Sentence Similarity
sentence-transformers
Safetensors
Korean
English
ministral3
feature-extraction
dense
matryoshka
Instructions to use nlpai-lab/KURE-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nlpai-lab/KURE-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nlpai-lab/KURE-v3") 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 tokenizer.json from nlpai-lab/KURE-v3: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/nlpai-lab/KURE-v3/resolve/main/tokenizer.json
- Command line
-
hf download hf://nlpai-lab/KURE-v3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/nlpai-lab/KURE-v3/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- d8a7d959323baeecf1ca3e77a7617cbe54675e056a79db91fc6dffb189164050
- Size of remote file:
- 17.1 MB
- SHA256:
- bed0c218bf2a85500014ecadb69d0f83c94b30526235f4bf2cac6d5c40bb5ced
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