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
Download 1_Pooling/config.json from whybe-choi/kovre: direct link, hf CLI and curl.
- Browser
- Download file 96 Bytes
-
https://huggingface.co/whybe-choi/kovre/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://whybe-choi/kovre/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/whybe-choi/kovre/resolve/main/1_Pooling/config.json
96 Bytes
| { | |
| "embedding_dimension": 2048, | |
| "pooling_mode": "lasttoken", | |
| "include_prompt": true | |
| } |