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tcapelle
/
celadon-pipeline

Text Classification
Transformers
Safetensors
multi-head-deberta-for-sequence-classification
custom_code
Model card Files Files and versions
xet
Community

Instructions to use tcapelle/celadon-pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use tcapelle/celadon-pipeline with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="tcapelle/celadon-pipeline", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModelForSequenceClassification
    model = AutoModelForSequenceClassification.from_pretrained("tcapelle/celadon-pipeline", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
celadon-pipeline
574 MB
Ctrl+K
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  • 1 contributor
History: 2 commits
tcapelle's picture
tcapelle
Upload CustomTextClassificationPipeline
6511d8d verified over 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    5.17 kB
    Upload CustomTextClassificationPipeline over 1 year ago
  • config.json
    1.44 kB
    Upload CustomTextClassificationPipeline over 1 year ago
  • configuration_deberta_multi.py
    273 Bytes
    Upload CustomTextClassificationPipeline over 1 year ago
  • custom_pipeline.py
    1.42 kB
    Upload CustomTextClassificationPipeline over 1 year ago
  • model.safetensors
    565 MB
    xet
    Upload CustomTextClassificationPipeline over 1 year ago
  • special_tokens_map.json
    970 Bytes
    Upload CustomTextClassificationPipeline over 1 year ago
  • tokenizer.json
    8.65 MB
    Upload CustomTextClassificationPipeline over 1 year ago
  • tokenizer_config.json
    1.29 kB
    Upload CustomTextClassificationPipeline over 1 year ago