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
File size: 1,014 Bytes
e9f912f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"model_type": "SentenceTransformer",
"__version__": {
"sentence_transformers": "5.2.2",
"transformers": "5.0.0",
"pytorch": "2.7.1+cpu"
},
"prompts": {
"query": "task: search result | query: ",
"document": "title: none | text: ",
"BitextMining": "task: search result | query: ",
"Clustering": "task: clustering | query: ",
"Classification": "task: classification | query: ",
"InstructionRetrieval": "task: code retrieval | query: ",
"MultilabelClassification": "task: classification | query: ",
"PairClassification": "task: sentence similarity | query: ",
"Reranking": "task: search result | query: ",
"Retrieval": "task: search result | query: ",
"Retrieval-query": "task: search result | query: ",
"Retrieval-document": "title: none | text: ",
"STS": "task: sentence similarity | query: ",
"Summarization": "task: summarization | query: "
},
"default_prompt_name": null,
"similarity_fn_name": "cosine"
} |