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: 770 Bytes
7ee8ac6 | 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 27 28 29 | {
"transformer_task": "feature-extraction",
"modality_config": {
"text": {
"method": "forward",
"method_output_name": "last_hidden_state"
},
"image": {
"method": "forward",
"method_output_name": "last_hidden_state"
},
"video": {
"method": "forward",
"method_output_name": "last_hidden_state"
},
"message": {
"method": "forward",
"method_output_name": "last_hidden_state",
"format": "structured"
}
},
"module_output_name": "token_embeddings",
"processing_kwargs": {
"chat_template": {
"add_generation_prompt": true
}
},
"unpad_inputs": false
} |