콘텐츠로 이동

트랜스포머 질문, 답변 및 문장 요약

필요한 모듈 설치하기

!pip install transformers --quiet
!pip install torch --quiet

Mask filling (마스트 필터링)

from transformers import pipeline

unmasker = pipeline("fill-mask")
unmasker("This course will teach you all about <mask> models.", top_k=2)

실행결과)

[{'sequence': 'This course will teach you all about mathematical models.',
  'score': 0.19619831442832947,
  'token': 30412,
  'token_str': ' mathematical'},
 {'sequence': 'This course will teach you all about computational models.',
  'score': 0.04052725434303284,
  'token': 38163,
  'token_str': ' computational'}]

Named entity recognition (NER 태깅)

from transformers import pipeline

ner = pipeline("ner", grouped_entities=True)
ner("My name is Sylvain and I work at Hugging Face in Brooklyn.")

실행결과)

[{'entity_group': 'PER', 'score': 0.99816, 'word': 'Sylvain', 'start': 11, 'end': 18}, 
 {'entity_group': 'ORG', 'score': 0.97960, 'word': 'Hugging Face', 'start': 33, 'end': 45}, 
 {'entity_group': 'LOC', 'score': 0.99321, 'word': 'Brooklyn', 'start': 49, 'end': 57}
]

Question answering (질문 & 답변)

from transformers import pipeline

question_answerer = pipeline("question-answering")
question_answerer(
    question="Where do I work?",
    context="My name is Sylvain and I work at Hugging Face in Brooklyn",
)

실행결과)

{'score': 0.6385916471481323, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}

Summarization (문장 요약하기)

from transformers import pipeline

summarizer = pipeline("summarization")
summarizer(
    """
    America has changed dramatically during recent years. Not only has the number of 
    graduates in traditional engineering disciplines such as mechanical, civil, 
    electrical, chemical, and aeronautical engineering declined, but in most of 
    the premier American universities engineering curricula now concentrate on 
    and encourage largely the study of engineering science. As a result, there 
    are declining offerings in engineering subjects dealing with infrastructure, 
    the environment, and related issues, and greater concentration on high 
    technology subjects, largely supporting increasingly complex scientific 
    developments. While the latter is important, it should not be at the expense 
    of more traditional engineering.

    Rapidly developing economies such as China and India, as well as other 
    industrial countries in Europe and Asia, continue to encourage and advance 
    the teaching of engineering. Both China and India, respectively, graduate 
    six and eight times as many traditional engineers as does the United States. 
    Other industrial countries at minimum maintain their output, while America 
    suffers an increasingly serious decline in the number of engineering graduates 
    and a lack of well-educated engineers.
"""
)

실행결과)

[{'summary_text': ' America has changed dramatically during recent years . The '
                  'number of engineering graduates in the U.S. has declined in '
                  'traditional engineering disciplines such as mechanical, civil '
                  ', electrical, chemical, and aeronautical engineering . Rapidly '
                  'developing economies such as China and India, as well as other '
                  'industrial countries in Europe and Asia, continue to encourage '
                  'and advance engineering .'}]

Translation (문장 번역하기)

from transformers import pipeline

translator = pipeline("translation", model="Helsinki-NLP/opus-mt-fr-en")
translator("Ce cours est produit par Hugging Face.")

실행결과)

[{'translation_text': 'This course is produced by Hugging Face.'}]