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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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- f1 |
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- recall |
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- precision |
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base_model: beomi/kcbert-base |
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model-index: |
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- name: kcbert-base-finetuned-nsmc |
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results: |
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- task: |
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type: text-classification |
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name: 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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- type: accuracy |
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value: 0.90198 |
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name: Accuracy |
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- type: f1 |
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value: 0.9033161705233671 |
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name: F1 |
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- type: recall |
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value: 0.9095062169785088 |
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name: Recall |
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- type: precision |
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value: 0.8972098126812446 |
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name: Precision |
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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-nsmc |
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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: 0.4197 |
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- Accuracy: 0.9020 |
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- F1: 0.9033 |
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- Recall: 0.9095 |
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- Precision: 0.8972 |
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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: 3 |
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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 | Accuracy | F1 | Recall | Precision | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:| |
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| 0.3028 | 0.32 | 3000 | 0.2994 | 0.8769 | 0.8732 | 0.8422 | 0.9066 | |
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| 0.2833 | 0.64 | 6000 | 0.2766 | 0.8880 | 0.8844 | 0.8512 | 0.9203 | |
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| 0.2719 | 0.96 | 9000 | 0.2527 | 0.8980 | 0.8981 | 0.8933 | 0.9030 | |
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| 0.1938 | 1.28 | 12000 | 0.2934 | 0.8969 | 0.8965 | 0.8869 | 0.9062 | |
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| 0.1907 | 1.6 | 15000 | 0.3141 | 0.8992 | 0.8999 | 0.9003 | 0.8996 | |
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| 0.1824 | 1.92 | 18000 | 0.3537 | 0.8986 | 0.8964 | 0.8711 | 0.9232 | |
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| 0.1261 | 2.24 | 21000 | 0.4197 | 0.9020 | 0.9033 | 0.9095 | 0.8972 | |
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| 0.1237 | 2.56 | 24000 | 0.4170 | 0.8995 | 0.9017 | 0.9156 | 0.8882 | |
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| 0.1182 | 2.88 | 27000 | 0.4165 | 0.9020 | 0.9036 | 0.9130 | 0.8945 | |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.9.1 |
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- Datasets 1.14.0 |
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- Tokenizers 0.10.3 |
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