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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: team-lucid/hubert-base-korean |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: for_test13 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# for_test13 |
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This model is a fine-tuned version of [team-lucid/hubert-base-korean](https://huggingface.co/team-lucid/hubert-base-korean) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 9.0800 |
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- Per: 0.8663 |
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- Learning Rate: 0.0000 |
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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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- learning_rate: 0.0001 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 20 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Per | Rate | |
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|:-------------:|:------:|:----:|:---------------:|:------:|:------:| |
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| 9.8433 | 1.0417 | 50 | 10.8927 | 1.9730 | 0.0001 | |
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| 6.2994 | 2.0833 | 100 | 10.1801 | 1.4889 | 0.0001 | |
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| 5.6979 | 3.125 | 150 | 9.8748 | 1.1627 | 0.0001 | |
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| 5.5696 | 4.1667 | 200 | 9.6279 | 0.9856 | 0.0001 | |
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| 5.5354 | 5.2083 | 250 | 9.4447 | 0.9282 | 0.0001 | |
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| 5.3749 | 6.25 | 300 | 9.3013 | 0.8952 | 4e-05 | |
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| 5.6517 | 7.2917 | 350 | 9.1784 | 0.8771 | 0.0000 | |
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| 5.1293 | 8.3333 | 400 | 9.0618 | 0.8661 | 0.0000 | |
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| 5.5912 | 9.375 | 450 | 9.0800 | 0.8663 | 0.0000 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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