Feature Extraction
sentence-transformers
Safetensors
xlm-roberta
sentence-similarity
Generated from Trainer
dataset_size:1879136
loss:CachedGISTEmbedLoss
text-embeddings-inference
Instructions to use nlpai-lab/KURE-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nlpai-lab/KURE-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nlpai-lab/KURE-v1") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from nlpai-lab/KURE-v1: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/nlpai-lab/KURE-v1/resolve/main/tokenizer.json
- Command line
-
hf download hf://nlpai-lab/KURE-v1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/nlpai-lab/KURE-v1/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 7802b7603b74807c999bf852f0eb558d6cb6c199e99603e990d9d36db2a2c412
- Size of remote file:
- 17.1 MB
- SHA256:
- fb3c3b93c46fd5a8634e262e1b7de7da11a18b527aa2282b312952b692781dfd
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