인공지능/ML, DL
[NLP] Sentence-transformer를 활용한 문장 임베딩
judy@
2023. 4. 28. 17:36
1. sentence-transformer 설치
$ pip install sentence-transformers
2. 모델 선택
- https://huggingface.co/sentence-transformers 에서 모델 선택
- 나는 이 모델 선택: https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2
3. 모델 불러오기
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("sentence-transformers/paraphrase-MiniLM-L6-v2")
4. 임베딩
my_sentence = 'I love you, darling'
response = model.encode(my_sentence)
5. 임베딩 결과 확인
response
Output exceeds the size limit. Open the full output data in a text editorarray([ 3.73888873e-02, 2.84339845e-01, 5.92863858e-01, -4.04151589e-01,
-3.62452984e-01, -1.35070413e-01, 9.10828412e-01, 2.13023871e-01,
8.73978212e-02, 3.87814462e-01, 1.87386442e-02, -8.04892361e-01,
-1.97961539e-01, 2.91577708e-02, 2.97898620e-01, 1.17376382e-02,
5.57748437e-01, -3.15046966e-01, -8.65592420e-01, -6.24333799e-01,
8.91279355e-02, -9.49146822e-02, -3.39516938e-01, -9.46855024e-02,
-4.84677464e-01, -2.08640546e-01, 1.27627596e-01, 3.56212914e-01,
1.70316055e-01, -1.92422360e-01, 4.13054734e-01, -8.34294915e-01,
5.01438737e-01, 4.00905520e-01, 2.16388777e-01, 7.73810208e-01,
-3.08173984e-01, -3.19728523e-01, -1.36708766e-01, -3.75668436e-01,
2.97464281e-01, -5.75280726e-01, -3.47505987e-01, 2.21415445e-01,
3.97491962e-01, -2.88253367e-01, 1.52879342e-01, -2.01121002e-01,
5.10286272e-01, 5.13169289e-01, -7.98814237e-01, -3.57702896e-02,
-3.14021148e-02, 3.62671137e-01, 4.97897804e-01, 7.65251040e-01,
3.25530440e-01, 6.36099726e-02, 2.32315615e-01, -2.28358701e-01,
5.67676425e-01, -3.68146934e-02, -5.27653813e-01, 5.21592438e-01,
-3.41809809e-01, -5.05130768e-01, -4.97012073e-03, 3.47984023e-02,
...
shape 확인
response.shape
(384,)
끝! 이제 프로젝트에 적용해보자!
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