Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:56358
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use hwjello/artwork_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hwjello/artwork_embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hwjello/artwork_embedding") sentences = [ "'신영복' 작가의 '설악일우'의 제작년도", "'원성원' 작가의 '드림룸-배경'은(는) 2004/2017에 제작되었습니다.", "'신영복' 작가의 '설악일우'은(는) 1989에 제작되었습니다.", "'강용운' 작가의 '비(秘)'의 부문은 회화 II입니다." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle