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mgfrantz
/
deberta_v3_finetuned_predicting_effective_arguments

Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
deberta-v2
text-embeddings-inference
Model card Files Files and versions
xet
Metrics Training metrics Community
1

Instructions to use mgfrantz/deberta_v3_finetuned_predicting_effective_arguments with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use mgfrantz/deberta_v3_finetuned_predicting_effective_arguments with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="mgfrantz/deberta_v3_finetuned_predicting_effective_arguments")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("mgfrantz/deberta_v3_finetuned_predicting_effective_arguments")
    model = AutoModelForSequenceClassification.from_pretrained("mgfrantz/deberta_v3_finetuned_predicting_effective_arguments", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
deberta_v3_finetuned_predicting_effective_arguments / runs
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  • 2 contributors
History: 4 commits
mgfrantz's picture
mgfrantz
commit files to HF hub
18cc8c4 about 4 years ago
  • Jul26_21-03-46_1fe91f198d71
    commit files to HF hub about 4 years ago
  • Jul26_21-10-23_1fe91f198d71
    commit files to HF hub about 4 years ago
  • Jul26_21-13-22_1fe91f198d71
    commit files to HF hub about 4 years ago