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wanderer2k1
/
M3-ViQuad2-VBPL

Feature Extraction
Transformers
Safetensors
xlm-roberta
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use wanderer2k1/M3-ViQuad2-VBPL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use wanderer2k1/M3-ViQuad2-VBPL with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="wanderer2k1/M3-ViQuad2-VBPL")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("wanderer2k1/M3-ViQuad2-VBPL")
    model = AutoModel.from_pretrained("wanderer2k1/M3-ViQuad2-VBPL")
  • Notebooks
  • Google Colab
  • Kaggle
M3-ViQuad2-VBPL
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  • 1 contributor
History: 3 commits
wanderer2k1's picture
wanderer2k1
Upload tokenizer
8f0c9ae verified almost 2 years ago
  • .gitattributes
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  • README.md
    5.17 kB
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  • config.json
    724 Bytes
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  • model.safetensors
    2.27 GB
    xet
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  • sentencepiece.bpe.model
    5.07 MB
    xet
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  • special_tokens_map.json
    964 Bytes
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  • tokenizer.json
    17.1 MB
    xet
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  • tokenizer_config.json
    1.17 kB
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