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
- Xet hash:
- e626b29e0ea784513a6fb056456979dac72a7c7b8ac3316ee51cdded23d4ce0e
- Size of remote file:
- 11.4 MB
- SHA256:
- f4deec245f9380efa31abe72adbcae78599405bf2e69e5828180a5a6e116c67d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.