8.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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