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vikp
/
pdf_postprocessor_t5

Token Classification
Transformers
PyTorch
t5
text-generation-inference
Model card Files Files and versions
xet
Community
1

Instructions to use vikp/pdf_postprocessor_t5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use vikp/pdf_postprocessor_t5 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("token-classification", model="vikp/pdf_postprocessor_t5")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForTokenClassification
    
    tokenizer = AutoTokenizer.from_pretrained("vikp/pdf_postprocessor_t5")
    model = AutoModelForTokenClassification.from_pretrained("vikp/pdf_postprocessor_t5")
  • Notebooks
  • Google Colab
  • Kaggle
pdf_postprocessor_t5
871 MB
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  • 1 contributor
History: 7 commits
vikp's picture
vikp
Update README.md
64e88b0 over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    135 Bytes
    Update README.md over 2 years ago
  • added_tokens.json
    3.06 kB
    Upload tokenizer over 2 years ago
  • config.json
    1.18 kB
    Upload WeightedT5EncoderForTokenClassification over 2 years ago
  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch._utils._rebuild_tensor_v2",
    • "collections.OrderedDict",
    • "torch.FloatStorage"

    What is a pickle import?

    871 MB
    xet
    Upload WeightedT5EncoderForTokenClassification over 2 years ago
  • special_tokens_map.json
    2.79 kB
    Upload tokenizer over 2 years ago
  • tokenizer_config.json
    25.6 kB
    Upload tokenizer over 2 years ago