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klue's Introduction

image

Data

  • premise와 hypothesis의 관계를 판별하는 모델을 개발

    • Train image
    • Test image
  • 외부데이터

EDA

  • KLUE Data Label
    • 5개의 label을 토대로 gold_label을 판별 image
  • premise와 hypothesis, label의 관계
    • 대체로 하나의 premise에 hypothesis 세 문장, 세 개의 label이 매칭 image
  • label 분포
    • 세 개의 label이 고르게 분포
      image

모델링

5 fold로 모델을 fine-tuning 후 Voting ensemble

추론

  • Hard voting
    • 5개의 모델로 label을 예측 image
    • 대부분의 모델이 neutral에 치중
      image
    • 5개의 label 중 같은 label이 3개 이상이면 해당 label 획득, 아니면 neutral
      def voting(df):
        cols = ['neutral', 'contradiction', 'entailment']
        for col in cols:
          if df[col] > 2:
            return col
        return 'neutral'
      
    • 가장 빈번한 label을 부여했을 때 label이 neutral에 치중
      image
    • label 별로 threshold 다르게 적용
        neutral: 3 / contradiction: 2 -> contradiction
        neutral: 3 / entailment: 2 -> entailment
        neutral: 4 / contradiction: 1 -> contradiction
        neutral: 4 / entailment: 1 -> entailment
      
    • 결과
      image
  • Soft voting
    • 추가

klue's People

Contributors

jungsungmoon avatar jeongname avatar seojiii avatar

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