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scales-okn
/
ontology-consent-decree-resolution

Text Classification
Transformers
PyTorch
TensorBoard
deberta-v2
Generated from Trainer
text-embeddings-inference
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use scales-okn/ontology-consent-decree-resolution with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use scales-okn/ontology-consent-decree-resolution with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="scales-okn/ontology-consent-decree-resolution")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("scales-okn/ontology-consent-decree-resolution")
    model = AutoModelForSequenceClassification.from_pretrained("scales-okn/ontology-consent-decree-resolution", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
ontology-consent-decree-resolution
1.75 GB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 8 commits
adamrpah's picture
adamrpah
Document GPL-3.0 public release
0e6c69c verified about 1 month ago
  • runs
    Model save over 3 years ago
  • .gitattributes
    1.48 kB
    initial commit over 3 years ago
  • .gitignore
    13 Bytes
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  • README.md
    4.32 kB
    Document GPL-3.0 public release about 1 month ago
  • added_tokens.json
    23 Bytes
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  • config.json
    889 Bytes
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  • pytorch_model.bin
    1.74 GB
    xet
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  • special_tokens_map.json
    173 Bytes
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  • spm.model
    2.46 MB
    xet
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  • tokenizer.json
    8.65 MB
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  • tokenizer_config.json
    406 Bytes
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  • training_args.bin
    3.45 kB
    xet
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  • training_progress.csv
    16.2 kB
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