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README.md
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---
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language: ko
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license: apache-2.0
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tags:
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- sql
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- text-to-sql
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- nl2sql
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- financial-domain
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- pytorch
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datasets:
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- custom
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metrics:
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- accuracy
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- f1
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---
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## Colab Notebook
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[](https://colab.research.google.com/drive/1vaGZTZ7y0SYLarCX0QemkUernLyohswz?usp=sharing)
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# NHSQLNL: κΈμ΅ μμ°μ΄ β SQL λ³ν λͺ¨λΈ
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`NHSQLNL`μ νκ΅μ΄ κΈμ΅ μμ°μ΄ μ§μλ₯Ό SQL μΏΌλ¦¬λ‘ λ³ννλ **Text-to-SQL (NL2SQL)** λͺ¨λΈμ
λλ€.
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μν λ° κΈμ΅κΆ λλ©μΈ μ§μλ₯Ό λ°μ΄ν°λ² μ΄μ€ μ§μ(SQL)λ‘ μλ λ³ννμ¬, κ³ κ° μ§μ μλ΅ μμ€ν
λ° κΈμ΅ λ°μ΄ν° λΆμμ νμ©ν μ μμ΅λλ€.
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---
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## μ£Όμ κΈ°λ₯ (Features)
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- νκ΅μ΄ κΈμ΅ λλ©μΈ μμ°μ΄ μ
λ ₯μ SQL μΏΌλ¦¬λ‘ λ³ν
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- μ¬μ μ μλ μ€ν€λ§μ λ§μΆ μμ ν SQL μμ±
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- PyTorch λ° Hugging Face `transformers` κΈ°λ°
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---
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## μ¬μ© λ°©λ² (How to Use)
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```python
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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# λͺ¨λΈ λ‘λ
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MODEL_PATH = "combe4259/NHSQLNL"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
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model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_PATH)
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# μ
λ ₯ μ§μ
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query = "2023λ
μ κ°μ€λ μκΈ κ³μ’ μλ₯Ό μλ €μ€"
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inputs = tokenizer(query, return_tensors="pt")
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# SQL μμΈ‘
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outputs = model.generate(**inputs, max_length=128)
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sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print("μ
λ ₯:", query)
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print("μμ±λ SQL:", sql)
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---
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## νμ΅ λ°μ΄ν° (Training Data)
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- μ체 ꡬμΆν κΈμ΅ λλ©μΈ **μμ°μ΄ β SQL λ§€ν λ°μ΄ν°μ
** μ¬μ©
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- λ°μ΄ν° μ μ²λ¦¬: SQL μ€ν€λ§ μ κ·ν λ° ν ν¬λμ΄μ κΈ°λ° μ
λ ₯ λ³ν
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---
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---
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## νμ© κ°λ₯ λΆμΌ (Applications)
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- κΈμ΅κΆ μ±λ΄ λ° μλ΄ μλν
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- μμ°μ΄ κΈ°λ° λ°μ΄ν° μ‘°ν λ° λ¦¬ν¬νΈ μμ±
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- λΉμ λ¬Έκ° λμ SQL νμ΅/μ°μ΅ λꡬ
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