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linu23
/
embeddinggemma-300M-KorSTS

Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:550146
loss:CosineSimilarityLoss
dataset_size:5696
text-embeddings-inference
Model card Files Files and versions
xet
Community

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
embeddinggemma-300M-KorSTS / 2_Dense
9.44 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
linu23's picture
linu23
embeddinggemma-300M-ko
2075549 verified 6 months ago
  • config.json
    134 Bytes
    embeddinggemma-300M-ko 6 months ago
  • model.safetensors
    9.44 MB
    xet
    embeddinggemma-300M-ko 6 months ago