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 bandi2716/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 bandi2716/klue-roberta-base-klue-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bandi2716/klue-roberta-base-klue-sts") sentences = [ "위치도 구성도 굉장히 만족스러운 숙소였습니다.", "숙박시설의 위치와 구성은 매우 만족스러웠습니다.", "주인은 친절하고 유익합니다.", "화장실과 현관 중 너가 켜길 원하는 조명은 어느 곳이야?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K