Sentence Similarity
Safetensors
sentence-transformers
Korean
English
PyLate
modernbert
ColBERT
late-interaction
multi-vector
feature-extraction
text-embeddings-inference
Instructions to use nlpai-lab/KURE-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nlpai-lab/KURE-v2 with sentence-transformers:
from pylate import models queries = [ "Which planet is known as the Red Planet?", "What is the largest planet in our solar system?", ] documents = [ ["Mars is the Red Planet.", "Venus is Earth's twin."], ["Jupiter is the largest planet.", "Saturn has rings."], ] model = models.ColBERT(model_name_or_path="nlpai-lab/KURE-v2") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from nlpai-lab/KURE-v2: direct link, hf CLI and curl.
- Browser
- Download file 1.09 MB
-
https://huggingface.co/nlpai-lab/KURE-v2/resolve/main/tokenizer.json
- Command line
-
hf download hf://nlpai-lab/KURE-v2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/nlpai-lab/KURE-v2/resolve/main/tokenizer.json
1.09 MB
File too large to display, you can check the raw version instead.