from transformers import pipeline
qqp_pipeline = pipeline(
task="text-classification",
model="textattack/bert-base-uncased-QQP"
)
question1 = "How can I learn Python?"
question2 = "What is the best way to study Python?"
result = qqp_pipeline({"text": question1, "text_pair": question2})
print(result)
{'label': 'LABEL_1', 'score': 0.6853412985801697}
QQP 파이프라인
from transformers import pipeline
qqp_pipeline = pipeline(
task="text-classification",
model="textattack/bert-base-uncased-QQP"
)
question1 = "How can I learn Python?"
question2 = "What is the capital of France?"
result = qqp_pipeline({"text": question1, "text_pair": question2})
print(result)
{'label': 'LABEL_0', 'score': 0.9999338388442993}
문법적 적합성 평가
문장의 문법적 적합성을 평가
활용:
교육 도구
문법 검사기
글쓰기 보조 도구
Corpus of Linguistic Acceptability(CoLA) 데이터셋으로 학습된 모델 사용
CoLA 파이프라인
from transformers import pipeline
cola_classifier = pipeline(
task="text-classification",
model="textattack/distilbert-base-uncased-CoLA"
)
result = cola_classifier("The cat sat on the mat.")
print(result)
from transformers import pipeline
cola_classifier = pipeline(
task="text-classification",
model="textattack/distilbert-base-uncased-CoLA"
)
result = cola_classifier("The cat on sat mat the.")
print(result)
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