Sentence Similarity
sentence-transformers
Safetensors
Korean
xlm-roberta
embedding
classification
mteb
korean
text-embeddings-inference
Instructions to use NGA-KR/ko-embed-cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NGA-KR/ko-embed-cls with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NGA-KR/ko-embed-cls") 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 NGA-KR/ko-embed-cls: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/NGA-KR/ko-embed-cls/resolve/main/tokenizer.json
- Command line
-
hf download hf://NGA-KR/ko-embed-cls/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/NGA-KR/ko-embed-cls/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 4b230c6f4591a71531d7d306f539bb5d9194b16f953402614741a82b511a6325
- Size of remote file:
- 17.1 MB
- SHA256:
- e87a2cff11ba953f3f63507aabe7630614da73797189a76b3b3a2a667707fcc1
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