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sanqiang
/
qa_base

Feature Extraction
Transformers
PyTorch
t5
Model card Files Files and versions
xet
Community
1

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

  • Libraries
  • Transformers

    How to use sanqiang/qa_base with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="sanqiang/qa_base")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("sanqiang/qa_base")
    model = AutoModel.from_pretrained("sanqiang/qa_base")
  • Notebooks
  • Google Colab
  • Kaggle
qa_base
894 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
Ubuntu
add model
d2a0d57 over 4 years ago
  • .gitattributes
    1.18 kB
    initial commit over 4 years ago
  • config.json
    1.47 kB
    add model over 4 years ago
  • pytorch_model.bin
    892 MB
    xet
    add model over 4 years ago
  • special_tokens_map.json
    1.79 kB
    add model over 4 years ago
  • spiece.model
    792 kB
    xet
    add model over 4 years ago
  • tokenizer.json
    1.39 MB
    add model over 4 years ago
  • tokenizer_config.json
    2.08 kB
    add model over 4 years ago