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--- |
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license: mit |
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base_model: beomi/KcELECTRA-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: 0322_cosmetic3_kcelectra |
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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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# 0322_cosmetic3_kcelectra |
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This model is a fine-tuned version of [beomi/KcELECTRA-base](https://huggingface.co/beomi/KcELECTRA-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3637 |
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- Accuracy: 0.8700 |
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- F1: 0.8703 |
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- Precision: 0.8789 |
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- Recall: 0.8700 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.4536 | 1.0 | 277 | 0.3556 | 0.8768 | 0.8734 | 0.8873 | 0.8768 | |
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| 0.2608 | 2.0 | 554 | 0.5060 | 0.8261 | 0.8252 | 0.8415 | 0.8261 | |
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| 0.1171 | 3.0 | 831 | 0.5406 | 0.8623 | 0.8571 | 0.8768 | 0.8623 | |
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| 0.1393 | 4.0 | 1108 | 0.5734 | 0.8768 | 0.8752 | 0.8862 | 0.8768 | |
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| 0.2115 | 5.0 | 1385 | 0.6661 | 0.8913 | 0.8915 | 0.8924 | 0.8913 | |
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| 0.0939 | 6.0 | 1662 | 0.5506 | 0.9058 | 0.9054 | 0.9057 | 0.9058 | |
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| 0.1122 | 7.0 | 1939 | 0.6672 | 0.8986 | 0.8985 | 0.8987 | 0.8986 | |
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| 0.2413 | 8.0 | 2216 | 0.7136 | 0.8949 | 0.8949 | 0.8950 | 0.8949 | |
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| 0.001 | 9.0 | 2493 | 0.6689 | 0.9058 | 0.9058 | 0.9058 | 0.9058 | |
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| 0.0013 | 10.0 | 2770 | 0.6764 | 0.9094 | 0.9094 | 0.9094 | 0.9094 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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