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mjaliz
/
siglip-text-encoder

Feature Extraction
sentence-transformers
Safetensors
xlm-roberta
text-encoder
contrastive-learning
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use mjaliz/siglip-text-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use mjaliz/siglip-text-encoder with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("mjaliz/siglip-text-encoder")
    
    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

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Gated model
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Preview of files found in this repository
  • .gitattributes
    1.57 kB
    Extract text encoder from mjaliz/vision-text-dual-encoder-v1 11 months ago
  • README.md
    1.36 kB
    Extract text encoder from mjaliz/vision-text-dual-encoder-v1 11 months ago
  • config.json
    658 Bytes
    Extract text encoder from mjaliz/vision-text-dual-encoder-v1 11 months ago
  • model.safetensors
    2.27 GB
    xet
    Extract text encoder from mjaliz/vision-text-dual-encoder-v1 11 months ago
  • special_tokens_map.json
    964 Bytes
    Extract text encoder from mjaliz/vision-text-dual-encoder-v1 11 months ago
  • tokenizer.json
    17.1 MB
    xet
    Extract text encoder from mjaliz/vision-text-dual-encoder-v1 11 months ago
  • tokenizer_config.json
    1.18 kB
    Extract text encoder from mjaliz/vision-text-dual-encoder-v1 11 months ago