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ragarwal
/
deberta-v3-base-nli-mixer-binary

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

Instructions to use ragarwal/deberta-v3-base-nli-mixer-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ragarwal/deberta-v3-base-nli-mixer-binary with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="ragarwal/deberta-v3-base-nli-mixer-binary")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("ragarwal/deberta-v3-base-nli-mixer-binary")
    model = AutoModelForSequenceClassification.from_pretrained("ragarwal/deberta-v3-base-nli-mixer-binary")
  • Notebooks
  • Google Colab
  • Kaggle
deberta-v3-base-nli-mixer-binary
749 MB
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  • 1 contributor
History: 20 commits
ragarwal's picture
ragarwal
Update README.md
e019e3b almost 4 years ago
  • .gitattributes
    1.17 kB
    initial commit almost 4 years ago
  • README.md
    2.63 kB
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  • added_tokens.json
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  • config.json
    958 Bytes
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  • pytorch_model.bin

    Detected Pickle imports (4)

    • "collections.OrderedDict",
    • "torch.FloatStorage",
    • "torch._utils._rebuild_tensor_v2",
    • "torch.LongStorage"

    What is a pickle import?

    738 MB
    xet
    Upload pytorch_model.bin almost 4 years ago
  • special_tokens_map.json
    156 Bytes
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  • spm.model
    2.46 MB
    xet
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  • tokenizer.json
    8.66 MB
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  • tokenizer_config.json
    368 Bytes
    Upload tokenizer_config.json almost 4 years ago