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flyyinghui
/
embeddinggemma-2-lean

Sentence Similarity
sentence-transformers
Safetensors
embedding_gemma2
feature-extraction
dense
Generated from Trainer
dataset_size:346930
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use flyyinghui/embeddinggemma-2-lean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use flyyinghui/embeddinggemma-2-lean with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("flyyinghui/embeddinggemma-2-lean")
    
    sentences = [
        "U card",
        "region",
        "clique",
        "tokens"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
embeddinggemma-2-lean / data
1.02 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 4 commits
flyyinghui's picture
flyyinghui
Upload index/names.json
6fad8d6 verified 25 days ago
  • index
    Upload index/names.json 25 days ago
  • declarations.jsonl
    426 MB
    xet
    Upload declarations.jsonl (185万 Lean 声明库) 25 days ago
  • declarations_18w.jsonl
    77.1 MB
    xet
    Upload declarations_18w.jsonl (16.7万 精选 theorem) 25 days ago