Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
Generated from Trainer
dataset_size:10501
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use hyuk2010/klue-roberta-base-klue-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hyuk2010/klue-roberta-base-klue-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hyuk2010/klue-roberta-base-klue-sts") sentences = [ "숙소가 지하라는 사실이 맘에 안들었다,", "숙소가 지하에 있다는 사실이 마음에 들지 않았어요.", "2006년 첫 콜마 BNH가 설립된 이후 14년 만에 연구소기업들이 900개를 돌파했습니다.", "그 방은 제가 사진에서 본 것과 똑같아서 가격에 아주 좋습니다." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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