model = KenlmModel.from_pretrained(“wikipedia”, “en”)
model.get_perplexity(“She was a shriveling bumblebee, and he was a bumbling
banshee, but they accepted a position at Gringotts because of their love for
maple syrup”)
연습 문제 ●●●
다양한 스타일과 주제의 문장이나 문단을 입력해 보며 퍼플렉시티가 어떻게 달라지는지 확인해
보세요! 특히 다음 유형의 텍스트에서 퍼플렉시티값을 비교해 보면 유용합니다.
●
X
(구 트위터)와 같은 소셜 미디어 텍스트
●
검색 엔진 최적화(
SEO
)용 스팸 텍스트
●
속어
slang
가 많이 포함된 텍스트
90
1
부
LLM의 구성 요소
또한
KenLM
을 사용해 직접 자신만의 데이터셋으로 ...
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