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electroglyph
/
snowflake2_m_uint8

Sentence Similarity
sentence-transformers
ONNX
Transformers.js
gte
feature-extraction
mteb
arctic
embedding
snowflake2_m_uint8
snowflake
custom_code
Model card Files Files and versions
xet
Community

Instructions to use electroglyph/snowflake2_m_uint8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use electroglyph/snowflake2_m_uint8 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("electroglyph/snowflake2_m_uint8", trust_remote_code=True)
    
    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]
  • Transformers.js

    How to use electroglyph/snowflake2_m_uint8 with Transformers.js:

    // npm i @huggingface/transformers
    import { pipeline } from '@huggingface/transformers';
    
    // Allocate pipeline
    const pipe = await pipeline('sentence-similarity', 'electroglyph/snowflake2_m_uint8');
  • Notebooks
  • Google Colab
  • Kaggle
snowflake2_m_uint8
328 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 20 commits
electroglyph's picture
electroglyph
Update README.md
aea19cf verified 8 months ago
  • .gitattributes
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  • README.md
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  • config.json
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  • config_sentence_transformers.json
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  • configuration_hf_alibaba_nlp_gte.py
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  • graph_old.png
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  • modeling_hf_alibaba_nlp_gte.py
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  • modules.json
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  • orig_model.png
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  • quant_model.png
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  • snowflake2_m_uint8.onnx
    311 MB
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  • special_tokens_map.json
    964 Bytes
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  • tokenizer.json
    17.1 MB
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  • tokenizer_config.json
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