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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 / scripts
16.3 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
flyyinghui's picture
flyyinghui
Upload lean_lemma_retriever.py
ff241f2 verified 25 days ago
  • build_18w_index.py
    2.36 kB
    Upload build_18w_index.py 25 days ago
  • lean_lemma_retriever.py
    8.16 kB
    Upload lean_lemma_retriever.py 25 days ago
  • parse_lean_declarations.py
    5.79 kB
    Upload parse_lean_declarations.py 25 days ago