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apperry-ai
/
fact_extraction_roberta_crf

Token Classification
Transformers
PyTorch
roberta
Model card Files Files and versions
xet
Community
3

Instructions to use apperry-ai/fact_extraction_roberta_crf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use apperry-ai/fact_extraction_roberta_crf with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("token-classification", model="apperry-ai/fact_extraction_roberta_crf")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForTokenClassification
    
    tokenizer = AutoTokenizer.from_pretrained("apperry-ai/fact_extraction_roberta_crf")
    model = AutoModelForTokenClassification.from_pretrained("apperry-ai/fact_extraction_roberta_crf")
  • Notebooks
  • Google Colab
  • Kaggle
fact_extraction_roberta_crf
503 MB
Ctrl+K
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  • 2 contributors
History: 3 commits
EvgeniaKomleva's picture
EvgeniaKomleva
Create README.md
d7a5310 almost 3 years ago
  • eval
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  • .gitattributes
    1.52 kB
    initial commit almost 3 years ago
  • README.md
    672 Bytes
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  • config.json
    8.01 kB
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  • merges.txt
    456 kB
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  • pytorch_model.bin

    Detected Pickle imports (4)

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

    What is a pickle import?

    496 MB
    xet
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  • special_tokens_map.json
    280 Bytes
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  • tokenizer.json
    2.11 MB
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  • tokenizer_config.json
    418 Bytes
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  • trainer_config.json
    517 Bytes
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  • vocab.json
    798 kB
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