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OpenSearch-AI
/
Ops-MoA-Yuan-embedding-1.0

Feature Extraction
sentence-transformers
Safetensors
Chinese
bert
mteb
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community
2

Instructions to use OpenSearch-AI/Ops-MoA-Yuan-embedding-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use OpenSearch-AI/Ops-MoA-Yuan-embedding-1.0 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("OpenSearch-AI/Ops-MoA-Yuan-embedding-1.0")
    
    sentences = [
        "The weather is lovely today.",
        "It's so sunny outside!",
        "He drove to the stadium."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [3, 3]
  • Notebooks
  • Google Colab
  • Kaggle
Ops-MoA-Yuan-embedding-1.0
1.37 GB
Ctrl+K
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  • 2 contributors
History: 10 commits
xzx22's picture
xzx22
Update README.md
23712d0 verified about 1 year ago
  • 1_Pooling
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  • 2_Dense
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  • 3_Adaptor
    sbert_integration (#1) about 1 year ago
  • .gitattributes
    1.52 kB
    initial commit about 1 year ago
  • README.md
    2.46 kB
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  • config.json
    864 Bytes
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  • config_sentence_transformers.json
    205 Bytes
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  • model.safetensors
    1.3 GB
    xet
    Upload model.safetensors with huggingface_hub about 1 year ago
  • modeling_adaptor.py
    3.82 kB
    sbert_integration (#1) about 1 year ago
  • modules.json
    455 Bytes
    sbert_integration (#1) about 1 year ago
  • sentence_bert_config.json
    53 Bytes
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  • special_tokens_map.json
    695 Bytes
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  • tokenizer.json
    439 kB
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  • tokenizer_config.json
    1.46 kB
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  • vocab.txt
    110 kB
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