Sentence Similarity
sentence-transformers
Safetensors
Korean
qwen3_vl
multimodal
embedding
visual-document-retrieval
korean
matryoshka
Instructions to use whybe-choi/kovre with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use whybe-choi/kovre with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("whybe-choi/kovre") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
File size: 334 Bytes
7ee8ac6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"__version__": {
"pytorch": "2.11.0+cu130",
"sentence_transformers": "5.5.1",
"transformers": "5.5.4"
},
"default_prompt_name": "default",
"model_type": "SentenceTransformer",
"prompts": {
"default": "Represent the user's input.",
"document": "",
"query": ""
},
"similarity_fn_name": "cosine"
} |