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Tverous
/
entailment-classification

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

Instructions to use Tverous/entailment-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Tverous/entailment-classification with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="Tverous/entailment-classification")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("Tverous/entailment-classification")
    model = AutoModelForSequenceClassification.from_pretrained("Tverous/entailment-classification")
  • Notebooks
  • Google Colab
  • Kaggle
entailment-classification
2.49 GB
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  • 2 contributors
History: 3 commits
Tverous's picture
Tverous
commit files to HF hub
52753e4 about 2 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 3 years ago
  • added_tokens.json
    23 Bytes
    commit files to HF hub almost 3 years ago
  • config.json
    1.28 kB
    commit files to HF hub about 2 years ago
  • model.safetensors
    1.74 GB
    xet
    commit files to HF hub about 2 years ago
  • pipeline.py
    2.71 kB
    commit files to HF hub almost 3 years ago
  • pytorch_model.bin
    738 MB
    xet
    commit files to HF hub almost 3 years ago
  • special_tokens_map.json
    971 Bytes
    commit files to HF hub about 2 years ago
  • spm.model
    2.46 MB
    xet
    commit files to HF hub almost 3 years ago
  • tokenizer.json
    8.65 MB
    commit files to HF hub about 2 years ago
  • tokenizer_config.json
    1.37 kB
    commit files to HF hub about 2 years ago