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mathislucka
/
deberta-large-hallucination-eval-v2

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

Instructions to use mathislucka/deberta-large-hallucination-eval-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use mathislucka/deberta-large-hallucination-eval-v2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="mathislucka/deberta-large-hallucination-eval-v2")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("mathislucka/deberta-large-hallucination-eval-v2")
    model = AutoModelForSequenceClassification.from_pretrained("mathislucka/deberta-large-hallucination-eval-v2")
  • Notebooks
  • Google Colab
  • Kaggle
deberta-large-hallucination-eval-v2
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  • 2 contributors
History: 3 commits
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SFconvertbot
Adding `safetensors` variant of this model
311d383 almost 3 years ago
  • .gitattributes
    1.52 kB
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  • added_tokens.json
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  • config.json
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  • model.safetensors
    1.74 GB
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  • optimizer.pt
    3.48 GB
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  • pytorch_model.bin
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  • rng_state.pth
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  • scaler.pt
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  • scheduler.pt
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  • special_tokens_map.json
    173 Bytes
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  • spm.model
    2.46 MB
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  • tokenizer.json
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  • tokenizer_config.json
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  • trainer_state.json
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  • training_args.bin
    3.44 kB
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