Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
dense
Generated from Trainer
dataset_size:9450
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use sept102/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 sept102/klue-roberta-base-klue-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sept102/klue-roberta-base-klue-sts") sentences = [ "전자레인지를 사용할 수 있고 레토르트 음식을 데우는 정도의 조리는 가능합니다.", "지자체별 내역은 보도자료를 참고하여 주시기 바랍니다.", "역까지는 버스를 타야 갈 수 있는 정도예요.", "부엌이나 세탁기에 아무런 문제가 없어요." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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