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LocalLaws
/
LOCUS-Problem-Salience

Text Classification
Transformers
Safetensors
modernbert
regression
legal
locus
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use LocalLaws/LOCUS-Problem-Salience with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use LocalLaws/LOCUS-Problem-Salience with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="LocalLaws/LOCUS-Problem-Salience")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("LocalLaws/LOCUS-Problem-Salience")
    model = AutoModelForSequenceClassification.from_pretrained("LocalLaws/LOCUS-Problem-Salience")
  • Notebooks
  • Google Colab
  • Kaggle
LOCUS-Problem-Salience
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  • 1 contributor
History: 2 commits
jbarrow's picture
jbarrow
Upload problem_salience weights, tokenizer, and model card
fa3cce9 verified 8 days ago
  • .gitattributes
    1.52 kB
    initial commit 8 days ago
  • README.md
    1.21 kB
    Upload problem_salience weights, tokenizer, and model card 8 days ago
  • config.json
    1.34 kB
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  • model.safetensors
    598 MB
    xet
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  • special_tokens_map.json
    694 Bytes
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  • tokenizer.json
    3.58 MB
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  • tokenizer_config.json
    20.8 kB
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  • training_args.bin
    5.84 kB
    xet
    Upload problem_salience weights, tokenizer, and model card 8 days ago