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spottedzebra
/
quantized_onnx_skill

Token Classification
Transformers
ONNX
deberta-v2
Model card Files Files and versions
xet
Community

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

  • Libraries
  • Transformers

    How to use spottedzebra/quantized_onnx_skill with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("token-classification", model="spottedzebra/quantized_onnx_skill")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForTokenClassification
    
    tokenizer = AutoTokenizer.from_pretrained("spottedzebra/quantized_onnx_skill")
    model = AutoModelForTokenClassification.from_pretrained("spottedzebra/quantized_onnx_skill")
  • Notebooks
  • Google Colab
  • Kaggle
quantized_onnx_skill
942 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 4 commits
shubhasz's picture
shubhasz
minor bug
3f160f6 almost 3 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 3 years ago
  • added_tokens.json
    23 Bytes
    Hola Amigo almost 3 years ago
  • config.json
    1.02 kB
    Hola Amigo almost 3 years ago
  • handler.py
    3.39 kB
    minor bug almost 3 years ago
  • model_optimized.onnx
    931 MB
    xet
    adding handler.py almost 3 years ago
  • ort_config.json
    726 Bytes
    Hola Amigo almost 3 years ago
  • requirements.txt
    36 Bytes
    adding handler.py almost 3 years ago
  • special_tokens_map.json
    173 Bytes
    Hola Amigo almost 3 years ago
  • spm.model
    2.46 MB
    xet
    Hola Amigo almost 3 years ago
  • tokenizer.json
    8.65 MB
    Hola Amigo almost 3 years ago
  • tokenizer_config.json
    412 Bytes
    Hola Amigo almost 3 years ago