Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
dense
Generated from Trainer
dataset_size:10501
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use eazzy/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 eazzy/klue-roberta-base-klue-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("eazzy/klue-roberta-base-klue-sts") sentences = [ "제습기 말고 에어컨이랑 선풍기는 같이 쓰도록해", "가습기말고 에어컨이랑 선풍기 동시에 틀지 그래?", "거실 화장실 침실 어디에서도 아름다운 뷰를 보실 수 있어요", "언제 에어컨이 작동하게 되어있니?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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