Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:112
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use acradin/DK_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use acradin/DK_embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("acradin/DK_embedding") sentences = [ "슈파인", "park | 장비를 파킹(대기) 위치로 이동 또는 튜브를 맨위로 | 파킹", "tubeToStandCenter | 튜브를 스탠드 센터를 향하도록 이동, 어브도민, 이렉트, 체스트, 홀스파인, 슈파인, abdomen, erect, chest, chest PA, Whole spine, supine | 튜브 스탠드 센터로", "tubeToTableCenter | 튜브를 테이블 센터를 향하도록 이동 | 튜브 테이블 센터로" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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