Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:10501
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use shinqhwa/bge-m3-klue-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use shinqhwa/bge-m3-klue-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("shinqhwa/bge-m3-klue-sts") sentences = [ "삼촌이 이 시간대에 보고싶은 티비 프로그램이 뭐여요?", "외출 시 방범 모드 변환하는 방법 좀 알려줘", "제2차 전략회의에서 대전, 경기, 강원, 전남, 제주, 경남 등 6개 시·도지사가 지역에서 추진 중인 뉴딜 관련 사례를 소개했습니다.", "학교가 보낸 메일은 차단하면 안돼" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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