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texturejc
/
texture-frames-de-frame

Text Classification
Transformers
German
frame-semantics
framenet
salsa
german
semantic-parsing
srl
Model card Files Files and versions
xet
Community

Instructions to use texturejc/texture-frames-de-frame with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use texturejc/texture-frames-de-frame with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="texturejc/texture-frames-de-frame")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("texturejc/texture-frames-de-frame", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
texture-frames-de-frame
1.36 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
texturejc's picture
texturejc
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31859a0 verified 12 days ago
  • .gitattributes
    1.52 kB
    initial commit 12 days ago
  • README.md
    3.96 kB
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  • added_tokens.json
    36 Bytes
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  • frame2_model.pt
    1.36 GB
    xet
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  • frame2id.json
    23.6 kB
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  • special_tokens_map.json
    437 Bytes
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  • tokenizer.json
    730 kB
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  • tokenizer_config.json
    1.69 kB
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  • vocab.txt
    240 kB
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