Sentence Similarity
sentence-transformers
Safetensors
Model2Vec
Korean
feature-extraction
static-embedding
korean
matryoshka
Instructions to use kekeappa/kor-static-embedding-512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kekeappa/kor-static-embedding-512 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kekeappa/kor-static-embedding-512") 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] - Model2Vec
How to use kekeappa/kor-static-embedding-512 with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("kekeappa/kor-static-embedding-512") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - Notebooks
- Google Colab
- Kaggle
Commit History
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Add Matryoshka family table (64/128/256/512)
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v2: 4-stage training (distill+KorNLI+xlingual+matryoshka) - 64/128/256/512 dims
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Expand installation guide, usage examples (FAISS, clustering, troubleshooting)
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Add detailed speed/throughput comparison vs BGE-M3 (% based)
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Initial upload: kor-static-embedding-512 (KorSTS 0.78, 68MB)
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initial commit
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