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aspire
/
acge_text_embedding

Sentence Similarity
sentence-transformers
PyTorch
Safetensors
bert
mteb
feature-extraction
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community
16

Instructions to use aspire/acge_text_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use aspire/acge_text_embedding with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("aspire/acge_text_embedding")
    
    sentences = [
        "That is a happy person",
        "That is a happy dog",
        "That is a very happy person",
        "Today is a sunny day"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Inference
  • Notebooks
  • Google Colab
  • Kaggle
acge_text_embedding / result
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  • 2 contributors
History: 1 commit
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aspire
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4ec0bf2 verified about 2 years ago
  • acge_text_embedding_a10_bf16
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  • acge_text_embedding_bf16
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  • acge_text_embedding_float
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  • acge_text_embedding_half
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