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FractalGPT
/
SbertSVDDistil

Feature Extraction
Transformers
Safetensors
sentence-transformers
Russian
English
bert
sentence-similarity
text-embeddings-inference
Model card Files Files and versions
xet
Community

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

  • Libraries
  • Transformers

    How to use FractalGPT/SbertSVDDistil with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="FractalGPT/SbertSVDDistil")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("FractalGPT/SbertSVDDistil")
    model = AutoModel.from_pretrained("FractalGPT/SbertSVDDistil")
  • sentence-transformers

    How to use FractalGPT/SbertSVDDistil with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("FractalGPT/SbertSVDDistil")
    
    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
SbertSVDDistil
45.7 MB
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  • 1 contributor
History: 7 commits
Ponimash's picture
Ponimash
Update README.md
92b0ec6 verified over 2 years ago
  • 1_Pooling
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  • 2_Dense
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  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    6.24 kB
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  • config.json
    685 Bytes
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  • config_sentence_transformers.json
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  • model.safetensors
    44.2 MB
    xet
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  • modules.json
    341 Bytes
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  • sentence_bert_config.json
    53 Bytes
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  • special_tokens_map.json
    695 Bytes
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  • tokenizer.json
    706 kB
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  • tokenizer_config.json
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  • vocab.txt
    241 kB
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