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bflhc
/
MoD-Embedding

Sentence Similarity
sentence-transformers
Safetensors
English
Chinese
multilingual
qwen3
feature-extraction
embedding
text-embedding
retrieval
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use bflhc/MoD-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use bflhc/MoD-Embedding with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("bflhc/MoD-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]
  • Notebooks
  • Google Colab
  • Kaggle
MoD-Embedding
8.06 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 4 commits
bflhc's picture
bflhc
add left padding
c04379e verified 4 months ago
  • 1_Pooling
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  • .gitattributes
    1.57 kB
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  • README.md
    3.69 kB
    add left padding 4 months ago
  • added_tokens.json
    605 Bytes
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  • chat_template.jinja
    2.43 kB
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  • config.json
    1.53 kB
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  • config_sentence_transformers.json
    216 Bytes
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  • merges.txt
    1.67 MB
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  • model-00001-of-00002.safetensors
    4.97 GB
    xet
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  • model-00002-of-00002.safetensors
    3.08 GB
    xet
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  • model.safetensors.index.json
    30.5 kB
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  • modules.json
    350 Bytes
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  • special_tokens_map.json
    613 Bytes
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  • tokenizer.json
    11.4 MB
    xet
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  • tokenizer_config.json
    4.69 kB
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  • vocab.json
    2.78 MB
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