Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:2838
loss:MSELoss
loss:CosineMSELoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Leejy0-0/kr-con-embedding-sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Leejy0-0/kr-con-embedding-sbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Leejy0-0/kr-con-embedding-sbert") sentences = [ "기초 부지 정지", "자재는 크레인이 아닌 인력으로 운반", "제작장 부지 정비", "잡철물 설치는 구조 순서에 맞춰 이루어진다" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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