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README.md
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@@ -10,17 +10,6 @@ formal classifier or honorific classifier
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## νκ΅μ΄ μ‘΄λλ§ λ°λ§ λΆλ₯κΈ°
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μ€λμ μ μ‘΄λλ§ , λ°λ§μ νκ΅μ΄ ννμ λΆμκΈ°λ‘ λΆλ₯νλ κ°λ¨ν λ°©λ²μ μκ°νλ€.<br>
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νμ§λ§ μ΄ λ°©λ²μ μ€μ λ‘ μ μ©νλ € νλλ, λ§μ λΆλΆμμ μ€λ₯κ° λ°μνμλ€.
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μλ₯Ό λ€λ©΄)
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```bash
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μ λ²μ κ΅μλκ»μ μλ£ κ°μ Έμ€λΌνλλ° κΈ°μ΅λ?
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```
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λΌλ 문ꡬλ₯Ό "κ»μ"λΌλ μ‘΄μΉλλ¬Έμ μ 체문μ₯μ μ‘΄λλ§λ‘ νλ¨νλ μ€λ₯κ° λ§μ΄ λ°μνλ€. <br>
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κ·Έλμ μ΄λ²μ λ₯λ¬λ λͺ¨λΈμ λ§λ€κ³ κ·Έ κ³Όμ μ 곡μ ν΄λ³΄κ³ μνλ€.
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#### λΉ λ₯΄κ² κ°μ Έλ€ μ°μ€ λΆλ€μ μλ μ½λλ‘ λ°λ‘ μ¬μ©νμ€ μ μμ΅λλ€.
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
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- HuggingFace : https://huggingface.co/beomi/kcbert-base
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***
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## λ°μ΄ν°
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```bash
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get_train_data.py
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```
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### μμ
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|sentence|label|
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|------|---|
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***
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## νμ΅(train)
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```bash
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python3 modeling/train.py
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```
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***
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## μμΈ‘(inference)
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```bash
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python3 inference.py
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```
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```python
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def formal_percentage(self, text):
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return round(float(self.predict(text)[0][1]), 2)
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def print_message(self, text):
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result = self.formal_persentage(text)
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if result > 0.5:
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print(f'{text} : μ‘΄λλ§μ
λλ€. ( νλ₯ {result*100}% )')
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if result < 0.5:
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print(f'{text} : λ°λ§μ
λλ€. ( νλ₯ {((1 - result)*100)}% )')
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```
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κ²°κ³Ό
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```
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μ λ²μ κ΅μλκ»μ μλ£ κ°μ Έμ€λΌνμ
¨λλ° κΈ°μ΅λμΈμ? : μ‘΄λλ§μ
λλ€. ( νλ₯ 99.19% )
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## νκ΅μ΄ μ‘΄λλ§ λ°λ§ λΆλ₯κΈ°
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
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- HuggingFace : https://huggingface.co/beomi/kcbert-base
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***
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### μμ
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|sentence|label|
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|------|---|
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***
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κ²°κ³Ό
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```
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μ λ²μ κ΅μλκ»μ μλ£ κ°μ Έμ€λΌνμ
¨λλ° κΈ°μ΅λμΈμ? : μ‘΄λλ§μ
λλ€. ( νλ₯ 99.19% )
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