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README.md
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---
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tags:
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- generated_from_trainer
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datasets:
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- nsmc
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metrics:
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- accuracy
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model-index:
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- name: kcbert-base-finetuned
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results:
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- task:
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name: Text Classification
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type: text-classification
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dataset:
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name: nsmc
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type: nsmc
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8978
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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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# kcbert-base-finetuned
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This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the nsmc dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6977
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- Accuracy: 0.8978
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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: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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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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- num_epochs: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|
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| 0.152 | 1.0 | 9375 | 0.3803 | 0.8880 |
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| 0.1741 | 2.0 | 18750 | 0.3669 | 0.892 |
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| 0.105 | 3.0 | 28125 | 0.5072 | 0.8975 |
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| 0.054 | 4.0 | 37500 | 0.6541 | 0.8966 |
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| 0.0302 | 5.0 | 46875 | 0.6977 | 0.8978 |
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### Framework versions
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- Transformers 4.11.3
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- Pytorch 1.9.0+cu111
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- Datasets 1.14.0
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- Tokenizers 0.10.3
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