Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:550146
loss:CosineSimilarityLoss
dataset_size:5696
text-embeddings-inference
Instructions to use linu23/embeddinggemma-300M-KorSTS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use linu23/embeddinggemma-300M-KorSTS with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("linu23/embeddinggemma-300M-KorSTS") sentences = [ "그 부품은, 158개의 부품이 있었고, 우리는 그것을 분해해야 했고 다시 조립해야 했습니다. 다시 분해하고 결코 실수하지 않았습니다.", "우리는 그것을 분해해서 다시 조립해야 했다.", "난 제3의 SS의 일원이 되고 싶지 않아.", "생물권이 성장했다." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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